The nature of breaking long bones for obtaining marrow is important in Paleolithic archaeology, due to its widespread, almost universal, character. Provided that hammer-stone percussion marks can be correctly identified using experimental datasets (e.g., [1]), the anatomical location and count of the marks may be taken to reflect recurrent “cultural” traditions in the Paleolithic [2]. Were MP humans breaking bones intuitively or did they abide by a strict “protocol”, and, if the latter, was this protocol optimized for marrow retrieval or geared towards another, less obvious goal?
The new preprint by Vettese and colleagues [3] took a novel approach to this question, by conducting an experiment which involved novice butchers, relying on nothing but their intuition, and recording the way in which they broke marrow-rich bones. Some variability was noted in the “intuitive” patterns; indeed, future studies replicating this experiment and adding more variables such as more experienced butchers and non-bovid bones are warranted. Similarities in the means by which novice butchers break bones was also observed, and especially telling is the strong effect of anatomical features in most bones, except for the femur.

This paper provides a baseline for location analyses of percussion marks. Their dataset may therefore be regarded as a null hypothesis according to which the archaeological data could be tested. If Paleolithic patterns of percussion marks differ from Vettese et al.’s [3] “intuitive” patterns, then the null hypothesis is disproved and one can argue in favor of a learned pattern. The latter can be a result of ”culture”, as Vettese et al. [3] phrase it, in the sense of nonrandom action that draws on transmitted knowledge. Such comparisons bear a great potential for understanding the degree of technological behavior in the Paleolithic by factoring out the “natural” constraints of bone breakage patterns. Vettese et al. [3: fig. 14] started this discourse by comparing their experimental dataset to some Middle and Upper Paleolithic faunas; we are confident that many other studies will follow.

Bibliography

[1]Pickering, T.R., Egeland, C.P., 2006. Experimental patterns of hammerstone percussion damage on bones: Implications for inferences of carcass processing by humans. J. Archaeol. Sci. 33, 459–469. https://doi.org/10.1016/j.jas.2005.09.001

[2]Blasco, R., Rosell, J., Domínguez-Rodrigo, M., Lozano, S., Pastó, I., Riba, D., Vaquero, M., Peris, J.F., Arsuaga, J.L., de Castro, J.M.B., Carbonell, E., 2013. Learning by Heart: Cultural Patterns in the Faunal Processing Sequence during the Middle Pleistocene. PLoS One 8, e55863. https://doi.org/10.1371/journal.pone.0055863

[3]Vettese, D., Stavrova, T., Borel, A., Marin, J., Moncel, M.-H., Arzarello, M., Daujeard, C. (2020) A way to break bones? The weight of intuitiveness. BioRxiv, 011320, ver. 4 peer-reviewed and recommended by PCI Archaeology. https://doi.org/10.1101/2020.03.31.011320

Over the last decade, a small but growing community of archaeologists, from a diversity of intellectual and demographic backgrounds, have been striving for computational reproducibility in their published research. In their survey of the accomplishments of this thriving community, Emma Karoune and Esther Plomp (2022) analyzed the wide variety of approaches researchers have taken to enhance the reproducibility of their research. A key contribution of this paper is their excellent synthesis of diverse approaches into three levels of increasing complexity. This is helpful because it provides multiple entry points for researchers new to the challenge of fortifying their research. Many researchers assume that computational reproducibility is only achievable if they have a high degree of technical skill with computers, or is only necessary if their work is very computationally intensive. Karoune and Plomp give three compelling reasons why reproducibility is important for all archaeological research, and through their three levels they demonstrate that how these levels can be accomplished with basic, non-specialized computer skills and widely used free software. They showcase exemplary work from a variety of archaeologists to show how practical and achievable reproducible research is for all archaeologists. They advocate for archaeologists to use the most widely used and supported tools and services to support their reproducible research, such as the R and Python programming languages for data analysis, and Git and GitHub for collaboration. 

This paper, with its extensive appendix including thoughtful responses to frequently asked questions about reproducible research in archaeology, is likely to have a wide reach and influence, beyond previous works on this topic that have largely focused on technical details. Karoune and Plomp have provided the on-ramp for a generation of archaeologists who will find their questions about reproducible research answered here. They will also find an agreeable entry point to reproducible research in one of the three levels described by the authors. Will every archaeologist embrace this way of working? Should they? The work of Leonelli (2018) can help us anticipate the answers to these questions. Leonelli asks where are the limits to reproducibility, and how do the characteristics of different ways of knowing affect the desirability of reproducibility? Leonelli's work invites us to consider that there will be archaeologists coming from different epistemic cultures for whom the motivations presented by Karoune and Plomp will not resonate. For example, archaeologists engaged in mostly hermeneutical social science and humanities research, who do little or no quantitative analysis and statistics, are unlikely to see reproducibility as meaningful or desirable for their work. We can describe these researchers as working in interpretative or constructivist epistemic cultures. In these cultures, the particulars of how an individual researcher engages with their subject are exclusive and unique, and they would argue it cannot be fully captured or shared in an meaningful way (Elman and Kapiszewski 2017). Here, knowledge is situational, emerging from a specific, once-off combination of people and circumstances. One example in archaeology is the chaîne opératoire approach of stone artefact analysis, which Monnier and Missal (2014:61) describe as "based upon the analyst's experience and intuition, and it is not replicable, nor quantifiable". To make sense of this example we can draw on Galison's (1997) concept of 'image traditions' and 'logic traditions'. An image tradition is a way of knowing that is qualitative, based on composing narratives from drawings and photographs. A logic tradition is based on the use of instruments and statistical methods to collect standardised quantitative data. Chaîne opératoire approaches fall into the image tradition, along with many other ways of working in archaeology that do not generate numbers or use them to support claims about the past. Archaeologists working in a logic tradition will find reproducible research to be more meaningful than those working in an image tradition.

We should be mindful not to claim that one epistemic culture is superior to another because reproducibility is not meaningful or attainable for researchers in one culture. Such a claim would threaten the plurality that is essential for the reliability of scientific knowledge (Massimi 2022). Instead we should identify those communities in archaeology where reproducible research is both meaningful and attainable, but has not yet been widely embraced. That is the where the most beneficial effects can be expected. According to Leonelli's (2018) framework, we can recognise these communities by a few basic characteristics. For example: they are doing computationally intensive archaeology, such as using or writing software to collect, simulate, analyse or visualise data; they are doing experimental archaeology; or they are making knowledge claims that are supported by tables of numeric data and data visualisations. Archaeologists whose work shares one or more of these characteristics will find the guidance provided here by Karoune and Plomp to be highly instructive and relevant, and stand the most to benefit from it.  ​​

But it is not only individual archaeological scientists that have potential to benefit from how Karoune and Plomp have lowered the barriers to reproducible research. An especially important implication of this paper is that by lowering the barriers to reproducible research, Karoune and Plomp help us all to lower barriers to participation in archaeology in general. Documenting our research transparently, and sharing our materials (such as data and code and so on) openly, can profoundly change how others can participate in archaeology. By doing this, we are enabling students and researchers elsewhere, for example in low and middle income locations, to use our materials in their teaching and learning. Other researchers and students can apply our methods to their data, and combine their data with ours to achieve syntheses beyond what a single project can do. Similarly, for archaeologists working with local, descendant or marginalized communities, the tools of reproducible research are vital for enabling community members to have full access to the archaeological process, and thus reproducibility may be considered a necessity for decolonising the discipline. Karoune and Plomp present the CARE principles (Carroll et al. 2020) to guide archaeologists in ensuring community control of data so that reproducibility can be ethically accomplished with community safety and well-being as a priority. This may have a profoundly positive impact on the demographics of archaeology, as it lowers the barriers of meaningful participation by people far beyond our immediate groups of collaborators. 

Making archaeology more accessible is of critical importance in stemming the negative social impacts of pseudoarchaeologists, who often claim that archaeologists actively suppress the truth of the archaeological record through secrecy, elitism, and exclusiveness. The harm in this is twofold. First, that pseudoarchaeology typically erases Indigenous heritage by claiming that their past achievements were due to an ancient, extinct advanced civilization, not Indigenous people. These claims are often adopted by white supremacists to support racist and antisemitic conspiracy theories (Turner and Turner 2021), which sometimes leads to prejudice, physical violence, radicalization and extremism. A second type of harm that can come from claims of secrecy and elitism is it drains public trust in experts, leading to science denial. Not only trust in archaeologists, but trust in many kinds of experts, including those working on urgent contemporary issues such as public health and climate change. Karoune and Plomp's work is important here because it provides a practical and affordable pathway for archaeologists to fight claims of secrecy and elitism by sharing their work in ways that make it possible for non-academics to inspect the analyses and logic in detail. Claims of secrecy and elitism can be easily countered by openness, transparently and reproducibility by archaeologists. This is not only useful for tackling pseudoarchaeologists, but also in enacting an ethic of care, framing members of the public as people that not only care about archaeology as part of humanity's shared heritage, but also care for the construction of reliable interpretations of the archaeological record to provide secure and authentic foundations for their social identities and relationships (Wylie et al 2018; de la Bellacasa 2011). By striving for reproducible research in the way described by Karoune and Plomp, we are practicing a kind of reciprocal care among ourselves as archaeologists, and between archaeologists and members of the public as two communities who care about the human past. 

References

Karoune, E., and Plomp, E. (2022). Removing Barriers to Reproducible Research in Archaeology. Zenodo, 7320029, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7320029

de la Bellacasa, M. P. (2011). Matters of care in technoscience: Assembling neglected things. Social Studies of Science, 41(1), 85–106. https://doi.org/10.1177/0306312710380301

Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J., and Hudson, M. (2020). The CARE Principles for Indigenous Data Governance. Data Science Journal, 19(1), Article 1. https://doi.org/10.5334/dsj-2020-043​

Elman, C., and Kapiszewski, D. (2017). Benefits and Challenges of Making Qualitative Research More Transparent. Inside Higher Ed 2017,  http://web.archive.org/web/20220407064134/https://www.insidehighered.com/blogs/rethinking-research/benefits-and-challenges-making-qualitative-research-more-transparent (accessed 21 Oct, 2022). 

Galison, P. (1997). Image and logic: a material culture of microphysics. Chicago (IL): University of Chicago Press.

Leonelli, S. (2018). Re-Thinking Reproducibility as a Criterion for Research Quality [preprint]. Available online: http://philsci-archive.pitt.edu/id/eprint/14352 (Accessed 21 Oct 2022).

Massimi, M. (2022). Perspectival realism. Oxford University Press.

Monnier, G. F., and Kele M.. "Another Mousterian debate? Bordian facies, chaîne opératoire technocomplexes, and patterns of lithic variability in the western European Middle and Upper Pleistocene." Quaternary International 350 (2014): 59-83. https://doi.org/10.1016/j.quaint.2014.06.053

Turner, D. D., and Turner, M. I. (2021). “I’m Not Saying It Was Aliens”: An Archaeological and Philosophical Analysis of a Conspiracy Theory. In A. Killin and S. Allen-Hermanson (Eds.), Explorations in Archaeology and Philosophy (pp. 7–24). Springer International Publishing. https://doi.org/10.1007/978-3-030-61052-4_2

​Wylie, C., Neeley, K., and Ferguson, S. (2018). Beyond Technological Literacy: Open Data as Active Democratic Engagement? Digital Culture & Society, 4(2), 157–182. https://doi.org/10.14361/dcs-2018-0209​​​​

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“Archaeology, Typology and Machine Epistemology” submitted by G. Lucas (1) offers a refreshing and welcome reflection on the role of computer-based practice, type-thinking and approaches to typology in the age of big data and the widely proclaimed ‘Third Science Revolution’ (2–4). At the annual meeting of the EAA in Maastricht in 2017, a special thematic block was dedicated to issues and opportunities linked to the Third Science Revolution in archaeology “because of [its] profound and wide ranging impact on practice and theory in archaeology for the years to come” (5). Even though the Third Science Revolution, as influentially outlined by Kristiansen in 2014 (2), has occasionally also been met with skepticism and critique as to its often implicit scientism and epistemological naivety (6–8), archaeology as a whole seems largely euphoric as to the promises of the advancing ‘revolution’. As Lucas perceptively points out, some even regard it as the long-awaited opportunity to finally fulfil the ambitions and goals of Anglophone processualism. The irony here, as Lucas rightly notes, is that early processualists initially foregrounded issues of theory and scientific epistemology, while much work conducted under the banner of the Third Science Revolution, especially within its computational branches, does not. Big data advocates have echoed Anderson’s much-cited “end of theory” (9) or at least emphatically called for an ‘empirization’ and ‘computationalization’ of theory, often under the banner of ‘data-driven archaeology’ (10), yet typically without much specification of what this is supposed to mean for archaeological theory and reflexivity. The latter is indeed often openly opposed by archaeological Third Science Revolution enthusiasts, arguably because it is viewed as part of the supposedly misguided ‘post-modernist’ project.

Lucas makes an original meta-archaeological contribution here and attempts to center the epistemological, ontological and praxeological dimensions of what is actually – in situated archaeological praxis and knowledge-production – put at stake by the mobilization of computers, algorithms and artificial intelligence (AI), including its many but presently under-reflected implications for ordering practices such as typologization. Importantly, his perspective thereby explicitly and deliberately breaks with the ‘normative project’ in traditional philosophy of science, which sought to nail down a universal, prescriptive way of doing science and securing scientific knowledge. He instead focuses on the practical dimensions and consequences of computer-reliant archaeologies, what actually happens on the ground as researchers try to grapple with the digital and the artefactual and try to negotiate new insights and knowledge, including all of the involved messiness – thereby taking up the powerful impetus of the broader practice turn in interdisciplinary science studies and STS (Science and Technology Studies (11)) (12–14), which have recently also re-oriented archaeological self-observation, metatheory and epistemology (15). This perspective on the dawning big data age in archaeology and incurred changes in the status, nature and aims of type-thinking produces a number of important insights, which Lucas fruitfully discusses in relation to promises of ‘automation’ and ‘novelty’ as these feature centrally in the rhetorics and politics of the Third Science Revolution. 

With regard to automation, Lucas makes the important point that machine or computer work as championed by big data proponents cannot adequately be qualified or understood if we approach the issue from a purely time-saving perspective. The question we have to ask instead is what work do machines actually do and how do they change the dynamics of archaeological knowledge production in the process? In this optic, automation and acceleration achieved through computation appear to make most sense in the realm of the uncontroversial, in terms of “reproducing an accepted way of doing things” as Lucas says, and this is precisely what can be observed in archaeological practice as well. The ramifications of this at first sight innocent realization are far-reaching, however. If we accept the noncontroversial claim that automation partially bypasses the need for specialists through the reproduction of already “pre-determined outputs”, automated typologization would primarily be useful in dealing with and synthesizing larger amounts of information by sorting artefacts into already accepted types rather than create novel types or typologies. If we identity the big data promise at least in part with automation, even the detection of novel patterns in any archaeological dataset used to construct new types cannot escape the fact that this novelty is always already prefigured in the data structure devised. The success of ‘supervised learning’ in AI-based approaches illustrates this. Automation thus simply shifts the epistemological burden back to data selection and preparation but this is rarely realized, precisely because of the tacit requirement of broad non-contentiousness. 

Minimally, therefore, big data approaches ironically curtail their potential for novelty by adhering to conventional data treatment and input formats, rarely problematizing the issue of data construction and the contested status of (observational) data themselves. By contrast, they seek to shield themselves against such attempts and tend to retain a tacit universalism as to the nature of archaeological data. Only in this way is it possible to claim that such data have the capacity to “speak for themselves”. To use a concept borrowed from complexity theory, archaeological automation-based type-construction that relies on supposedly basal, incontrovertible data inputs can only ever hope to achieve ‘weak emergence’ (16) – ‘strong emergence’ and therefore true, radical novelty require substantial re-thinking of archaeological data and how to construct them. This is not merely a technical question as sometimes argued by computational archaeologies – for example with reference to specifically developed, automated object tracing procedures – as even such procedures cannot escape the fundamental question of typology: which kind of observations to draw on in order to explore what aspects of artefactual variability (and why). The focus on readily measurable features – classically dimensions of artefactual form – principally evades the key problem of typology and ironically also reduces the complexity of artefactual realities these approaches assert to take seriously. The rise of computational approaches to typology therefore reintroduces the problem of universalism and, as it currently stands, reduces the complexity of observational data potentially relevant for type-construction in order to enable to exploration of the complexity of pattern. It has often been noted that this larger configuration promotes ‘data fetishism’ and because of this alienates practitioners from the archaeological record itself – to speak with Marxist theory that Lucas briefly touches upon. We will briefly return to the notion of ‘distance’ below because it can be described as a symptomatic research-logical trope (and even a goal) in this context of inquiry. 

In total, then, the aspiration for novelty is ultimately difficult to uphold if computational archaeologies refuse to engage in fundamental epistemological and reflexive self-engagement. As Lucas poignantly observes, the most promising locus for novelty is currently probably not to be found in the capacity of the machines or algorithms themselves, but in the modes of collaboration that become possible with archaeological practitioners and specialists (and possibly diverse other groups of knowledge stakeholders). In other words, computers, supercomputers and AI technologies do not revolutionize our knowledge because of their superior computational and pattern-detection capacities – or because of some mysterious ‘superintelligence’ – but because of the specific ‘division of labour’ they afford and the cognitive challenge(s) they pose. Working with computers and AI also often requires to ask new questions or at least to adapt the questions we ask. This can already be seen on the ground, when we pay attention to how machine epistemologies are effectively harnessed in archaeological practice (and is somewhat ironic given that the promise of computational archaeology is often identified with its potential to finally resolve "long-standing (old) questions"). The Third Science Revolution likely prompts a consequential transformation in the structural and material conditions of the kinds of ‘distributed’ processes of knowledge production that STS have documented as characteristic for scientific discoveries and knowledge negotiations more generally (14, 17, 18). This ongoing transformation is thus expected not only to promote new specializations with regard to the utilization of the respective computing infrastructures emerging within big data ecologies but equally to provoke increasing demand for new ways of conceptualizing observations and to reformulate the theoretical needs and goals of typology in archaeology. The rediscovery of reflexivity as an epistemic virtue within big data debates would be an important step into this direction, as it would support the shared goal of achieving true epistemic novelty, which, as Lucas points out, is usually not more than an elusive self-declaration. Big data infrastructures require novel modes of human-machine synergy, which simply cannot be developed or cultivated in an atheoretical and/or epistemological disinterested space. 

Lucas’ exploration ultimately prompts us to ask big questions (again), and this is why this is an important contribution. The elephant in the room, of course, is the overly strong notion of objectivity on which much computational archaeology is arguably premised – linked to the vow to eventually construct ‘objective typologies’. This proclivity, however, re-tables all the problematic debates of the 1960s and – to speak with the powerful root metaphor of the machine fueling much of causal-mechanistic science (19, 20) – is bound to what A. Wylie (21) and others have called the ‘view from nowhere’. Objectivity, in this latter view, is defined by the absence of positionality and subjectivity – chiefly human subjectivity – and the promise of the machine, and by extension of computational archaeology, is to purify and thus to enhance processes of knowledge production by minimizing human interference as much as possible. The distancing of the human from actual processes of data processing and inference is viewed as positive and sometimes even as an explicit goal of scientific development. Interestingly, alienation from the archaeological record is framed as an epistemic virtue here, not as a burden, because close connection with (or even worse, immersion in) the intricacies of artefacts and archaeological contexts supposedly aggravates the problem of bias. The machine, in this optic, is framed as the gatekeeper to an observer-independent reality – which to the backdoor often not only re-introduces Platonian/Aristotelian pledges to a quasi-eternal fabric of reality that only needs to be “discovered” by applying the right (broadly nonhuman) means, it is also largely inconsistent with defendable and currently debated conceptions of scientific objectivity that do not fall prey to dogma.  

Furthermore, current discussions on the open AI ChatGPT have exposed the enormous and still under-reflected dangers of leaning into radical renderings of machine epistemology: precisely because of the principles of automation and the irreducible theory-ladenness of all data, ecologies such as ChatGPT tend to reinforce the tacit epistemological background structures on which they operate and in this way can become collaborators in the legitimization and justification of the status quo (which again counteracts the potential for novelty) – they reproduce supposedly established patterns of thought. This is why, among other things, machines and AI can quickly become perpetuators of parochial and neocolonial projects – their supposed authority creates a sense of impartiality that shields against any possible critique. With Lucas, we can thus perhaps cautiously say that what is required in computational archaeology is to defuse the authority of the machine in favour of a new community archaeology that includes machines as (fallible) co-workers. Radically put, computers and AI should be recognized as subjects themselves, and treated as such, with interesting perspectives on team science and collaborative practice.

Bibliography

1. Lucas, G. (2022). Archaeology, Typology and Machine Epistemology. https:/doi.org/10.5281/zenodo.7620824.

2. Kristiansen, K. (2014). Towards a New Paradigm? The Third Science Revolution and its Possible Consequences in Archaeology. Current Swedish Archaeology 22, 11–34. https://doi.org/10.37718/CSA.2014.01.

3. Kristiansen, K. (2022). Archaeology and the Genetic Revolution in European Prehistory. Elements in the Archaeology of Europe. https://doi.org/10.1017/9781009228701

4. Eisenhower, M. S. (1964). The Third Scientific Revolution. Science News 85, 322/332. https://www.sciencenews.org/archive/third-scientific-revolution.

5. The ‘Third Science Revolution’ in Archaeology. http://www.eaa2017maastricht.nl/theme4 (March 16, 2023).

6. Ribeiro, A. (2019). Science, Data, and Case-Studies under the Third Science Revolution: Some Theoretical Considerations. Current Swedish Archaeology 27, 115–132. https://doi.org/10.37718/CSA.2019.06

7. Samida, S. (2019). “Archaeology in times of scientific omnipresence” in Archaeology, History and Biosciences: Interdisciplinary Perspectives, pp. 9–22. https://doi.org/10.1515/9783110616651

8. Sørensen, T. F.. (2017). The Two Cultures and a World Apart: Archaeology and Science at a New Crossroads. Norwegian Archaeological Review 50, 101–115. https://doi.org/10.1080/00293652.2017.1367031

9. Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/.

10. Gattiglia, G. (2015). Think big about data: Archaeology and the Big Data challenge. Archäologische Informationen 38, 113–124. https://doi.org/10.11588/ai.2015.1.26155

11. Hackett, E. J. (2008). The handbook of science and technology studies, Third edition, MIT Press/Society for the Social Studies of Science.

12. Ankeny, R., Chang, H., Boumans, M. and Boon, M. (2011). Introduction: philosophy of science in practice. Euro Jnl Phil Sci 1, 303. https://doi.org/10.1007/s13194-011-0036-4

13. Soler, L., Zwart, S., Lynch, M., Israel-Jost, V. (2014). Science after the Practice Turn in the Philosophy, History, and Social Studies of Science, Routledge.

14. Latour, B. and Woolgar, S. (1986). Laboratory life: the construction of scientific facts, Princeton University Press.

15. Chapman, R. and Wylie, A. (2016) Evidential reasoning in archaeology, Bloomsbury Academic.

16. Greve, J. and Schnabel, A. (2011). Emergenz: zur Analyse und Erklärung komplexer Strukturen, Suhrkamp.

17. Shapin, S., Schaffer, S. and Hobbes, T. (1985). Leviathan and the air-pump: Hobbes, Boyle, and the experimental life, including a translation of Thomas Hobbes, Dialogus physicus de natura aeris by Simon Schaffer, Princeton University Press.

18. Galison, P. L. and Stump, D. J. (1996).The Disunity of Science: Boundaries, Contexts, and Power, Stanford University Press.

19. Pepper, S. C. (1972). World hypotheses: a study in evidence, 7. print, University of California Press.

20. Hussain, S. T. (2019). The French-Anglophone divide in lithic research: A plea for pluralism in Palaeolithic Archaeology, Open Access Leiden Dissertations. https://hdl.handle.net/1887/69812 

21. A. Wylie, A. (2015). “A plurality of pluralisms: Collaborative practice in archaeology” in Objectivity in Science, pp. 189-210, Springer. https://doi.org/10.1007/978-3-319-14349-1_10

The community of archaeologists, bioanthropologist and paleontologists relying on tools use-wear and dental microwear has grown in the recent years, mainly driven by the spread of confocal microscopes in the laboratories. If the diversity of microscopes is quite high, the main software used for 3D surface texture data analysis are mostly different versions of the same Mountains Map core. In addition to this software, since the beginning of 3D surface texture analysis in dental microwear, surface sensitive fractal analysis (SSFA) initially developed for industrial research (Brown & Savary, 1991) have been performed in our disciplines with the Sfrax/Toothfrax software for two decades (Ungar et al., 2003). This software being discontinued, these calculations have been integrated to the new versions of Mountains Map, with multi-core computing, full integration in the software and an update of the calculation itself.

New research based on these standard parameters of surface texture analysis will be, from now on, mainly calculated with this new add-on of Mountains Map, and will be directly compared with the important literature based on the previous software. The question addressed by Calandra et al. (2022), gathering several prominent researchers in this domain including the Mountains Map developer F. Blateyron, is key for the future research: can we directly compare SSFA results from both software?

Thanks to a Bayesian approach to this question, and comparing results calculated with both software on three different datasets (two on dental microwear, one on lithic raw materials), the authors show that the two software gives statistically different results for all surface texture parameters tested in the paper. Nevertheless, applying the new calculation to the datasets, they also show that the results published in original studies with these datasets would have been similar. Authors also claim that in the future, researchers will need to re-calculate the fractal parameters of previously published 3D surfaces and cannot simply integrate ancient and new data together.

We also want to emphasize the openness of the work published here. All datasets have been published online and will be probably very useful for future methodological works. Authors also published their code for statistical comparison of datasets, and proposed a fully reproducible article that allowed the reviewers to check the content of the paper, which can also make this article of high interest for student training.

This article is therefore a very important methodological work for the community, as noted by all three reviewers. It will certainly support the current transition between the two software packages and it is necessary that all surface texture specialists take these results and the recommendation of authors into account: calculate again data from ancient measurements, and share the 3D surface measurements on open access repositories to secure their access in the future.

References

Brown CA, and Savary G (1991) Describing ground surface texture using contact profilometry and fractal analysis. Wear, 141, 211–226. https://doi.org/10.1016/0043-1648(91)90269-Z

Calandra I, Bob K, Merceron G, Blateyron F, Hildebrandt A, Schulz-Kornas E, Souron A, and Winkler DE (2022) Surface texture analysis in Toothfrax and MountainsMap® SSFA module: Different software packages, different results? Zenodo, 7219877, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7219877

Ungar PS, Brown CA, Bergstrom TS, and Walker A (2003) Quantification of dental microwear by tandem scanning confocal microscopy and scale-sensitive fractal analyses. Scanning: The Journal of Scanning Microscopies, 25, 185–193. https://doi.org/10.1002/sca.4950250405

The paper entitled “Probabilistic Modelling using Monte Carlo Simulation for Incorporating Uncertainty in Least Cost Path Results: a Roman Road Case Study” [1] submitted by J. Lewis presents an innovative approach to applying Least Cost Path (LCP) analysis to incorporate uncertainty of the Digital Elevation Model used as the topographic surface on which the path is calculated.

The proposition of using Monte Carlo simulations to produce numerous LCP, each with a slightly different DEM included in the error range of the model, allows one to strengthen the method by proposing a probabilistic LCP rather than a single and arbitrary one which does not take into account the uncertainty of the topographic reconstruction. This new method is integrated in the R package leastcostpath [2].

The author tests the method using a Roman road built along a ridge in Cumbria, England. The integration of the uncertainty of the DEM, thanks to Monte Carlo simulations, shows that two paths could have the same probability to be the real LCP. One of them is indeed the path that the Roman road took. In particular, it is one of two possibilities of LCP in the south to north direction.

This new probabilistic method therefore strengthens the reconstruction of past pathways, while also allowing new hypotheses to be tested, and, in this case study, to suggest that the northern part of the Roman road’s location was selected to help the northward movements.

[1] Lewis, J., 2020. Probabilistic Modelling using Monte Carlo Simulation for Incorporating Uncertainty in Least Cost Path Results: a Roman Road Case Study. SocArXiv, mxas2, ver 17 peer-reviewed and recommended by PCI Archaeology, 10.31235/osf.io/mxas2.

[2] Lewis, J., 2020. leastcostpath: Modelling Pathways and Movement Potential Within a Landscape. R package. Version 1.7.4.