Connecting Middle Palaeolithic Datasets: the Interplay of Zooarchaeological and Lithic Data for Unravelling Neanderthal Behaviour
Bibliographic record
Abstract
Abstract The ongoing refinement of archaeological excavation and recording methods over the last decades has led to a significant increase in quantitative Middle Palaeolithic datasets that provide a record of past Neanderthal behaviour. Stone tools and butchered animal remains are the two main categories of Middle Palaeolithic archaeological remains and both provide distinctive insights into site formation and Neanderthal behaviour. However, the integration of these quantitative lithic and zooarchaeological datasets is key for achieving a full understanding of both site-specific and broader-scale patterns of Middle Palaeolithic subsistence. To explore novel ways to enhance the incorporation of these datasets, we organised a session at the 82nd annual meeting of the Society for American Archaeology in Vancouver. An underlying theme was the problem of linking lithic and faunal variability. Are variations in subsistence strategies reflected by changes in toolmaking decisions? This paper will briefly introduce the possible ways these Middle Palaeolithic datasets can be integrated, illustrated with the papers included in this special volume, and discuss its potential for understanding the variability and interconnectedness of Neanderthal technologies and subsistence strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".