Investigating Species Composition in the Early Aurignacian of Le Piage (France) Through Collagen Fingerprinting (ZooMS) of Screen-Recovered Small Bone Fragments
Bibliographic record
Abstract
In the paleoproteomic field, ZooMS (Zooarchaeology by Mass Spectrometry) has been developed to identify morphologically non-diagnostic animal remains to taxon, offering insights into human subsistence practices. Here, we report new ZooMS analyses of 1,050 Early Aurignacian (ca. 37,000-34,000 cal BP) bone fragments from the site of Le Piage (Lot, France). The studied sample is heavily fragmented and was retrieved through water sieving. In our analysis, we compare the taxonomic identifications of bone remains using traditional morphological attributes with remains identified using ZooMS and discuss the implications of the taxonomic patterns. Our results indicate that the faunal spectrum identified through ZooMS differs from the species composition obtained through morphological analyses. While reindeer remains the dominant species, bovids and other cervids are better represented in the ZooMS fraction. Two rare taxa, a hare (Lepus sp.) and a previously unidentified carnivore (Pantherinae/Hyaenidae/Mustelidae), were also identified using ZooMS. We also observe an increase of Bos/Bison remains in the spongious assemblage that is possibly explained by the use of spongy portions of long bones as fuel. Our work adds new data on patterns of reindeer dominance during the Early Aurignacian and illustrates how ZooMS identifications of screen-recovered small bone fragments can enhance our understanding of Paleolithic site occupation 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".