A double-blind comparison of morphological and collagen fingerprinting (ZooMS) methods of skeletal identifications from Paleolithic contexts
Bibliographic record
Abstract
Modeling the subsistence strategies of prehistoric groups depends on the accuracy of the faunal identifications that provide the basis for these models. However, our knowledge remains limited about the reproducibility of published taxonomic identifications and how they accurately reflect the range of species deposited in the archaeological record. This study compares taxonomic identifications at three Paleolithic sites (Saint-Césaire and Le Piage in France, Crvena Stijena in Montenegro) characterized by high levels of fragmentation. Identifications at these sites were derived using two methods: morphological identification and collagen fingerprinting, the latter a peptide-based approach known as ZooMS. Using a double-blind experimental design, we show that the two methods give taxonomic profiles that are statistically indistinguishable at all three sites. However, rare species and parts difficult to identify such as ribs seem more frequently associated with errors of identification. Comparisons with the indeterminate fraction indicate that large game is over-represented in the ZooMS sample at two of the three sites. These differences possibly signal differential fragmentation of elements from large species. Collagen fingerprinting can produce critical insights on the range distribution of animal prey in the past while also contributing to improved models of taphonomic processes and subsistence behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.021 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".