Dental tissue proportions and enamel thickness in Rangifer tarandus: testing the potential of intra-tooth measurements for identifying subspecies
Bibliographic record
Abstract
The remains found in a large number of archaeological sites attest to the essential role that the reindeer (Rangifer tarandus) had in the economic system of European hunter-gatherer societies during several periods of the Palaeolithic. When examining present-day Rangifer populations, a great deal of variability between different subspecies can be observed in terms of ecology, genetics, behaviour and morphology. However, very little information is known to date about the past ecological plasticity of this species, and how its ethological traits might have influenced the hunting dynamics of prehistoric human groups.Within the framework of the DeerPal project, we have attempted to access this information by studying the internal tooth morphology. Indeed, the study of the of dental tissues proportions and enamel thickness has already provided numerous results allowing the study of taxonomy and phylogenetic relationships, and a better understanding of adaptive strategies in extinct and present-day primates. However, to our knowledge, these parameters are little or not studied in other taxa. In this work, we have, for the first time, explored the information contained within the molars of two present-day Rangifer populations, one from Canada and the other from Norway, by studying the proportions of dental tissues in two (2D) and three (3D) dimensions. The results show differences in the 3D data between the two populations and, to a lesser extent, between the sexes within the same population, which could result from the adaptative response to possible ecological constraints. Additional samples andmethodological developments on the 2D protocol will be added before applying this method to fossil samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".