Metric and non-metric guides for the determination between fore- and hindlimb phalanges of Rangifer tarandus
Bibliographic record
Abstract
Phalanges are a great untapped resource in the zooarchaeology of Rangifer tarandus. The utilization of this resource, however, is constrained by a current inability to consistently differentiate fore- from hindlimb phalanges in a mixed assemblage. The ability to separate and identify forelimb and hindlimb phalanx 1 (PI) and phalanx 2 (PII), as well as to recognize and identify other small bones of the hoof, leads to great opportunities for archaeologists. In large scale-analysis, this capacity allows a greater ability to determine minimum number of individuals and assess butchery and transport practices. In the examination of individual life histories of Rangifer tarandus, these designations allow a more precise study of pathology and entheseal change, which can shed light on adaptation, foraging strategy, and human-animal interactions. This study presents qualitative and quantitative methods for the differentiation of PI and PII of the fore- and hindlimbs and describes other bones of the hoof. Metric techniques were developed to differentiate fore- from hindlimb phalanges using non-invasive, non-destructive, and simple methods. The efficacy and accuracy of these methods were assessed using blind testing by students and staff. The average success rates of metric analysis yielded 87% accuracy for determinations of fore- versus hindlimb PI and 92% accuracy for determination of fore- versus hindlimb PII. These results show that this method could benefit researchers working with Rangifer tarandus remains.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".