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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.003 |
| 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".