A resampling approach and implications for estimating the phalangeal index from unassociated hand bones in fossil primates
Bibliographic record
Abstract
Primate fossil assemblages often have metacarpals and phalanges from which functional/behavioral interpretations may be inferred. For example, intrinsic hand proportions can indicate hand function and substrate use. But, estimates of intrinsic hand proportions from unassociated hand elements can be imperfect due to digit misattribution. Although isolated metacarpals can be identified to a specific digit, phalanges are difficult to assign to a specific ray. We used a resampling approach to evaluate how estimates of intrinsic hand proportions are affected by such uncertainty. First, the phalangeal index-intermediate phalanx length plus proximal phalanx length divided by metacarpal length-for the third digit was calculated for associated specimens of terrestrial, semiterrestrial, and arboreal taxa. We then used resampling procedures to generate distributions of "composite digits" based on resampled ratios in which phalanges from the second, fourth, and fifth rays, and from different individuals, were chosen randomly. Results confirm that the phalangeal index for associated third digits significantly discriminates groups. We also found that resampled ratios had significantly lower means, indicating that using composite digits is prone to systematic underestimation. Resampled ratios also generated distributions with greater variance around the means that obscured distinctions between groups, although significant differences between the most arboreal and terrestrial taxa are maintained. We conclude that using unassociated phalanges to calculate a phalangeal index is prone to sampling bias. Nevertheless, a resampling approach has the potential to inform estimates of hand proportions for fossil taxa, provided that the comparative sample is constrained to mimic the fossil composition.
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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 | 0.070 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".