Comparing ungulate dietary proxies using discriminant function analysis
Bibliographic record
Abstract
A variety of tooth-wear and morphological dietary proxies have been proposed for ungulates. In turn, they have been applied to fossil specimens with the purpose of reconstructing the diets of extinct taxa. Although these dietary proxies have been used in isolation and in combination, a consistent set of statistical analyses has never been applied to all of the available datasets. The purpose of this study is to determine how well the most commonly used dietary proxies classify ungulates as browsers, grazers, and mixed feeders individually and in combination. Discriminant function analysis is applied to individual dietary proxies (hypsodonty, mesowear, microwear, and several cranial dietary proxies) and to combinations thereof to compare rates of successful dietary classification. In general, the tooth-wear dietary proxies (mesowear and microwear) perform better than morphological dietary proxies, though none are strong proxies in isolation. The success rates of the cranial dietary proxies are not increased substantially when ruminants and bovids are analyzed separately, and significance among the three dietary guilds is reduced when controlling for phylogenetic relatedness. The combination of hypsodonty, mesowear, and microwear is found to have a high rate of successful dietary classification, but a combination of all commonly used proxies increases the success rate to 100%. In most cases, mixed feeders bear the greatest resemblance to browsers suggesting that a morphology intermediate to browsers and grazers may represent a fitness valley resulting from the inability to exploit both browse and graze efficiently. These results are important for future paleoecological studies and should be used as a guide for determining which dietary proxies are appropriate to the research question.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".