An updated global data set for diet preferences in terrestrial mammals: testing the validity of extrapolation
Bibliographic record
Abstract
Abstract Diet is a key trait of an organism's life history that influences a broad spectrum of ecological and evolutionary processes. Kissling et al. (2014; Ecology and Evolution 4: 2913–2930) compiled a species‐specific data set of diet preferences of mammals for 38% of a total of 5364 terrestrial mammalian species assessed for the International Union for Conservation of Nature's Red List, to facilitate future studies. The authors imputed dietary data for the remaining 62% by using extrapolation from phylogenetic relatives. We collected dietary information for 1261 mammalian species for which data were extrapolated by Kissling et al. (2014), in order to evaluate the success with which such extrapolation can predict true diets. The extrapolation method devised by Kissling et al. (2014) performed well for broad dietary categories (consumers of plants and animals). However, the method performed inconsistently, and sometimes poorly, for finer dietary categories, varying in accuracy in both dietary categories and mammalian orders. The results of the extrapolation performance serve as a cautionary tale. Given the large variation in extrapolation performance, we recommend a more conservative approach for inferring mammalian diets, whereby dietary extrapolation is implemented only when there is a high degree of phylogenetic conservatism for dietary traits. Phylogenetic comparative methods can be used to detect and measure phylogenetic signal in diet. If data for species are needed, then only the broadest feeding categories should be used. This would ensure a greater level of accuracy and provide a more robust data set for further ecological and evolutionary analysis.
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 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".