A comprehensive review of dietary strategies of sigmodontine rodents of central-eastern Argentina: linking diet, body mass, and stomach morphology
Bibliographic record
Abstract
Information on dietary ecology plays a key role in a wide array of biological research and conservation decisions, but its availability is biased towards large and charismatic taxa, whereas small mammals are underrepresented. Of the scarce publications on the Neotropical sigmodontine rodents, most are concentrated in central-eastern Argentina and, up until now, no revision of the totality of these data has been made available. In this work, we performed a thorough review of the dietary information from over 50 publications on 22 species, finding omissions and errors propagated up to the present time. This resulted in a robust database, the proposal of a posteriori dietary categories, and a list of the species that have been neglected in feeding ecology research. In turn, we used that database to test whether the patterns which associate diet and morphology in medium-sized and large mammals could be replicated in these small rodents, despite their overall generalist habits and the restrictions characteristic to their size. We found that larger species tend towards a low-protein diet. Additionally, we identified some stomach types that are restricted to specific diets. These results are consistent with the observations for larger species and shed light on the biology of small mammals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".