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Record W3184320930 · doi:10.1139/cjz-2021-0042

A comprehensive review of dietary strategies of sigmodontine rodents of central-eastern Argentina: linking diet, body mass, and stomach morphology

2021· review· en· W3184320930 on OpenAlexvenueno aff
Sofía Barbero, Pablo Teta, Guillermo H. Cassini

Bibliographic record

VenueCanadian Journal of Zoology · 2021
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGeneralist and specialist speciesEcologyTaxonZoologyHabitat

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.294
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.289
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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