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Record W2784788646 · doi:10.1139/cjz-2017-0243

Comparative bite force in two syntopic murids (Rodentia) suggests lack of competition for food resources

2018· article· en· W2784788646 on OpenAlexvenueno aff
Samuel Ginot, Camille Le Noëne, Jacques Cassaing

Bibliographic record

VenueCanadian Journal of Zoology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersCentre National de la Recherche Scientifique
KeywordsApodemusBiologyWood mouseBite force quotientCompetition (biology)RodentSexual dimorphismZoologyMediterranean climateNiche differentiationEcologyNiche

Abstract

fetched live from OpenAlex

Closely related syntopic species have been shown to avoid competition by differentiating in the type of food they process. This can be achieved by changes in size or in the masticatory apparatus that produce modifications in bite force. The wood mouse (Apodemus sylvaticus (Linnaeus, 1758)) and Western Mediterranean mouse (Mus spretus Lataste, 1883) are two murid rodent species found in syntopy in the south of France. We measured bite force in wild specimens of both species to test for differences in performance. Despite its greater body mass, the wood mouse showed only slightly higher bite force than the Western Mediterranean mouse. We found no clear sexual dimorphism in either species; however, among the males of the Western Mediterranean mouse, two groups appeared in terms of bite force. This bite force difference may correspond to a hierarchical organization of these males. Overall, it seems that both species have similar bite forces and accordingly overlap in the resources they use. Other factors may exist that create a niche differentiation between the wood mouse and the Western Mediterranean mouse. Another explanation may be a great abundance of food, which would cancel competition for this resource in these species.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.312
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations3
Published2018
Admission routes1
Has abstractyes

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