MétaCan
Menu
Back to cohort
Record W6948592320 · doi:10.5061/dryad.pq1js52

Data from: Wild and laboratory exposure to cues of predation risk increases relative brain mass in male guppies

2018· dataset· en· W6948592320 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldArts and Humanities
TopicLinguistics and Cultural Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPredationGuppyPoeciliaPoeciliidaePredatorPopulation

Abstract

fetched live from OpenAlex

There is considerable diversity in brain size within and among species, and substantial dispute over the causes, consequences and importance of this variation. Comparative and developmental studies are essential in addressing this controversy. Predation pressure has been proposed as a major force shaping brain, behaviour and life history. The Trinidadian guppy, Poecilia reticulata, shows dramatic variation in predation pressure across populations. We compared the brain mass of guppies from high and low predation populations collected in the wild. Male but not female guppies exposed to high predation possessed heavier brains for their body size compared to fish from low predation populations. The brain is a plastic organ, so it is possible that the population differences we observed were partly due to developmental responses rather than evolved differences. In a follow‐up study, we raised guppies under cues of predation risk or in a control condition. Male guppies exposed to predator cues early in life had heavier brains relative to their body size than control males, while females showed no significant effect of treatment. Collectively our results suggest that male guppies exposed to predation invest more in neural tissue, and that these differences are at least partly driven by plastic responses.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.010

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 head0.032
GPT teacher head0.256
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueData Archiving and Networked Services (DANS)Same topicLinguistics and Cultural StudiesFrench-language works237,207