MétaCan
Menu
Back to cohort
Record W6948478750 · doi:10.5061/dryad.vhhmgqnqr

Data from: Temporally-variable predation risk and fear retention in Trinidadian guppies

2020· dataset· en· W6948478750 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsPredationAffect (linguistics)GuppyPredatorRisk perception

Abstract

fetched live from OpenAlex

Predation fear is a unifying theme across vertebrate taxa. Here, we explored how the frequency and duration of predation risk affects post-risk fear behaviour in Trinidadian guppies. We first exposed individuals to visual cues of potential predators for 3 days, either frequently (6×/day) or infrequently (1×/day). Each exposure lasted for either a relatively brief (5 min) or long (30 min) duration, whereas a control group consisted of no risk exposures. One day later, we quantified guppy behaviour. All background risk treatments induced a fear response toward a novel odour (i.e., neophobia), and individuals previously exposed to frequent bouts of brief risk showed elevated baseline fear. Although neophobic responses were initially similar across risk treatments (1 day later), retention of this response differed. After 8 days, only individuals previously exposed to brief bouts of risk (both frequent and infrequent) maintained neophobic responses, whereas their initially higher level of baseline fear remained elevated but was no longer significantly different from the control. These results increase our understanding of temporal factors that affect the intensity and retention of fear that persists following risk exposure, which may have applications across vertebrates in relation to problems with fearful phenotypes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.016
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.068
GPT teacher head0.247
Teacher spread0.179 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicForensic Entomology and Diptera StudiesFrench-language works237,207