MétaCan
Menu
Back to cohort

Ecology and Neurobiology of Fear in Free-Living Wildlife

2020· article· en· W3097842351 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Ecology Evolution and Systematics · 2020
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPredationEcologyTrophic cascadePredatorPopulationTrophic levelEcosystemApex predatorWildlifePsychologyBiologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The ecology of fear concerns the population-, community-, and ecosystem-level consequences of the behavioral interactions between predators and prey, i.e., the aggregate impacts of individual responses to life-threatening events. We review new experiments demonstrating that fear itself is powerful enough to affect the population growth rate in free-living wild birds and mammals, and fear of large carnivores—or the human super predator—can cause trophic cascades affecting plant and invertebrate abundance. Life-threatening events like escaping a predator can have enduring, even lifelong, effects on the brain, and new interdisciplinary research on the neurobiology of fear in wild animals is both providing insights into post-traumatic stress (PTSD) and reinforcing the likely commonality of population- and community-level effects of fear in nature. Failing to consider fear thus risks dramatically underestimating the total impact predators can have on prey populations and the critical role predator-prey interactions can play in shaping ecosystems.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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