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Record W3200600841 · doi:10.1163/1568539x-bja10122

Urban-nesting mountain chickadees have a reduced response to a simulated predator

2021· article· en· W3200600841 on OpenAlexafffundabout
Blaire L. Smith, Cara Snell, Matthew W. Reudink, Ken A. Otter

Bibliographic record

VenueBehaviour · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsThompson Rivers UniversityUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaThompson Rivers UniversityUniversity of Northern British Columbia
KeywordsPredatorForagingNest (protein structural motif)BoldnessHabitatEcologyNesting (process)PredationPredator avoidanceGeographyBiologyPsychology

Abstract

fetched live from OpenAlex

Abstract Anti-predator behaviour is common among birds, but little research exists on whether differences in the predator landscape between urban and rural habitats results in differential anti-predator behaviour. We compared nest-defence behaviour of mountain chickadees ( Poecile gambeli ) in urban and rural habitats in Kamloops, BC, Canada to a simulated predator model (snake) on top of nest boxes while incubating females were away from nests on foraging bouts. Upon their return, we recorded proximity to the predator model, latency to contact the nest box and enter the nest, and number of gargle and chick-a-dee calls as measures of anti-predator behaviour and compared multivariate “predator aversion scores” across birds occupying either rural or urban landscapes. Rural-nesting birds had more aversive reactions to the predator model than the urban-nesting birds, which may suggest differences in perceived threat of the model, in combination with increased boldness associated with urban-nesting birds.

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)0.000
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations4
Published2021
Admission routes3
Has abstractyes

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