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Record W2943504020 · doi:10.1139/cjz-2018-0161

Supplemental feeding may reduce responsiveness of Black-capped Chickadees (<i>Poecile atricapillus</i>) to avian mobbing calls during gap-crossing experiments

2019· article· en· W2943504020 on OpenAlexafffundvenue
Jacob Bailey, Matthew W. Reudink, Ken A. Otter

Bibliographic record

VenueCanadian Journal of Zoology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsThompson Rivers UniversityUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Ornithological Society
KeywordsPredationMobbingBiologyHabitatEcologyZoologySocial psychology

Abstract

fetched live from OpenAlex

Deforestation creates gaps in forest habitat, which can limit the movements of many avian species. Increased predation risk associated with crossing open habitats is often considered the primary impediment to crossing gaps. However, other factors such as energetic reserves may also influence these decisions. We conducted playback experiments before and after supplemental feeding of Black-capped Chickadees (Poecile atricapillus (Linnaeus, 1766)) to investigate how energetic reserves influenced gap-crossing decisions. Black-capped Chickadees were less likely to respond to playbacks, whether conducted within forests or across gaps, after supplemental feeding. This suggests that energetic reserves may have less influence on gap-crossing decisions than on the willingness of birds to respond to mobbing calls in general. We recommend that future studies investigating gap-crossing decisions using playbacks account for differences in resource availability among sampling locations, especially when making comparisons across discrete habitats.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
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 head0.026
GPT teacher head0.299
Teacher spread0.272 · 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

Citations0
Published2019
Admission routes3
Has abstractyes

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