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Record W1605060274 · doi:10.22621/cfn.v128i2.1586

Effect of food patch discovery on the number of American Crows (<em>Corvus brachyrhynchos</em>) using a flight lane

2014· article· en· W1605060274 on OpenAlexvenueno aff
William M. Langley

Bibliographic record

VenueThe Canadian Field-Naturalist · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsForagingProvisioningCorvidaeGeographyBiologyEcologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

In winter, American Crows (Corvus brachyrhynchos) move back and forth between night roosts and foraging sites along flight lanes. If communal roosts act as information centres, we would expect more birds to use a particular flight lane after discovery of a new food patch on that route. In this study, I investigated how the number of crows using different flight lanes was affected by the establishment of artificial food patches, as well as how crows responded to multiple days of provisioning and to the location of the food patch relative to the flight lane. After discovery of a food patch, the number of crows using the flight lane closest to it increased, while numbers using adjacent flight lanes remained the same or decreased, particularly when the patch was in the path of the flight lane and when food provisioning occurred for 2 consecutive days. These results support the idea that crows using winter roosts may make use of information on food availability obtained at the roost.

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)0.003
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.008
GPT teacher head0.232
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 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
Published2014
Admission routes1
Has abstractyes

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