MétaCan
Menu
Back to cohort
Record W3091716176 · doi:10.1111/jav.02448

Evening locomotor activity during stopover differs on pre‐departure and departure days in free‐living songbirds

2020· article· en· W3091716176 on OpenAlexaff
Yolanda E. Morbey, Andrew T. Beauchamp, Simon J. Bonner, Greg W. Mitchell

Bibliographic record

VenueJournal of Avian Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsCarleton UniversityWestern University
Fundersnot available
KeywordsSunsetNocturnalEveningBiologyWarblerLocomotor activityEcologyZoologyHabitat

Abstract

fetched live from OpenAlex

The length of time songbirds remain at a migratory stopover site is likely regulated by a daily stay/go decision informed by fat stores and weather conditions, but the finer‐scale timing of this decision and associated pre‐departure behaviours are still poorly understood. Using automated radiotelemetry of free‐living songbirds captured at a migratory stopover site in spring, we tested whether individuals change their locomotor activity near sunset on their migratory departure day compared to their non‐departure days. To do so, we extracted precise transition times between diurnal activity and nocturnal inactivity, which always precedes departure, using changepoint analysis of radio transmission signal strength. Among four warbler species, individuals extended diurnal activity by 8–19 min towards sunset on their departure day. In three species, this extension was significant. In contrast, white‐throated sparrows significantly shortened diurnal activity on their departure day by 13 min, also towards sunset. This is the first study to detect and quantify a change in locomotor activity schedule on departure versus non‐departure days in free‐living songbirds, and is consistent with the hypothesis that birds engage in pre‐departure preparatory behaviours near sunset.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.032
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.010
GPT teacher head0.233
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 teacher head, 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Avian BiologySame topicAvian ecology and behaviorFrench-language works237,207