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Record W2132254908 · doi:10.1525/auk.2011.09161

Weather Effects on Autumn Nocturnal Migration of Passerines on Opposite Shores of the St. Lawrence Estuary

2011· article· en· W2132254908 on OpenAlexafffundabout
François Gagnon, Jacques Ibarzabal, Jean‐Pierre L. Savard, Pierre Vaillancourt, Marc Bélisle, Charles M. Francis

Bibliographic record

VenueThe Auk · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversité de SherbrookeNatural Resources CanadaEnvironment and Climate Change CanadaUniversité du Québec à Chicoutimi
FundersBrock UniversityUniversité de SherbrookeNatural Resources CanadaUniversité du Québec à ChicoutimiMcGill University
KeywordsBird migrationPrecipitationWind directionEstuaryPrevailing windsShoreWind speedNocturnalDiel vertical migrationOceanographyClimatologyGlobal wind patternsGeographyAtmospheric sciencesEnvironmental scienceGeologyMeteorologyEcologyBiology

Abstract

fetched live from OpenAlex

Nous avons modlis l'intensit migratoire automnale en fonction de la mto, en utilisant des mesures de la migration nocturne prises la fois sur la rive nord (Cte-Nord) et la rive sud (Gaspsie) de l'estuaire du Saint-Laurent, Qubec, Canada, l'aide d'un radar Doppler de surveillance mtorologique. Ce radar effectue des balayages des angles ngatifs, une caractristique rare chez les radars mto qui permet entre autres, de relever des donnes de migration d'oiseaux basse altitude et simultanment de chaque ct de l'estuaire. Nos rsultats montrent que les prcipitations et le vent avaient de forts effets sur l'intensit migratoire. Peu d'oiseaux migraient quand % ou plus du territoire tait affect par des prcipitations, particulirement en combinaison avec des vents forts. Les plus fortes intensits migratoires taient associs avec des vents lgers, peu importe la direction du vent; par vents forts, la migration tait plus probable quand les vents avaient une composante nord. Un vnement de conditions mto adverses la migration s'ensuivait d'une augmentation de l'intensit migratoire sur la Cte-Nord, mais pas en Gaspsie. Le passage d'un front -99 -

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 score0.104
Threshold uncertainty score0.555

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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

Citations8
Published2011
Admission routes3
Has abstractyes

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