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Record W3044731200 · doi:10.1676/1559-4491-132.1.72

Use of discrete molting grounds by migrant passerines undergoing prebasic molt in southern Quebec

2020· article· en· W3044731200 on OpenAlexaboutno aff
James H. Junda, Simon Duval, Marcel A. Gahbauer

Bibliographic record

VenueThe Wilson Journal of Ornithology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsWarblerFeatherFlight featherMoultingEcologyBiologyZoologyPlumageGeographyBreedMudaHabitat

Abstract

fetched live from OpenAlex

We documented prebasic flight feather molt of passerines captured in fall 2013 and 2015 at McGill Bird Observatory (MBO) in Montreal, Quebec. We recorded active molt of flight feathers (remiges) in 11 species that do not breed on site. Flight feather molt was frequent among Swainson's Thrush (Catharus ustulatus; 64% of adults), Tennessee Warbler (Oreothlypis peregrina; 57%), Nashville Warbler (Leiothlypis ruficapilla; 67%), and Yellow-rumped Warbler (Setophaga coronata; 44%), and was observed less frequently in other species. The minimum stopover length of molting individuals was on average 8 times longer than that of non-molting individuals of the same species. Among Swainson's Thrushes and Yellow-rumped Warblers, far more females were undergoing molt than males, whereas for Tennessee Warblers molt was slightly more frequent among males. Frequency of molt was similar between years for most species but not Yellow-rumped Warbler, with 59% of adults captured in 2013 molting compared to none in 2015. We also observed molting site fidelity with multiyear returns of Tennessee and Nashville warblers. The use of separate breeding and molting sites is not well understood among eastern North American species, and with recent studies highlighting the importance of molt locations in western North America, we demonstrate the value in additional study of the use of discrete molt locations in the East.

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 score0.026
Threshold uncertainty score0.335

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)0.0000.000

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 head0.026
GPT teacher head0.237
Teacher spread0.211 · 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
Published2020
Admission routes1
Has abstractyes

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