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Record W4385194957 · doi:10.5751/jfo-00276-940305

No effect of geolocators on apparent return rates of a declining Neotropical migrant, the Canada Warbler ( Cardellina canadensis ).

2023· article· en· W4385194957 on OpenAlexaboutno aff
Peyton Caylor, Stephanie Augustine, Christopher T. Rota

Bibliographic record

VenueJournal of Field Ornithology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureEastern Bird Banding AssociationAmerican Ornithological SocietyWest Virginia UniversityU.S. Department of Agriculture
KeywordsSongbirdWarblerEcologyGeographyPopulationBiologyDemographyHabitat

Abstract

fetched live from OpenAlex

Canada Warblers (<em>Cardellina canadensis</em>) are small Neotropical migrants whose populations are declining across most of their range. Understanding factors limiting Canada Warbler populations requires knowledge of population ecology across the full annual cycle, including migratory pathways and over-winter locations. Light-level geolocator tags have offered unprecedented insight into migratory ecology for many species, but previous studies suggest that geolocators may influence apparent return rates. We sought to determine if geolocators influence apparent return rates of adult male Canada Warblers breeding in West Virginia, USA. In 2020, we deployed geolocators on 32 birds and color banded an additional 78 birds without geolocators. The following year, 13 of 32 (40.6%) geolocator birds and 37 of 78 (47.4%) color-banded birds were detected with no significant difference in apparent return rates between groups (χ² = 0.19, p = 0.66). Although further evaluation of additional groups will be valuable, the lack of significant effect on adult male Canada Warblers suggests that the slightly lower return rate does not preclude the use of geolocators as a tool to assess the migration ecology of this small songbird of conservation concern.

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 score0.186
Threshold uncertainty score0.977

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 head0.012
GPT teacher head0.260
Teacher spread0.247 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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