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Record W6967500690 · doi:10.5061/dryad.bcc2fqzk0

Natal forest fragment size does not predict fledgling, pre-migration, or apparent annual survival in Wood Thrushes

2023· dataset· en· W6967500690 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsJuvenileFledgePopulationSurvivorship curveSurvival analysisSongbirdWildlifeMark and recaptureAge structure

Abstract

fetched live from OpenAlex

Determining the drivers and mechanisms for first-year survival of migratory songbirds has been an understudied area in population dynamics due to the difficulty in tracking juveniles once they have dispersed from the natal site. With the advancement in miniaturization of radio-tags (battery life ~400 days) and the development of the Motus Wildlife Tracking System, we tracked 189 Wood Thrush nestlings through independence and to fall migration departure, and their return the following spring. Natal forest fragment size was not a good predictor of survival at any of the main life stages and onset of fall migration was predicted by fledge date but not natal fragment size. The percent forest cover in the landscape (at 2-km scale) had only a weak effect on fledgling survival. Survival probability was lowest for fledglings on their natal territory (70%, or 0.86 weekly survival probability), very high for juveniles as they explored the local landscape prior to fall migration (89%, or 0.99 weekly survival probability) and low during their first migration and wintering season (26%, or 0.95 weekly survival probability). To our knowledge, this is the first study to directly estimate annual apparent juvenile survival in a migratory songbird using year-round radio-tracking. Our study suggests that small forest fragments are important for the conservation for forest songbirds because they can support high survival of juveniles.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.349
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.010

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 head0.017
GPT teacher head0.288
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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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