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Record W7065918607

Environmental determinants Of reproductive success in cavity nesting songbirds of a semi-arid grassland in British Columbia

2016· article· en· W7065918607 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodExclosureWindageHyporeflexiaLiquation
DOInot available

Abstract

fetched live from OpenAlex

I investigated the influence of regional climate, local weather, and nest box features on the reproductive success of mountain bluebirds (Sialia currucoides)\nand tree swallows (Tachycineta bicolor). I also evaluated the relative influence of local weather and parental care behaviour on mountain bluebird nestling growth\nand mortality. My results demonstrate that local weather can strongly influence the breeding performance of mountain bluebirds, with improved reproductive\nsuccess during years of less rainfall. I conclude that this influence is likely exerted directly through acute nestling mortality rather than through nestling growth and prolonged nestling stress. As well, I show that regional climate plays an important role in tree swallow reproductive success, with improved tree swallow breeding performance during years of lower Southern Oscillation Index values (El Niño conditions). I suggest that the affect of regional climate on tree swallow reproductive success is likely due to influences that ENSO and regional climatic patterns may have on the aerial insect prey base of tree swallows in our study region near Kamloops, BC. For mountain bluebirds, I found the influence of weather on reproductive success is dependent on nest box features, including nest box entrance type. This study has implications for conservationists and managers of grassland passerines in British Columbia and beyond, especially in light of global climate change.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0340.000

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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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.

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
Published2016
Admission routes1
Has abstractyes

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