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Record W4308131953 · doi:10.21199/wb53.4.2

NESTING ECOLOGY OF THE BARN SWALLOW ON AGRICULTURAL LANDS IN YUKON

2022· article· en· W4308131953 on OpenAlexaboutno aff
Maria Leung, Donald Reid

Bibliographic record

VenueWestern Birds · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBarnNest (protein structural motif)EcologyAvian clutch sizeBarn-owlGeographyPredationRange (aeronautics)SteppeBird nestAbundance (ecology)Nest boxTemperate climateHirundoBiologyFisheryReproductionArchaeology

Abstract

fetched live from OpenAlex

Since the 1980s, the abundance of the Barn Swallow (Hirundo rustica) in north america, including the far north, has declined. To better understand the species’ biology north of 60° n, near the northern limit of its range, and in a region of expanding agriculture, we studied its nesting ecology on farms in southern Yukon Territory, Canada, in 2019 and 2020. We followed 21 attempted nests in 2019, 20 in 2020, of which 52% and 60%, respectively, were inside buildings with permanently open entrances. other nests were built on the outside of buildings. in both years we inferred successful double brooding by three pairs, which is rarely reported north of 60°n latitude. We found the swallows’ reproductive output to be similar to that at temperate latitudes: first clutches ranged from three to six eggs (mean 4.8 in 2019; 4.2 in 2020); second clutches may have averaged marginally smaller (n = 6). Themean number of fledglings per nest was 3.3 in 2019 and 3.0 in 2020. Twenty-one percent of nests failed, either by falling off a vertical substrate or because of predation by deer mice (Peromyscus spp.), Black-billed Magpies (Pica hudsonia), or domestic cats. We also compared the air temperatures at nests, usually near building roofs, to ambient temperatures, finding them on average 1.6°C warmer than temperatures outside buildings. We set out 33 platforms and 20 wooden cups designed for Barn Swallow nesting but over the two years of our study the birds did not use any of them.

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 categoriesInsufficient 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 score0.002
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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