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Record W4381613791 · doi:10.22621/cfn.v136i4.2963

Impact of anthropogenic disturbance on nesting Chimney Swift (<i>Chaetura pelagica</i>) including best practices for conservation

2023· article· en· W4381613791 on OpenAlexaffvenueabout
Timothy F. Poole, B. A. Stewart, Robert Stewart

Bibliographic record

VenueThe Canadian Field-Naturalist · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsGovernment of Manitoba
Fundersnot available
KeywordsChimney (locomotive)Nest (protein structural motif)DemolitionNesting seasonDisturbance (geology)HabitatEnvironmental scienceGeographyEcologyRoofMeteorologyArchaeologyBiology

Abstract

fetched live from OpenAlex

The effect of anthropogenic disturbance on nesting Chimney Swift (Chaetura pelagica) is poorly described. We review five case studies of anthropogenic disturbance around Chimney Swift nest sites caused by building construction, demolition, and maintenance activities in St. Adolphe, Manitoba. Chimney Swift behaviour and nest site activity did not appear to be overtly influenced by building demolition and construction conducted on adjacent buildings or lots within 13–30 m of nest chimneys. In contrast, Chimney Swift behaviour and breeding success appeared to be negatively affected by loud interior renovations and rooftop work conducted in or on the same building as the nest chimneys. The presence of humans on the roof of the nest building prevented Chimney Swifts from entering the nest site and reduced the overall rate of feeding young. Based on these observations, we provide conservation best practices for building construction and maintenance projects conducted within or on the same building as nest chimneys to help ensure protection of Chimney Swifts and their nesting habitat during the breeding season.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.564
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.055
GPT teacher head0.324
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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 routes3
Has abstractyes

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