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Record W4386600869 · doi:10.5751/jfo-00348-940316

Influence of localized artificial light on calling activity of Common Poorwill ( Phalaenoptilus nuttallii )

2023· article· en· W4386600869 on OpenAlexfundno aff
Paul J.E. Preston, R. Mark Brigham

Bibliographic record

VenueJournal of Field Ornithology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNature ConservancyNature Conservancy of CanadaUniversity of ReginaNational Geographic Society
KeywordsGeographyArtificial lightZoologyBiologyPhysicsOptics

Abstract

fetched live from OpenAlex

The presence of localized artificial light at night (or ALAN) drastically changes the landscape for organisms by providing areas of darkness and light within an area that was once only dark. This change can influence the behaviors of organisms in many ways. One such behavior is bird song. Many studies have examined how ALAN influences diurnal bird song, but few have examined its influence on nocturnal birds. We examined the influence of lunar illumination and localized artificial light on the calling behavior of the nocturnal Common Poorwill (<em>Phalaenoptilus nuttallii;</em> hereafter poorwills). To do so, we erected artificial light stations in poorwill territories and then conducted point count surveys at these stations to count poorwill calls with the lights turned on and off. We hypothesized that artificial light would have a similar effect to lunar illumination, and we would see an increase in calling when the lights were on. However, the results we obtained were mixed. We found there was no significant effect of artificial light on calling rate and, as expected, a strong positive effect of moonlight. Surprisingly, however, there was a negative effect of the interaction between moonlight and artificial light, with birds calling less when artificial light was on during nights with high lunar illumination. One possible reason for this result is increased visibility leading to increased predation risk under high levels of ambient illumination.

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 score0.286
Threshold uncertainty score0.471

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)0.0000.000

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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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