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Roost Selection and Roosting Behavior of Male Common Nighthawks

2004· article· en· W2011205793 on OpenAlexafffundabout
Ryan J. Fisher, Quinn E. Fletcher, Craig K. R. Willis, R. Mark Brigham

Bibliographic record

VenueThe American Midland Naturalist · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsCanopyCypressMicroclimateEcologyPredator avoidanceGeographyNational parkSelection (genetic algorithm)PredationBiologyPredator

Abstract

fetched live from OpenAlex

Many studies to date have documented clear energetic costs and benefits of avian roost selection. Male Common Nighthawks (Chordeiles minor) spend at least half of each day during the summer on a day-roost. Therefore selection of roost-sites likely has implications for survival and long-term fitness. Our objective was to identify characteristics of day-roosts used by male Common Nighthawks in Cypress Hills Provincial Park, Saskatchewan, Canada. We measured features of roost trees and monitored behavior of roosting birds. Nighthawks preferred trees situated on north facing slopes surrounded by trees with significantly lower canopy height compared to randomly measured trees. Roost trees were taller than random trees and occurred in less dense patches of forest. Birds always roosted parallel to branches and adopted a motionless posture. There was a non-significant trend for birds to roost on branches facing east. Birds typically roosted in a direction pointing away from the sun and away from the roost tree trunk. Roost trees emerging from the canopy may provide landmarks for birds as they search for suitable day-roosts, whereas a low tree density surrounding roost trees likely reduces flight costs associated with maneuvering. Our results suggest that roosts chosen by male nighthawks may provide selective benefits in terms of microclimate, energetics and predator avoidance, but further studies are needed to determine which is the most important.

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 score0.149
Threshold uncertainty score0.792

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations25
Published2004
Admission routes3
Has abstractyes

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