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ASSESSING EDGE AVOIDANCE AND AREA SENSITIVITY OF RED-EYED VIREOS IN SOUTHCENTRAL ONTARIO

2002· article· en· W2210340921 on OpenAlexaffabout
Wendy Dunford, Dawn M. Burke, Erica Nol

Bibliographic record

VenueThe Wilson Bulletin · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsTrent University
Fundersnot available
KeywordsNest (protein structural motif)CowbirdEcologyGeographyForest fragmentationBiologyHabitatHost (biology)

Abstract

fetched live from OpenAlex

We assessed edge avoidance, area sensitivity, and the relationship between local and regional forest cover for nesting Red-eyed Vireos (Vireo olivaceus) in 13 forest fragments (1–2,353 ha in size) in southcentral Ontario, Canada. Red-eyed Vireo territories and nests were not significantly farther from the edge than random points in any of the forest fragments, and there was no relationship between the probability of a male pairing and the distance of the territory from the edge of the forest fragment. The density of singing males and the probability of a male being paired increased significantly with increasing local forest cover within a 2-km radius of a study site, but not with forest fragment area or regional forest cover within a 10-km radius. Nest success was low and the probability of a nest being parasitized by the Brown-headed Cowbird (Molothrus ater) or successfully fledging ≥1 host young did not vary with distance of the nest from the forest edge or with any of our area or forest cover measures. Red-eyed Vireos did not display edge avoidance nor did they appear to be area sensitive within our study region, but there was a positive relationship with the amount of local (2-km radius) forest cover. Maintaining localized regions with high forest cover has been recommended on numerous occasions for the conservation of area sensitive species; our results suggest high forest cover also may benefit species that do not appear to be area sensitive.

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 score0.014
Threshold uncertainty score0.997

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)0.0040.000

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 head0.215
Teacher spread0.193 · 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

Citations10
Published2002
Admission routes2
Has abstractyes

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