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Record W1482668019 · doi:10.22621/cfn.v126i1.1289

Greater Scaup, <em>Aythya marila</em>, Nest Site Characteristics on Grassy Island, New Brunswick

2012· article· en· W1482668019 on OpenAlexaffvenueabout
Jon T. McRoberts, Nicole T. Quintana, Woutrina Smith, Warren B. Ballard, F. Patrick Kehoe, T. G. Dilworth

Bibliographic record

VenueThe Canadian Field-Naturalist · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsDucks Unlimited CanadaUniversity of New Brunswick
Fundersnot available
KeywordsAythyaNest (protein structural motif)HabitatVegetation (pathology)EcologyVegetation coverCover (algebra)GeographyEnvironmental scienceBiologyWaterfowlGrazing

Abstract

fetched live from OpenAlex

We studied Greater Scaup (Aythya marilla) nest site selection on Grassy Island, New Brunswick, during 1995 and 1996 by describing site selection in relation to habitat characteristics and association with larids using univariate comparisons. We pooled nesting data from both years and found that nesting sites were significantly closer to larid colonies and the edge of the patch of vegetation in which the nests were situated, had less forb canopy cover, more sedge cover, greater overhead concealment and lateral cover at 0–0.25 m, and less ground moisture than random sites. We evaluated Greater Scaup nests delimited as close to or far from larid colonies at 30 m and documented that nests closer to larid colonies were found in shorter vegetation that was closer to the edge of the patch of vegetation with less lateral cover at 0.25–0.5 m, but had greater overhead concealment than nests farther away. Advancements in the ecological understanding of the species, including habitat use patterns and species associations, will increase the likelihood of conservation successes.

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)0.000
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 score0.287
Threshold uncertainty score0.578

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.216
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 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

Citations3
Published2012
Admission routes3
Has abstractyes

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