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Record W4386830598 · doi:10.1675/063.045.0410

Group Adherence in Endangered California Least Terns (Sternula antillarum browni)

2023· article· en· W4386830598 on OpenAlexaff
Patricia A. Baird

Bibliographic record

VenueWaterbirds · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPredationEcologyBiologyNest (protein structural motif)Endangered speciesIntraspecific competitionPredatorMobbingTernHabitat

Abstract

fetched live from OpenAlex

Colonial nesting in seabirds is advantageous for protection from predators—spotting a predator, mobbing, and predator swamping. Familiarity with nesting areas gives knowledge of protected sites and may promote site fidelity. Familiarity with nearest neighbors helps nesting success by lessening intraspecific aggression and increasing social facilitation, and may promote group adherence. Group adherence has been proposed as more important than site tenacity for some species where nesting areas are frequently disturbed. Ground-nesting terns often nest at disturbed sites, and their colonies are accessible to predators. Serendipitously, I was able to test the concept of group adherence in individually color-marked California Least Terns Sternula antillarum browni during early egg-laying when some nests in a colony in southern California were depredated, and the adults deserted. A week later, I found the majority of those birds nesting at the edge of a small Least Tern colony 28 km distant, where they laid a second clutch and remained at the site the rest of the breeding season. The following breeding season, no color-marked terns nested again at the small colony where they had moved after disturbance.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.998

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)0.0030.017

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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