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Record W2914325141 · doi:10.5038/2074-1235.47.1.1290

Eastern coyote Canis latrans predation on adult and pre-fledgling Northern Gannets Morus bassanus nesting on mainland cliffs at Cape St. Mary's, Newfoundland, Canada

2019· article· en· W2914325141 on OpenAlexfundaboutno aff
William A. Montevecchi, Kelley C. Power, E. White, Chris Mooney, Leanne Guzzwell, Jean-François Lamarre, M. Aeberhard, Jonathan Fiely

Bibliographic record

VenueMarine ornithology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapeSeabirdOrnithologyMainlandPredationCanisNesting (process)GeographyNest (protein structural motif)FisheryEcologyBiologyArchaeologyEngineeringSouthern Hemisphere

Abstract

fetched live from OpenAlex

We document the first evidence of predation by invasive eastern coyotes Canis latrans on breeding seabirds on the island of Newfoundland, Canada.We detail kills of 110 Northern Gannets Morus bassanus (50 adults, 60 large pre-fledgling chicks) nesting on mainland cliffs at the Cape St. Mary's Ecological Reserve.During nocturnal predation, late in the Northern Gannets' nesting season (September/October), coyotes killed 68 birds (30 adults, 38 large pre-fledgling chicks) in 2016 and 42 birds (20 adults, 22 pre-fledgling chicks) in 2018.Most birds were killed by bites to the head and cranial punctures.Approximately one-quarter of the birds were partially (pectoral muscle) or fully consumed.Based on carcass condition, it appeared that coyotes killed, consumed, and left intact gannets for one week or longer.Although coyotes are not a significant threat to seabirds, they could increase selection pressure on seabirds nesting at mainland sites.Coyote-seabird interactions are likely to increase as the canids venture to coastal seabird nesting areas and islands.

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 score0.650
Threshold uncertainty score1.000

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)0.0010.000

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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