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Record W4412442398 · doi:10.5038/2074-1235.52.2.1602

Seasonal and Spatial Variations in Densities of Marbled Murrelets <i>Brachyramphus marmoratus</i> off Southwestern Vancouver Island

2024· article· en· W4412442398 on OpenAlexafffundabout
Alan E. Burger, E. Anne Stewart

Bibliographic record

VenueMarine ornithology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaHabitat Conservation Trust FoundationStrongWorld Wildlife Fund
KeywordsSeabirdOrnithologyMarbled meatOceanographyGeographyFisheryGeologyBiologySouthern HemisphereEcologyClimatology

Abstract

fetched live from OpenAlex

The ocean off Vancouver Island, British Columbia, is a productive area supporting diverse marine life, including a globally important breeding population of Marbled Murrelets Brachyramphus marmoratus. We report on the year-round at-sea distribution and densities of this murrelet off southwestern Vancouver Island based on monthly vessel surveys in 1993–2000. Surveys covered three ocean zones: Nearshore (sheltered waters, usually < 20 m deep, within 1–2 km of shore; 69 surveys), Inshore (exposed coastal waters, < 50 m deep, within 6 km of shore; 38 surveys), and Offshore (exposed open seas over the continental shelf, > 50 m deep, generally > 6 km from shore; 29 surveys). Year-round mean densities (± standard deviation, in birds/km²) for Nearshore, Inshore, and Offshore waters were 4.5 ± 5.8, 1.5 ± 1.2, and 0.2 ± 0.2, respectively. During peak breeding season (May–July), densities were 13.7 ± 2.3, 3.3 ± 0.2, and 0.2 ± 0.1 birds/km², respectively. Our data confirm that most murrelets leave this area outside of the breeding season, and very few move into the open ocean at this time. Post-breeding movements, molt locations, and winter distribution of these birds remain poorly known, highlighting the need for more surveys outside the breeding season and across the murrelet’s range. Due to high vessel traffic, including many oil tankers, and ongoing chronic oil spills, murrelets off southwestern Vancouver Island are exposed to high risk of oil pollution, particularly during the summer and close to shore. Gill nets and disturbance from boats are additional risks.

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 score0.146
Threshold uncertainty score0.990

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.007
GPT teacher head0.222
Teacher spread0.214 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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