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Record W2066827406 · doi:10.1139/f10-137

Sea lice (Lepeophtheirus salmonis) infestations and the productivity of pink salmon (Oncorhynchus gorbuscha) in the Broughton Archipelago, British Columbia, Canada

2011· article· en· W2066827406 on OpenAlexfundvenueaboutno aff
Martin Krkošek, Ray Hilborn

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLepeophtheirusOncorhynchusBiologyInfestationFisheryProductivityJuvenileArchipelagoPopulationFishingEcologyAquacultureFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

The spread of salmon lice ( Lepeophtheirus salmonis ) from salmon farms may threaten some wild salmon populations. Infestations of wild juvenile pink salmon ( Oncorhynchus gorbuscha ) have been associated with high mortality and population decline. Using stock–recruit data for pink salmon from the central coast of British Columbia, we analyzed how fishing mortality and spatial covariation combine with louse infestation to affect pink salmon population dynamics. The results indicate substantial coherence in survival at nested spatial scales — large-scale regional covariation and smaller scale covariation within management areas. Populations exposed to salmon farms (those from the Broughton Archipelago) show a sharp decline in productivity during sea lice infestations relative to pre-infestation years. Unexposed populations (comprising four management areas) did not experience a change in productivity during infestation years and had similar productivity to exposed populations before infestations. Our results suggest that sea lice infestations may result in declines of pink salmon populations and that management and policy of salmon farms should consider protecting wild juvenile salmon from exposure to sea lice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.010
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.234
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.

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

Citations37
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicParasite Biology and Host InteractionsFrench-language works237,207