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Record W2152869640 · doi:10.1890/12-0519.1

Cessation of a salmon decline with control of parasites

2012· article· en· W2152869640 on OpenAlexaffabout
Stephanie J. Peacock, Martin Krkošek, Stan Proboszcz, Craig Orr, Mark A. Lewis

Bibliographic record

VenueEcological Applications · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsPacific Salmon FoundationVancouver Coastal HealthUniversity of Alberta
Fundersnot available
KeywordsEcologyBiologyFishery

Abstract

fetched live from OpenAlex

The resilience of coastal social-ecological systems may depend on adaptive responses to aquaculture disease outbreaks that can threaten wild and farm fish. A nine-year study of parasitic sea lice (Lepeophtheirus salmonis) and pink salmon (Oncorhynchus gorbuscha) from Pacific Canada indicates that adaptive changes in parasite management on salmon farms have yielded positive conservation outcomes. After four years of sea lice epizootics and wild salmon population decline, parasiticide application on salmon farms was adapted to the timing of wild salmon migrations. Winter treatment of farm fish with parasiticides, prior to the out-migration of wild juvenile salmon, has reduced epizootics of wild salmon without significantly increasing the annual number of treatments. Levels of parasites on wild juvenile salmon significantly influence the growth rate of affected salmon populations, suggesting that these changes in management have had positive outcomes for wild salmon populations. These adaptive changes have not occurred through formal adaptive management, but rather, through multi-stakeholder processes arising from a contentious scientific and public debate. Despite the apparent success of parasite control on salmon farms in the study region, there remain concerns about the long-term sustainability of this approach because of the unknown ecological effects of parasticides and the potential for parasite resistance to chemical treatments.

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 score0.041
Threshold uncertainty score0.999

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)0.0020.000

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 head0.307
Teacher spread0.295 · 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

Citations49
Published2012
Admission routes2
Has abstractyes

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