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Record W4292672697 · doi:10.1016/j.hal.2022.102310

Indications that algal blooms may affect wild salmon in a similar way as farmed salmon

2022· article· en· W4292672697 on OpenAlexaffabout
Svetlana Esenkulova, Chrys Neville, Emiliano DiCicco, Isobel Pearsall

Bibliographic record

VenueHarmful Algae · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsVancouver Island UniversityFisheries and Oceans CanadaAsia Pacific Foundation of Canada
Fundersnot available
KeywordsBiologyBayAlgal bloomFisheryJuvenilePhytoplanktonGillAlgaeAquacultureJuvenile fishFish <Actinopterygii>ZoologyEcologyOceanographyNutrient

Abstract

fetched live from OpenAlex

Based on a four year study conducted in Cowichan Bay, Canada, potential linkages between composition and abundance of phytoplankton and the feeding and histopathology of juvenile salmon were noted. During two dense blooms (Skeletonema spp. and Pseudo-nitzschia spp.), feeding of juvenile Chinook salmon decreased (n=202, empty stomachs >50%). All collected salmon gills (n=5) were damaged following high levels of mechanically harmful Chaetoceros convolutus in the water column; all collected livers (n=5) showed signs of pathological changes during Octactis speculum bloom. These observations were consistent with effects previously reported from salmon farms, however this agreement must be treated with caution as it is based on a limited number of samples. We suggest that there is a need for comprehensive studies to evaluate the potential role of harmful algae as a stressor to wild fish in a coastal environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2022
Admission routes2
Has abstractyes

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