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Record W4412779950 · doi:10.1038/s41597-025-05653-x

Sea lice infestation dataset for wild and farmed salmon populations on the Pacific coast of Canada (2001–2023)

2025· article· en· W4412779950 on OpenAlexaffabout
Crawford W. Revie, Thitiwan Patanasatienkul, Gregor McEwan

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of TorontoHealth PEIUniversity of Prince Edward Island
Fundersnot available
KeywordsInfestationFisheryBiologyAquacultureLepeophtheirusJuvenileFishingGeographyEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Monitoring sea lice infestation levels on populations of farmed and wild salmonids is critical to the development of evidence-based policy designed to mitigate the risk these ectoparasites represent to wild juvenile salmon and the on-going sustainability of salmon aquaculture. The data described relate to sea lice monitoring along the coast of British Columbia (BC), Canada from all areas where Atlantic salmon farms are present, spanning over two decades of observations from these farms and adjacent wild Pacific salmonid populations. Around 10,000 mean monthly sea lice estimates are included from almost 100 salmon farms spread across seven 'fish health' zones along the BC coast. Sea lice infestation data from over 365,000 wild hosts, observed as part of almost 17,000 sampling events in these zones, are also reported. While observations were made in the same broad geographical area, temporal coverage varies by zone. These data provide valuable insights into long-term trends, including spatial variability and demographic patterns within the sea lice populations observed on various host species along the BC coast.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.949

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 studies0.0010.000
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 head0.056
GPT teacher head0.354
Teacher spread0.297 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

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