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Record W6920826715 · doi:10.60825/swhf-m095

Sediment concentrations of emamectin benzoate (EMB) detected around some Canadian finfish aquaculture sites sampled as part of the Aquaculture Monitoring Program (2018-2023) and comparison with the published EMB environmental quality standard used in Scotland

2025· report· en· W6920826715 on OpenAlexaffabout

Bibliographic record

VenueFisheries and Oceans Canada / Pêches et Océans Canada - Publications · 2025
Typereport
Languageen
Field
Topic
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsAquacultureSedimentEmamectin benzoateWater qualityFish <Actinopterygii>Environmental impact assessmentEnvironmental quality

Abstract

fetched live from OpenAlex

This is a descriptive summary of data collected as part of the Aquaculture Monitoring Program (AMP) from 2018 to 2023. The AMP generate a database of measurements and indicators assessed in marine sediments at and around finfish sites. This report focuses on Emamectin Benzoate (EMB) measurements of sediment samples, EMB being the main in-feed drug in Canadian salmon aquaculture. We report percentages and locations of samples collected at some British Columbia (BC), Newfoundland and Labrador (NL), and Maritimes’ sites that are above the EMB Environmental Quality Standard (EQS) concentration. The EQS is not an adopted value in Canada but is used in Scotland as a threshold to manage drug deposits at finfish sites. From 2018 to 2020, 33% to 77% samples had EMB concentrations above the EQS of 0.272 μg/kg with most samples located within 200 m from cage edge. For the 2021 to 2023 data, 69 to 81% were over the EQS and located within 400 m of cages highlighting that this dataset exhibit a higher percentage of stations above EQS relative to the previous 3 years. In both datasets, NL sites had the highest EMB concentrations while EMB levels at BC sites were consistently lower than in NL and Maritimes.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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 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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Same venueFisheries and Oceans Canada / Pêches et Océans Canada - PublicationsFrench-language works237,207