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Record W2051734227 · doi:10.1021/es0520161

Ecosystemic Effects of Salmon Farming Increase Mercury Contamination in Wild Fish

2006· article· en· W2051734227 on OpenAlexafffundabout
Adrian M.H. deBruyn, Marc Trudel, Nicola Eyding, Joel Harding, Heather McNally, Robert Mountain, Craig Orr, Diane J. Urban, Sergei S. Verenitch, Asit Mazumder

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of VictoriaFisheries and Oceans Canada
FundersHealth Canada
KeywordsMercury (programming language)FisheryDemersal zoneDemersal fishEnvironmental scienceAquaculturePredationTrophic levelFish farmingRockfishEcologyPelagic zoneBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Net-pen salmon aquaculture has well-known effects on coastal ecosystems: farm waste increases sediment organic content and the incidence of sediment anoxia, supports increased production of deposit-feeding invertebrates, and attracts higher densities of demersal fish and other mobile carnivores. These impacts are widely considered to be localized and transitory, and are commonly managed by imposing a period of fallowing between cycles of production. The implications of these ecosystemic effects for contaminant cycling, however, have not previously been considered. We found elevated levels of mercury in demersal rockfishes near salmon farms in coastal British Columbia, Canada, attributable to a combination of higher rockfish trophic position and higher mercury levels in prey near farms. Mercury concentrations in long-lived species such as rockfishes change over a longer time scale than cycles of production and fallowing, and thus at least some important effects of fish farms may not be considered transitory.

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)0.000
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 score0.099
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.204
Teacher spread0.201 · 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

Citations44
Published2006
Admission routes3
Has abstractyes

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