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Noise results in lower quality of an important forage fish, the Pacific sand lance, Ammodytes personatus

2025· article· en· W4407430447 on OpenAlexafffund
Nora V. Carlson, Meredith A V White, José Tavera, Patrick D. O’Hara, M. R. Baker, Douglas F. Bertram, Adam P. Summers, David A. Fifield, Francis Juanes

Bibliographic record

VenueMarine Pollution Bulletin · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Victoria
FundersEnvironment and Climate Change CanadaMitacsUniversity of VictoriaWildlife Conservation Society Canada
KeywordsFisheryForageFish <Actinopterygii>Forage fishGeographyNoise (video)Environmental scienceBiologyEcologyComputer science

Abstract

fetched live from OpenAlex

Anthropogenic noise is a pervasive environmental pollutant that continues to expand and increase globally, especially in marine environments, affecting many marine animals, especially fish. Although interest and concern regarding the effects of noise on fish has increased, most studies still focus on the effects noise has on individual species, often overlooking wider system-level consequences. This is particularly true of trophically important species such as forage fish. We investigated how different types of anthropogenic noise affect the quality of an important forage fish species, Pacific sand lance, Ammodytes personatus, which could impact the many species that rely on them. We found that, compared to controls, fish in noisy environments had lower energy density and lower weight at a given length. These results suggest that even over shorter periods of time the anthropogenic noise could reduce sand lance quality, which in-turn could cascade up the food chain causing drastic ecosystem-level consequences.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

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)0.0020.000

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations7
Published2025
Admission routes2
Has abstractyes

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