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Record W2985426347 · doi:10.1002/lol2.10122

Microplastic occurrence and effects in commercially harvested North American finfish and shellfish: Current knowledge and future directions

2019· article· en· W2985426347 on OpenAlexaboutno aff
Britta R. Baechler, Cheyenne Stienbarger, Dorothy A. Horn, Jincy Joseph, Alison R. Taylor, Elise F. Granek, Susanne M. Brander

Bibliographic record

VenueLimnology and Oceanography Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
FundersOregon Sea Grant, Oregon State UniversityOregon State UniversityNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsMicroplasticsFisheryShellfishPlastic pollutionEnvironmental sciencePollutantMarine debrisPopulationEnvironmental protectionFish <Actinopterygii>GeographyEcologyAquatic animalBiologyEnvironmental healthDebris

Abstract

fetched live from OpenAlex

Abstract Commercial fisheries yield essential foods, sustain cultural practices, and provide widespread employment around the globe. Commercially harvested species face a myriad of anthropogenic threats including degraded habitats, changing climate, overharvest, and pollution. Microplastics are pollutants of increasing concern, which are pervasive in the environment and can harbor or adsorb pollutants from surrounding waters. Aquatic organisms, including commercial species, encounter and ingest microplastics, but there is a paucity of data about those caught and cultured in North America. Additional research is needed to determine prevalence, physiological effects, and population‐level implications of microplastics in commercial species from Canada, the United States, and Mexico. Investigations into possible human health effects of microplastic exposure from seafood are also greatly needed. This synthesis summarizes current knowledge, identifies data gaps, and provides future research directions for addressing microplastics effects in commercially valuable North American fishery species.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.710

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 studies0.0000.001
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 head0.003
GPT teacher head0.192
Teacher spread0.188 · 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 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

Citations80
Published2019
Admission routes1
Has abstractyes

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