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Record W4286213339 · doi:10.1139/as-2021-0057

Current efforts on microplastic monitoring in Arctic fish and how to proceed

2022· article· en· W4286213339 on OpenAlexaffvenue
Tanja Kögel, Bonnie M. Hamilton, Maria Granberg, Jennifer F. Provencher, Sjúrður Hammer, Alessio Gomiero, Kerstin Magnusson, Amy Lusher

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Toronto
Fundersnot available
KeywordsMicroplasticsArcticPlastic pollutionMarine debrisComparabilityFish <Actinopterygii>LimitingThe arcticEnvironmental scienceFisheryFood chainSubsistence agricultureEnvironmental resource managementEcologyGeographyBiologyOceanographyEngineeringDebris

Abstract

fetched live from OpenAlex

In this review, we investigated published data on the occurrence of microplastic in Arctic fish, and the suitability of the data and species for risk assessment and monitoring. As of 11 November 2021, we found nine studies in the peer-reviewed literature, one thesis and one report, confirming the occurrence of microplastic in fishes from multiple Arctic regions. The studies varied in methodology, detection, and quantification limitations, reported categories of size, shape, and chemical identity. All these factors influence the numbers of microplastic reported, thus limiting comparability and hindering integrative analysis. The physiological impacts of the reported microplastic contamination cannot be determined, as all studies targeted stomach/intestine contents and did not use methods with limits of detection low enough to determine particle translocation from the intestine to other organs, tissues, or body fluids within the fish. Furthermore, there is a fundamental lack of understanding the transfer and the effects of plastic additives to Arctic fishes. In addition to discussing methodological challenges and knowledge gaps, we consider ecosystem needs, commercial interests, Indigenous people’s subsistence, food safety and food sovereignty concerns, and developed a framework to harmonize and facilitate pan-Arctic microplastic monitoring.

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)0.001
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 score0.191
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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

Citations28
Published2022
Admission routes2
Has abstractyes

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