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Record W4403082646 · doi:10.1039/d4va00174e

Monitoring plastic pollution using bioindicators: a global review and recommendations for marine environments

2024· review· en· W4403082646 on OpenAlexaff
Matthew S. Savoca, Neil Angelo S. Abreo, Andrés H. Arias, Laura Baes, Matteo Baini, Elisa Bergami, Susanne M. Brander, Miquel Canals, C. Anela Choy, Ilaria Corsi, Bavo De Witte, Camila Domit, Sarah E. Dudas, Emily M. Duncan, Claudia E. Fernández, María Cristina Fossi, Ostin Garcés-Ordóñez, Brendan J. Godley, Daniel González‐Paredes, Victoria González Carman, Bonnie M. Hamilton, Britta Denise Hardesty, Sang Hee Hong, Shirel R. Kahane‐Rapport, Lauren Kashiwabara, Mariana Baptista Lacerda, Guillermo Luna‐Jorquera, Clara Manno, Sarah E. Nelms, Cristina Panti, Diego Pérez‐Venegas, Christopher K. Pham, Jennifer F. Provencher, Sara Purca, Harunur Rashid, Yasmina Rodríguez, Conrad Sparks, Chengjun Sun, Martín Thiel, Catherine Tsangaris, Robson G. Santos

Bibliographic record

VenueEnvironmental Science Advances · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsEnvironment and Climate Change CanadaFisheries and Oceans Canada
FundersNational Oceanic and Atmospheric AdministrationMinistry of Oceans and FisheriesNatural Environment Research CouncilConselho Nacional de Desenvolvimento Científico e TecnológicoSight Research UK
KeywordsBioindicatorPlastic pollutionPollutionEnvironmental scienceMarine pollutionEnvironmental resource managementEnvironmental planningEnvironmental protectionOceanographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Monitoring the movement of plastic into marine food webs is central to understanding and mitigating the plastic pollution crisis.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.327
Teacher spread0.301 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations27
Published2024
Admission routes1
Has abstractyes

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