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Record W4386001130 · doi:10.1002/lob.10597

Plastics as Non‐Toxic Disruptors of Aquatic Ecosystems

2023· article· en· W4386001130 on OpenAlexafffund
Andrew J. Tanentzap

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsTrent University
FundersCanada Research Chairs
KeywordsAquatic ecosystemEcosystemFood webProductivityEnvironmental scienceEcologyEnvironmental resource managementBiology

Abstract

fetched live from OpenAlex

Abstract Plastics are receiving widespread attention as an environmental contaminant, but their role in disrupting the behavior and physiology of organisms remains neglected. Here, I highlight growing evidence that plastics have pervasive, non‐toxic effects in aquatic ecosystems, especially on feeding interactions. These effects ultimately have consequences for the flow of energy through food webs and the productivity of entire ecosystems. New research efforts are now needed to link synthetic polymers and their additive chemistries to food web interactions under field conditions. This knowledge could be used to strengthen regulatory risk assessments, similar to the process for other ecological‐disrupting compounds, such as those that interfere with hormone systems.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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