MétaCan
Menu
Back to cohort
Record W4294225537 · doi:10.3389/fmars.2022.981439

Achieving sustainable production and consumption of virgin plastic polymers

2022· article· en· W4294225537 on OpenAlexaboutno aff
Tim Grabiel, Tom Gammage, Clare Perry, Christina Dixon

Bibliographic record

VenueFrontiers in Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsPlastic pollutionMandateConsumption (sociology)Production (economics)TreatyBusinessMarine debrisSustainabilityOzone layerMidstreamEnvironmental scienceNatural resource economicsEnvironmental economicsPollutionEconomicsEnvironmental engineeringDebrisPolitical scienceLawGeographyEcology

Abstract

fetched live from OpenAlex

The United Nations Environment Assembly (UNEA) recently adopted a resolution with a mandate to negotiate a new international legally binding instrument (a treaty) on plastic pollution. The mandate includes the need to ‘prevent’ as well as ‘reduce’ and ‘eliminate’ plastic pollution through a ‘comprehensive approach that addresses the full life cycle of plastic’. Unsustainable production and consumption of virgin (primary) plastic polymers represents the single greatest threat to preventing plastic pollution and risks undermining the incoming treaty. However, current discussions on a global plastics treaty overlook upstream measures that address virgin plastic production and consumption, focusing instead on midstream and downstream measures on product design and waste management. This article presents the justification for and benefits of a stepwise approach for controlling virgin plastic production and consumption internationally, inspired by the Montreal Protocol on Substances that Deplete the Ozone Layer;

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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 designNot applicable
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

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Marine ScienceSame topicMicroplastics and Plastic PollutionFrench-language works237,207