MétaCan
Menu
Back to cohort
Record W6929809059 · doi:10.5281/zenodo.10048129

Breaking the Plastic Cycle: A Policy Roadmap to Eliminate One-Third of Canada's Plastic Packaging Waste

2023· report· en· W6929809059 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typereport
Languageen
FieldMedicine
TopicBoron Compounds in Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsPlastic wastePlastic packagingPlastic pollutionPlastic bagPlastics industry

Abstract

fetched live from OpenAlex

Today, Canada produces nearly 2.4 million tonnes of plastic packaging waste each year. And that number keeps growing dramatically. Typically, this plastic is used just once, sometimes for minutes. But it lasts for centuries in the environment, where it harms oceans, ecosystems and life itself. Oceana Canada's roadmap provides an evidence-based guide to eliminating one-third of our country's plastic packaging. By implementing the recommended interventions, Canada can prevent the generation of nearly nine million tonnes of single-use plastic by 2040.

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)0.009
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0050.002
Scholarly communication0.0060.003
Open science0.0050.003
Research integrity0.0110.005
Insufficient payload (model declined to judge)0.0300.008

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 head0.072
GPT teacher head0.312
Teacher spread0.241 · 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
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicBoron Compounds in ChemistryFrench-language works237,207