Bibliographic record
Abstract
Plastic pollution is a concern to the entire planet. The Government of Canada has taken action on its promise to achieve the goal of zero plastic waste by 2030 and has adopted a Strategy on Zero Plastic Waste. Canada's Plastics Science Agenda (CaPSA) is a framework to inform future science and research investments, as well as decision-making. Among other actions, Canada is supporting and conducting research to improve our understanding of plastics in the environment and funded scientific research projects under the Increasing Knowledge on Plastic Pollution Initiative. In 2020, the Science assessment of plastic pollution was published by Environment and Climate Change Canada (ECCC) and Health Canada (HC). This state of the science report summarized the occurrence on plastic pollution in Canada and its potential impacts on human health and the environment. It found that macroplastics cause physical harm to biota and may negatively affect their habitat. Applying a precautionary approach, the report recommended action to reduce macroplastics and microplastics in the Canadian environment. The report also found that there was uncertainty surrounding microplastics (≤5 mm). As a result, ECCC and HC are currently drafting a supplementary update to the 2020 report which aims to address some of the knowledge gaps previously identified and to help direct future research. The report will summarize the state of microplastics in the Canadian environment, as well as their potential effects to animals and to human health. Whereas microplastic methodologies continue to evolve, the existing primary literature has already demonstrated that microplastics are found throughout the Canadian environment in all environmental compartments and that they are ingested by Canadian organisms at nearly all trophic levels. And, similar to the 2020 report, microfibres have been found to be one of the most common shapes of microplastics in the aquatic compartment, along with microplastic fragments. Also see: https://micro2024.sciencesconf.org/555196/document
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.144 | 0.020 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".