Developing SAICM into a framework for the international governance of chemicals throughout their lifecycle: Looking beyond 2020
Bibliographic record
Abstract
Chemicals play a vital role in global human development.Pharmaceuticals save lives from otherwise fatal diseases.Agrochemicals protect food crops from pests.Thousands of industrial chemicals provide the basis for consumer products.The global chemical industry is one of the largest business sectors in the world economy.In 2016, the roughly 100 000 chemicals produced, processed, and finally sold generated a turnover of USD 5.2 trillion (Statista 2018).Worldwide chemical production is projected to double between 2010 and 2030, and more than 70% of this increase will take place outside developed countries, in particular in countries with economies in transition such as Brazil, India, and China (OECD 2012).However, chemical production and use are not only a success story.Chemical pollution adversely impacts human health and the environment on a global scale (UN Environment 2013, 2017).Environmental pollution ignores national borders and can therefore be addressed effectively first and foremost through international cooperation.Approximately 200 multilateral environmental agreements (MEAs) address pollution issues on global, continental, and regional scales.Important global MEAs that deal with chemical pollution include the Montreal Protocol on Substances that Deplete the Ozone Layer, the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal, the Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade, the Stockholm Convention on Persistent Organic Pollutants, and the Minamata Convention on Mercury.
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".