Challenges with managing hazardous chemicals in the international frame - opportunity for educating on sustainable chemistry and alternatives assessment
Bibliographic record
Abstract
Introduction Within international chemicals management treaties like the Stockholm Convention on Persistent Organic Pollutants (POPs) or the Vienna Convention/Montreal Protocol on Ozone Depleting Substances (ODS) the phase out and management of hazardous chemicals is addressed globally. Decades of experiences have unveiled how difficult and expensive the management of listed chemicals is in particular the end of life. Method Past failures and on-going challenges to manage hazardous chemicals in the international context in particular the Stockholm Convention were assessed with considerations of a way forward. Results and Discussion The assessment shows the challenge with managing organohalogen compounds (chlorinated, brominated and fluorinated) in their life-cycle in particular in developing countries. In the past, often certain types of hazardous or unsustainable chemicals are continued to be used or replaced by similarly unsustainable chemicals for different reasons in various cases leading to regrettable substitutions. Examples are the substitution of POPs like lower brominated PBDEs with highly brominated PBDEs and other brominated aromatic compounds or the substitution of PCBs with chlorobenzyltoluenes and chlorinated paraffins. Since some of the recently listed POPs have a range of exemptions, a process on compiling information on alternatives has been initiated in the Stockholm Convention. Therefore, international conventions provide opportunities to substitute hazardous chemicals with more sustainable alternatives. A bottle neck is, however, that chemists, engineers, industrial designers, and policy makers often lack knowledge on green/sustainable chemistry and alternatives assessment. This is particularly a challenge in developing and transition countries. Methodologies and tools are hence needed to disseminate information on and guidance on how to phase in more sustainable alternatives. In this presentation, we suggest a comprehensive yet efficient approach of alternatives assessment which could be integrated in the education of stakeholders within the process of the substitution of chemicals e.g. in international chemical conventions.
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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 | 0.022 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".