Sustainable Management Practices to Include Mixtures of Chemicals in Regulatory Approaches Focusing on the Global South
Bibliographic record
Abstract
Environmental pollution is becoming increasingly unpredictable over time due to its complexity, given the number of new chemicals produced annually and the constantly changing environmental conditions. Regulation has yet to keep pace with the rapid changes posed by chemical mixtures, especially in the Global South. Understanding the potential outcomes of co-exposure to multiple compounds can be challenging, even for professionals with a background in sustainability and mixture toxicity, due to the complexity of the issue. Some tools have been developed to tackle this uncertainty like the Species Sensitivity Distribution curve (SSD), the Adverse Outcome Pathways (AOP), and the Mixture Assessment Factor (MAF). This study aims to bridge the gap between knowledge generated in the field of mixture toxicity and regulatory practices by proposing sustainable management practices at the local scale, particularly for countries in the Global South. The proposed framework is called GlORIES and comprises the following measures. The first proposed step is to describe the chemicals used in industries or identified in existing environmental studies and/or monitoring campaigns on a watershed basis. Having a watchlist of compounds and organisms present in the region, and by generating a regionalized SSD, it is possible to use models such as AOPs to try to predict which compounds could potentially interact and thus generate a correcting factor, such as a MAF. A MAF could then be incorporated into regulations to further protect the environment by reducing the concentration of the compound in the mixture. Including local communities in reporting human and environmental health alterations could be a key to identifying the possible harmful emissions. It is proposed that watershed management committees be established to integrate all stakeholders and promote workshops organized by academia, industry, regulatory agencies, and civil society, leveraging existing structures to conserve energy in the process. The proposed framework can improve the sustainability of the process and the knowledge flow from academia to regulatory bodies, increasing the efficacy of the chosen water quality thresholds by adapting to real-life scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".