Overcoming challenges measuring SDG 12 progress using national registers to track chemicals in waste
Bibliographic record
Abstract
Abstract The United Nations’ Sustainable Development Goal 12 contains ambitions to reduce human and ecological harm from chemicals, including those in waste, but current official indicators (measurable parameters used to evaluate sustainable development conditions) do not measure variables relevant to these goals, such as impact to humans or the environment from chemicals. Pollutant Release and Transfer Registers from around the world comprise rich datasets on chemicals in industrial waste that can and should be used to measure progress towards Sustainable Development Goal 12. However, translation of these data to inform evaluation of the subsequent human and ecological impacts is impeded by gaps in assessment models. Here, data from Canada’s Pollutant Release and Transfer Register – the National Pollutant Release Inventory is used in a case study to offer perspectives on future directions to fill these gaps. The use of such Pollution Release and Transfer Registers will substantially advance the ability to quantify progress towards the Sustainable Development Goal 12 aims of sound management of chemicals in waste and, importantly, human health and ecological harm reduction.
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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 | 0.033 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".