Integrated Approach to PBT and POP Prioritization and Risk Assessment
Bibliographic record
Abstract
This article summarizes discussions at the SETAC Pellston Workshop on "Science-Based Guidance and Framework for the Evaluation and Identification of PBTs and POPs" and provides an overview of other articles from that workshop that are also published in this issue. Identification of persistent, bioaccumulative, and toxic substances (PBTs) and persistent organic pollutants (POPs) and evaluation of their impact are more complicated than those for other chemicals and remain a challenge. The main reason for this is that PBT substance and POP assessment is associated with higher uncertainty and generally requires more data. However, for some data-rich PBTs and POPs, that identification and assessment of impact are feasible has been clearly demonstrated. New scientific developments and techniques are able to significantly increase the certainty of the various elements of PBT and POP assessment, and the current scientific literature provides many successful and illustrative examples that can be used as methodologies to build on. Applying multiple approaches for assessment is advisable, because it will reduce uncertainty and may increase confidence and improve the quality of decision-making.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".