Selecting High-priority Hazardous Chemicals for Tri-national Control: A Maximum-utility Method Applied to Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The dispersion of persistent, bioaccumulative toxic chemicals poses risks to human health and the integrity of the ecosystem on a continental scale. Mexico, the United States, and Canada sought to add two pollutants to an existing list of four subject to North American Regional Action Plans (chlordane, DDT, mercury, PCBs). Mexican negotiators used results from an internal selection process, applying 14 criteria in five categories-physicochemical, health-endpoint, data quality/quantity, exposure potential, and control feasibility-to a baseline group of over 4,700 substances. Using policy analysis by the multiattribute maximum-utility method, progressive application of criteria and weighting algorithms acted like successive filters to identify priority lists of 15 and 7 substances/substance groups for Mexico. The 15 are: 1) benzo-a-pyrene (1 other PAHs); 2) cadmium; 3) heptachlor; 4) hexachlorobenzene; 5) lead; 6) lindane (+ other HCH isomers); 7) 2,3, 7,8-tetrachlorodibenzo-p-dioxin (&plus other PCDDs); 8) aldrin; 9) arsenic; 10) chromium; 11) carbon tetrachloride; 12) 3-3'-dichlorobenzidine; 13) dieldrin; 14) nickel; and 15) toxaphene. The first seven are the priority list of seven.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it