Bibliographic record
Abstract
This article describes the approach used to develop a prioritized list of toxic and hazardous industrial chemical hazards considered to pose substantial risk to deployed troops and military operations. The U.S. Army Center for Health Promotion and Preventive Medicine published the prioritized list in November 2003. The work was performed as part of a multinational military effort supported by Canada, the United Kingdom, and the United States. Previous chemical priority lists had been developed to support military as well as homeland defense research, development, and acquisition communities to determine enhanced detection and protection needs. However, there were questions as to the adequacy of the methodologies and focus of the previous efforts. This most recent effort is a more extensive evaluation of over 1700 industrial chemicals, with a modified methodology that includes not only the assessment of acute inhalation toxic industrial chemicals (TICs), but also chemicals/compounds that pose substantial physical risk (from fire/explosion) and those that may pose acute ingestion risks (such as in water supplies). The methodology was designed to rank such hazards from a strategic (global) military perspective, but it may be adapted to address more site/user specific needs. Users of this or any other chemical priority list are cautioned that the derivation of such lists is largely influenced by subjective decisions and significant variability in chemical-specific data availability and quality.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".