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
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.
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 | 0.000 | 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) | 0.000 | 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 it