Key Words Coalinga chrysotile • Jeffrey chrysotile • Asbestos
Bibliographic record
Abstract
Significant results from one of the largest asbestos inhalation studies ever done were never published. Over the last 10 years, some of these have been found and reported in a series of papers in this journal. The “missing ” data from the study were largely concerned with the potential chronic effects of short fibre chrysotile but also dealt with the alleged ability of a single, high dose exposure to long fibre chrysotile to produce a risk of disease for life (so-called “irreversibil-ity”). Given its ubiquity and the notion held by the US Government and Plaintiff that all forms of asbestos are equally potent in even the smallest doses, the where-withal to scientifically “exonerate ” short fibre chrysotile as a human health hazard would have very major regu-latory, socio-economic and legal implications. The US Government was aware that the issues of fibre length and irreversibility had to be scientifically resolved and so funded the study. California Coalinga chrysotile was used as the “standard ” short fibre material for the chronic inhalation assay: initially a 12-month exposure to fibre and then lifetime follow up. The “irreversibil-ity ” question was tested with a long fibre chrysotile from the Canadian Jeffrey mine: an initial high dose 1-hour to 1-day exposure and then 2-year follow-up. This report summarises how some of these missing data were found and discusses their relevance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.024 |
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".