Coalinga Chrysotile: A Short-Fibre, Amphibole-Free, Chrysotile: Part VI - Additional Evidence for Lack of Fibrogenicity
Bibliographic record
Abstract
This report describes additional unpublished data that support the notion that Coalinga chrysotile is not fibrogenic. These data are from the same long-term, NTP-NIEHS, chronic chrysotile inhalation study that served as the basis for our earlier publications which concluded that Coalinga chrysotile was not fibrogenic due to its lack of biopersistence. Wagner-scored 12 and 24 month interim sacrifice data from the NTP-NIEHS investigation and an unpublished data set from the comparison MRC study both previously unreported, were recently found in the author’s files. The histology slides and attendant data should have been amongst the materials archived at the NTP but were not present at the time the author reviewed those files in 1995 and 1996. Review of these new materials showed that Coalinga chrysotile did not produce fibrosis either at 12 or 24 months in contrast to the two Canadian preparations which were both fibrogenic even at this early stage of the study. These new data and related testimony greatly strengthen our earlier conclusions and serve to refute earlier apparently contradictory reports. International and Governmental Reports support the conclusion reached in our papers that Coalinga chrysotile is not pathogenic. In fact, the only scientists to criticise our conclusions have been in litigation against Union Carbide.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".