Bibliographic record
Abstract
Thank you for the opportunity to reply to the letter by Roggli, Sporn, Case and Butnor (RSCB). I would like to begin by addressing the areas in which I am in agreement with the writers. I agree that neither amosite nor crocidolite was used in friction products in the US. I agree that the finding of elevated levels of those fibres in the lung tissues of some of their subjects indicates asbestos exposure from other sources in addition to exposure from the dusts of friction products. This is not necessarily surprising. In Hessel’s reanalysis of the National Institutes of Health mesothelioma study, it was noted that 10 of 12 brake workers with mesothelioma had also had asbestos exposures in shipbuilding or insulation (Hessel et al., 2004). The trivial conclusion of the study of Butnor, Sporn and Roggli (BSR) (Butnor et al., 2003) is thus that they misclassified their study subjects. Although occupational contact with brake dust was the only information about asbestos exposure available to BSR, some of their subjects had other exposures. I presume that this misclassification was related to the poor quality of some of the exposure histories, as exemplified by the observation that BSR did not have such basic information as age for 20% of their subjects.
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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.050 | 0.045 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".