Bibliographic record
Abstract
Thank you for the opportunity to respond to Dr Teta's letter about Finkelstein and Meisenkothen (2010). Before I make any substantive reply, I think that the background for this correspondence, namely litigation in the American legal system, needs to be brought to the attention of your readers. My coauthor, Chris Meisenkothen, is an attorney who represents individuals alleging illness caused by exposure to asbestos. Although my introduction to the friction materials debate was through a commission by a defense attorney to review the literature and discuss my opinion about causation, I have never been hired by attorneys representing defendants. Since my retirement from the Ontario Ministry of Labour, I have served as a medical epidemiologic consultant to Mr Meisenkothen and other plaintiff's attorneys. Exponent conducts research for companies who are defendants in asbestos litigation, and their staff are designated as experts in litigation on asbestos-containing products. David Egilman, a physician who consults to plaintiffs’ attorneys, has published a paper entitled ‘Exposing the “myth” of ABC, “anything but chrysotile”, in which he alleges that defense attorneys may argue that it is not chrysotile, but exposure to amphiboles that is the cause of an individual's mesothelioma (Egilman and Billings 2005). In the fourth paragraph of her letter, Dr Teta writes: ‘The key issue is not whether there are former employees of the CT plant who developed mesothelioma, but whether they are attributable to chrysotile exposure at this facility’. Dr Teta's letter might thus be seen as part of the “anything but chrysotile” defense.
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 | 0.042 |
| 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 | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.042 | 0.040 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".