229 Exposure and risk assessment for elongate mineral particles
Bibliographic record
Abstract
Abstract Asbestos and other fibrous minerals remain significant occupational and environmental concerns worldwide. Despite being effectively banned from commercial utilization in the Western world, asbestos is still produced and utilized in many countries. Exposure to so called “legacy” asbestos, still present in buildings and other objects and products, also causes concerns. This Professional Development Course (PDC) will present a framework for exposure assessment and risk characterization based on the most advanced methods and recently published models. We will show the applications of quantitative risk assessment for different situations that occupational and environmental health professionals worldwide may encounter. Examples will include potential exposure to erionite fibers during forestry activities; exposure to legacy asbestos in buildings with partially disturbed asbestos-containing materials; exposure of populations near current or former mines or other point sources such as former asbestos cement plants and shipyards; exposure during recreational activities at sites containing naturally occurring asbestos (NOA), and others. Participants will review the most recent scientific approaches to the pathogenesis and pathology of asbestos-associated diseases, important for hazard identification and a proper understanding of the health effects of asbestos. The most innovative approaches to asbestos exposure assessment will be characterized, including real time monitoring instruments, artificial intelligence tools, and exposure reconstruction from the lung burden levels. The PDC will be presented by the leading scientists in the area of asbestos risk assessment, known for their numerous peer-reviewed publications. As an added value, participants will be provided with copies of some of the most recent papers authored by the presenters, and the asbestos “risk calculator” for practical utilization.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".