Bibliographic record
Abstract
Journal, celebrating the 50th anniversary of the Canadian Thoracic Society (CTS), provides me with an opportunity to document what, in my view, is one of the most effective collective interventions in the field of epidemiology and occupational health.The present essay gives the reasons why the Society addressed the question of whether grain dust is not just a nuisance dust, and how it did so.My main source of information is from a 1978 report in the Canadian Lung Association Bulletin (1).Grain growing, handling and processing have long been major Canadian industries.In the 1971 census of Canada, there were in Canada approximately 253,000 farmers, farm managers and farm workers; approximately 5000 small elevator workers and 7200 large elevator workers; and approximately 15,000 workers in the flour, feed and seed mills.The dust to which grain workers are exposed is complex, whether it be wheat, barley, rye, oats or corn.It consists of the grain, hairs from the epicarp and the germ.Plant contaminants include weeds and pollens.Fungi grow in grain depending on its freshness.Rodents may infest it, leaving their spoor.Chemicals may be added to control fungi, arthropods and rodents.Free silica from the soil may be present or incorporated into the plant as phytoliths.Finally, grain workers often handle oil seeds such as sunflower, flax and mustard.Grain dust had been classified as a 'nuisance' dust; it does not, in the view of the influential American Conference of Government and Industrial Hygienists, require regulation because of its ill health effects on exposed workers.This, however, was contrary to the experience of several members of the CTS, who had been involved in an impressive number of epidemiological research studies of workers in different branches of the grain handling industry before 1977.
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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".