Bibliographic record
Abstract
F arming, one of the oldest professions of mankind, is by far the one that employs the largest number of individuals worldwide.Although outdoor country work is supposedly healthy, farmers are at risk of respiratory diseases because of their work environment.This essay summarizes the major respiratory health risks to farmers in Canada.Farming is a major industry in Canada.Prince Edward Island and New Brunswick have potatoes, Nova Scotia has apples, Quebec and Ontario have dairy, the Prairies have wheat and British Columbia has fruits.But then we all have pigs, lots of pigs.In Quebec, there are as many pigs as there are humans, and in Saskatchewan, there are three or four pigs per person.Canada exports hog products around the world, mostly to the United States and Asia.Because of the importance of this industry and the potential associated health risks, most often respiratory-related, Canadian researchers have developed internationally recognized expertise in this area.As a respirologist born and raised on a small mixed farm in Prince Edward Island, research on the respiratory health impact of the farm environment was a natural choice for me.My background allowed me to communicate with farmers in their terms and to understand their interest and concerns.This connection gave me a privileged relationship with farmers in Quebec, especially dairy farmers and swine producers, and made most of my work on farmer's lung (FL) and swine building environments possible.Dr James Dosman from Saskatoon, Saskatchewan, is by far the champion of Canada's research in respiratory health in farmers.He has led the way in defining the respiratory health impact of grain handling, and swine and poultry production.Besides his excellence as a researcher, he shines in his promotional and leadership role.Because of him, the research community in this field now holds a seven-year Canadian Institutes of Health Research training program, a multimillion dollar grant for the study of endotoxins in swine buildings, and recently, a large Canada Foundation for Innovation cross-Canada infrastructure grant.Dr James Dosman is what we say in French a 'rassembleur', one who brings people together for a cause.Researchers across Canada who work together largely because of his initiatives include Judy Guernsey from Halifax, Nova Scotia;
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".