Carcinogens in the workplace? Think about it!
Bibliographic record
Abstract
The risk of developing work-related cancer is far less obvious than the risk of falling or being injured on the job.Yet, according to the CSST [workers' compensation board], there were 68 deaths in Quebec as a result of workplace accidents in 2011, as opposed to 100 caused by occupational cancer.This leaflet concerns all types of workplaces, because asbestos isn't the only carcinogen! CarcinogensWe can be exposed to carcinogens in dust, liquid, gas, waves or other forms.They can be colourless, odourless and invisible.See the table below for examples.To view a list of carcinogens, go to the International Agency for Research on Cancer (IARC) Web site: http://monographs.iarc.fr/ENG/Classification/index.php CARCINOGENS IN THE WORKPLACE? RR-796Even very low exposure can increase the risk of cancer.We spend an average of 40 hours a week at work for 30 or 40 years.We should reduce our exposure as much as possible.
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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.034 |
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".