Bibliographic record
Abstract
As more and more Canadian municipalities consider banning the use of lawn pesticides, Ottawa has announced a plan to reduce domestic use and speed up the re-evaluation of certain chemicals. The Action Plan for Urban Use Pesticides (www.hc-sc.gc.ca/pmra-arla/english/pdf/hl-ActionPlan-e.pdf) provides consumer information on pest prevention and the use of reduced-risk products. Because of public concern, the plan gives re-evaluation priority to the 7 most commonly used active ingredients in lawn-care products. The 7 products include the insecticides diazinon, carbaryl and malathion, and the herbicides 2,4-D and mecoprop. This re-evaluation, due for completion this year, will include exposure guidelines for children. Marc Richard, spokesperson for Health Canada's Pest Management Regulatory Agency (PMRA), says Canada will rely on US reports on basic toxicology in conducting its re-evaluations, which will save taxpayers about 80% of the cost of an evaluation. A risk assessment that considers uniquely Canadian conditions will be performed by Health Canada. For the past 5 years Canadian and American scientists have collaborated on researching low-risk products by sharing expertise and dividing up the work. Considering that each chemical can be accompanied by about 20 000 pages of data, says Richard, it's “extremely efficient” to work together. The action plan also addresses the problem of manufacturers who fail to comply with requests to withdraw products voluntarily or restrict their uses. In June 2000, US manufacturers agreed to phase out production of chlorpyrifos, but Dow AgroSciences, Canada's largest manufacturer of the chemical, refused to do the same here. Under the action plan, PMRA will ask Canadian-based companies to comply with US re-evaluation activities that result in these requests for voluntary action. —
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.060 | 0.019 |
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".