Influence of herbicide‐resistant canola on the environmental impact of weed management
Bibliographic record
Abstract
The growth of herbicide-resistant canola varieties increased from 10% of the canola area in Canada in 1996, when the technology was first introduced, to 80% in 2000. From 1995 to 2000, the amount of herbicide active ingredient applied per hectare of canola declined by 42.8% and the Environmental Impact (EI) per hectare, calculated using the Environmental Impact Quotient for individual herbicides and the amounts of active ingredients applied, declined 36.8%. The amount of herbicide active ingredient per hectare applied to conventional canola was consistently higher than that applied to herbicide-resistant canola each year between 1996 and 2000. Similarly, the EI of herbicide use per hectare in conventional canola was higher than that of herbicide-resistant canola during the same time period. Since 1996, herbicide use has shifted from broadcast applications of soil-active herbicides to post-emergence applications of herbicides with broad-spectrum foliar activity. The decline in herbicide use and EI since the introduction of herbicide-resistant varieties was due to increased use of chemicals with lower application rates, a reduced number of applications and a decreased need for herbicide combinations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".