Transgenic crops : new weed problems for Canada?
Bibliographic record
Abstract
Over 25 000 transgenic field trials were conducted globally from 1986-1997, and many transgenic crops, including soybean (Glycine max), maize (Zea mays), tobacco (Nicotiana tabaccum), cotton (Gossypium hirsutum), canola (Brassica napus, B. rapa), tomato (Lycopersicon esculentum) and potato (Solarium tuberosum) have been commercially released. There has been a high adoption rate, with at least 28 million ha reported for 1998, with herbicide- and insect-resistant plants occupying 71 and 28% of the releases, respectively. The current status of commercial production of transgenic crops in Canada is summarized. Transgenic crops have the potential to change weed communities/populations in three principal ways, via: 1 ) escape and proliferation of the transgenic plants as 'weedy' volunteers with subsequent displacement of the crop, weed and/or natural vegetation; 2) hybridization with and transgene infiltration into related weedy and/or wild species, resulting in invigorated weeds and/or alteration of natural gene frequencies in these species; and 3) genetic changes in populations of unrelated species, as a result of changes to the environment, in particular herbicide-resistant (HR) transgenic crops and the development of HR weeds. Potential risk can be estimated a priori using knowledge of the systematics of crop/wild/weed complexes. Risk must be assessed on a case-by-case basis for each crop, each country/ecological region, and for each trait. Potential weed risks will be greater if crop volunteers are predisposed to becoming weedy, are well adapted to the Canadian climate and if sexually compatible wild species are present.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".