Transgenic crops : new weed problems for Canada?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it