Comparison of a Glyphosate-Resistant Canola (<i>Brassica napus</i>L.) System with Traditional Herbicide Regimes
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
Herbicide-resistant cultivars account for over 90% of the canola grown in western Canada and cultivars resistant to glyphosate dominate the market. Field experiments were conducted at three locations in Alberta to compare the glyphosate system with more traditional herbicide regimes. Glyphosate applied before seeding in spring resulted in better weed control, lower dockage, and higher canola yield and net return than 2,4-D applied in the fall. Glyphosate applied once (two- to four-leaf canola) or twice (two- to four-leaf followed by five- to six-leaf canola) in-crop provided similar weed control, dockage, and canola yield as ethalfluralin applied PRE in the fall followed by an in-crop mixture of sethoxydim, ethametsulfuron, and clopyralid; and superior weed control and canola yield and lower dockage than ethalfluralin alone or an in-crop mixture of sethoxydim and ethametsulfuron. The in-crop glyphosate applications resulted in higher net revenues than the other treatments. There was little or no advantage to applying glyphosate twice compared with once in-crop. The amount of active ingredient entering the environment varied with the herbicide regime but was lower with the glyphosate system than with most of the traditional regimes, especially when glyphosate was applied only once in-crop.
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 | 0.001 |
| 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