Reproductive phenology of transgenic<i>Brassica napus</i>cultivars: Effect on intraspecific gene flow
Bibliographic record
Abstract
Pollen-mediated gene flow in space is well documented and isolation distances are recommended to ensure genetic purity of Brassica napus seed crops. Isolation in time could also contribute to gene flow management but has been little investigated. We assessed the effects of asynchronous and synchronous flowering on intraspecific B. napus gene flow by seeding adjacent plots of transgenic spring canola cultivars, either resistant to glyphosate or glufosinate, over a 0-4 week interval and measuring outcrossing rates and seed-set. Outcrossing rates, evaluated in the center of the first adjacent row, were reduced to the lowest level in plots flowering first when the seeding interval > 2 weeks. Increasing the time gap increased outcrossing rates in plots flowering second up to a seeding interval of two weeks. Flowers that opened during the last week of the flowering period produced fewer seed (< 10% of total seed production) and a smaller fraction of outcrossed seed (-25%). Observed time gap effects were likely caused by extraneous pollen load during the receptivity of productive seed-setting early flowers. Clearly, manipulation of B. napus flowering development through staggered planting dates can contribute to gene flow management. The approach will need to be validated by additional site-years and increased isolation distances.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".