Mitigation of establishment of <i>Brassica napus</i> transgenes in volunteers using a tandem construct containing a selectively unfit gene
Bibliographic record
Abstract
Transgenic oilseed rape (Brassica napus) plants may remain as 'volunteer' weeds in following crops, complicating cultivation and contaminating crop yield. Volunteers can become feral as well as act as a genetic bridge for the transfer of transgenes to weedy relatives. Transgenic mitigation using genes that are positive or neutral to the crop, but deleterious to weeds, should prevent volunteer establishment, as previously intimated using a tobacco (Nicotiana tabacum) model. A transgenically mitigated (TM), dwarf, herbicide-resistant construct using a gibberellic acid-insensitive (Deltagai) gene in the B. napus crop was effective in offsetting the risks of transgene establishment in volunteer populations of B. napus. This may be useful in the absence of herbicide, e.g. when wheat is rotated with oilseed rape. The TM dwarf B. napus plants grown alone had a much higher yield than the non-transgenics, but were exceedingly unfit in competition with non-transgenic tall cohorts. The reproductive fitness of TM B. napus was 0% at 2.5-cm and 4% at 5-cm spacing between glasshouse-grown plants relative to non-transgenic B. napus. Under screen-house conditions, the reproductive fitness of TM B. napus relative to non-transgenic B. napus was less than 12%, and the harvest index of the TM plants was less than 40% of that of the non-transgenic competitors. The data clearly indicate that the Deltagai gene greatly enhances the yield in a weed-free transgenic crop, but the dwarf plants can be eliminated when competing with non-transgenic cohorts (and presumably other species) when the selective herbicide is not used.
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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".