Multiple herbicide-resistant kochia (<i>Bassia scoparia</i>) control in glufosinate-resistant canola
Bibliographic record
Abstract
Multiple herbicide-resistant kochia ( Bassia scoparia (L.) A.J. Scott) has grown in prevalence in the canola ( Brassica napus L.) production region of North America. Glufosinate-resistant canola facilitates kochia management since glufosinate-resistant kochia is not known to occur. Field experiments were conducted from 2013 to 2015 in five environments near Lethbridge and Coalhurst, Alberta, to identify herbicide strategies targeting acetolactate synthase inhibitor-resistant kochia with and without glyphosate resistance in glufosinate-resistant canola. Sequential glufosinate treatments (500 g ai ha−1) postemergence (POST) caused excellent (≥90%) kochia control and biomass reduction across environments. Preplant (PP) carfentrazone + sulfentrazone (9 + 105 g ai ha−1) alone or followed by (fb) POST glufosinate (9 + 27 fb 500 or 9 + 105 fb 500 g ai ha−1) resulted in excellent kochia control in all environments tested. PP carfentrazone + sulfentrazone (9 + 53 g ai ha−1) alone, and a single POST treatment with glufosinate (500 or 590 g ai ha−1) alone or preceded by fall-applied ethalfluralin (1100 fb 500 g ai ha−1) with or without PP carfentrazone (1100 fb 9 fb 500 g ai ha−1) caused ≥80% kochia control and biomass reduction in all environments tested. However, treatments containing PP carfentrazone + sulfentrazone caused unacceptable canola injury or yield loss in at least one environment. In conclusion, single or sequential treatments of glufosinate POST managed multiple herbicide-resistant kochia effectively in canola. Layering fall-applied/PP ethalfluralin and/or PP carfentrazone with glufosinate POST may help alleviate resistance selection pressure placed on glufosinate in canola.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".