Tolerance of flax (Linum usitatissimum) to fluthiacet-methyl, pyroxasulfone, and topramezone
Bibliographic record
Abstract
Abstract Flax yield can be severely reduced by weeds. The combination of limited herbicide options and the spread of herbicide-resistant weeds across the prairies has resulted in a need for more weed control options for flax producers. The objective of this research was to evaluate the tolerance of flax to topramezone, pyroxasulfone, flumioxazin, and fluthiacet-methyl applied alone as well as in a mix with currently registered herbicides. These herbicides were applied alone and in mixtures at the 1X and 2X rates and compared with three industry standards and one nontreated control. This experiment was conducted at Carman, MB, and Saskatoon, SK, as a randomized complete block with four replications. Data were collected for crop population, crop height, yield, and thousand-seed weight. Ratings for crop damage (phytotoxicity) were also taken at three separate time intervals: 7 to 14, 21 to 28, and 56+ d after treatment. Crop tolerance to these herbicides varied between site-years. This was largely attributed to differences in spring moisture conditions and the differences in soil characteristics between sites. Herbicide injury was transient. Hence, no herbicide or combination of herbicides significantly impacted crop yield consistently. Flumioxazin was the least promising herbicide evaluated, as it caused severe crop damage (>90%) when conditions were conducive. Overall, flax had excellent tolerance to fluthiacet-methyl, pyroxasulfone, and topramezone. Flax had excellent crop safety to the combination of pyroxasulfone + sulfentrazone. However, mixing fluthiacet-methyl and topramezone with MCPA and bromoxynil, respectively, increased crop damage and would not be recommended.
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) | 0.000 | 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) | 0.001 | 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".