Weed management in white beans with postemergence herbicide tankmixes
Bibliographic record
Abstract
Soltani, N., Nurse, R. E. and Sikkema, P. H. 2013. Weed management in white beans with postemergence herbicide tankmixes. Can. J. Plant Sci. 93: 669-674. Weed control efficacy of cloransulam-methyl, halosulfuron, bentazon, fomesafen and their tankmixes applied post-emergence (POST) for weed management in white bean was evaluated in field studies conducted in four Ontario locations during 2008-2011. Cloransulam-methyl, halosulfuron, bentazon, fomesafen, bentazon plus fomesafen, cloransulam-methyl plus bentazon, cloransulam-methyl plus fomesafen, cloransulam-methyl plus bentazon plus fomesafen, halosulfuron plus bentazon, halosulfuron plus fomesafen, and halosulfuron plus bentazon plus fomesafen caused white bean injury ranging from 1 to 18%. Control for AMARE, AMBEL and CHEAL ranged from 47 to 92%, 66 to 98% and 36 to 91%, respectively, among herbicide treatments. Similar results were observed for weed density and biomass of AMARE, AMBEL, and CHEAL. Cloransulam-methyl, halosulfuron, and cloransulam-methyl plus fomesafen reduced the yield of white bean by 34, 21, and 17%, respectively, compared with the weed-free control. However, bentazon, fomesafen, bentazon plus fomesafen, cloransulam-methyl plus bentazon, cloransulam-methyl plus bentazon plus fomesafen, halosulfuron plus bentazon, halosulfuron plus fomesafen, and halosulfuron plus bentazon plus fomesafen had no adverse effect on yield of white bean. Based on these results, tankmixes of cloransulam-methyl and halosulfuron with bentazon and fomesafen can reduce crop injury and provide control of broadleaf weeds in white bean.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Agronomy field trials of postemergence herbicide tankmixes in white beans; the object is weed control.
The study evaluates herbicide treatments for white beans rather than research practice.
Agronomic field trials of postemergence herbicides in white bean; crop science.
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".