Variable response of kochia [<i>Kochia scoparia</i> (L.) Schrad.] to auxinic herbicides dicamba and fluroxypyr in Montana
Bibliographic record
Abstract
Jha, P., Kumar, V. and Lim, C. A. 2015. Variable response of kochia [Kochia scoparia (L.) Schrad.] to auxinic herbicides dicamba and fluroxypyr in Montana. Can. J. Plant Sci. 95: 965–972. Herbicide-resistant kochia is an increasing concern for growers in the Northern Great Plains of United States and Canada. The objective of the research was to characterize the response of the three putative auxinic herbicide-resistant kochia inbreds (derived from accessions collected from wheat/chemical fallow fields in northern Montana) to dicamba and fluroxypyr relative to a susceptible (SUS) inbred. A dicamba dose-response study indicated that the three putative resistant inbreds (Chot-01, Chot-02, and Chot-03) had R/S ratios of 1.3 to 6.1 based on the visible control response (I50 values), and R/S ratios of 1.5 to 6.8 based on the shoot dry weight response (GR50 values). Dose-response experiments with fluroxypyr determined I50 R/S ratios of 1.4 to 5.7 and GR50 R/S ratios of 1.6 to 4.0 for the three putative resistant inbreds. The selected inbreds showed variable symptomology (phenotype) in response to dicamba and fluroxypyr. Among the three inbreds, Chot-01 exhibited the least epinasty, stem curling/swelling, and chlorosis/necrosis symptoms, and was resistant to dicamba and fluroxypyr. Growers should diversify their weed management tools to manage further spread of auxinic or multiple herbicide-resistant kochia in the region.
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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 | 0.001 | 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".