Assessment of crop and weed management strategies prior to introduction of auxin-resistant crops in Brazil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A stakeholder survey was conducted from April through June of 2018 to understand stakeholders’ perceptions and challenges about cropping systems and weed management in Brazil. The dominant crops managed by survey respondents were soybean (73%) and corn (66%). Approximately 75% of survey respondents have grown or managed annual cropping systems with two to three crops per year cultivated in succession. Eighteen percent of respondents manage only irrigated cropping systems, and over 60% of respondents adopt no-till as a standard practice. According to respondents, the top five troublesome weed species in Brazilian cropping systems are horseweed (asthmaweed, Canadian horseweed, and tall fleabane), sourgrass, morningglory, goosegrass, and dayflower (Asiatic dayflower and Benghal dayflower). Among the nine species documented to have evolved resistance to glyphosate in Brazil, horseweed and sourgrass were reported as the most concerning weeds. Other than glyphosate, 31% and 78% of respondents, respectively, manage weeds resistant to acetyl-CoA carboxylase (ACCase) inhibitors and/or acetolactate synthase (ALS) inhibitors. Besides herbicides, 45% of respondents use mechanical, and 75% use cultural (e.g., no-till, crop rotation/succession) weed control strategies. Sixty-one percent of survey respondents adopt cover crops to some extent to suppress weeds and improve soil chemical and physical properties. Nearly 60% of survey respondents intend to adopt the crops that are resistant to dicamba or 2,4-D when available. Results may help practitioners, academics, industry, and policy makers to better understand the bad and the good of current cropping systems and weed management practices adopted in Brazil, and to adjust research, education, technologies priorities, and needs moving forward.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it