Impact of common ragweed (<i>Ambrosia artemisiifolia</i>) aggregation on economic thresholds in soybean
Why this work is in the frame
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Bibliographic record
Abstract
One approach to site-specific weed control is to map weeds within a field and then divide the field area into smaller grid units. The decision to apply a herbicide to individual grid units, or decision units, is made by using yield loss models to establish an economic threshold level. However, decision units often contain weed populations with aggregated distributions. Many yield loss models have not considered this because experiments dealing with weed–crop competition typically assume uniform weed distributions. Therefore, these models may overestimate yield losses. Field experiments conducted in 1999 and 2000 compared the effects of common ragweed having a uniform distribution vs. an aggregated distribution on soybean seed yield, moisture content, and dockage. Field experiment data were used to calculate and compare economic thresholds for both distributions. Economic thresholds that considered drying costs and dockage also were compared. There was no significant difference in I parameters (yield loss as density approaches zero) between the two ragweed distributions in either year. Seed moisture content and dockage increased with increasing common ragweed densities, but increases were not significant at the break-even yield loss level. Economic threshold values were similar for both distributions with differences between aggregated and uniform of 0.14 and 0.01 plants m −2 in 1999 and 2000, respectively. The economic threshold values were reduced by 0.01 to 0.06 plants m −2 when drying costs and dockage were considered.
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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 | 0.001 | 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