Impact of common ragweed (Ambrosia artemisiifolia) aggregation on economic thresholds in soybean (Glycine max.)
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 treatment to individual grid, or decision units is made by using yield loss models to determine an economic threshold. However, decision units often contain weed populations with aggregated distributions. Many yield loss models have not considered this since experiments dealing with weed-crop competition typically use uniform weed distributions. Therefore, these models may be overestimating yield losses. Field experiments conducted in 1999 and 2000 compared the effects of common ragweed, a prominent Ontario weed, having a uniform distribution versus 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 were also compared. There was no significant difference in 'I' parameters (yield loss as density approaches zero) between the two distributions in both years. 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 ranging from 0.01 to 0.14 plants m -2. Consideration of drying costs and dockage reduced the economic threshold values by 0.01-0.06 plants m-2.
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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 | 0.001 |
| 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".