The production economics of dandelion control in forage production in Ontario
Bibliographic record
Abstract
Forage is the fourth most widely grown crop in Ontario. Few herbicides had been developed for forage and the existing products did not offer adequate control. The purpose of this thesis is to analyze the profitability of dandelion control in Ontario for a selected set of control measures. A survey of forage producers was conducted to characterize their perceptions of dandelion infestations in their stands. The survey data indicated a demand for a dandelion control product. A literature review was conducted which indicated that there was a reduction in crude protein content during the first cut of the forage. A number of field trials were conducted at the University of Guelph, BASF research farms and at the author's home farm, using a number of different combinations of imazethapyr and 2, 4 DB. Data obtained from these trials were used to estimate a number of different functional forms for a control function. Fox and Weersink (1995) showed that damage control inputs can be subject to increasing returns. Applying each of the different types of functional forms to the data available, combined with a linear damage function, showed that none of the estimated control functions produced increasing returns. A profitability analysis of dandelion control was conducted. The results of this analysis indicated that none of the dandelion control strategies considered in this study were profitable using the manufactures retail sales cost over a single season. Dandelion does become profitable does become profitable if the application effort is about half of its original value.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".