Through a farmer's eyes: Adoption of best management practices in southern Ontario watersheds
Bibliographic record
Abstract
This study analyzed 317 Ausable Bayfield, Grand River and Lake Simcoe farmers' questionnaires obtained from a stratified random sample of these watersheds 'best' and 'worst' quality sub-watersheds. Logistic regression models for adoption rate and gross sales helped identify the variables that predicted the dependant variables {Adoption Rate Index (ARI) and Gross Farm Sales (GFS)}. Area farmed was the only variable which was significant in the ARI model. The GFS model where GFS = 'f' (education, gender, age, off farm income, watersheds, total land farmed, Adoption Rate Index), had a much better fit (Pseudo R2=70.8%) than the ARI model and had more significant variables. Farm size had a significant influence on both adoption rates and gross sales. Thus, farmers with good management practices (higher adoption rates) and large farm sizes are likely to have better sales. Face to face interviews and data from the open ended questions in the questionnaires also supported the above results. The findings showed that these watersheds have many small operation farmers and this explains why the overall adoption rate was low at 0.27. Environmental programs have to find better ways to influence small farm operation farmers to adopt BMPs as financial and technical adoption incentives are perceived to be inadequate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".