Bibliographic record
Abstract
Agricultural activities alter landscapes to produce food and fiber and can pose a risk to the health of the soil, water and air, and impact biodiversity. The environmental impact of agriculture is largely influenced by the management practices implemented on farms. Beneficial management practices (BMPs) can help mitigate risk to the environment and improve the health of the soil, water, air and biodiversity. In order to develop effective agri-environmental policies and programs to promote environmental sustainability, decision-makers at all levels of government require science-based information on the environmental performance of agriculture, including information on practices being implemented on the farm. Information about BMP adoption in Canada has, until now, been largely fragmented and not widely available. In this study I developed a BMP Adoption Index, which is a reporting tool that measures the level BMP adoption in Canada and can be used to inform policy and program development. The BMP Adoption Index suggests that average adoption by both crop and livestock farmers across the country is in the medium range, with producers implementing more BMPs in areas where agriculture is a dominant land use. BMP Adoption across Canada does not appear to be motivated by a particular environmental issue or high environmental risk. Further investigation to identify the drivers of BMP adoption will enable decision makers to help farmers increase BMP adoption where they will be best able to mitigate the environmental risks of agriculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".