Bibliographic record
Abstract
The literature on the interface between agriculture and the environment is highly diverse. This paper organizes this literature into three categories: regulation; adoption of environmental best management practices (BMPs) by farmers; and conservation programs. Within each category, the main research questions are set forth. After reviewing select papers, suggestions for future research are discussed. Academic research in this area has been impressive, but many issues and research questions remain unanswered. La littérature sur la corrélation entre l’agriculture et l’environnement est très diversifiée. Dans le présent article, nous avons divisé l’ensemble de cette littérature en trois catégories: la réglementation, l’adoption des meilleures pratiques de gestion environnementale de la part des agriculteurs et les programmes de conservation. Pour chaque catégorie, les principales questions de recherche ont étéénoncées. Une fois l’analyse des articles choisis terminée, nous avons examiné les suggestions en matière de recherche future. Bien que la recherche universitaire effectuée sur le sujet soit impressionnante, de nombreux problèmes et de nombreuses questions de recherche demeurent sans réponse.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".