Sustainable agricultural practices adoption
Bibliographic record
Abstract
Abstract As it has been practiced for many decades, agriculture has had a significant negative impact on the environment. More land, fertiliser, and pesticides had been used to increase the yield to meet the demands of an expanding population. Consequences included deforestation and soil degradation as well as the extinction of biodiversity, irrigation issues, and pollution, among other things. This has resulted in developing a new type of agriculture known as sustainable agriculture to remedy the situation. Specifically, the goal is to “meet the food and textile needs of society in the present without risking the ability of future generations to meet their own needs.” Using appropriate agricultural practices to implement sustainable agriculture is the most effective method of accomplishing this goal. According to research, farmers’ decisions to effectively adopt sustainable agricultural practices are influenced by a variety of factors. In this paper, we firstly give an overview of sustainable agriculture practices. Then, we review the various factors affecting the adoption of these practices, and finally, we highlight the gap found in the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".