Selection of Winter Season Crop Pattern for Environmental-Friendly Agricultural Practices in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Owing to the sudden changes in climatic conditions, monsoon failure, and scarce availability of resources because of population hike, yielding a minimum profit has become a challenge for Indian farmers. This is a severe problem for India, as a major part of the Nation’s Gross Domestic Product (GDP) depends on agriculture. To change this dreadful situation, Indian farmers must employ sustainable agricultural practices in farming, as it will help them to meet their agricultural needs and economic stability. Here, we have built a framework for selecting the ideal crop pattern for Winter Cropping Season (Rabi Season), as crop pattern plays a vital role in the effective function of sustainable agricultural practices. We have used the rough AHP-TOPSIS (Analytical Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution) method for finding the best crop pattern for the Rabi season, by considering all the influential criteria in terms of agriculture sustainability. Our study demonstrates an overall idea to the farmers and stakeholders about attaining maximum crop productivity with optimum use of available resources, without compromising the economic, social, and ecological aspects of agriculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it