Elucidating Traditional Rice Varieties for Consilient Biotic and Abiotic Stress Management under Changing Climate with Landscape-Level Rice Biodiversity
Bibliographic record
Abstract
Rice is grown under diverse agro-climatic conditions and crop management regimes across the globe. Emerging climatic-vulnerabilities and the mismatched farm practices are becoming major challenges for poor or declining rice productivity in potential rice growing regions, especially South Asia. In the biodiversity-rich landscapes of South Asia, many traditional rice varieties (TRVs) are known to exhibit resilience to climate change and climate adaptation besides their therapeutic benefits. Hence, a random sample survey of farmers (n = 320), alongwith secondary data collection from non-governmental organizations/farmers’ organizations/farmers, led to documentation of the information on TRVs’ biodiversity in South Asia. The current study (2015–2019) explored and documented ~164 TRVs which may enhance the resilience to climatic-risks with improved yields besides their unique therapeutic benefits. A large number of TRVs have still not been registered by scientific organizations due to poor awareness by the farmers and community organizations. Hence, it is urgently needed to document, evaluate and harness the desired traits of these TRVs for ecological, economic, nutritional and health benefits. This study suggests taking greater cognizance of TRVs for their conservation, need-based crop improvement, and cultivation in the niche-areas owing to their importance in climate-resilient agriculture for overall sustainable rice farming in South Asia so as to achieve the UN’s Sustainable Development Goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".