The Scale and Complexity of Salinity Impacts on Sri Lankan Rice Farming Systems: Actionable Insights
Bibliographic record
Abstract
Saline-affected rice (Oryza sativa L.) production environments in Sri Lanka can be divided into three categories including: late-season salinity in irrigated mega-cultivation environments during the minor cultivation season where soil EC ≥7 dSm−1, late season salinity in rain-fed farming systems in the west, southwest, and eastern coastal line during the minor cultivation season where soil EC ≥20 dSm−1, and early season salinity in selected irrigated and rain fed sites during major and minor cultivation seasons as a result of residual overload of salts that was not washed off due to inadequate rain. In the west and southern coast early season salinity salinity can exceed EC ≥12 dSm−1. The proposed zones of saline-afflicted production environments permit designing of target ideotypes and locally adapted rice varieties. Accordingly, high yielding, 3 to 3.5 months duration varieties that are tolerant at >7 dSm−1 are recommended for intensive irrigated farming systems affected due to late season salinity (panicle initiation stage of the crop, PI); high yielding, 2.5 to 3 months duration varieties can avoid late season salinity in intensive irrigated farming, and varieties tolerant up to EC = 12 to 20 dSm−1 throughout the crop life including seedling and PI stages can target saline affected, semi-subsistence rice cultivation in rain-fed systems. In fact, secondary salinization in local rice farming environments is resulting from interaction among multiple factors; therefore, system-level interventions are necessary to manage the impacts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".