Bibliographic record
Abstract
Abstract The suitability of a given water source for irrigation warrants the diagnosis of potential salinity, sodicity and toxicity hazards. Region‐specific irrigation water quality standards and guidelines formulated worldwide are too conservative. After a comprehensive assessment of newer insights from re‐analysis of the available data and examination of the associated evidence covering diversified conditions of saline water use, existing guidelines are updated to adjust for site‐specific conditions. The updated versions for rating both saline and alkali waters include the chemical composition of irrigation waters, soil type (texture, clay mineralogy), salt tolerance rating of crops and rainfall for broader adaptability. As modified by soil texture and rainfall, crop salt tolerance is the decisive factor for fixing upper salinity limits in hyper‐arid, arid, semi‐arid and semi‐humid regions. The customized recommendations include conjunctive use, leaching/rainfall management, irrigation methods, ionic ratios, fertilizers and manures. Guidelines for sodic/alkali waters are based on both crop responses and sodicity‐induced water infiltration problems. The amendment requirements stand defined. In view of the increasing water scarcity, the established guidelines are expected to promote saline and alkali water irrigation in arid and semi‐arid regions while safeguarding the environment.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".