A systematic review of agricultural use water quality indices
Bibliographic record
Abstract
Water Quality Indices (WQIs) are increasingly being applied for reporting on the suitability of water for a variety of human uses including agriculture. This systematic review identified and compared 42 examples of Agricultural use Water Quality Indices (AgWQIs) for surface waters in published literature. The review confirmed the growing popularity in AgWQI reporting, particularly in the last six years. All studies incorporated the suitability of water for irrigated cropping into their AgWQI with three also addressing stock watering. The review confirmed that all parameter thresholds adopted by AgWQI studies originated from either the Food and Agriculture Organisation of the United Nations publication Water quality for agriculture publication or National Standards. An AgWQI common key was developed to overcome interstudy method variability and facilitate comparative assessment. This assessment determined that all study methods originated from two sources, the Canadian Council of Ministers of the Environment Water Quality Index, and the National Sanitation Foundation Water Quality Index. For studies adopting the latter method, a further three strategies for parameter weightings and eight functions for developing water quality ratings were identified. Our assessment also identified and explored limitations with some equations, including a method known as the proportionality constant. Significant variation in parameters, classes, thresholds, subindices, and weightings between studies was found, but also some areas of agreement. Based on the review findings, a guide has been developed to assist in future AgWQI development.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".