Sustainability Index for the Management of River Basins Based Upon Ecological, Environmental and Hydrological Integrity and the Minimization of Long Term Risks to Supply
Bibliographic record
Abstract
A new methodology for determining a sustainability index (SI) for the management of river basins is developed. Sustainability is defined in terms of minimizing the long-term risks to supply and maintaining the ecological, environmental and hydrological integrity of a river resource. The SI procedure developed uses two groups of performance criteria. The first group is based on demand-supply deficits and measures the risk to water supplies. The second group is only applied to river demands and compares a river’s allocation to a target flow regime using the Range of Variability Approach (RVA) and the Modified Hydrological Alteration factor. The RVA measures differences in flow regimes and is used to compare a projected flow regime to a targeted flow regime. This is the first attempt to use the RVA to develop a sustainability index for river basin management. A combined sustainability metric for the system (SS) is also determined. The methodology is applied to an area including the Prescott Active Management Area (AMA) in north-central Arizona. Sustainability for the entire system is determined using the weighted sum of the sustainability indices. The methodology has been used to measure and compare the sustainability of two allocation scenarios for the Prescott AMA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".