Combining hydro-economic and water quality modeling for optimal management of a degraded watershed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Increase of economic and productivity efficiencies intensifies environmental pressures, too. Agriculture is one of the most common examples of this phenomenon. The sector is lacking proper management, which is especially prominent in Mediterranean areas. To address the situation, a holistic modeling approach, combining hydrological, economic and water quality aspects, is recommended for implementation in a Greek watershed. The broader area is degraded regarding its water availability, quality, and management. The model provides insights into water balance, net profit from agricultural activities, presents water quality data from simulations, and introduces two useful parameters informing the decision-maker's knowledge and understanding: the deficit irrigation water's value and a hydro-economic index which estimates (socio-)economic benefits over environmental balance. A combined demand-management plan is also examined considering the above outputs in investigating the multiple effects of the suggested policy measures. Furthermore, to discuss the optimal approach depending on data availability and scope, we compare two different settings of the proposed model. The results of the study confirmed the continuous quantitative and qualitative water resources' deterioration and economic overexploitation of the watershed. The study reveals the immediate need for management actions, integrated modeling approaches, and provides future recommendations on hydro-economic modeling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it