Meta-Evaluation of Water Quality Indices. Application into Groundwater Resources
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
Until now, there was no simple procedure to test the performance of water quality indices (WQIs) or, in other words, to perform their meta-evaluation. The purpose of this study is to provide a meta-evaluation approach of two widely used WQIs and suggestions for selecting one or both of them for application in groundwater quality assessment as proposed by the European Union. The meta-evaluation concept is based on testing the performance of two widely known WQIs by applying classification of Water Framework Directive (WFD; 2000/60/EC) and Groundwater Directive (GWD; 2006/118/EC) which was used as a reference. The Canadian Council of Ministers of Environment (CCME) and National Sanitation Foundation (NSF-WQI) have been selected for evaluation. These WQIs were applied in an agricultural area of the Mediterranean region where six sub-datasets for an entire hydrological year were available. This study uses all the available water quality data (52 monitoring stations × 2 sampling periods × 15 parameters) which is systematically collected at the area studied. The CCME-WQI is a rather strict index since it estimates statistically significantly lower values than the NSF-WQI. Based on the performance of the examined indices, it is shown that, mostly, the CCME-WQI classification findings are close to those of the GWD.
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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 | 0.002 | 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) | 0.009 | 0.002 |
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