Efficiency Criteria for Water Quality Monitoring
Bibliographic record
Abstract
The issues of possible improvements, increased efficiency and/or optimizationof a monitoring system in general, and a monitoring design in particular, are urgent. Sincemonitoring activities are always limited by financial and logistics constraints, algorithms ofconstrained optimization are deemed more suitable for this purpose. Monitoring designs aredeveloped as solutions of an operation research model. In order to formulate such modelthe effectiveness function has been introduced. The effectiveness reflects the extent towhich a monitoring design meets the objectives of the monitoring program and can be usedfor comparison of different monitoring designs. The effectiveness function depends on theinvestigated water quality parameters, selected indicators of water quality and theirestimators. The function properties suggest the selection of an optimization algorithm. Theproposed approach has been applied to a case study in order to develop temporalmonitoring designs. It has been shown that the designs differ significantly only when thelevels of the effectiveness are high. With the effectiveness of 80% or less the designs fordifferent water quality parameters and the same indicator can be compromised. Sincemonitoring data are usually used for various purposes, the preference should be given tosimple monitoring designs or to the designs which support efficient reconstruction ofchemographs of investigated water quality parameters
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".