UV Spectrophotometry as a Non-parametric Measurement of Water and Wastewater Quality Variability
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The composition of water and wastewater, varying temporally and spatially, depends on factors such as environmental context, types of pollution sources, weather conditions leading to dilution or solids transportation, length of sewer network, etc. Because quantitative parameters are often not adapted for the characterization of wastewater quality variability, a non-parametric measurement is proposed, based on comparison of the UV absorption spectra of samples. The presence of isosbestic points, occurring in the set of spectra either directly or indirectly after normalization, allows quantification of the variability of a given water or effluent. A normalization step is used when dilution exists in the case of a mixture of water types (discharge or rain). Several examples show how to calculate the variability or to estimate the dilution factor from UV spectra data, even without results of physicochemical parameters.
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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 | 0.053 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it