Single- and Multievent Optimization in Combined Sewer Flow and Water Quality Model Calibration
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
Over the last three decades, storm-water quality modeling has been used increasingly commonly to describe the general system behavior and assess the pollution loads transferred in and spilled out of combined sewer systems. The calibration of quality models is, in most cases, based on conventionally obtained calibration data, e.g., by automated sampling. Long-term high-resolution online measurement data are available for the Graz West catchment (Graz, Austria), allowing an assessment of the full dynamics of discharge and pollution concentrations. This paper focuses on the application and comparison of single-event and two different multievent optimization schemes for sewer-water quality model calibration. While both single- and multievent optimization lead to satisfying results for the calibration events in discharge calibration, it is shown that validation events are better reproduced by using multievent calibration. Single- and multievent autocalibration of pollution concentration is based on the best dataset obtained from the discharge calibration. As for discharge, the pollutographs are reproduced satisfactorily, and multievent calibration is more stable. In all cases, the two multievent approaches performed equally well.
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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 | 0.001 |
| 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