The dissolved oxygen ramp is immoral: facing global water challenges with mathematical analysis
Bibliographic record
Abstract
The water sector is increasingly relying on data-driven approaches to create proxy measurements. These approaches are often trained with data covering only few situations, resulting in a lack of robustness and increasing the risk of false predictions. Therefore, robust approaches are needed for data-driven proxy measurements (i.e. soft sensors), especially for water recovery and reuse. In climate science and robotics, Dynamical Systems Analysis) is used to explore a wide range of system behaviour and uncover conditions of high uncertainty, and potential tipping points. DSA allows the systematic analysis of model dynamics, and the measurable features caused by these dynamics. We created a novel DSA workflow for soft-sensor development to measure water quality. Herein, we demonstrate that the integration of DSA into soft-sensor development adds robustness by revealing all mathematically possible combinations of state variables that lead to a feature. It can thus detect possible interferences and help design the soft sensor to avoid these. We used DSA for the falsification of a ramp-feature-based soft sensor and found that, despite a published, successful laboratory and real-world application, the ramp feature is immoral. Such an immorality implies that false predictions could occur. However, the DSA analysis uncovered under which operational conditions the ramp becomes a robust soft sensor feature. Targeted experiments will be necessary to confirm the boundary of the robust conditions in the real world.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".