Long-term evaluation of a spectral sensor for nitrite and nitrate
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
A spectral in-situ UV sensor was investigated to measure nitrite and nitrate concentrations in the effluent of the EAWAG pilot-scale plant. The sensor was used with a calibration that was based on data from another WWTP and was operated over a period of 1.5 years. The results showed constant accuracy although the sensor was operated with minimal maintenance (manual cleaning once a month). It could be shown that the sensor was able to accurately predict the nitrite and nitrate concentration with a precision of 0.32 mg N/l (95% prediction interval at mean lab value of 1.15 mg N/l) and 1.08 mg N/l (at 5.55 mg N/l) for nitrite and nitrate, respectively. The UV sensor showed good results for nitrite in the low concentration range and very accurate results for higher concentrations (up to 10 mg N/l). This allows using the sensor for alarm systems as well as for control concepts at WWTPs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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