Machine Learning for the Estimation of COD from UV-Vis Spectrometer in Leather Industries Wastewater
Bibliographic record
Abstract
In this paper, we introduce a method for analysing wastewater from the leather industry with a specific focus on determining the Chemical Oxygen Demand parameter, which plays a vital role in evaluating water pollution levels.Conventional methods for measuring it involve extensive laboratory analysis, sample preparation, and the usage of hazardous substances.To overcome these limitations, we propose a machine learning-based approach that employs nonspecific sensors and soft sensing techniques to derive indicators of wastewater quality.Our method leverages ultraviolet and visible spectroscopy measurements, which provide valuable insights into the light absorption characteristics of the wastewater sample, enabling us to estimate Chemical Oxygen Demand.Importantly, our approach includes an analysis of the input wavelengths, allowing us to identify the spectra for accurate Chemical Oxygen Demand estimation.Once deployed, our method offers the potential for real-time monitoring systems of wastewater in leather production contexts, by eliminating the need for timeconsuming laboratory analyses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".