Could we estimate industrial wastewater flows composition using the UN-ISIC classification system?
Bibliographic record
Abstract
Although we have extensive datasets on the location and typology of industries, we do not know much on their generated and discharged wastewater. This lack of information compromises the achievement of the sustainable development goals focused on water (Sustainable Development Goal 6) in Europe and globally. Thus, our goal was to assess to which degree the chemical composition of industrial wastewater could be estimated based on the industry's typology according to its International Standard Industrial Classification of All Economic Activities (ISIC) class. We collected wastewater effluent water samples from 60 industrial wastewater effluents (before any wastewater treatment process), accounting for 5 samples each of 12 ISIC classes, analyzed the composition of key contaminants (i.e. European Commission rated priority compounds and watchlist), and statistically assessed the similarities and differences amongst ISIC classes using ordination and random forest analyses. The results showed statistically significant linkages between most ISIC classes and the composition of produced wastewater. Among the analytical parameters measured, the random forest methodology allowed identifying a sub-set particularly relevant for classification or eventual contamination prediction based on ISIC class. This is an important applied research topic with strong management implications to (i) determine pollution emission caps for each individual ISIC class, (ii) define monitoring schemes to sample and analyze industrial wastewater, and (iii) enable predicting pollutant loads discharged in river basins with scarce information. These encouraging results urge us to expand our work into other ISIC classes and water quality parameters to draw a full picture of the relationship between ISIC classes and produced wastewater.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".