LOFT-ERI : un outil d'aide au choix de filières de traitement d'eaux résiduaires industrielles
Bibliographic record
Abstract
This study contributed to the automatic choice of industrial wastewater treatment lines after obtaining UV spectra. Treatment lines taken into account are: advanced oxidation processes (H2O2/UV), biological treatment, coagulation/flocculation/precipitation, activated carbon adsorption or destruction methods like incineration. For the choice of treatment processes, a UV spectra mathematical treatment aims at the determination of several parameters. A decision making tool based on the parameters and developed with Excel® has been validated with 4 different industrial samples. Cette étude contribue au choix automatique de filières de traitement d'eaux résiduaires industrielles après obtention de leur spectre UV Les filières prises en compte sont les suivantes : procédés d'oxydation avancée (H2O2/UV),tratement biologique, coagulation/floculation/filtratioaadsorption sur charbon actif ou méthodes de destruction comme l'évapo-incinération. Un traitement mathématique des spectres UV permet la détermination de plusieurs paramètres conduisant au choix de filières par l'intermédiaire d'un outil informatique développé sous Excel®. L'outil a été validé avec une bonne adéquation, sur quatre échantillons provenant d'industries chimique, pharmaceutique ou mécanique.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".