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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".