Needs-oriented approach for decision support of industrial wastewater management
Bibliographic record
Abstract
Historically, wastewaters are mostly discharged in rivers and more recently in wastewater treatment plants.These last few years the paradigm shift toward industrial ecology brought up the necessity of matching natural and anthropogenic cycles and so raised the interest for reuse of wastewater as raw material.Thereby, wastewater, depending on its characteristics, can be discharged to different types of receiving media -natural environment (river), urban wastewater treatment plants (WWTP), internal or external industrial units -after being properly processed.Thereby, the characteristics of the discharged water must meet the regulatory requirements.In Europe, two directives deal with this question.The first one, the Industrial Emission Directive (IED) is based on four principles: the integrative pollution prevention, the use of Best Available Techniques (BAT), flexibility, public participation and information access.The second one, the Water Framework Directive (WFD), gives general objectives for maintaining and restoring the European water bodies' quality.The application at the industrial level remains variable.In this context, we explored the possibility of considering the wastewater as a product, via a quality approach, linking the two European directives in an industrial ecology strategy.The thought process questions the possibility to transpose the principles and steps of quality management described in the ISO 9000 norm to industrial wastewater management.One key point of the transposition is the evaluation of the "client's" needs, which is quite easy when the receiving media is an industrial unit, becomes more difficult when it is a WWTP and even more in the case of a river.
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 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".