Bibliographic record
Abstract
STMI, subsidiary company of the AREVA Group with over 40 years in the D&D business, has been continuously innovating and developing new decontamination techniques, with the objectives of achieving more efficient decontaminations on a growing spectrum of media. In the field of liquid waste treatment, STMI manufactures uses and commercialises selective inorganic ion exchangers (RAN). These are hydrated synthetic inorganic compounds prepared from very pure raw materials. Different types of RANs (POLYAN, OXTAIN, Fe-Cu, Fe-CoK, Si-Fe-CoK) can be used to trap a large number of radioactive elements in contaminated effluents. Different implementations could be applied depending on technical conditions. STMI’s offers consist in building global solution and preliminary design of installation either in dispersed form (batch) or in column (cartridge filtration). Those products are used all over the world not only in the nuclear business (Canada, US, Belgium, France…) but also in other fields. Indeed, it provides competitive solutions to many domains of application especially water pollution control, liquid waste treatment in the nuclear business by decreasing the activity level of waste. The following paper will focus on the theoretical principle of the mineral exchanger, its implementation and the feed back collected by STMI.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".