Établissement d'une méthode de caractérisation minéralogique décrivant les sols contaminés par le plomb
Bibliographic record
Abstract
Many metal-contaminated soils originate in old abandoned industrial sites. One of the problems encountered in the reclamation of soils lies in the selection of the decontamination techniques. Few data are available to predict the efficiency of the extraction of metals from the contaminated soils. Moreover, a signifiant part of the contamination is often found as particles. These can be extracted from the soils by means of mineralurgical separation techniques. A trial and error procedure is often used for selecting the technique and the procedure parameters. The purpose of this study is to develop a method of mineralogical characterization for the identification and localisation of the metal contamination so as to allow a more enlightened choice of the mineralurgical treatments. Besides the identification of the contaminant particles, the method takes into account the distribution of contaminants, which can be found on the surface of the particles or included within the volume of the particle, the average proportion and the size of the contaminants in the contaminated particles, and the association of the iron oxide contaminant. The frequency of appearance of the particles depending on the different categories of the method guides the choice of the treatment technologies to be used so as to optimize the extraction of contaminant particles.Key words: metals, contamination, soils, lead, mineralurgical techniques, mineralogy.[Journal translation]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".