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Record W2029959983 · doi:10.1139/l09-034

Traitement et stabilisation chimique de déchets d’aluminerie contaminés en ions fluoruresArticles envoyés à la Revue du génie et de la science de l'environnement

2009· article· en· W2029959983 on OpenAlexaffvenueabout
Ghislain Bongo, Guy Mercier, Patrick Drogui, Jean-François Blais

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFluorideChemistryLeaching (pedology)LeachateNuclear chemistryToxicity characteristic leaching procedureHydroxideAluminium hydroxideAluminiumEnvironmental chemistryInorganic chemistrySoil waterHeavy metalsEnvironmental science

Abstract

fetched live from OpenAlex

Aluminium plant production wastes are contaminated by fluoride according to TCLP (toxicity characteristics leaching procedure) and Quebec legislation (>150 mg F – /L). Those wastes contain 2–323 g F – /kg. The extraction of fluoride by acid leaching (H 2 SO 4 , pH 1.5) at total solids content between 1% and 16% allows the removal of approximately 33% of F – but does not respect the TCLP regulation. However, the chemical stabilization of aluminium wastes by use of calcium hydroxide (Ca(OH) 2 ) at a concentration varying from 10 to 12 g/L in the suspension of E C (100 g/L) allowed us to record lower concentrations of fluoride ions (110–115 mg F – /L) in the leachate from the TCLP test. The subsequent treatment of the dehydration filtrate of stabilized residues by decreasing the pH from the initial value (pH 11.9 or 12.0) to pH values between 7.0 and 8.5 by addition of an inorganic acid (H 2 SO 4 , 10 mol/L) removed up to 98% of fluoride ions by precipitation.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.202
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes3
Has abstractyes

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