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Laboratory-Scale Flotation Process for Treatment of Soils Contaminated with Both PAH and Lead

2010· article· en· W2065288929 on OpenAlexafffundabout
Julia Mouton, Guy Mercier, Jean‐François Blais

Bibliographic record

VenueJournal of Environmental Engineering · 2010
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsEnvironmental scienceContaminationSoil contaminationSoil waterWaste managementLead (geology)Process (computing)Scale (ratio)Environmental chemistryEnvironmental engineeringChemistryEngineeringSoil scienceGeologyComputer scienceEcology

Abstract

fetched live from OpenAlex

A soil decontaminating process has been studied at laboratory scale for the treatment of one soil polluted by both polycyclic aromatic hydrocarbons (PAHs) and lead (Pb). This process first includes attrition and sieving steps to separate the coarse (>2 mm) from the fine (<2 mm) fractions, followed by a flotation step using an amphoteric surfactant in acid and saline conditions for the treatment of the fine contaminated particles. Electrodeposition and chemical precipitation using sodium hydroxide have been compared to ensure a possible reuse of wastewaters without disturbing the efficiency of the process. The performance of the process has been estimated considering soil quality after treatment with respect to the limit regulatory levels for commercial or industrial use in Quebec (Canada). Precipitation of lead hydroxides was efficient after five cycles of wastewaters reuse, while electrodeposition did not maintain efficiency of the flotation step with regard to PAH levels in soil after treatment. The complete process including Pb precipitation ensured the removal of 89±8 and 76±10% of total PAH, respectively, for the coarse (>2 mm) and fine (<2 mm) fractions, while Pb was removed at 88±10 and 65±2% , respectively, for the same fractions of the soil.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.227
Teacher spread0.220 · 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 designBench or experimental
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

Citations13
Published2010
Admission routes3
Has abstractyes

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