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Record W2301806028 · doi:10.1080/09593330.2015.1137636

Remediation of inorganic contaminants and polycyclic aromatic hydrocarbons from soils polluted by municipal solid waste incineration residues

2016· article· en· W2301806028 on OpenAlexaff
Philippe Jobin, Lucie Coudert, Vincent Taillard, Jean‐François Blais, Guy Mercier

Bibliographic record

VenueEnvironmental Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicRecycling and utilization of industrial and municipal waste in materials production
Canadian institutionsCegep de ThetfordInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsIncinerationLeaching (pedology)Soil waterEnvironmental chemistryMunicipal solid wasteContaminationSoil contaminationEnvironmental scienceWaste managementEnvironmental remediationHuman decontaminationBottom ashChemistryEnvironmental engineeringFly ashSoil science

Abstract

fetched live from OpenAlex

Three soils polluted by municipal solid waste (MSW) incineration residues and containing various concentrations of Cu, Pb, Sb, Sn and Zn were treated using magnetism, gravity separation (jig and shaking table) and flotation/leaching. The process removed between 18% and 39% of the contaminants present in soil 1, between 31% and 53% of the contaminants present in soil 2 and between 42% and 56% of the contaminants present in soil 3. Polycyclic aromatic hydrocarbons were present only in soil 3, and the process removed 64% of its PAHs total content. Magnetism seemed to be the most efficient technique to remove metals from contaminated soils, followed by gravity separation and finally flotation/leaching. The global efficiency of the process was higher when the initial contaminant concentrations were lower (smaller proportions of MSW incineration residues). The estimated costs of the process, including direct and indirect costs, varied from $82 to $88 per ton of treated soil depending on the proportion of MSW incineration residues mixed with 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 score0.009
Threshold uncertainty score0.423

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 head0.008
GPT teacher head0.201
Teacher spread0.194 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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