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Record W2088523820 · doi:10.1139/s04-077

Applications of data on the mobility of heavy metals in contaminated soil to the definition of site-specific remediation criteria

2005· article· en· W2088523820 on OpenAlexvenueno aff
Jean‐Sébastien Dubé, Rosa Galvez‐Cloutier

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental remediationFractionationEnvironmental chemistrySoil acidificationContaminationSoil contaminationCadmiumSoil pHHeavy metalsDissolutionChemistryEnvironmental scienceZincSoil waterTitrationSoil scienceInorganic chemistryEcology

Abstract

fetched live from OpenAlex

More than a decade of research on the fractionation of heavy metals in soil has led scientists and engineers to the conclusion that the mobility of heavy metals can be defined by the relative sensitivity of their solid species to environmental stresses. The following paper presents a procedure to obtain the fractionation of heavy metals during the acidification of the soil and discusses the use such data. The procedure used combines an acid–base titration of the soil with the Tessier scheme of sequential chemical extractions (SCEs). The soil was strongly buffered against acidification by the dissolution of solid carbonates. Cadmium and zinc were nevertheless significantly dissolved by acidification from pH 8 to pH 5. All other heavy metals were dissolved once pH 5 was attained. The most soluble heavy metals were those that had formed less stable acid-soluble and reducible solid species. These results are used as an example to demonstrate the importance of knowing the buffering capacity of the soil and the fractionation of heavy metals to obtain a more rigorous assessment of the mobility of heavy metals and to determine site-specific remediation criteria. Key words: contaminated soil, heavy metals, mobility, sequential chemical extractions, buffering capacity, remediation criteria.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.236

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.248
Teacher spread0.221 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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