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Record W2154181033 · doi:10.1139/l06-038

Determination of transport parameters for heavy metal in residual compacted soil using two methodologies

2006· article· en· W2154181033 on OpenAlexvenueno aff
Izabel Christina Duarte Azevedo, Cleidimar Rejane Nascentes, Antônio Teixeira de Matos, Roberto Azevêdo

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisUniversidade Federal de Viçosa
KeywordsRetardation factorResidualLeachateDispersion (optics)CadmiumEnvironmental scienceSoil scienceGeotechnical engineeringChemistryEnvironmental chemistryMaterials scienceGeologyMathematicsMetallurgyChromatographyColumn chromatography

Abstract

fetched live from OpenAlex

Values of the hydrodynamic dispersion coefficient and retardation factor obtained using the traditional and cumulative mass methods of column test analysis for zinc, manganese, and cadmium in a compacted soil are compared. The soil under study is from the B horizon of a residual gneissic tropical soil used for construction of the liner for the sanitary landfill in the District of Visconde do Rio Branco, Minas Gerais (MG), Brazil. To evaluate the behavior of landfill leachate heavy metals through the soil, soil column tests were performed on samples of compacted soil. A computational program that uses an optimization procedure to generate values of the hydrodynamic dispersion coefficient and retardation factor was developed to facilitate interpretation of the results obtained by the cumulative mass method. Values of the retardation factor and hydrodynamic dispersion coefficient were not influenced by the method of determination, even when a reduced number of effluent samples was used by the cumulative mass method. The use of the cumulative mass method, based on a reduced number of pore volumes, reduces the time and cost involved in the tests.Key words: heavy metals, column test, cumulative mass method.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.992

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 head0.037
GPT teacher head0.259
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 designSimulation or modeling
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

Citations4
Published2006
Admission routes1
Has abstractyes

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