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Record W2153011837 · doi:10.1139/t03-011

Sorption of Cd<sup>2+</sup>, K<sup>+</sup>, F<sup></sup>, and Cl<sup></sup> on some tropical soils

2003· article· en· W2153011837 on OpenAlexvenueno aff
Adilson do Lago Leite, Antenor Braga Paraguassú, R. Kerry Rowe

Bibliographic record

VenueCanadian Geotechnical Journal · 2003
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSorptionSoil waterFreundlich equationChemistryLangmuirSalt (chemistry)AdsorptionEnvironmental chemistrySoil scienceGeologyPhysical chemistry

Abstract

fetched live from OpenAlex

The potential for the retention of K + , Cd 2+ , F – , and Cl – by lateritic soils from the Botucatu (an important sandy aquifer in the southeastern part of Brazil) and Serra Geral (a clayey material) formations and three mixtures of these soils is examined. Batch equilibrium results are presented for both single salt (CdCl 2 and KF) and multiple salt (CdCl 2 + KF) solutions. The soil retention parameters are obtained by fitting sorption models (linear, Freundlich, and Langmuir) to the experimental isotherm plots using linear and nonlinear regression analyses. The results show that the so called varying soil:solution ratio isotherms (VSI) are in fact less variant in terms of pH than constant soil:solution isotherms (CSI). However, because of restrictions related to the VSI approach and the "soil solution ratio effect," the CSI approach is considered to be a more reasonable way of constructing sorption isotherms. It is shown that under certain conditions, the Cl – anion can be sorbed while for others it is not sorbed. Sorption was observed for F – and K + . The sorption of Cd 2+ is also shown to be highly sensitive to leachate chemistry. It is shown that a knowledge of pH 0 is quite important for the interpretation of sorption data.Key words: sorption, inorganic chemicals, isotherms, tropical soils.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.253
Teacher spread0.233 · 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.

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

Citations15
Published2003
Admission routes1
Has abstractyes

Explore more

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