MétaCan
Menu
Back to cohort
Record W2053981394 · doi:10.1081/css-200059044

Equilibrium Speciation of Cadmium, Copper, and Lead in Soil Solutions

2005· article· en· W2053981394 on OpenAlexafffundabout
Ying Ge, Sébastien Sauvé, William H. Hendershot

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2005
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversité de MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsCadmiumCopperChemistryGenetic algorithmTrace metalMetalEnvironmental chemistryAnodic stripping voltammetryMetal ions in aqueous solutionIon exchangeInorganic chemistryNuclear chemistryIonElectrodeElectrochemistryEcology

Abstract

fetched live from OpenAlex

Abstract Determination of chemical speciation of trace metals may help us to better understand the bioavailability and toxicity to living organisms in terrestrial and aquatic environments. This paper reports the speciation data of cadmium (Cd), copper (Cu), and lead (Pb) in soil solutions sampled from areas around metal smelters. A column ion exchange technique (IET) using a cation exchange resin was applied to determine the concentrations of Cd2+, Cu2+, and Pb2+. It was shown that the pH and the amount of calcium (Ca) in solutions affected the distribution of metal ions to the resin. In the synthetic solutions involving Cl, SO4, and citrate, the metal‐ligand complexes did not affect the measurement of free metal ions by the IET. In soil solutions, the concentrations of free ions were affected by solution pH, varying within 10−9–10−7 M for Cd, 10−9–10−6 M for Cu, and 10−9–10−7 M for Pb. The IET results were comparable to those measured by Cu‐ion selective electrode (ISE) and anodic stripping voltammetry (ASV). These findings suggest that the IET can be applied to soil solutions for metal speciation measurements. Keywords: Trace metalsspeciationsoil solution Acknowledgments We thank Hélène Lalande, Peter Campbell, Claude Fortin, John Luong, and Abdelkader Hilmi for their help with the various speciation techniques. The financial support from a doctoral scholarship from “Fonds québécois de la recherche sur la nature et les technologies” (formerly FCAR) Toxic Substance Research Initiative (TSRI), Health Canada, and the Natural Sciences and Engineering Research Council of Canada is greatly appreciated.

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)0.001
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 score0.889
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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

Citations23
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueCommunications in Soil Science and Plant AnalysisSame topicElectrochemical Analysis and ApplicationsFrench-language works237,207