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Record W2092883902 · doi:10.1021/es0609001

Impact of Redox Conditions on Arsenic Mobilization from Tailings in a Wetland with Neutral Drainage

2006· article· en· W2092883902 on OpenAlexafffundabout
Suzanne Beauchemin, Y. T. John Kwong

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsNatural Resources Canada
FundersNatural Resources CanadaU.S. Geological SurveyU.S. Department of Energy
KeywordsTailingsArsenicEnvironmental chemistryRedoxDissolutionChemistryGenetic algorithmX-ray absorption spectroscopyCobaltEnvironmental scienceEnvironmental engineeringInorganic chemistryAbsorption spectroscopyEcology

Abstract

fetched live from OpenAlex

More than 80 years of silver mining in the Cobalt area (Ontario, Canada) has led to widespread contamination of water with arsenic. The objective of this study was to determine the impact of changes in redox conditions on the stability of As in samples collected from a tailings wetland in the historic mining camp. Dissolved metal concentrations were monitored while tailings samples (approximately 1300 mg of As kg(-1), pH 7.4) were subjected to 30 days of reduction. Reoxidation of the samples was accomplished by air drying. The As oxidation states in the original, reduced, and reoxidized samples were determined using X-ray absorption spectroscopy (XAS). Arsenic speciation was affected by changes in redox conditions, resulting in rapid mobilization of As during reduction. Glucose input had a significant impact on the dissolution and speciation of As, suggesting that the As transformation was microbially mediated. When carbon was not limiting, the combination of reducing conditions and lower pH favored the formation of As(-I) species.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.213
Teacher spread0.210 · 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 designObservational
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

Citations46
Published2006
Admission routes3
Has abstractyes

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