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Record W2031130359 · doi:10.2134/jeq2003.2410

Use of Alkaline Extraction to Quantify Sulfate Concentration in Oxidized Mine Tailings

2003· article· en· W2031130359 on OpenAlexafffund
Guohong Yin, Lionel J.J. Catalan

Bibliographic record

VenueJournal of Environmental Quality · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsSulfateChemistryExtraction (chemistry)ChlorideInductively coupled plasmaInductively coupled plasma mass spectrometryEnvironmental chemistryMass spectrometryChromatography

Abstract

fetched live from OpenAlex

An alkaline extraction method has been developed for the determination of total sulfate in mine tailings containing secondary sulfate minerals formed by the oxidation of primary sulfides. Oxidized tailings were extracted with a 0.3 M NaOH solution at a liquid/solid ratio of 30 at room temperature for 16 h. The sulfate concentration in the extracts was determined by ion chromatography (IC). The coefficient of variation for sulfate determinations ranged from 1.9 to 3.2% for five tailings samples collected at two tailings impoundments. Mineralogical analysis of the tailings by scanning electron microscopy/X-ray energy dispersive spectrometry (SEM/EDS) demonstrated that the extraction of sulfate was complete, with the exception of extremely insoluble barite. The proposed method is simple, yields an accurate yet rapid measurement of sulfate, and involves a safer laboratory operation than conventional methods that make use of strong HCl acid solutions. Moreover, this method allows the specific measurement of sulfate in the extract, whereas conventional methods are generally limited to the measurement of total S by inductively coupled plasma atomic emission spectrometry (ICP-AES) due to the interference of chloride with sulfate in IC.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.315
Teacher spread0.275 · 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 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

Citations13
Published2003
Admission routes2
Has abstractyes

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