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Record W1981599662 · doi:10.2134/jeq2003.2373

Effect of Peroxide on Neutralization‐Potential Values of Siderite and Other Carbonate Minerals

2003· article· en· W1981599662 on OpenAlexaff
J. L. Jambor, J. E. Dutrizac, Mati Raudsepp, Lee A. Groat

Bibliographic record

VenueJournal of Environmental Quality · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRhodochrositeSideriteChemistrySulfide mineralsCarbonateCarbonate mineralsSilicate mineralsFerrousGoethiteSulfideSilicateDissolutionInorganic chemistryMineralogyEnvironmental chemistryNuclear chemistryPyrite

Abstract

fetched live from OpenAlex

To assess quantitatively the effect of peroxide addition to standard static tests of the neutralization potential (NP) of mine wastes, 10 specimens of carbonate minerals, including five of siderite (FeCO3) and two of rhodochrosite (MnCO3), were analyzed by electron microprobe. The compositions of the siderite span a range from 60 to 86 mol % Fe. Tests of NP for the siderite diluted with 80% (w/w) kaolinite gave values of 647 to 737 kg CaCO3 equivalent per Mg for determinations by the standard Sobek method. However, if it is assumed that the ferrous carbonate component of the mineral does not contribute to NP in field situations because oxidation of Fe(II) to Fe(III) and the subsequent hydrolysis of Fe(III) leads to the release of an equivalent amount of acid, then the calculated NP for the samples ranges from 110 to 390 kg CaCO3 equivalent per Mg. Two different methods involving the addition of peroxide to the test solutions were successful in bringing the measured NP values closer to the theoretical ones. By contrast, the tests with rhodochrosite indicated the Mn(II) to be stable. For long-term environmental planning, especially for wastes from metalliferous sulfide-poor deposits in which gradual dissolution of silicate and aluminosilicate minerals may be involved in attenuating the acidity, consideration in the overall NP budget needs to be given to the ferrous iron content of those minerals. The presence of Fe2+-bearing minerals, especially carbonates, in tested mine-waste materials may lead to overestimated Sobek NP values, thus increasing the risk of poor-quality drainage and the need for costly remediation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.679

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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 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

Citations30
Published2003
Admission routes1
Has abstractyes

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