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Record W1973833425 · doi:10.1021/es020781z

Comparison of Calcite to Quicklime for Amending Partially Oxidized Sulfidic Mine Tailings before Flooding

2003· article· en· W1973833425 on OpenAlexafffund
Lionel J.J. Catalan, Guohong Yin

Bibliographic record

VenueEnvironmental Science & Technology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCalciteTailingsDissolutionAcid mine drainageSulfide mineralsChemistryEnvironmental chemistryBicarbonateGeologyMineralogyPyrite

Abstract

fetched live from OpenAlex

Flooding partially oxidized mine tailings for the purpose of mitigating further oxidation of sulfide minerals and generation of acid drainage is generally preceded by treatment with alkaline amendments to prevent releasing previously accumulated acidity to the water cover. This work compares the ability of calcite (CaCO 3 ) and quicklime (CaO), two common amendments, to establish and maintain pH conditions and dissolved metal concentrations within environmentally acceptable ranges over long time periods. Although higher initial pH values were obtained with quicklime, the pH of quicklime treated tailings decreased over time. This was attributed to the low buffering capacity of quicklime treated tailings and to the consumption of hydroxide ions by incongruent dissolution of water-insoluble iron oxyhydroxysulfate minerals. In contrast, the pH of tailings treated with calcite increased initially and then remained stable at pH ≈ 6.7. This pH behavior was due to the lower reactivity of iron oxyhydroxysulfates with calcite, the increased buffering capacity provided by bicarbonate ions, and the incomplete dissolution of calcite. Overall, calcite was found preferable to quicklime for maintaining long-term neutral pH conditions in the treated tailings. With the exception of zinc, acceptable dissolved metal concentrations were achieved with calcite treated tailings.

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 score0.031
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.286
Teacher spread0.274 · 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

Citations23
Published2003
Admission routes2
Has abstractyes

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