MétaCan
Menu
Back to cohort
Record W2000053414 · doi:10.1021/es025589b

The Effect of Natrojarosite Addition to Mine Tailings

2002· article· en· W2000053414 on OpenAlexafffund
Jasna Jurjovec, Carol J. Ptacek, David W. Blowes, J. L. Jambor

Bibliographic record

VenueEnvironmental Science & Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of Waterloo
FundersMinistry of Colleges and Universities
KeywordsTailingsEffluentEnvironmental scienceAcid mine drainageWaste managementEnvironmental chemistrySulfuric acidRefineryEnvironmental engineeringMining engineeringChemistryGeologyEngineering

Abstract

fetched live from OpenAlex

An increasingly common practice for metallurgical plants is to discard their wastes by combining them with mine tailings prior to disposing the blended material to a containment facility. This practice has occurred since 1985 at the Kidd Creek tailings impoundment where natrojarosite, a waste produced from the adjacent Zn refinery, is combined with mine tailings and is deposited in a single impoundment. To assess the environmental impact of the co-disposal, a laboratory column experiment was conducted. The column material was flotation tailings from the Kidd Creek site containing 3 wt % natrojarosite residue. Dilute sulfuric acid was passed through the column to simulate the acid generated in the unsaturated zone of the tailings impoundment. The results of this experiment were compared to the results of a previous experiment conducted on unamended flotation tailings. The results showed that the effluent from the column containing the natrojarosite-bearing mixture had a faster decrease in pH, earlier increases in the concentrations of dissolved metals such as Pb and Cd, and a greater persistence in effluent metal concentrations such as Pb, Zn and Ni. To prevent the observed enhanced release of dissolved metals from mine waste disposal areas, natrojarosite should not be co-disposed with 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.180
Teacher spread0.177 · 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

Citations15
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicMetal Extraction and BioleachingFrench-language works237,207