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Record W4285496446 · doi:10.5382/econgeo.4939

Aqueous Geochemistry and Mineralogy of Tungsten with Emphasis on Mine Wastes

2022· article· en· W4285496446 on OpenAlexaffabout
Brent G. Kazamel, Heather E. Jamieson, Matthew I. Leybourne, Hendrik Falck, Karen H. Johannesson

Bibliographic record

VenueEconomic Geology · 2022
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsArthur B. McDonald-Canadian Astroparticle Physics Research InstituteGovernment of Northwest TerritoriesQueen's University
Fundersnot available
KeywordsScheeliteTungstenWolframiteLeaching (pedology)WeatheringTailingsDissolutionAdsorptionAqueous solutionMetallurgyWastewaterEnvironmental scienceGeologyGeochemistryEnvironmental chemistryChemistryMaterials scienceMining engineeringEnvironmental engineeringSoil water

Abstract

fetched live from OpenAlex

Abstract Tungsten is a valuable industrial metal with limited effective substitutes for its many industrial uses. Tungsten is classified as a critical mineral by many countries, including Canada and the United States. As such, tungsten mining is expected to increase in the future, generating waste with potential as point sources of metal leaching to the environment. Concerns about the toxicity of tungsten have recently emerged, but the element is not regulated in mine waste. The geochemical behavior of tungsten is understudied, hampering the ability to predict tungsten leaching from past, present, and future mining operations. The low-temperature aqueous mobility of tungsten is pH dependent, with adsorption as a primary factor limiting its dissolved concentrations in the environment. Tungsten, which forms strictly anionic species, adsorbs to mineral surfaces at low pH, and is most mobile in alkaline waters. The speciation of tungsten has been shown to affect its adsorption propensity, with WO42- more strongly adsorbed than polymeric and thiolated species. Aqueous tungsten concentrations generally decrease with decreasing pH in surface waters, groundwaters, geothermal waters, and mine tailings. Although progress has been made, the mechanisms of scheelite and wolframite dissolution are still poorly understood. Mine waste generated from processing tungsten-rich ores contains elevated tungsten concentrations; the weathering behavior of scheelite and wolframite need more research to mitigate the potential for tungsten leaching during the long-term storage of mine waste. Identifying mineralogical changes to scheelite and wolframite that may occur during ore processing and weathering of mine waste would be beneficial for understanding the leaching behavior of these minerals. Based on current knowledge, it is likely that tungsten is mobilized as both dissolved anions and adsorbed to colloidal particles. Improved understanding of the environmental geochemistry of tungsten will help ensure its sustainable supply in the future and permit more effective remediation at abandoned and existing mines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.005
GPT teacher head0.171
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2022
Admission routes2
Has abstractyes

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