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Record W2964460772 · doi:10.1130/abs/2019am-333580

FACTORS CONTROLLING TUNGSTEN MOBILITY IN W-CU SKARN TAILINGS AT THE CANTUNG MINE, NORTHWEST TERRITORIES, CANADA

2019· article· en· W2964460772 on OpenAlex
Brent G. Kazamel, Heather E. Jamieson, Matthew I. Leybourne, Hendrik Falck

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAbstracts with programs - Geological Society of America · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsSkarnTailingsTungstenGeologyMining engineeringGeochemistryMetallurgyMaterials scienceSeismologyHydrothermal circulation

Abstract

fetched live from OpenAlex

Few studies have addressed the mobility of tungsten in mine waste, which could act as a point source for metal leaching. This study addresses the behaviour of tungsten in tailings at the Cantung Mine in the Northwest Territories, Canada. In addition to this study, this thesis includes a literature review regarding the environmental geochemistry of tungsten.
\n\tIn the Cantung tailings, tungsten is present as scheelite (CaWO4; 0.1 – 0.7 wt.%), copper is hosted in chalcopyrite (CuFeS2; 0.1 – 1.5 wt.%), and gangue mineralogy consists of abundant pyrrhotite (Fe1-xS; 0.2 – 49 wt.%), calc-silicate minerals (26 – 62 wt.%), and carbonate minerals (0 – 30 wt.%). In July 2018, nine surface water samples, five tailings pore-water samples, and thirteen tailings samples were collected from the Cantung Mine’s tailings. Water samples have been analyzed by high resolution inductively coupled mass spectrometry (HR-ICP-MS), yielding tungsten concentrations ranging from 5.3 to 26.3 μg/L, exclusively in samples with pH values between 7.05 – 8.05. Tungsten and iron concentrations are both on average 1.6 x higher in unfiltered aliquots compared to filtered aliquots, suggesting that tungsten is transported as dissolved species but is also adsorbed to suspended Fe-oxyhydroxide minerals. Tailings were analyzed by scanning electron microscopy (SEM) paired with automated mineralogy software (MLA), synchrotron-based μXRD-XRF, and partial leach extractions. The scheelite content of Tailings Pond 3 (TP3) and the FRT are similar (0.15 wt.% and 0.21 wt.%, respectively), and scheelite shows no evidence of alteration. Synchrotron-based μXRD of Fe-oxyhydroxide minerals in the FRT identify goethite (FeOOH) and lepidocrocite (γ-FeOOH), whereas μXRD spectra of pyrrhotite rims from TP3 do not match Fe-minerals, with the exception of rare rims that match hematite (Fe2O3) and maghemite (γ-Fe2O3). The μXRF maps of the hematite-maghemite rims have prominent tungsten peaks, which represents included scheelite grains and possibly structurally incorporated tungsten, likely formed during ore processing. The hydroxylamine leaches yield higher tungsten concentrations in tailings samples from the impoundments than samples from the FRT, suggesting the tailings impoundments have more tungsten that is associated with poorly crystalline and amorphous Fe-oxyhydroxide phases than the FRT. Over time, labile-hosted tungsten in the FRT may have been washed down the Flat River during Fe-oxyhydroxide recrystallization and high energy flooding events.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.201
Teacher spread0.188 · 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