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Record W4206901027 · doi:10.26480/esmy.01.2021.58.64

ASSESSING THE ACID MINE DRAINAGE POTENTIAL OF A STOCKPILED GOLD BEARING SULPHIDE ORE

2020· article· en· W4206901027 on OpenAlexaff
Bennetta Koomson, Shadrack Fosu, Blestmond A. Brako, E. K. Asiam, Ernest K. Yanful

Bibliographic record

VenueEarth Science Malaysia · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsWestern University
FundersAngloGold Ashanti
KeywordsArsenopyritePyriteAcid mine drainageMetallurgyArsenicGold oreSphaleriteGold extractionBase metalMineralogySulfide mineralsMining engineeringEnvironmental chemistryGeologyEnvironmental scienceChemistryChalcopyriteMaterials scienceCopper

Abstract

fetched live from OpenAlex

This work seeks to assess the acid mine drainage (AMD) potential of stockpiled sulphidic gold bearing ore that was left untreated for a period of four (4) years. The mineralogical composition of the stockpiled sulphidic gold ore was determined using X-Ray Diffractometry (XRD) and Reflected Light Microscope. The AMD potential and kinetic studies on the ore sample were determined using Acid Base Accounting (ABA) and Humidity Cell (ASTMD 5744). The XRD and microscope analyses showed that the gold ore contain arsenopyrite and pyrite as the major sulphide minerals. The ABA confirmed that the gold sulphide ore could be a potential source of AMD with respect to AMD marginal of safety if the ore is left untreated for a very long period. Humidity Cell kinetic analysis showed a possibility of mobilizing Arsenic (As) into the geo-environment when there is a long period of oxidation of abandoned sulphide run-of-mine (ROM) ore.

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: Bench or experimental · Consensus signal: Bench or experimental
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
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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