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Record W4392649349 · doi:10.5194/egusphere-egu24-17054

Exploration and reevaluation of a tailing storage facility from a high-sulfidation epithermal gold deposit

2024· preprint· en· W4392649349 on OpenAlexaff
Petya Gutzmer, Kai Bachmann, Raimon Tolosana‐Delgado, Laura Tuşa, Cecilia Contreras, Philipp Büttner, Jens Gutzmer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsSulfidationMining engineeringGeologyGeochemistryMetallurgyMaterials scienceSulfur

Abstract

fetched live from OpenAlex

Reevaluating tailings material originating from a high-sulfidation epithermal gold deposit has unveiled the potential for a holistic remining endeavour. The primary objective of this operation would be the mitigation of minerals containing penalty elements, specifically sulphur in pyrrhotite, a major contributor to acid mine water drainage, alongside the concentration of precious elements like gold. Furthermore, exploring the applicability of the silicate fraction for industries such as ceramics, glass, and geopolymer production has been considered.To accomplish these objectives, an initial drill core campaign featuring six drill holes was executed, accompanied by a thorough material characterization using automated mineralogy, geochemical assays, and hyperspectral analysis. The subsequent step involved a geometallurgical domaining process based on 64 geochemical assays, particle size measurements, and mineralogy assessments. A Mahalanobis distance hierarchical cluster analysis was employed to differentiate domains, and predictions for these domains were extended to all hyperspectral imaging samples.The outcome of this comprehensive approach revealed the delineation of four distinct domains, each characterized by variations in modal mineralogy and trace elemental contents. This strategic analysis provides valuable insights into the heterogeneity of the tailings material, laying the groundwork for targeted interventions to address environmental concerns and maximize the extraction of valuable resources.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.719

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 head0.029
GPT teacher head0.230
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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