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Record W4312989003 · doi:10.46427/gold2022.8864

Assessment of water quality evolution in filtered tailings storage facilities

2022· article· en· W4312989003 on OpenAlexaff
Ikram Elkhoumsi, Thomas Pabst, Carmen Mihaela Neculita

Bibliographic record

VenueGoldschmidt2022 abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversité du Québec en Abitibi-TémiscaminguePolytechnique Montréal
Fundersnot available
KeywordsTailingsQuality (philosophy)Environmental scienceComputer scienceMetallurgyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Neutral mine drainage (NMD) and contaminants leaching are common problems at mine effluents. They occur as metal(oids) become soluble in water system of mine tailings at near-neutral pH. Filtered tailings storage facilities (TSF) are particularly prone to contaminants released because of their unsaturated state and continuous interactions with atmosphere. Metal leaching can be made even more challenging in TSF where salinity is high. A potential approach to improve water quality in TSF consists in using waste rock inclusions (WRI) to control contaminants flow and reduce water contamination.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGoldschmidt2022 abstractsSame topicTailings Management and PropertiesFrench-language works237,207