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Record W2885630074 · doi:10.11159/mmme18.117

Study on Chemical Plug Removal Technology for Acid In-situ Leaching Uranium

2018· article· en· W2885630074 on OpenAlexvenueno aff
Po Li, Xu Ying, Tan Ya-Hui, Cheng Wei, Hu Bo-Shi

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2018
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsnot available
Fundersnot available
KeywordsUraniumLeaching (pedology)Plug-inSpark plugIn situEnvironmental scienceMetallurgyMaterials scienceChemistryComputer scienceEngineeringOperating systemSoil science

Abstract

fetched live from OpenAlex

In-situ leaching mining technology refers to selective dissolution of metallic element containing in ore under the natural burial conditions through the chemical reaction of leaching agent and mineral.During the process of in-situ leaching of uranium by acid leaching, the chemical reaction between sulfuric acid and ore dissolving out iron, aluminum and uranium, and these metal ions are hydrolyzed and precipitated under the influence of pH.Meanwhile, under the long-term effect of sulphuric acid, the silicate ore formed silica gel hydrate of silicon dioxide and deposited in ore bed, which causes the chemical blockage.In this text, a method of chemical plug removal of ammonium bifluoride + sulfuric acid + citric acid is introduced for the improvement of permeability of ore bed and production capacity of drilling process, which is achieved by dissolving the siliceous compounds in ore bed by hydrofluoric acid.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicRadioactive element chemistry and processingFrench-language works237,207