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Record W1978772959 · doi:10.1080/08827500500339331

DISSOLVED GAS FLOTATION IN MINERAL PROCESSING

2006· article· en· W1978772959 on OpenAlex
T. Yalçın, Amy Byers

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

VenueMineral Processing and Extractive Metallurgy Review · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsLaurentian University
Fundersnot available
KeywordsArgonPulp (tooth)Argon gasMineral processingDissolved air flotationChemistryMineralogyMetallurgyPulp and paper industryEnvironmental scienceMaterials scienceEnvironmental engineeringWastewater

Abstract

fetched live from OpenAlex

Potential use of dissolved gas bubbles in mineral flotation processes was investigated by conducting tests on the copper–nickel ore of Inco Ltd. in Sudbury, Ontario, Canada. Such bubbles were generated by pressurizing the ore pulp in an air or argon atmosphere at a 276 kPa gauge (40 psig) for a period of about 1 min and then releasing the pressure by discharging the pressurized pulp into a column where flotation took place. Based on the conclusions of an earlier work, dissolved gas bubbles were employed together with conventional bubbles, the latter produced by a gas sparger located inside the flotation column. The presence of dissolved gas bubbles in the flotation pulp was found to have a significant impact, particularly when argon was used as the flotation gas, resulting in substantially higher grades and recoveries in the concentrate. At the same time, mass recoveries by size showed a 20% increase across all sizes when air was used as the flotation gas and a 40–100% increase in the case of argon.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.875

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 communication0.0000.001
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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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