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Record W2539407086 · doi:10.1080/00084433.2016.1245255

Break-up in formation of small bubbles: an energy consideration

2016· article· en· W2539407086 on OpenAlexafffund
Pengbo Chu, Kristian E. Waters, J.A. Finch

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsMcGill University
FundersResearch and DevelopmentNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsEnergy (signal processing)Environmental scienceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

The formation of small bubbles in flotation is usually accomplished by the use of frothers, or sometimes is due to the presence of inorganic salts in the process water. The effective mechanism associated with the presence of these solutes is generally considered to be coalescence inhibition. However, recent work has demonstrated that the presence of frothers and salts also reduces the size of bubble at the initial formation stage. In this paper, we adapt the same experimental setup to investigate the time that it takes for a bubble to form; for a known system power input, the energy to form a bubble can then be estimated from the time. The hypothesis is that the presence of frother and salt will reduce the time for a bubble to form, which can be interpreted as an added energy component being derived from the solute. Testing different types and concentrations of frothers and salts, the hypothesis is supported: the mechanical energy required to form a bubble decreases by about 10% in the presence of these solutes.

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)0.002
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Metallurgical QuarterlySame topicMinerals Flotation and Separation TechniquesFrench-language works237,207