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Record W2324026515 · doi:10.1021/ie201698r

Limestone Particle Attrition in High-Velocity Air Jets

2011· article· en· W2324026515 on OpenAlexaff
Gang Xiao, John R. Grace, C. Jim Lim

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAttritionParticle (ecology)Particle sizeJet (fluid)MechanicsMineralogyExponential functionMaterials scienceVolume (thermodynamics)ThermodynamicsChemistryGeologyMathematicsPhysics

Abstract

fetched live from OpenAlex

Experiments were carried out with limestone particles of several narrow size intervals (125–180, 250–300, 355–425, 500–600, 600–710, 710–850, and 850–1037 μm) for times ranging from 0.5 to 144 h in a high-velocity jet apparatus to provide a more comprehensive understanding of jet attrition. The theory of cumulative damage for fatigue is applied to explain the particle attrition mechanisms and to build an attrition model. Fines generation processes differed for limestone particles of different initial sizes, especially in the initial stage, because of the effects of rough surfaces and cumulative damage needed for attrition. In the model, the fines generation rates in the initial stage was well fitted by an exponential function with an index inversely proportional to the particle volume until stable stages were reached, whereas the rate of fines generation during the stable stage appeared to be constant for narrowly sized limestone particles.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.160
GPT teacher head0.308
Teacher spread0.148 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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