MétaCan
Menu
Back to cohort
Record W1978878914 · doi:10.1002/aic.12182

Particle size monitoring in dense suspension using ultrasound with an improved model accounting for low‐angle scattering

2010· article· en· W1978878914 on OpenAlex

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.

Bibliographic record

VenueAIChE Journal · 2010
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsDeconvolutionParticle-size distributionAttenuationSuspension (topology)OpacityParticle (ecology)Ultrasonic sensorParticle sizeMaterials scienceDetectorAcousticsBiological systemOpticsComputational physicsPhysicsEngineeringMathematicsChemical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract The inherent ability of ultrasonic wave to propagate in dense and opaque suspensions makes it a desirable method for online measurement of particle size distribution in industrial operations. The ability of ultrasonic attenuation spectroscopy to determine particle size distribution has been extended to dense suspensions of particles lying predominantly in the intermediate wave propagation regime at the measurement frequencies. This was achieved by accounting for the effect of detector size and shift in the frequency spectrum under dense conditions in the theoretical model and deconvolution algorithm, respectively. The proposed modifications enable the application of this technique in various industrial processes requiring in situ and real‐time measurement of particle size distribution such as crystallization, mineral processing and food processing. © 2010 American Institute of Chemical Engineers AIChE J, 2010

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.016
GPT teacher head0.283
Teacher spread0.268 · 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