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Record W4235146699 · doi:10.32920/ryerson.14646120.v1

Study of impact of sonic energy waves on the rate of precipitation of particles in liquid

2021· preprint· en· W4235146699 on OpenAlex
Abdisamed Sheik-Qasim

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

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSettlingViscosityAmplitudeRange (aeronautics)Particle (ecology)Materials scienceMechanicsParticle velocityParticle sizeParticle densityPhysicsThermodynamicsChemistryComposite materialOpticsGeologyVolume (thermodynamics)

Abstract

fetched live from OpenAlex

The effects of sonic energy waves on the settling velocity of small particles in water were studied. A design of experiment (DOE) with five variables (frequency, amplitude, particle diameter, particle density and fluid viscosity) at two or three levels was conducted to obtain the particle settling velocity as the response. The DOE data were analyzed both experimentally and by a statistical multiple regression software. It was concluded that when sound frequency and amplitude in the range of 0 to 500 Hz and 2 to 3 Vrms (root mean square) respectively were applied to plastic particles of three different diameters (2,381 μm, 3,175 μm, and 4,763 μm) and two different densities ... their effects on the particle settling velocity in hydroxypropyl cellulose (HPC) solutions of three different viscosities ... were insignificant. The regression analysis gave an equation that is in good agreement with the experimental data.

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 score0.007
Threshold uncertainty score0.508

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.310
Teacher spread0.286 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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