MétaCan
Menu
Back to cohort
Record W2131184667 · doi:10.1080/10837450500463778

Powder and Other Divided Solids Mixing. Scale-Up and Parametric Study of a Ribbon Blender Used in Pharmaceutical Powders Mixing

2006· article· en· W2131184667 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

VenuePharmaceutical Development and Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMicrocrystalline celluloseImpellerMixing (physics)RibbonRotational speedMaterials scienceAgitatorComposite materialWork (physics)Parametric statisticsSCALE-UPScalingMechanical engineeringProcess engineeringEngineering drawingCelluloseMathematicsEngineeringChemical engineeringGeometryPhysicsStatistics

Abstract

fetched live from OpenAlex

This work is aimed at evaluating the effect of ribbon blender operational parameters on mixture quality. Mix quality parameters and blend uniformity limits are enforced by regulatory bodies. These limits have served in this present work as targets for blending end-points. In a laboratory-scale model ribbon blender, built by scaling down a real industrial unit, powder mixtures composed of white and blue microcrystalline cellulose (MCC) were blended. Blend uniformity was evaluated using a statistical analysis method under various operating conditions such as loading patterns, blender filling percentage, impeller rotational speeds, and mixing times. It was shown that the filling percentage is the most influential mixing parameter. At high impeller rotational speed, the blending end-point was never reached during experimentation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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