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Record W2054452588 · doi:10.3109/03639045.2014.983931

Twin screw granulation – review of current progress

2014· review· en· W2054452588 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDrug Development and Industrial Pharmacy · 2014
Typereview
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaDow Chemical Company
KeywordsGranulationGranule (geology)Consistency (knowledge bases)Process engineeringExtrusionComputer scienceMaterials scienceEngineeringComposite materialArtificial intelligence

Abstract

fetched live from OpenAlex

Twin screw granulation (TSG) is a new process of interest to the pharmaceutical community that can continuously wet granulate powders, doing so at lower liquid concentrations and with better product consistency than found by a high shear batch mixer. A considerable body of research has evolved over the short time since this process was introduced but generally with little comparison of results. A certain degree of confidence has been developed through these studies related to how process variables and many attributes of machinery configuration will affect granulation but some major challenges still lay ahead related to scalability, variations in the processing regimes related to degree of channel fill and the impact of wetting and granulation of complex powder formulations. This review examines the current literature for wet granulation processes studied in twin screw extrusion machinery, summarizing the influences of operational and system parameters affecting granule properties as well as strives to provide some practical observations to newly interested users of the technique.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.099
GPT teacher head0.348
Teacher spread0.249 · 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