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Record W2924202459 · doi:10.22215/etd/2018-12966

Experimental and Numerical Study of Inhaler Spray Characterization (Size Distributions and Velocity)

2018· dissertation· en· W2924202459 on OpenAlexaff
Abubaker Alatrash

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsCarleton University
Fundersnot available
KeywordsMouthpieceInhalerPropellantMaterials scienceMetered-dose inhalerParticle sizeSpray characteristicsParticle-size distributionMedicineAsthmaSpray nozzleThermodynamicsChemical engineeringAerospace engineeringPhysicsEngineeringInternal medicineNozzle

Abstract

fetched live from OpenAlex

Particle size and velocity are two of the most signicant factors that impact the deposition of pressurized metered-dose inhaler (pMDI) sprays in the mouth cavity. pMDIs are prominently used around the world in the treatment of patients suering from a variety of lung diseases such as asthma and chronic obstructive pulmonary disease. Moreover, Soft Mist Inhalers (SMIs) are a new-generation of propellant free Inhalers. The rst delivery inhalation spray system of this kind is the Spiriva Respimat inhaler, which has its advantages, such as better medication delivery to the lungs.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.293
Teacher spread0.280 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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