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Record W2155688078

Multiport averaging pitot tube to measure airflow rates from exhaust fans

2008· article· en· W2155688078 on OpenAlexaffabout
Oliver Clark, J. Chiva, Jeannine Ouellette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsPitot tubeAirflowAnemometerAcousticsTurbulenceAirspeedFlow measurementMechanicsEnvironmental scienceFlow (mathematics)EngineeringMeteorologyMechanical engineeringPhysicsAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

Multiport averaging Pitot tube to measure airflow rates from exhaust fans. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 50: 5.1- 5.7. Reliable airflow measurements for mechanically-ventilated animal confinement facilities are necessary to accurately determine gas emission rates. Instrumentation for this purpose must, however, be inexpensive, not intrusive, functional in very turbulent airflow, and robust under demanding field conditions. A multiport, averaging Pitot tube was constructed, calibrated in the laboratory using a standardized testing facility, and then tested and refined under simulated and actual field conditions as part of an airflow measurement system. Special features of the measurement system included a flow settling means, downstream orientation of the pressure inlets on the Pitot tube, and a physical filter to dampen pressure fluctuations in the signal line from the Pitot tube to the transducer. The relationship between the measurement signal and air

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)0.002
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.202
Teacher spread0.173 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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