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Record W2300066691 · doi:10.18260/p.23437

A Remote Access Laboratory for Fluids Education in Mechanical Engineering

2015· article· en· W2300066691 on OpenAlexaff
Gwen Ellis, C. D. Richards, Brad Thompson, R. F. Richards

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
FundersAir Force Institute of TechnologyAir Force Research LaboratoryU.S. Air Force AcademyUniversity of California, IrvineU.S. Air ForceUniversity of WashingtonNational Institute of Standards and TechnologyWashington State UniversityArizona State University
KeywordsVenturi effectRemote laboratoryComputer scienceClass (philosophy)MultimediaSimulationEngineeringMechanical engineeringWorld Wide WebThe InternetArtificial intelligence

Abstract

fetched live from OpenAlex

His research is in thermodynamics and heat and mass transfer.Over the last five years he has become involved in developing and disseminating research based learning methods.He was a participant in the NSF Virtual Communities of Practice (VCP) program in Spring, 2013, learning research based methods to instruct thermodynamics.More recently he introduced the concept of fabricating very low cost thermal fluid experiments using 3-D printing and vacuum forming

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0630.018

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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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