MétaCan
Menu
Back to cohort
Record W4385394746 · doi:10.55037/lxlaser.20th.187

Progress Towards Full-Scale (In Situ) Flow Measurements

2022· article· en· W4385394746 on OpenAlexaff
Frieder Kaiser, Raymond H. Chan, David E. Rival

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsQueen's University
FundersCalifornia Institute of Technology
KeywordsFocus (optics)Computer scienceScale (ratio)Flow (mathematics)Full scaleReal-time computingComputer visionArtificial intelligenceOpticsPhysicsGeographyCartography

Abstract

fetched live from OpenAlex

Recent advances in tracer, illumination, and camera technology, paired with new processing algorithms, have been pushing the limit of scale for three-dimensional flow measurements. The present study reflects on the state-of-the-art and discusses the required steps to enable full-scale, in situ flow measurements in very large measurement volumes. In particular, we focus on industrial and environmental applications, where the measurement time, the processing time, and the costs all have to be minimized. A single-camera approach that enables measurements with natural illumination in very large volumes is presented. The required infrastructure is discussed along with experiments to quantify the experimental errors of the proposed single-camera approach.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.220
Teacher spread0.193 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicFlow Measurement and AnalysisFrench-language works237,207