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Record W3035441133 · doi:10.2514/6.2020-2826

Ice Crystal Environment-Modular Axial Compressor Rig: Evaluation of Measured Water Content and Melt

2020· article· en· W3035441133 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAIAA AVIATION 2020 FORUM · 2020
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsModular designGas compressorMaterials scienceWater contentEnvironmental scienceIce crystalsMechanical engineeringComputer scienceGeologyEngineeringGeotechnical engineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

The National Research Council of Canada (NRC) has developed the Ice-Crystal Environment Modular Axial Compressor Rig (ICE-MACR) for simulating altitude ice crystal icing of aircraft engines in altitude facilities. Commissioning of the rig under altitude icing conditions was conducted in the NRC’s altitude icing wind tunnel (AIWT) in May-June 2019. This paper examines the water content at the inlet and in the test section of the rig. A compact isokinetic probe was used to determine the total water content and a new version of Science Engineering Associates multi-element probe was used to differentiate water phase. In addition, a particle imaging system was used to determine particle size but also provided insight into whether the particles were in a solid or liquid state. These results will be compared to those from the multi-element probe.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.221
Threshold uncertainty score0.396

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)0.0000.000

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 head0.040
GPT teacher head0.212
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