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Record W2526042832 · doi:10.1299/jsmepes.2014.19.375

F224 Study of washing by using supersonic wet steam

2014· article· en· W2526042832 on OpenAlexaboutno aff
Takumi Komori, Tsugue Ito, Fumio Inoue, Masahiro Osakabe, Sachiyo Horiki

Bibliographic record

VenueDoryoku, Enerugi Gijutsu Shinpojiumu koen ronbunshu/Doryoku, enerugi gijutsu no saizensen koen ronbunshu · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsSupersonic speedNozzleChoked flowJet (fluid)Flow (mathematics)Mechanical engineeringEnvironmental scienceMechanicsMaterials scienceWaste managementEngineeringNuclear engineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Generally for biological and environmental safety, the de-oiling or cleaning should be done with the physical method instead of the chemical method using detergent or solvent. As one of the physical method, a cleaning by a supersonic steam flow impinging on machine parts was proposed. The simple de Laval nozzle to make the supersonic flow was designed and manufactured with the electric discharging machine. The impinging jet structure was carefully investigated by using air and steam. The under-expansion and super-saturation of jet is expected to increase cleaning ability. The cleaning experiment of oily machine parts and coins were conducted with the supersonic steam flow and the good results were obtained.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.234
Teacher spread0.222 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDoryoku, Enerugi Gijutsu Shinpojiumu koen ronbunshu/Doryoku, enerugi gijutsu no saizensen koen ronbunshuSame topicErosion and Abrasive MachiningFrench-language works237,207