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Record W3134318864 · doi:10.3811/jjmf.2021.001

International Transportation of Ultrafine Bubble Dispersions

2021· article· en· W3134318864 on OpenAlexaboutno aff
Shunya Tanaka, Yuri Naruse, Koichi Terasaka, Satoko Fujioka, Yoko Yamamoto, Yusuke Noguchi, Kyoka Yamazaki, Takuya SHOMURA, Shun Harada

Bibliographic record

VenueJAPANESE JOURNAL OF MULTIPHASE FLOW · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsDispersion (optics)Materials scienceDispersion stabilityDynamic light scatteringBubbleAccelerationStability (learning theory)MechanicsComposite materialPolymerNanotechnologyComputer scienceOpticsPhysicsNanoparticle

Abstract

fetched live from OpenAlex

Interlaboratory comparison can be effective to obtain consistent findings on ultrafine bubbles (UFBs). In this study, the stability of UFBs during international transportation was reported for the first time. We conducted stability tests in cooperation with German and Canadian institutions. UFBs were measured using two laser-based characterization techniques: particle tracking analysis (PTA) and dynamic light scattering (DLS). Acceleration and temperature during transportation were also measured. The results showed that regardless of transportation, the number concentration of UFBs decreased, while the size of UFBs increased with time. By filling the glass vial with UFB dispersion and minimizing the liquid flow in the vial, the effects of transportation were virtually eliminated. It was also suggested that the liquid flow caused by vibration during transportation may have enhanced the aggregation of UFBs. We report the results of an inter-instrumental comparison among PTA and DLS instruments using the transported UFB dispersions.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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
Published2021
Admission routes1
Has abstractyes

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