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Record W4281775791 · doi:10.1063/5.0088065.5

10.1063/5.0088065.5

2022· dataset· en· W4281775791 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.

Bibliographic record

VenueDefault Digital Object Group · 2022
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Waterloo
FundersOffice of Naval ResearchJapan Society for the Promotion of Science
KeywordsBubbleRADIUSMechanicsAir bubbleEnvironmental scienceMaterials sciencePetroleum engineeringMeteorologyPhysicsGeologyComputer science

Abstract

fetched live from OpenAlex

The interaction between a heated oil bath and water droplets commonly occurs in the kitchen and has important implications for cooking, fire safety, and indoor air pollution. The interplay between the bubble dynamics in a heated oil bath, the generated sound, and the ligament-like expulsion to the surrounding air is examined. We focus on an explosion of a millimeter-sized water droplet in heated oil as a simplified case. We discuss three typical bubble types that can be classified as a function of the stand-off parameter h/R, where h is the distance between the oil surface and bubble and R is the maximum bubble radius. Our data describe the morphology of bubble dynamics inside a heated oil bath and represent those found in the cooking pan. This paper also highlights potential applications of our findings.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.5800.027

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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