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Record W2806982727 · doi:10.11159/ffhmt18.120

Heat Transfer inside a Highway Tunnel under Vehicle Thermal Effect

2018· article· en· W2806982727 on OpenAlexvenueno aff
Tao Zhang, Hiroshi Katsuchi, Hitoshi Yamada

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsHeat transferThermalTransfer (computing)Automotive engineeringEnvironmental scienceMaterials scienceAerospace engineeringEngineeringComputer scienceMechanicsPhysicsMeteorology

Abstract

fetched live from OpenAlex

A highway tunnel in Tokyo experiences high inside temperature, especially during summer season. Vehicle heat was roughly calculated based on fuel consumption of different vehicle under observed traffic volume. In a certain range of tunnel, the amount of heat absorbed by road surface and concrete wall, extracted by ventilation system and went out by tunnel exit, were calculated respectively in short time period. These results are used to verify a heat-balance model to determine each proportion of vehicle heat dissipated in road, concrete wall and outside air. Finally, cooling measure based on this is preliminary discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.841

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 science0.0010.000
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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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.

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
Published2018
Admission routes1
Has abstractyes

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