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Record W2965666041 · doi:10.11159/mmme19.143

In-Situ Measurement of Sensible Heat Ratio and Wetness Coefficient in Coal Mine Roadways

2019· article· en· W2965666041 on OpenAlexvenueno aff
Jianliang Gao, Quanfu He

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSensible heatCoalIn situGeologyMeteorologyAtmospheric sciencesWaste managementEngineering

Abstract

fetched live from OpenAlex

The sensible heat ratio and wetness coefficient of roadway are important parameters for calculating the heat exchange between the wall of surrounding rock and air flow. Firstly, the variation pattern of sensible heat factor is analysed and illustrated theoretically. The sensible heat ratio decreases with the increase of the ventilation time after the rock surface is exposed, because of the decreasing of the surface temperature of the rock. The sensible heat ration increases with the enhancement of the relative humidity of airflow and decreases with the increase of the wetness coefficient of the rock surface. Secondly, according to the theory of heat and moisture exchange between mine air flow and surrounding rock, a method for calculating sensible heat ratio and wetness coefficient of underground roadway is proposed. The in-situ measurement has been carried out at several coal mines. The results show that the sensible heat ratio of rock roadway is larger than that of coal roadway, in which rock roadway is in the range of 0.4-0.7, coal roadway is in the range of 0.1-0.4, upper and lower hills are in the range of 0.2-0.4, and coal mining face is in the range of 0.1-0.4. For roadways with a small amount of water on the floor, the wetness coefficient is between 0.06 and 0.06. For roadways with a small amount of water on the floor, the wetness coefficient is between 0.06 and 0.1.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.525

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 head0.008
GPT teacher head0.181
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

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicCoal Properties and UtilizationFrench-language works237,207