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Hierarchical occurrence law of gas content in deep coal seams and its relationship with outburst prevention

2025· article· en· W6885390971 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoal miningCoalComputer simulationContent distributionGas pressureSensitivity (control systems)

Abstract

fetched live from OpenAlex

The prediction indexes currently used in China for coal and gas outburst area include gas pressure and gas content, which exhibit varying sensitivity to outbursts in different mining areas. The formulation of rational prediction indexes for outburst risk is based on understanding the mechanism of coal and gas outbursts. According to the “internal gas controlled theory”, it is inferred that under deep conditions, the sensitivity of gas content is higher than that of gas pressure. To verify this inference, this study selects Pingmei No.8 Coal Mine as a typical research site due to its high temperature and high pressure conditions. These conditions result in a different occurrence pattern of gases in deep coal seams compared to shallow ones, where below a “critical depth”, the gas content exhibits negative growth leading to a step-like occurrence pattern, distinct from the linear increase observed in gas pressure. Using a combination of theoretical calculation, experimental analysis, numerical simulation, and on-site verification, the hierarchical occurrence law of gas content in Pingmei No. 8 Mine was first theoretically calculated, and a s solid-flow-heat three-field coupling model considering the competition effect of temperature and pressure was established to analyze the influencing factors of the reverse decrease of gas content with depth. Furthermore, the differential occurrence patterns of gas content and gas pressure during geological exploration/mining periods were compared, verifying the research results of theoretical research and numerical simulation. Additionally, the vertical distribution pattern of outburst energy within the study area was theoretically analyzed while verifying step-like distribution characteristics through positive lateral verification using data on emitted gases amounts and documented outburst accidents cases. Finally,the differences between sensitivities towards outbursts were compared and analyzed between deep conditions for bothgas pressureandgascontent,andthe underlying reasons behind these differences are clarified. The research results of this paper verify the correctness of “internal gas controlled theory” from a macro perspective, and have certain guiding significance for the prevention and control of deep coal gas dynamic disasters.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.484

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 communication0.0000.001
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 head0.253
GPT teacher head0.481
Teacher spread0.227 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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