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Record W4317459798 · doi:10.55365/1923.x2022.20.68

Theoretical Foundations of Building A Mathematical Model of Crushed Wood As An Object Of Fire Safety

2022· article· en· W4317459798 on OpenAlexvenueno aff
Vladimir Pavlyust, Yuri Gradysky, Anastasiia Suska, Serhii Shevchenko, Oleksii Diakonov

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldEngineering
TopicCoal Combustion and Slurry Processing
Canadian institutionsnot available
Fundersnot available
KeywordsCrushed stoneEnvironmental scienceTask (project management)Waste managementComputer scienceEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The article considers the issues related to the possibility of using wood waste as an alternative energy source that can partially solve the problem of energy independence.However, the processing and storage of bioraw materials are associated with the risk of spontaneous inflammation of crushed wood.To prevent this, it is necessary to cope with the task of a safe method of long-term storage at processingcompanies.The paper presents the preconditions and methodology for creating a mathematical model of crushed wood during its storage in bunker bays with forced ventilation as an object of fire safety.The model takes into account the processes of heat, moisture, and gas exchange of crushed wood with the environment, along with biochemical processes, which will allow assessing the probability of spontaneous combustion of crushed wood effectively.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.252
Teacher spread0.235 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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