MétaCan
Menu
Back to cohort
Record W4412411151 · doi:10.5539/ijc.v17n2p63

Organic Combustion Model for Determining Higher Heating Value: Mathematical Framework and Thermochemical Equation

2025· article· en· W4412411151 on OpenAlexvenueno aff
Pong Kau Yuen, Cheng Man Diana Lau, Karen Yuen

Bibliographic record

VenueInternational Journal of Chemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryCombustionHeat of combustionThermodynamicsValue (mathematics)Organic chemistryStatistics

Abstract

fetched live from OpenAlex

Organic combustion is a classic redox reaction which uses molecular oxygen as an oxidizing agent. It pertains to two critical parameters in thermochemistry, i.e., higher heating value and heat of organic combustion. In this research, an organic combustion model which consists of two sections is established. The first section is a mathematical framework for counting theoretical higher heating value and the second section is a thermochemical equation for calculating theoretical heat of organic combustion. This research shows that based on any given empirical or structural formula of organic matter, the theoretical higher heating value can be calculated by parameters of organic matter, parameters of organic combustion, or a combination of both. The general balanced organic combustion and thermochemical mathematical equation can be deduced, and consequently the theoretical heat of organic combustion can be counted. Using this organic combustion model, the study reveals the quantitative difference between two approaches that are used for calculating theoretical higher heating value and heat of organic combustion.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of ChemistrySame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207