An Exploration of Ionic Buswell’s Equation for Biomethane
Bibliographic record
Abstract
Anaerobic digestion is a sustainable process that occurs under anaerobic microorganism-mediated conditions. In this process, organic matters generate biogas and digestate in the gas-aqueous solution-solid multiphases system. Molecular Buswell’s equation has been widely applied for neutral organic matter. In contrast, ionic Buswell’s equation has been given little attention. This article uses the proton method to develop a stoichiometric ionic Buswell’s equation for neutral and ionic organic matters. When an empirical formula of organic matter is given, its stoichiometric ionic Buswell’s equation can be balanced and deduced. Conversely, when a structural formula is given, it must go through either the fragmentation method to identify the designated products or the carbon-atom method to identify the organic fragmented formula. The designated products or the organic fragmented formula can then be input into the proton method to balance the ionic Buswell’s equation. Based on any given organic matter, regardless of its electrical charge and nature of formula, the mean oxidation number of organic carbons, parameters of organic matter, parameters of Buswell’s equation, and ionic Buswell’s equation can be determined. Compared to molecular Buswell’s equation, the established ionic Buswell’s equation is an extended model for understanding physical, chemical, and biochemical processes among water molecules, ionic species, and neutral species in the multiphases anaerobic digestion system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".