Calculations Reconsidered: From Mass Percentages of Elements to Empirical Formula and From Empirical Formula to Theoretical Biomethane Potential
Bibliographic record
Abstract
Anaerobic digestion is an organic carbon-based process and sustainable technology. In this process, empirical formulas and theoretical biomethane potentials of organic matters are two important parameters. Mass percentages of elements can be found by ultimate analysis, and then empirical formula can be calculated by the known mass percentages of elements. Other data, such as quantity amount of biomethane and theoretical biomethane potential can also be attained by calculations. Although empirical formulas are important for quantifying theoretical biomethane potentials, they are not always available in published papers. In some cases, the theoretical biomethane potential cannot be verified. This article has two purposes. First, it identifies the valid empirical formulas of organic matters. Second, it examines the correctness of a series of calculations: from mass percentages of elements to empirical formulas and from empirical formulas to theoretical biomethane potential. The mean oxidation number of organic carbons is used as an assessor that validates the empirical formulas and their corresponding mass percentages of elements. The relative percentage of theoretical biomethane potential is designed as an indicator that measures the discrepancy between the published theoretical biomethane potential and the recalculated theoretical biomethane potential.
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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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".