Using the Carbon-Atom Method to Determine Mean Oxidation Number of Organic Carbons
Bibliographic record
Abstract
The notion of oxidation number acting as an electron-counting concept is crucial for balancing redox reactions, and for understanding organic and biological redox conversions. Chemical formula methods are widely used for counting oxidation numbers. There are three types of chemical formula methods. They are molecular formula method, structural formula method, and Lewis formula method. Each type has its own rules and procedures, and they are difficult for students to fully understand and remember. In addition, the capability of the molecular formula method to assign mean oxidation number of organic carbons for organic molecules or molecular ions is limited. To overcome these drawbacks, this article explores a new half reaction approach, the carbon-atom method, which can count the mean oxidation number of organic carbons for both organic and bioorganic compounds. The quantitative relationships among the number of transferred electrons, change in oxidation numbers of organic carbons, and mean oxidation number of organic carbons can also be established by balancing half organic reactions. Furthermore, the mean oxidation number of organic carbons for any given organic or bioorganic compounds with known structural formulas can be determined by using the carbon-atom method and the fragmentation operation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".