Data‐driven hypotheses of reaction networks for thermochemical conversion of a physical mixture of levoglucosan and 2‐phenoxyethyl benzene
Bibliographic record
Abstract
Abstract In this work, we analyze the hydrous pyrolysis of a physical mixture of the model components representing cellulose (levoglucosan) and lignin (2‐phenoxyethyl benzene). Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (1H‐NMR) spectroscopy was used to characterize the products of the reaction. The main objective of the work was to use data‐driven methods to develop a reaction network for this system based on the spectroscopic data. This was achieved using Bayesian hierarchical clustering to identify pseudocomponents and Bayesian networks to develop a reaction network between these pseudocomponents. The data‐driven reaction network was shown to be consistent with the known chemistry of the pyrolysis of cellulose and lignin, and the chemistry of the physical mixture incorporated/combined elements of the reaction mechanisms of cellulose and lignin.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".