Quantitative Molecular Representation and Sequential Optimization of Athabasca Asphaltenes
Why this work is in the frame
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Bibliographic record
Abstract
The chemical complexity and diversity of an Athabasca asphaltene sample was described using a series of molecular representations. The molecular representations were created with a Monte Carlo construction method that represented molecules with a series of aromatic and aliphatic groups. After the groups were randomly sampled for a molecule, a connection algorithm linked them together to form molecules consisting of aromatic groups connected by aliphatic chains and thioethers. A sequential nonlinear optimization algorithm was used to select a small subset of molecules that were consistent with elemental, molecular weight, and NMR spectroscopy (both 13 C and 1 H) data. To accurately represent the analytical data for the asphaltene sample, a minimum of five molecules was needed. On the basis of the results of the sequential optimization, at least 50 molecules in the starting population were required to produce an analytically consistent molecular representation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it