Bibliographic record
Abstract
Abstract A complete methodology (including synthesis, purification and analysis) for the preparation of 1,3‐DAG is described. For a successful synthesis project, the strengths and weaknesses of each particular process should be taken into account and measures taken to offset or balance potential weaknesses. To this end, we describe some of the challenges associated with: chemically and enzymatically catalyzed acylglycerol syntheses; recrystallization and flash chromatography for purification of partial acylglycerols; and thin‐layer chromatography (TLC) separation of DAG. For this work, 1‐MAG intermediates and subsequent diacid 1,3‐DAG were prepared using non‐enzymatic methods, whereas, monoacid 1,3‐DAG were prepared by enzymatic methods. It was not always possible to obtain pure samples of target compounds—in recrystallizations this is due to solid solution formation and co‐crystallization and in chromatographic separations it is due to co‐elution of components with similar R f . Furthermore, TLC R f of DAG is determined by two main factors: acyl chain length and positional isomerism. Interestingly, while the role of positional isomerism is well‐known, the role of acyl chain length in these separations has only recently come to light.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".