Laminar flamelet decomposition for conditional source-term estimation
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Bibliographic record
Abstract
A new decomposition approach to conditional source-term estimation (CSE) is proposed and discussed. The new approach is tested in the a priori sense using direct numerical simulations (DNS). It is found that—where CSE had previously been found to provide closure for chemical source-terms with arbitrary chemistry in the large eddy simulation paradigm—it can provide this closure in the Reynolds averaged Navier–Stokes paradigm as well. Using the proposed decomposition improves the predictions of CSE considerably. Only the assumptions that gradients in conditional averages are small and that the probability density function of mixture fraction can be adequately approximated using a presumed functional form are needed. The computational cost of the new laminar flamelet decomposition approach to CSE is also substantially lower than that of the original approach.
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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