Stochastic Reconstruction and Transport Simulation of PEFC Catalyst Layers
Bibliographic record
Abstract
A stochastic method for analysis and reconstruction of PEFC porous media is presented. The method uses statistical correlation function to analyze and parametrize the porous media structure. The improved reconstruction method uses a multigrid hierarchical simulated annealing technique to significantly reduce the reconstruction time and ascertains a long range connectivity. Analysis of the catalyst layers show it to be a homogeneous porous media with strong anisotropy in in-plane and through-plane directions. The stochastic reconstruction procedure is able to reconstruct 3D structures of the catalyst layer with similar statistical correlation functions as of the reference image. Molecular and Knudsen diffusion are simulated on the reconstructions using the open source package OpenFCST. The effective transport properties of the reconstructions is similar to that of the reference structure; however, the accuracy of the reconstructions has been limited by the imaging methods.
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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".