Bibliographic record
Abstract
The first part of this thesis (Chapters 1--4) addresses accelerated algorithms for coarsening systems---we review unconditionally stable algorithms for the study of coarsening systems with a conserved or non-conserved scalar order parameter. These algorithms allow us to take arbitrarily large time-steps constrained only by desired accuracy. For conserved coarsening systems, these accelerated algorithms provide maximally-fast numerical algorithms---we can actually use the natural time-step Δt = At2/3s . To study the accuracy we compare the scaling structure obtained from our maximally-fast conserved systems directly against the standard fixed-time-step Euler algorithm, and find that the error is time-independent in the scaling regime and scales as A ---this is consistent with an approximate bound of the error. Arbitrary accuracy is accessible for these maximally driven coarsening algorithms. These algorithms provide the most efficient and accurate means to reach the scaling regime for large systems. For non-conserved systems, however, with these accelerated algorithms, only effectively finite time-steps are accessible. The maximal time-step obtained by these algorithms is about four times the time-step of the Euler algorithm. The second part of this thesis (primarily Chapter 5) applies these accelerated algorithms to the study of universality classes of scaled correlations in coarsening systems. Specifically, we study the universality classes found by introducing asymmetric bulk mobilities. We also develop accelerated algorithms for the study of systems with anisotropic surface tension.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".