Corrections to “A Dissipation Theory for Three-Dimensional FDTD With Application to Stability Analysis and Subgridding” [Dec 18 7156-7170]
Bibliographic record
Abstract
In the above article[1], the interpolation conditions (42)–(45) are too restrictive and the proposed updated equation (57) does not ensure that these conditions hold simultaneously with (50) and (51). The update equation (57) proposed in[1]instead enforces a weaker interpolation condition at the interface between a coarse and a fine finite-difference time-domain (FDTD) grid, as discussed in this correction. It can be shown that this weaker condition is sufficient to ensure dissipativity of the system and that the update equations described in the original publication[1]give rise to a stable subgridding algorithm. The only equations of[1]affected by this correction are (43a)–(43c), (61a)–(61c), (67), and (68). Also, the proofs in (47)–(49), (69), and (70) need to be updated accordingly, but the conclusion remains unchanged. All the other equations, theorems and numerical results in[1]are correct as stated in the manuscript.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.026 |
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".