Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract FDTD method plays an important role for simulation of different structures in various fields of engineering, such as RF/microwaves, photonics and VLSI. However, due to the CFL stability constraint, the FDTD time step is still small and the related CPU time is still large for modelling fine geometry where small cell sizes are required to resolve fields. As a result, the unconditionally stable CFL‐condition‐free ADI‐FDTD method is becoming a popular alternative to the FDTD method. The ADI‐FDTD method allows the use of larger time steps; however, it comes at the cost of larger errors. To mitigate the problem of these larger errors, in this paper we propose to modify the conventional ADI‐FDTD algorithm. The modifications are based on the fact that because the ADI‐FDTD is a truncated form of the Crank–Nicolson (CN) method, the truncated terms can be re‐introduced approximately into the ADI algorithms to improve accuracy. Two accuracy‐improved ADI‐FDTD algorithms are derived and then validated for two‐dimensional cases. Unfortunately, in the three‐dimensional case the proposed methods are not found to be unconditionally stable. Copyright © 2006 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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