Thermal-wave resonant cavity signal processing
Bibliographic record
Abstract
The thermal-wave resonant cavity (TWRC) technique has been used for thermal diffusivity measurements by many researchers. This study aims to reduce the uncertainty associated with TWRC signal processing (curve fitting) by means of numerical simulation and experimental verification. Simulations show that the plot of signal amplitude versus cavity length can be fitted to a simplified model reported previously when the initial fitting position is at least twice the thermal-wave diffusion length (2 μg), and that the uncertainty caused by different end positions is negligible in the range of 6–10 μg. Upon consideration of the simulation results, signal-to-noise ratio, and clearly defined amplitude curve shape, fitting ranges of about 2.2–8.0 μg and 2.2–8.7 μg were chosen for the experimental data. Thermal diffusivity values (1.438 ± 0.001) × 10−7 and (1.436 ± 0.001) × 10−7 m2 s−1, respectively, were obtained for distilled water, in excellent agreement with the accepted literature value. The ratio of standard deviation to the mean value is smaller than 0.07%, one order of magnitude lower than typical results reported in the literature. Similar simulation results were obtained for air and methanol as intra-cavity samples.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".