FMC/TFM Technique Design Using the FMC Beamset in BeamTool
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
Eclipse Scientific’s BeamTool software has supported Full Matrix Capture/Total Focusing Method (FMC/TFM) technique design through the FMC Beamset since version 9.0 (released in 2017). With the release of BeamTool 10.1, however, the FMC Beamset now includes more sophisticated tooling to help users design FMC/TFM based inspection techniques – these include: Sensitivity, Focal Area and Resolution maps which together allow users to quickly assess how similar reflectors will be imaged (amplitude, shape, size) throughout the chosen region of interest. For each of these focal metrics, the absolute minimum and maximum values are provided along with other helpful derived quantities (amplitude fidelity, maximum sensitivity difference) which allows the influence of probe and wedge parameters to be compared directly. This document details what these new focal metrics are as well as how to use them to optimize FMC/TFM based techniques for various common applications. It is assumed that the reader is familiar with the principles of FMC/TFM and the BeamTool software.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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