FMC/TFM Technique Design Using the FMC Beamset in BeamTool
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".