Auto-thresholding for unbiased electron counting
Bibliographic record
Abstract
As interest in fast real-space frame-rate scanning transmission electron microscopy for both structural and functional characterization of materials increases, so does the need for precise and fast electron detection techniques. Electron counting, with monolithic, segmented, or 4D detectors, has been explored for many years. Recent studies have shown that a retrofittable signal digitizer for a monolithic or segmented detector can be a sustainable and accessible way to enhance the performance of existing detectors, especially for imaging at fast scan speeds. Since such signal digitization uses a threshold on the gradient of the detector signal to identify electron events, appropriate threshold choice is key. Previously, this threshold has been set manually by the operator and is therefore inherently susceptible to human bias. In this work, we introduce an auto-thresholding approach for electron counting to determine the optimal threshold by maximizing the difference in identified counts from a stream with real electron events and a stream with only noise. This leads to easier operation, increased throughput and eliminates human bias in signal digitization. When pixel dwell time becomes shorter than scintillator response time, digitization of the detector signal is needed to avoid artefacts in STEM images. Optimizing the threshold for this digitization process automatically is crucial to achieve high-quality quantitative digitized images, free of human bias for what threshold yields the best digitization.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".