Laser Probe Techniques with Adaptive FPGA Device
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 This paper introduces an accessible implementation of laser probe (LP) techniques using a single adaptive field-programmable gate array (FPGA) device. Through several case studies, we demonstrate different integration methods and improvements over conventional ways. First, we investigate two alternative implementations of frequency-domain mapping (FM): one using a software-defined lock-in amplifier, and another using a dual boxcar averager module. Second, we explore a time-domain mapping (TM) technique implemented with the dual boxcar averager. Third, we introduce a customized solution to enhance signal quality in combination with a software-defined lock-in amplifier. Lastly, we examine the use of artificial intelligence (AI), specifically neural networks (NN), to improve LP signal acquisition during real-time signal processing. These approaches reduce barriers to innovation by eliminating the need for substantial upfront legal or financial commitments between collaborating organizations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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