A Fast and Precise Digital Lock-In Amplifier for Accessible in-situ Fluorometry
Bibliographic record
Abstract
The in-situ fluorometer is a ubiquitous tool for monitoring marine and freshwater environments, from detection of algal blooms to performing dye-tracing studies for Ocean Alkalinity Enhancement for Marine Carbon Dioxide Removal. Low-cost open-source instrumentation has been identified as a potential solution to both the demand for data and the need for greater accessibility. In this paper, we present a Digital Lock-In Amplifier designed for the Texas Instruments MSP430 platform, in the context of its central role in a new open-source in-situ fluorometer, the PIXIE. The amplifier is implemented digitally, using the mixed signal peripherals of the TI MSP430FG6626 microcontroller to minimize PCB complexity. DSP is performed using a Lattice Wave Digital Filter and a Q3.28 fixed-point arithmetic designed to maximize use of the MSP430's hardware multiplier for speed, precision, and numerical stability. The design and implementation of the lock-in amplifier, along with its performance metrics and example data collected by the PIXIE, are provided.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".