The PIXIE: A Low-Cost, Open-Source, Multichannel In Situ Fluorometer Applied To Dye-Tracing in Halifax Harbor
Bibliographic record
Abstract
Fluorometers are ubiquitous tools in the fields of oceanography, limnology, and water quality assessment. Fluorescent species in our waters range from in vivo chlorophyll, contaminants like crude oil, or intentionally added agents like rhodamine. Submersible in situ fluorometers can collect real-time data at scales that cannot be matched by discrete bottle samples with lab/shore-side analysis. However, accessibility of sensors remains a problem recognized by the United Nations Sustainable Development Goals. Here, we introduce the PIXIE, an open-source, multichannel, in situ fluorometer that performs high-quality fluorometry at a low cost. The PIXIE is assembled by simple means from almost entirely off-the-shelf components. The few necessary custom parts are either easily outsourced or printed by consumer-grade 3D printers. The PIXIE draws an average of 225 mW during measurement and has been tested to depths of 45 m. It has been calibrated to demonstrate a limit of detection 0.01 ppb rhodamine WT (a fluorescent dye) in a range up to 60 ppb, and a limit of detection of 0.02 ppb chlorophyll a. The PIXIE has been deployed as part of a dye-tracer experiment in Halifax Harbor, Canada, demonstrating its performance in a quasi-simultaneous profiling of rhodamine WT dye and chlorophyll a.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".