Simultaneous in situ Nitrate and Orthophosphate Measurement Using a Dual Chemistry Microfluidic Sensor
Bibliographic record
Abstract
Anthropogenic nitrogen and phosphorus loading can have significant impact on land and marine based ecosystems with global impact. The use of nitrogen- and phosphorus-based fertilizers has continued to increase over recent years to stimulate agriculture production, and nutrient runoff from land can impact the stability of marine environments. As it stands, mitigation efforts are limited by a lack of comprehensive visibility over a wide spatial and temporal range, and severely impacted zones are often identified after significant ecological harm. Here, we present a submersible microfluidic sensor designed to simultaneously measure two prevalent forms of nitrogen and phosphorus, nitrate and orthophosphate, in situ within the same instrument. The device consists of two parallel analysis chambers, used to interrogate the same sample fluid with a different colourimetric assay for each species. To maintain small fluid volumes, light absorption spectrophotometry measurements are obtained using integral optical cells based on “inlaid microfluidics The performance of the dual-species nitrate and phosphate “NP Sensor” was first investigated in a controlled laboratory setting using prepared nutrient standards. A comprehensive temperature study followed to calibrate the sensor over a wide range of deployment conditions. Finally, the NP Sensor was deployed to acquire measurements in situ at two sites: a 60m vertical profile, and a long-term stationary shallow water study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".