The Nutrient App: Developing a smartphone application for on-site instantaneous community-based [formula omitted] and [formula omitted] monitoring
Bibliographic record
Abstract
Freshwater ecosystems, particularly those in agricultural areas, remain at risk of eutrophication due to anthropogenic inputs of nutrients. While community-based monitoring has helped improve awareness and spur action to mitigate nutrient loads, monitoring is challenging due to the reliance on expensive laboratory technology, poor data management, time lags between measurement and availability of results, and risk of sample degradation during transport or storage. In this study, an easy-to-use smartphone-based application (The Nutrient App) was developed to estimate NO3 and PO4 concentrations through the image-processing of on-site qualitative colorimetric-based results obtained via cheap commercially-available instantaneous test kits. The app was tested in rivers, wetlands, and lakes across Canada and relative errors between 30% (filtered samples) and 70% (unfiltered samples) were obtained for both NO3 and PO4. The app can be used to identify sources and hotspots of contamination, which can empower communities to take immediate remedial action to reduce nutrient pollution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.061 | 0.021 |
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".