The Nutrient App: Developing a smartphone application for on-site instantaneous community-based [formula omitted] and [formula omitted] monitoring
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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