Ultraviolet‐visual spectroscopy estimation of nitrate concentrations in surface waters via machine learning
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
Abstract High‐frequency acquisition of nutrient concentrations in rivers is needed to generate nutrient loading estimates commensurate with flow and discharge data. Although the combination of field sampling and laboratory analysis is the standard approach to riverine water quality analysis, this strategy is expensive and can miss important storm‐related events. Ultraviolet‐visual (UV–Vis) spectroscopy is widely used in drinking water and wastewater systems for high‐frequency concentration estimates. However, surface waters present a unique challenge as co‐occurring constituents in environmental samples cause spectral interference at the wavelengths used to measure concentrations of dissolved nutrients. Partial least squares regression (PLSR), Lasso regression (Lasso), and stepwise multivariate linear regression (Stepwise) models can be effective predictors of nitrate concentrations using UV–Vis absorbance and are used in many available in‐situ nitrate sensors; however, the proliferation of user‐friendly open‐source machine learning (ML) algorithms offers an opportunity to use sophisticated big‐data techniques to predict nutrient concentrations in surface waters. We collected samples from four rivers across southern Ontario with a variety of nitrate concentrations, flow regimes, and interfering co‐contaminants. We demonstrated that ML applications of random forest and gradient boosting models significantly outperformed PLSR, Lasso, and Stepwise methodologies to estimate nitrate concentrations in complex environmental samples via UV–Vis absorbance. Importantly, ML applications outcompete current models at low concentrations. This new predictive methodology provides regulators and stakeholders an opportunity to establish low cost, continuous monitoring environmental programs using UV–Vis approaches.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 it