Ultraviolet-visual spectroscopy estimation of nitrate concentrations in surface waters via machine learning
Bibliographic record
Abstract
This is the code and data associated with a manuscript "Ultraviolet-visual spectroscopy estimation of nitrate concentrations in surface waters via machine learning" submitted to Limnology and Oceanography Methods. Below is a copy of the abstract of our manuscript. "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 cooccurring 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 bigdata 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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".