Reimagining the Kendall plot: using <i>δ</i> <sup>15</sup> N and <i>δ</i> <sup>18</sup> O of nitrate and advanced machine learning to improve N pollutant source classification
Bibliographic record
Abstract
Nitrate (NO3−) pollution is a serious water quality issue in many countries due to contamination of lakes, rivers, and aquifers by intensive agriculture practices and inadequate wastewater management. Nitrate pollution and associated cultural eutrophication are anticipated to increase worldwide, highlighting the need to control and reduce nitrogen pollution. The stable isotope ratios of nitrate (δ15N, δ18O) are widely used as tracers of nitrogen pollution sources. The primary technique for identifying nitrate sources has been the longstanding Kendall boxplot, despite improved methods using Bayes’ theorem and the R language for estimating source fractions using hydrogeochemical context, N source data and expert assessment. This article improves the classification of aqueous nitrate sources using comprehensive published stable isotope data for nitrate from four known pollutant types and applying machine learning algorithms. AI modelling results reveal improved source depictions and offer a robust statistical framework for identifying N pollution sources. This is essential given the increased published data sources and the need for better-informed water quality management strategies.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".