A global assessment of nitrogen concentrations using spatiotemporal random forests
Bibliographic record
Abstract
Abstract. Anthropogenic nitrogen fluxes into surface freshwater bodies significantly impair water quality (WQ), pose serious health hazards, and create critical environmental threats. Quantification of the magnitude and impact of WQ issues requires identifying the key controls of nitrogen dynamics and assessing past and future patterns of global nitrogen flows. To achieve this, we adopted a data-driven, machine learning approach to build a space-time random forest model for simulating nitrogen concentration in 115 major river basins of the world. The proposed random forest-based WQ model regressed the monthly measured nitrogen concentration collected at 718 river stations across the globe for the period of 1992–2010 onto a set of 17 predictor variables with a spatial resolution of 0.5-degree. The resulting model was validated with data from river basins outside the training dataset, and was used to predict nitrogen concentrations in all river basins globally, including many with scarce or no observations. We predict that the regions with highest median nitrogen concentrations in their rivers (in 2010) were: United States, India, Pakistan, Bangladesh, China, and most of Europe. Furthermore, our results showed that the rate of increase between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, southern Finland, Pakistan, parts of Russia, mainland southeast Asia, and south-eastern Australia. We found that, globally, the most influential predictors of nitrogen concentrations are temporal: month of the year and cumulative month count, reflecting the secular trend. Apart from temporal variables, cattle density, nitrogen fertilizer application, temperature, precipitation, and pig population are the most influential predictors of nitrogen pollution of the river systems. The proposed global WQ model will provide a new tool to explore agricultural and land management strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.
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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 | 0.003 |
| 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 | 0.000 |
| 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 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".