A Bayesian Approach to Infer Nitrogen Loading Rates from Cropand Landuse Types Surrounding Private Wells in the Central Valley,California
Bibliographic record
Abstract
Abstract. Nitrate contamination of alluvial aquifers in agricultural areas is a typical and major problem around the world. Nitrogen applied to crops, in the form of synthetic fertilizers or manure, in excess of plant uptake, largely leaches to groundwater in the form of nitrate, which is stable and highly mobile in oxygen-rich groundwaters. Increased awareness of the impact that excess nitrogen has had on groundwater and major health concerns about nitrate are prompting new regulations for farmers, e.g., in Europe and California, USA. This study is focused in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Though nitrogen loading rates for several crop and landuse types in the Central Valley have been estimated or measured in a handful of studies, nitrogen loading rates for specific crop or landuse types and their impact to groundwater quality remain largely unknown. Knowledge of crop or other landuse specific groundwater nitrate impact may aid future regulatory decisions. Nitrogen loading rates for specific crop or landuse types are expected to vary depending on individual landuse practices; and interactions with hydrogeologic parameters that may promote or inhibit nitrate leaching. In this study, we developed a novel Bayesian regression model that allowed us to estimate crop or other landuse-specific groundwater nitrogen loading rate probability distributions from surveys of private wells, each of which is likely impacted by more than one landuse. We used recent nitrate measurements from 2149 wells in the Central Valley. We estimated nitrogen loading rate distributions for 15 crop and landuse groups. These were shown to compare favorably with prior mass-balance estimates of loading rates based on agronomic estimates of nitrogen loading.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".