Unprocessed\nAtmospheric Nitrate in Waters of the Northern\nForest Region in the U.S. and Canada
Bibliographic record
Abstract
Little is known about\nthe regional extent and variability of nitrate\nfrom atmospheric deposition that is transported to streams without\nbiological processing in forests. We measured water chemistry and\nisotopic tracers (δ<sup>18</sup>O and δ<sup>15</sup>N)\nof nitrate sources across the Northern Forest Region of the U.S. and\nCanada and reanalyzed data from other studies to determine when, where,\nand how unprocessed atmospheric nitrate was transported in catchments.\nThese inputs were more widespread and numerous than commonly recognized,\nbut with high spatial and temporal variability. Only 6 of 32 streams\nhad high fractions (>20%) of unprocessed atmospheric nitrate during\nbaseflow. Seventeen had high fractions during stormflow or snowmelt,\nwhich corresponded to large fractions in near-surface soil waters\nor groundwaters, but not deep groundwater. The remaining 10 streams\noccasionally had some (<20%) unprocessed atmospheric nitrate during\nstormflow or baseflow. Large, sporadic events may continue to be cryptic\ndue to atmospheric deposition variation among storms and a near complete\nlack of monitoring for these events. A general lack of observance\nmay bias perceptions of occurrence; sustained monitoring of chronic\nnitrogen pollution effects on forests with nitrate source apportionments\nmay offer insights needed to advance the science as well as assess\nregulatory and management schemes.
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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 | 0.000 | 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) | 0.002 | 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".