High-frequency NO <sub>3</sub> <sup>−</sup> isotope ( <i>δ</i> <sup>15</sup> N, <i>δ</i> <sup>18</sup> O) patterns in groundwater recharge reveal that short-term changes in land use and precipitation influence nitrate contamination trends
Bibliographic record
Abstract
Abstract. Poultry manure is the primary cause of nitrate (NO3-) exceedances in the transboundary Abbotsford–Sumas aquifer (ASA; Canada–USA) based on synoptic surveys two decades apart, but questions remained about seasonal and spatial aspects of agricultural nitrate fluxes to the aquifer to help better focus remediation efforts. We conducted over 700 monthly δ15N and δ18O of nitrate assays, focusing on shallow groundwater (< 5 years old) over a 5-year period to gain new insight on spatio-temporal sources and controls of groundwater nitrate contamination. NO3- concentrations in these wells ranged from 1.3 to 99 mg N L−1 (n=1041) with a mean of 16.2±0.4 mg N L−1. The high-frequency 15N and 18O isotope data allowed us to identify three distinctive NO3- source patterns: (i) primarily from synthetic fertilizer, (ii) dynamic changes in nitrate due to changes in land use, and (iii) from a mix of poultry manure and fertilizer. A key finding was that the source(s) of nitrate in recharge could be quickly influenced by short-term near-field management practices and stochastic precipitation events, which ultimately impact long-term nitrate contamination trends. Overall, the isotope data affirmed a subtle decadal-scale shift in agricultural practices from manure increasingly towards fertilizer nitrate sources; nevertheless, poultry-derived N remains a predominant source of nitrate contamination. Because the aquifer does not generally support denitrification, remediation of the Abbotsford–Sumas aquifer is possible only if agricultural N sources are seriously curtailed, a difficult proposition due to longstanding high-value intensive poultry and raspberry and blueberry operations over the aquifer.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".