Use of δ<sup>15</sup>N and δ<sup>18</sup>O Values for Nitrate Source Identification under Irrigated Crops: A Cautionary Vadose Zone Tale
Bibliographic record
Abstract
Source nitrogen (N) identification of leachate or groundwater nitrate is complicated by N source mixing and N and oxygen (O) isotope fractionation caused by microbial N transformations. This experiment examined the δ 15 N NO3 and δ 18 O NO3 values in leachate collected over 1 yr at 55 cm below raspberry ( Rubus idaeus L.) plots receiving either synthetic fertilizer (FT) or poultry manure (MT). The large ranges of δ 15 N NO3 (FT: −2.4 to +8.7‰, MT: +1.6 to +9.6‰) and δ 18 O NO3 (FT: −9.9 to −0.3‰, MT: −10.9 to +1.7‰) values in leachate collected under crop rows prohibited the reliable identification of the applied N sources on individual sampling dates. However, the mass‐weighted average δ 15 N NO3 (FT: +3.2‰, MT: +7.3‰) values in leachate were significantly different and can be explained by accounting for the estimated contributions of nitrate and δ 15 N NO3 values of the various N sources, including applied fertilizer (−0.7‰) or manure (+7.9‰), nitrate‐rich irrigation water (+9.0‰), and nitrate from soil N mineralization and nitrification (FT: +3.7‰, MT: +4.6‰; the seasonal timing of which is unknown). This study illustrates the importance of characterizing all major N sources and considering the seasonal variation of these sources and of N cycling processes, as they contribute to the δ 15 N NO3 values of leachate. Core Ideas Temporal variation in δ 15 N NO3 and δ 18 O NO3 prevented reliable N source distinction. N treatments differed using mass‐weighted annual average δ 15 N NO3 and δ 18 O NO3 values. Annual average δ 15 N NO3 was predictable using estimated N source contributions. Soil N and irrigation water NO 3 contributed significantly to the leachate mass flux.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".