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 δ15NNO3 and δ18ONO3 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 δ15NNO3 (FT: −2.4 to +8.7‰, MT: +1.6 to +9.6‰) and δ18ONO3 (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 δ15NNO3 (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 δ15NNO3 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 δ15NNO3 values of leachate. Core Ideas Temporal variation in δ15NNO3 and δ18ONO3 prevented reliable N source distinction. N treatments differed using mass‐weighted annual average δ15NNO3 and δ18ONO3 values. Annual average δ15NNO3 was predictable using estimated N source contributions. Soil N and irrigation water NO3 contributed significantly to the leachate mass flux.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".