Diurnal Variation and Sampling Frequency Effects on Nitrous Oxide Emissions Following Nitrogen Fertilization and Spring‐Thaw Events
Bibliographic record
Abstract
Core Ideas High‐frequency N 2 O emission data was subsampled to assess sampling frequency errors. Mid‐morning sampling was adequate to derive daily average N 2 O emission during events. Twice‐weekly sampling gave an uncertainty of –6 to +12% during spring thaw. Twice weekly + sample after >10 mm rainfall had –1 to +19% error after fertilization. An infrequent sampling protocol can introduce bias into N 2 O studies. Different methods are used to measure nitrous oxide (N 2 O), a potent greenhouse gas emitted from agricultural soils. While some methods (e.g. micrometeorological methods) conduct near‐continuous measurements, manual chambers measure discontinuously. Estimates of N 2 O emissions based on discontinuous measurements can carry errors due to: (i) diurnal variation and (ii) integration of emissions over time. This study evaluated these two sources of uncertainties and identified the optimal sampling strategy for emission following spring‐thaw events (ST) and nitrogen fertilization (NF). Two times of day (mid‐morning [MM] and mid‐afternoon [MA]) and three sampling frequencies suggested in the literature on N 2 O emissions (bi‐weekly [BW], weekly [W], twice weekly [TW] for the ST, and W, TW, and TW plus extra sample after rainfall events >10 mm for the NF) were created by subsampling a high‐frequency reference dataset. We show that the mid‐morning sampling strategy effectively represented the daily N 2 O emission average, while the mid‐afternoon strategy overestimated fluxes by 14 and 32% for the ST and NF, respectively. For the ST, the weekly mid‐morning sampling protocol resulted in errors ranging from –3 to +33%, with lower uncertainties when sampling frequency increased to TW (–6 to +12%). The TW mid‐morning sampling also was adequate for the NF datasets (–5 to +19%), with narrower uncertainty levels when an additional sample was taken after >10 mm rainfall (–1 to +19%). These results provide increased confidence in selecting the sampling strategy for discontinuous measurements following N fertilization and STs in cropping systems.
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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 | 0.001 | 0.000 |
| 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".