Wavelet Spectra of Nitrous Oxide Emission from Hummocky Terrain during Spring Snowmelt
Bibliographic record
Abstract
Soil N 2 O emission data is typically highly skewed. In terrain where the spatial distribution of soil processes is controlled by topography, extreme N 2 O flux events can be highly localized and nonstationary. Wavelet analysis can be used to describe the spatial variation of these nonstationary processes. The objectives of this study were to use wavelet analysis to determine the spatial variation, scales of variability and their change over the snowmelt period for soil N 2 O flux. On a hummocky, agricultural landscape in the Dark Brown soil zone of Saskatchewan, N 2 O flux measurements were taken from a 128‐point linear transect five times over the spring snowmelt season of 2004. Localized variance was determined using a continuous wavelet transform (Mexican Hat) and the local spectrum was compared with the distribution of fluxes along the transect and the relative elevation. Two spatial patterns of soil N 2 O emission were revealed. The first was a cyclic, landscape‐element‐controlled pattern with a scale of variation that ranged between 20 and 60 m. Changes in the spatial scale were due to a shift in importance between landscape elements as sources of peak N 2 O flux. The second pattern was composed of non‐cyclic, localized features that were due to extreme flux events at specific landscape positions. These extreme flux events were not temporally persistent, but represent a large, non‐random contribution to mean and variance on the dates they occurred. Sample strategies to capture the full range of soil N 2 O emission at this site would probably require separate approaches for these two spatial patterns.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".