Time, location, and scale dependence of soil nitrous oxide emissions, soil water, and temperature using wavelets, cross‐wavelets, and wavelet coherency analysis
Bibliographic record
Abstract
Soil N2O emission measurements have high‐spatial and temporal variability that results in poor field scale predictive relationships, and this leads to high uncertainty in estimates of flux. Correlation between variables may be location and scale dependent. This needs to be understood to identify at what scales or locations predictive relationships may be used to reduce uncertainty in estimates. The purpose of this study was to describe the scale and location dependency of N2O emission on soil water and temperature using wavelet coherency and their change through time. Measurements of N2O flux, soil water content, and temperature were made multiple times from a 128‐point transect on a hummocky, agricultural landscape in the Dark Brown soil zone of Saskatchewan, Canada. Using three sampling dates in the spring of 2004, the local wavelet, cross‐wavelet, and wavelet coherency spectra were generated. In late March, the spatial pattern of wavelet coherency between soil N2O flux and temperature was scale dependent. At the end of April, there was no significant relationship between N2O and temperature. Instead, a location‐dependent spatial pattern existed between flux and water‐filled pore space. By late June, there were no significant scale‐ or location‐dependent relationships. The results indicate that N2O emission models for this landscape cannot rely on a single predictive relationship, but may have to adjust between scale‐ and location‐dependent relationships at different times of the year.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".