Probability Distribution and Spatial Dependence of Nitrous Oxide Emission
Bibliographic record
Abstract
Climate controls soil N 2 O emissions via its effects on soil properties such as water‐filled pore space. Changes in climate should produce changes in the probability distribution and spatial dependency of soil N 2 O data. Knowing the extent of the changes in the distribution of this data is important for validating model predictions. The objectives of this study were to describe the probability distributions and estimate the spatial dependency of soil N 2 O emission data. On a hummocky, agricultural landscape in Saskatchewan, N 2 O emission data and related soil variables were taken from a 128‐point transect 15 times over 2 yr. Probability distributions were compared using a Chi‐square test. The range in spatial correlation was determined using the indicator semivariogram with a nested model fit approach. The mean N 2 O flux ranged from 25.3 to −0.2 ng N 2 O–N m −2 s −1 Probability distributions ranged in shape from reverse J‐shape through log normal to symmetrical. The majority of distributions were statistically different from each other, showing a lack of temporal stability. Mean N 2 O flux and distribution shape followed an event‐based/background emission pattern. High flux events had statistically similar, reverse J‐shaped distributions. As mean N 2 O flux decreased to a background level distribution, shape changed to log normal and symmetrical forms. A high nugget/sill ratio characterized the majority of sampling dates, although spatial dependency was generally moderate. Flux values in the fourth quartile tended to have a spatial dependency of 15 m, probably reflecting a topographic control at a landform element scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".