Spatial variation of nitrous oxide fluxes during growing and non-growing seasons at a location subjected to seasonally frozen soils
Bibliographic record
Abstract
Nitrous oxide (N2O) emissions from soils have been widely studied in the literature — mostly with the chamber method — due to the importance of this gas to climate change. Emissions of N2O derive from biological reactions and are controlled by soil parameters, which are by nature heterogeneous (i.e., “hot spots” for N2O emissions) — a source of uncertainty in chamber-based studies. Spatial variation in N2O fluxes has been assessed in the literature, but the information is still needed for contrasting soil management practices (e.g., tillage) and for specific bioclimatic situations [e.g., non-growing seasons (NGS) under cold weather]. Here, we subsampled daily N2O data to assess within-plot and between-block spatial variation from an agronomic experiment under conventional tillage (CT) and no-tillage (NT), identifying if patterns differ between growing seasons (GS) and NGS datasets. Within-plot spatial variation in N2O fluxes was a small source of uncertainties, but half of the comparisons in GS datasets presented a slope different from 1 for the regression of N2O averages from two vs. one chamber per plot — a source of uncertainty mitigated when within-plot duplication occurred during N2O “hot moments”. Between-block spatial variation in N2O emissions was much larger than within-plot errors — an effect more accentuated for NGS and CT than GS and NT datasets. Decreasing the number of sampled blocks resulted in averages that did not represent the N2O daily average of the whole field, but exceptions occurred. The methodology proposed here may be used in other locations, after appropriate verification, for improved planning and maximization of the resources associated with N2O measurements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".