Thick ice layers in snow and frozen soil affecting gas emissions from agricultural soils during winter
Bibliographic record
Abstract
We investigated soil and snow cover gas concentrations at two agricultural sites (St‐Lambert; Chapais) in Quebec, Canada, during winter 1998–1999. Both sites showed frozen and unfrozen soils and complex snow cover structure. At St‐Lambert we measured higher average concentrations of N2O (35 to 62 μl L−1) and CO2 (3 to 19 mL L−1) below the frozen soil surface of plots subjected to a treatment of pig slurry than in the control plot (N2O, 9 to 30 μL L−1; CO2, 3 to 7.5 mL L−1). The lack of vertical gaseous concentration gradients in the snowpack was due to the trapping of accumulating gas below the impermeable frozen soil layer. Soil gas concentrations decreased sharply when soil warmed to the freezing point. At the same time, the snow cover was isothermal. N2O could have been lost at spring thaw through gaseous emissions and/or dissolved in meltwaters and leached to the drainage system. High N2O fluxes were measured using closed chambers (215 ng m−2 s−1, slurry treatment; 55 ng m−2 s−1, control) as soon as snow ablation was completed, but became negligible 2 days later, suggesting that emissions were the result of passive degassing rather than of increased biological activity. At Chapais, N2O and CO2 accumulated in the unfrozen soil surface below a thick (0.1 m) basal ice layer. The basal ice layer and the continuous ice layer above it were impermeable to gas diffusion, as demonstrated by the accumulation of a tracer gas (Ar, >50 mL L−1) introduced by a diffuser into the soil. The existence of a basal ice layer is uncommon in eastern Canada. The occurrence of such a phenomenon may increase with climate change due to more frequent rain events during the cold season and affect the dynamics of winter gas emissions from soils.
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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 | 0.001 | 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".