N2O and CO2 dynamics in a pasture soil across the frozen period
Bibliographic record
Abstract
Since the process of gas dynamics in agricultural soils is mainly studied during plant growth, only a few studies have focused on these dynamics in frozen soils covered with snow. Nevertheless, gas dynamics during the cold season is important to quantify the yearly mass balance of gas emitted to the atmosphere. Spatiotemporal concentrations of CO2 and N2O have been measured from the prefreezing to the thawing period in a pasture soil during two cold seasons along with soil temperature and other soil properties. The spatial dynamics of these gases differed from each other and depended on the spatial and temporal variability of soil temperature as long as the soil surface temperature was above 0 °C. Two main occurrences of gas release occurred during thawing, one related to trapped gases, similar for both gases, and the other to reactivation of microorganisms, different between both gases. Once the soil was frozen, both gas concentrations increased throughout the frozen period, even during very cold conditions, indicated a gases production faster than the loss. Under frozen condition, their spatial variability was independent of soil temperature during which their correlation was up to 90%. Three periods related to gas dynamics were observed during both cold seasons: freezing with spatiotemporal trends different between both gases, completely frozen with similar trends, and partial to complete thawing with trends different between both gases.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".