Slow pyrolysis pine wood-derived biochar reduces nitrous oxide production from surface but not subsurface soil
Bibliographic record
Abstract
Soil amended with biochar is expected to produce less nitrous oxide (N2O), although this may depend on nitrate (NO3-N) availability. Our objective was to determine how pine wood biochar, slow pyrolyzed at 500 °C, affects N2O production in soil having different denitrification potentials with variable NO3-N concentrations under controlled laboratory conditions. Sandy loam surface soils (0–30 cm, pH 5.7) and sandy clay loam subsurface soils (40–60 cm, pH 5.6) were amended with four biochar rates (0, 10, 20, and 30 g·kg−1), two nitrogen fertilizer rates (0 and 100 mg·kg−1 NO3-N) and two acetylene levels (0% and 10% headspace), arranged as a full factorial. Soil moisture content was adjusted to 80% water-filled pore space, and flasks were incubated at 20 °C for 30 h. Headspace gas was collected from each flask at 25, 26, 28, and 30 h. There was a significant reduction in N2O production with an increasing rate of biochar in the surface soil but not in the subsurface soil. On average, less N2O was produced in the subsurface soil than in the surface soil. As the NO3-N concentration was not a limiting factor for denitrification, the most likely explanation was that denitrifier activity was influenced by the availability of soluble organic carbon in the soil–biochar mixtures. We recommend further study of the coupled carbon–nitrogen transformations during denitrification to understand how biochar influences soil N2O production in sandy loam 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 | 0.000 | 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".