Bibliographic record
Abstract
gas contributions and mitigation potential www.elsevier.com/locate/still Soil & Tillage Research 83 (2005) 25–52Concern over human impact on the global environment has generated increased interest in quantifying agricultural contributions to greenhouse gas fluxes. As part of a research effort called GRACEnet (Greenhouse Gas Reduction through Agricultural Carbon Enhancement Network), this paper summarizes available information concerning management effects on soil organic carbon (SOC) and carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) fluxes in cropland and rangeland in northwestern USA and western Canada, a region characterized by its inherently productive soils and highly variable climate. Continuous cropping under no-tillage in the region increased SOC by 0.27 0.19 Mg C ha1 yr1, which is similar to the Intergovernmental Panel on Climate Change (IPCC) estimate for net annual change in C stocks from improved cropland management. Soil organic C sequestration potential for rangelands was highly variable due to the diversity of plant communities, soils, and landscapes, underscoring the need for additional long-term C cycling research on rangeland. Despite high variability, grazing increased SOC by 0.16 0.12 Mg C ha1 yr1 and converting cropland or reclaimed mineland to grass increased SOC by 0.94 0.86 Mg C ha1 yr1. Although there was generally poor geographical coverage throughout the region with respect to estimates of N2O and CH4 flux, emission of N2O was greatest in irrigated cropland, followed by non-irrigated cropland, and
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.153 | 0.081 |
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".