Hydrologic profiling for greenhouse gas effluxes from natural grasslands in the prairie pothole region of Canada
Bibliographic record
Abstract
Abstract The prairie pothole physiographic region of North America is likely to be affected by climate change, and it is important to establish its baseline global warming potential as a basis for assessing global change effects. This study estimated the hydrologic effects on soil greenhouse gas efflux during the growing season along hydrologic profiles within natural prairie potholes ranging from the southern to northern limits of the prairie pothole region within central Canada. Soil moisture was found to be an important driver of differences in soil efflux along the hydrologic profiles and along the south‐to‐north gradient. The position of peak cumulative soil efflux varied along the hydrologic profile, with the N2O peak occurring at the backslope, the CO2 peaks at footslope and toeslope, and the CH4 peaks at toeslope and surface waters. When cumulative soil efflux was converted to global warming potential (Mg CO2 eq), the largest values were restricted to the narrow land‐water interface in the south but expanded to a broader area of the hillslope in the north. CO2 was the major (> 95%) contributor to global warming potential. Omitting hydrologic controls on greenhouse gas fluxes from estimates of global warming potential led to substantial underestimates of the contributions of N2O and CH4. Arid regions in the south had smaller global warming potential than wetter regions in the north. If future climate projections for a warmer, drier climate in this region are realized, global warming potential from soil greenhouse gas fluxes will be smaller in this landscape, because the changing hydrologic conditions should result in a reduction of global warming potential from land surfaces, even as global warming potential from surface waters increases.
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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 | 0.001 |
| 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".