A comparison of methane flux in a boreal landscape between a dry and a wet year
Bibliographic record
Abstract
We used field measurements of methane (CH4) flux from upland and wetland soils in the Northern Study Area (NSA) of BOREAS (BOReal Ecosystem‐Atmosphere Study), near Thompson, Manitoba, during the summers of 1994 and 1996 to estimate the overall CH4 emission from a 1350 km2 landscape. June–September 1994 and 1996 were both drier and warmer than normal, but summer 1996 received 68 mm more precipitation than 1994, a 40% increase, and had a mean daily air temperature 0.6°C warmer than 1994. Upland soils consumed CH4 at rates from 0 to 1.0 mg m−2 d−1, with small spatial and temporal variations between years, and a weak dependence on soil temperature. In contrast, wetlands emitted CH4 at seasonal average rates ranging from 10 to 350 mg CH4 m−2 d−1, with high spatial and temporal variability, and increased an average of 60% during the wetter and warmer 1996. We used Landsat imagery, supervised classification, and ground truthing to scale point CH4 fluxes (<1 m2) to the landscape (>1000 km2). We performed a sensitivity analysis for error terms in both areal coverage and CH4 flux, showing that the small areas of high CH4 emission (e.g., small ponds, graminoid fens, and permafrost collapse margins) contribute the largest uncertainty in both flux measurements and mapping. Although wetlands cover less than 30% of the landscape, areally extrapolated CH4 flux for the NSA increased by 61% from 10 to16 mg CH4 m−2 d−1 between years, entirely attributed to the increase in wetland CH4 emission. We conclude that CH4 fluxes will tend to be underestimated in areas where much of the landscape is covered by wetlands. This is due to the large spatial and temporal variability encountered in chamber‐based measurements of wetland CH4 fluxes, strong sensitivity of wetland CH4 emission to small changes in climate, and because most remote sensing images do not adequately identify small areas of high CH4 flux.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".