Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands
Bibliographic record
Abstract
Peatlands are an important component of Canada’s landscape, however there is little information on their national-scale net emissions of carbon dioxide [Net Ecosystem Exchange (NEE)] and methane (CH 4 ). This study compiled results for peatland NEE and CH 4 emissions from chamber and eddy covariance studies across Canada. The data were summarized by bog, poor fen and rich-intermediate fen categories for the seven major peatland containing terrestrial ecozones (Atlantic Maritime, Mixedwood Plains, Boreal Shield, Boreal Plains, Hudson Plains, Taiga Shield, Taiga Plains) that comprise > 96% of all peatlands nationally. Reports of multiple years of data from a single site were averaged and different microforms (e.g., hummock or hollow) within these peatland types were kept separate. A new peatlands map was created from forest composition and structure information that distinguishes bog from rich and poor fen. National Forest Inventory k-NN forest structure maps, bioclimatic variables (mean diurnal range and seasonality of temperatures) and ground surface slope were used to construct the new map. The Earth Observation for Sustainable Development map of wetlands was used to identify open peatlands with minor tree cover. The new map was combined with averages of observed NEE and CH 4 emissions to estimate a growing season integrated NEE (± SE) at − 108.8 (± 41.3) Mt CO 2 season −1 and CH 4 emission at 4.1 (± 1.5) Mt CH 4 season −1 for the seven ecozones. Converting CH 4 to CO 2 equivalent (CO 2 e; Global Warming Potential of 25 over 100 years) resulted in a total net sink of − 7.0 (± 77.6) Mt CO 2 e season −1 for Canada. Boreal Plains peatlands contributed most to the NEE sink due to high CO 2 uptake rates and large peatland areas, while Boreal Shield peatlands contributed most to CH 4 emissions due to moderate emission rates and large peatland areas. Assuming a winter CO 2 emission of 0.9 g CO 2 m −2 day −1 creates an annual CO 2 source (24.2 Mt CO 2 year −1 ) and assuming a winter CH 4 emission of 7 mg CH 4 m −2 day −1 inflates the total net source to 151.8 Mt CO 2 e year −1 . This analysis improves upon previous basic, aspatial estimates and discusses the potential sources of the high uncertainty in spatially integrated fluxes, indicating a need for continued monitoring and refined maps of peatland distribution for national carbon and greenhouse gas flux estimation.
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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 | 0.002 | 0.003 |
| 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) | 0.001 | 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".