Ecosystem Productivity and Carbon Exchange in Northern Peatlands
Bibliographic record
Abstract
The annual net primary productivity of northern peatlands is relatively small compared to that of many other ecosystems, yet peatlands contain between 200 and 450 Gt C, or 15 to 20% of the world�_s terrestrial organic carbon. This large carbon store, which is 99% in the form of peat, has resulted from the inhibition of decomposition due to water saturated anoxic conditions. Five years of CO$_{2}$ eddy covariance measurements at the Fluxnet-Canada eastern peatland station, Mer Bleue, indicate that the net annual exchange between the peatland and the atmosphere varies between -60 (sink from the atmosphere) and 10 (source to the atmosphere) g C m$^{-2}$ yr$^{-1}$. Additional carbon losses as methane, DOC and DIC, yield annual changes in the peatland's carbon store of between 50 to -20 g C m$^{-2}$ yr$^{-1}$. The annual NPP of the Mer Bleue bog is quite small, ranging from 290 to 360 g m$^{-2}$ yr$^{-1}$ (�� 145 to 180 g C m$^{-2}$ yr$^{-1}$) depending on location within the peatland. The long-term carbon accumulation rate for the last 3,000 years, estimated from age-depth relationships and carbon content and bulk desnity profiles of the peat, varies between -10 and -20 g C m$^{-2}$ yr$^{-1}$. Analyses of ancillary environmental variables and simulations of the short-term carbon dynamics using the Peatland Carbon Simulator (PCARS) show a combination of factors control the variations in ecosystem productivity. However, the peat winter thermal regime and the summer temperatures and moisture storage appear of be of primary importance. Over the long term (decades to millennia) the feedbacks among peatland wetness, primary productivity, and the partitioning of decomposition between aerobic and anaerobic pathways simulated by the Peat Accumulation Model (PAM) explain a significant portion of the dynamic changes in the carbon storage.
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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) | 0.001 | 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".