Variability in exchange of CO<sub>2</sub> across 12 northern peatland and tundra sites
Bibliographic record
Abstract
Abstract Many wetland ecosystems such as peatlands and wet tundra hold large amounts of organic carbon (C) in their soils, and are thus important in the terrestrial C cycle. We have synthesized data on the carbon dioxide (CO2) exchange obtained from eddy covariance measurements from 12 wetland sites, covering 1–7 years at each site, across Europe and North America, ranging from ombrotrophic and minerotrophic peatlands to wet tundra ecosystems, spanning temperate to arctic climate zones. The average summertime net ecosystem exchange of CO2 (NEE) was highly variable between sites. However, all sites with complete annual datasets, seven in total, acted as annual net sinks for atmospheric CO2. To evaluate the influence of gross primary production (GPP) and ecosystem respiration (Reco) on NEE, we first removed the artificial correlation emanating from the method of partitioning NEE into GPP and Reco. After this correction neither Reco (P= 0.162) nor GPP (P= 0.110) correlated significantly with NEE on an annual basis. Spatial variation in annual and summertime Reco was associated with growing season period, air temperature, growing degree days, normalized difference vegetation index and vapour pressure deficit. GPP showed weaker correlations with environmental variables as compared with Reco, the exception being leaf area index (LAI), which correlated with both GPP and NEE, but not with Reco. Length of growing season period was found to be the most important variable describing the spatial variation in summertime GPP and Reco; global warming will thus cause these components to increase. Annual GPP and NEE correlated significantly with LAI and pH, thus, in order to predict wetland C exchange, differences in ecosystem structure such as leaf area and biomass as well as nutritional status must be taken into account.
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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 | 0.000 | 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) | 0.000 | 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".