Stimulation of both photosynthesis and respiration in response to warmer and drier conditions in a boreal peatland ecosystem
Bibliographic record
Abstract
Peatland ecosystems have been consistent carbon (C) sinks for millennia, but it has been predicted that exposure to warmer temperatures and drier conditions associated with climate change will shift the balance between ecosystem photosynthesis and respiration providing a positive feedback to atmospheric CO2 concentration. Our main objective was to determine the sensitivity of ecosystem photosynthesis, respiration and net ecosystem production (NEP) measured by eddy covariance, to variation in temperature and water table depth associated with interannual shifts in weather during 2004–2009. Our study was conducted in a moderately rich treed fen, the most abundant peatland type in western Canada, in a region (northern Alberta) where peatland ecosystems are a significant landscape component. During the study, the average growing season (May–October) water depth declined approximately 38 cm, and temperature [expressed as cumulative growing degree days (GDD, March–October)] varied approximately 370 GDD. Contrary to previous predictions, both ecosystem photosynthesis and respiration showed similar increases in response to warmer and drier conditions. The ecosystem remained a strong net sink for CO2 with an average NEP (± SD) of 189 ± 47 g C m−2 yr−1. The current net CO2 uptake rates were much higher than C accumulation in peat determined from analyses of the relationship between peat age and cumulative C stock. The balance between C addition to, and total loss from, the top 0–30 cm depth (peat age range 0–70 years) of shallow peat cores averaged 43 ± 12 g C m−2 yr−1. The apparent long-term average rate of net C accumulation in basal peat samples was 19–24 g C m−2 yr−1. The difference between current rates of net C uptake and historical rates of peat accumulation is likely a result of vegetation succession and recent increases in tree establishment and productivity.
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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) | 0.000 | 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".