Contrasting Temperature Sensitivity of CO<sub>2</sub> Exchange in Peatlands of the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
Abstract It remains uncertain how the net ecosystem CO2 exchange (NEE) of diverse peatlands will respond to warming. Here we compare five years of eddy covariance measurements of NEE and estimates of gross primary productivity and ecosystem respiration between a fen dominated by deciduous vegetation and an adjacent bog with evergreen vegetation in the Canadian Hudson Bay Lowlands. At the bog, daily net CO2 uptake lasted from snowmelt to snow cover onset, while at the fen, net CO2 uptake was delayed in spring and ended earlier in fall. Greater midsummer net CO2 uptake at the fen compensated for shoulder season net CO2 losses resulting in similar annual NEE at the two sites (fen: −52 ± 16 g C m−2, bog: −80 ± 14 g C m−2). Observations of a satellite‐based productivity index also suggest lower shoulder season and higher peak vegetation productivity at these deciduous versus evergreen plant‐dominated peatlands. The response of NEE to warmer weather differed between sites. Warming during the shoulder seasons increased net CO2 uptake at the evergreen plant‐dominated bog, while it increased net CO2 losses at the fen where deciduous leaves had not yet emerged or had senesced. In contrast, warmer weather during the peak growing season appeared to reduce net CO2 uptake more at the bog than the fen resulting from both increasing ecosystem respiration and decreasing gross primary productivity. In the short term, warming will likely decrease annual net CO2 uptake of these and similar peatlands, although the magnitude will depend on factors including vegetation dynamics and seasonality of warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".