Climate and peat type in relation to spatial variation of the peatland carbon mass in the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
Abstract Northern peatlands store ~500 Pg of carbon (C); however, controls on the spatial distribution of the stored C may differ regionally, owing to the complex interaction among climate, ecosystem processes, and geophysical controls. As a globally significant C sink, elucidation of controls on the distribution of C in the Hudson Bay Lowlands, Canada (HBL), is of particular importance. Although peat age is related to timing of land emergence and peat depth in the HBL, considerable variation in the total C mass (kg m−2) among sites of similar peat age suggests that other factors may explain spatial patterns in C storage (Pg) and sequestration. Here we quantify the role of two key factors in explaining the spatial distribution of the C mass in the HBL (n = 364 sites), (i) climate variability and (ii) peat lithology, for two major peatland classes in the HBL (bogs and fens). We find that temperature, precipitation, and evapotranspiration each explained nearly half of the C mass variability. Regions characterized by warmer and wetter conditions stored the most C as peat. Our results show that bogs and fens store similar amounts of C within a given climate domain, although via distinct storage mechanisms. Namely, fen peats tend to be shallower and more C dense (kg m−3) compared to bogs. Following geophysical controls on the timing of peat initiation, our results reveal that both the widespread bog‐fen patterning and variability in regional climate contribute to explaining the spatial distribution of the peat C mass in the HBL.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".