Stability of fen and bog peatland types over the Holocene Epoch in the Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
The Hudson Bay Lowlands (HBL) is a vast continuous peatland in Northern Canada. The landscape is a mosaic of mostly bogs and fens, with more limited swamp, marsh, forest and open water. Owing to rapid rates of isostatic uplift, younger peats are found closer to the coasts of Hudson and James Bays, with fen-type peatlands somewhat more prevalent on these younger surfaces. More than 30 Pg of carbon have accumulated in the HBL over the Holocene. The rates of Holocene carbon accumulation vary considerably both spatially and temporally, with some sites showing more rapid rates of carbon accumulation in the first 2-3 millennia following peatland initiation. We evaluate here the hypothesis that vegetation changes over the course of the Holocene, including fen-to-bog transitions, partially explain the variability in carbon accumulation. We find that in some cases, more rapid rates of C accumulation in the middle Holocene (5000-8000 yrs before present) are associated with early successional minerotrophic fens with higher carbon densities. Fen-to-bog transitions are recorded in many peat cores collected from present day bogs; however, these transitions are time transgressive, and can depend on the time since initiation, suggesting that climate changes may play a secondary role, relative to hydrological changes and local ecological processes. Fens are highly prevalent in the HBL landscape (covering about 38% of land cover). Cores taken from present day fens and analyzed for carbon accumulation and vegetation change indicate that many fen sites have remained fens since peat initiation. Variability in rates of Holocene carbon accumulation within fen records which have not been subject to any major vegetation change may more closely reflect climate drivers.
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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 | 0.002 | 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".