Assessing Shifts in 20th Century Carbon Stocks in NE Canadian Permafrost Peatlands
Bibliographic record
Abstract
Recent and rapid changes in climate and permafrost thaw are affecting carbon dynamics in high-latitude peatlands. There is growing interest in evaluating the C sink potential of peatlands for conservation as nature-based climate solutions. However, rapid decadal-to centennial-scale changes are poorly understood, in part due to poor dating resolution in surface peat. Here, we evaluate the timing of vegetation shifts and rates of carbon accumulation for the past ~200 years peatlands for 100 cores from boreal and subarctic regions in Québec and Labrador (Eastern Canada). We used classical (Constant Rate of Supply - CRS) and Bayesian (Plum) approaches to model age-depth relationships from lead-210 (210Pb) and radiocarbon (14C) dates. Results highlight the important role of permafrost thaw in altering local peatland hydrological conditions, favouring Sphagnum growth and new peat addition in subarctic regions. While both models provide similar ages for the last century in complete cores, the CRS model tends to overestimate peat ages compared to Plum prior to ~1900CE. We recommend using Plum when constructing combined age-depth models, and importantly when if cores are incomplete. While 210Pb activity profiles are a clear indicator of disturbance in the peat column from permafrost thaw, the addition of independent dating markers (e.g. postbomb 14C dates) is especially important to validate age-depth models. The choice of age-depth model and user decisions can have important knock-on effects for interpreting timings of environmental shifts, as well as estimating the order of magnitude of C stocks for policy and conservation purposes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".