Carbon accumulation in permafrost peatlands in the Northwest Territories and Nunavut, Canada
Bibliographic record
Abstract
Average long-term apparent rates of carbon (C) accumulation (LARCA) were estimated for four peat cores from Arctic and Subarctic Canada. Detailed analyses of dry bulk-density and C content were used to determine variations in C accumulation rates throughout the cores. LARCA range from 12.5 to 16.5 g C m-2 yr-1 over the past 6700-10000 years. Rates are lower for the surface layers of Arctic high-centred peat polygons, at 5.3 to 7.1 g C m-2 yr-1 for the last 3500-4500 years. By comparison, the rate for the near-surface peat from a Sphagnum fuscum hummock in the high Subarctic was considerably higher, at 24.1 g C m-2 yr-1. The highest carbon accumulation rates were from core segments older than 4500 BP, which represent fen stages according to palaeoecological analysis. The average LARCA in our study are considerably lower than recent estimates of average carbon accumulation in Boreal peatlands. This difference is attributable partly to lower carbon percentages in our cores compared to the mean or estimated values of 50 to 51.7% used in those studies. Another factor is the presence of ground ice, which exaggerates the apparent peat depth and leads to erroneously high values if cumulative carbon estimates are based on depth. Using cumulative dry bulk-density, as we have done, eliminates the influence of ground ice and thus makes more accurate estimates possible.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".