Peat carbon stocks in the southern Mackenzie River Basin: uncertainties revealed in a high‐resolution case study
Bibliographic record
Abstract
Abstract The organic carbon (C) stocks contained in peat were estimated for a wetland‐rich boreal region of the Mackenzie River Basin, Canada, using high‐resolution wetland map data, available peat C characteristic and peat depth datasets, and geostatistics. Peatlands cover 32% of the 25 119 km2 study area, and consist mainly of surface‐ and/or groundwater‐fed treed peatlands. The thickness of peat deposits measured at 203 sites was 2.5 m on average but as deep as 6 m, and highly variable between sites. Peat depths showed little relationship with terrain data within 1 and 5 km, but were spatially autocorrelated, and were generalized using ordinary kriging. Polygon‐scale calculations and Monte Carlo simulations yielded a total peat C stock of 982–1025 × 1012 g C that varied in C mass per unit area between 53 and 165 kg m−2. This geostatistical approach showed as much as 10% more peat C than calculations using mean depths. We compared this estimate with an overlapping 7868 km2 portion of an independent peat C stock estimate for western Canada, which revealed similar values for total peatland area, total C stock, and total peat C mass per unit area. However, agreement was poor within ∼875 km2 grids owing to inconsistencies in peatland cover and little relationship in peat depth between estimates. The greatest disagreement in mean peat C mass per unit area occurred in grids with the largest peatland cover, owing to the spatial coincidence of large cover and deep peat in our high‐resolution assessment. We conclude that total peat C stock estimates in the southern Mackenzie Basin and perhaps in boreal western Canada are likely of reasonable accuracy. However, owing to uncertainties particularly in peat depth, the quality of information regarding the location of these large stocks at scales as wide as several hundreds of square kilometers is presently much more limited.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".