THE EFFECT OF CLIMATE CHANGE ON PEATLANDS IN THE CANADIAN BOREAL AND SUBARCTIC
Bibliographic record
Abstract
Most of Canadas peatlands (97% by area) occur in the Boreal (64%) and Subarctic (33%) wetland regions. They contain large amounts of organic carbon (3050% by weight) and water as liquid or ice. The active layer (seasonal thaw layer) of peat contains 2090% water (by volume), while the perennially frozen layer and underlying mineral soil contain approximately 7080% ice. For perennially frozen peatlands, this represents approximately 506 billion m3 stored water, 85% as ice. The increase in air temperature (approximately 6 C) predicted for a 2x CO2 environment would result in degradation of perennially frozen peatlands in the Subarctic and northern Boreal wetland regions and in severe drying in the southern Boreal Wetland Region. The Peatland Sensitivity Model used to estimate the effect of climate warming on organic carbon, water and ice contents, indicates that approximately 61% of the Subarctic and Boreal peatlands (by area), containing 74 Gt organic carbon, will be severely to extremely severely affected by climate change, as will approximately 87% of perennially frozen Subarctic and Boreal peatlands, containing approximately 37 Gt organic carbon and 440 billion m3 water, stored primarily as ice (85%). The release of this large amount of carbon into the atmosphere could trigger further increases in climate warming. Melting of ground ice as a result of climate warming could release large amounts of water, causing water-logged conditions and landscape changes. In addition, water released from perennially frozen peatlands may contain toxic materials.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".