Using Holocene paleo-fire records to estimate carbon stock vulnerabilities in Hudson Bay Lowlands peatlands
Bibliographic record
Abstract
Holocene fire records from charcoal are critical to understand linkages between regional climate and fire regime and to create effective fire management plans. The Hudson Bay Lowlands (HBL) of Canada is one of the largest continuous peatland complexes in the world and is predicted to be increasingly impacted by wildfire. We present three charcoal records from a bog in the western HBL and demonstrate that median fire frequency was higher in the Middle Holocene, related to warmer regional temperatures and higher evaporative demand. Holocene fire frequencies are lower than in western Canadian peatlands, supporting that the HBL lies in the transition between continental and humid boreal fire regimes. Apparent carbon accumulation rates at the site were not significantly different between the Middle and Late Holocene, suggesting that higher fire frequency and enhanced decomposition offset the potential for higher rates of biomass production. We compile records from the boreal region and demonstrate that increasing fire frequency is significantly correlated with diminishing long-term carbon accumulation rates, despite large variation in response of peatlands to fire frequency changes. Therefore, the paleo-record supports that higher fire frequencies will likely weaken the capacity of some northern peatlands to be net carbon sinks in the future.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".