Effects of vegetation zones and climatic changes on fire-induced atmospheric carbon emissions: a model based on paleodata
Bibliographic record
Abstract
An original method is proposed for estimating past carbon emissions from fires in order to understand long-term changes in the biomass burning that, together with vegetation cover, act on the global carbon cycle and climate. The past carbon release resulting from paleo-fires during the Holocene is examined using a simple linear model between measured carbon emissions from modern fires and sedimentary charcoal records of biomass burning within boreal and cold temperate forests in eastern Canada (Quebec, Ontario). Direct carbon emissions are estimated for each ecozone for the present period and the fire anomaly per kilo annum (ka) v. present day (0 ka) deduced from charcoal series of 46 lakes and peats. Over the postglacial, the Taiga Shield ecozone does not match the pattern of fire history and carbon release of Boreal Shield, Atlantic Maritime, and Mixedwood Plains ecozones. This feature results from different air mass influences and the timing of vegetation dynamics. Our estimations show, first, that the contribution of the Mixedwood Plains and the Atlantic Maritime ecozones on the total carbon emissions by fires remains negligible compared with the Boreal Shield. Second, the Taiga Shield plays a key role by maintaining important carbon emissions, given it is today a lower contributor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".