North American boreal forests are a large carbon source due to wildfires from 1986 to 2016
Bibliographic record
Abstract
The dataset contains all files to reproduce the figures in the paper North American boreal forests are a large carbon source due to wildfires from 1986 to 2016. These figures are created by Matlab, Python and ArcGIS. For Python, a environment of Python 2.7 or Python 3.7 with packages (pandas, numpy, scipy, matplotlib) pre-installed is required. The files with the extension of *.sglburnemit are essentially text files. Wildfires are a major disturbance to influence forest carbon balance through both immediate combustion emissions and post-fire ecosystem carbon dynamics. Here we use a process-based biogeochemistry model, the Terrestrial Ecosystem Model, to simulate carbon budget in Alaska and Canada during 1986-2016 considering fire disturbances. The difference Normalized Burn Ratio (dNBR) data for fires are extracted from Landsat TM/ETM imagery, and used to estimate the proportion of vegetation and soil carbon combustion. We find that the region is a carbon source of 2.74 Pg C during the 31-year period. The loss is attributed to fire emissions at 57.1 Tg C/yr, overwhelming the net ecosystem production at 1.9 Tg C/yr in the region. Our during-fire emission for Alaska and Canada are lower than some field measurements and model estimations (for Alaska: 1.4 Tg C/yr versus 1.6-3.3 Tg C/yr; for Canada: 2.1 Tg C/yr versus 1.3-4.3 Tg C/yr). Fire severity complicates after-fire carbon dynamics, with low severity fires increase soil temperature and decrease soil moisture, stimulating soil respiration. However, the opposite trend is found under moderate or high fire severity. Net nitrogen mineralization rates gradually recovered after fire, enhancing net primary production. Net ecosystem production recovers quicker under higher burn severities. Overall, our carbon budget analysis might be biased mainly due to the burn severity uncertainty.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".