Partitioning carbon losses from fire combustion in a montane Valley, Alberta Canada
Bibliographic record
Abstract
Direct carbon (C) emissions from wildland fires have been difficult to quantify, especially in montane environments where sites are difficult to access. Here we examined pre-fire C partitioning and losses in a southern Canadian montane valley ecosystem, in Waterton Lakes National Park, Alberta Canada. The objectives of this study were to: (a) quantify the C loss due to combustion at a moist riparian site compared with a dry undulating upland site and (b) compare C loss observations to an active multi-spectral lidar remote sensing index. C losses from wildfire were consistently greater at the wet riparian site compared with the dry valley site. Average soil C losses were 92.92 Mg C ha −1 (st. dev. ± 48.60 Mg C ha −1 ) and 58.05 Mg C ha −1 (st. dev. ± 37.19 Mg C ha −1 ). Average tree C losses were 114.0 Mg C ha −1 (std.dev. ± 9.9 Mg C ha −1 ) and 86.9 Mg C ha −1 (std.dev. ± 13.5 Mg C ha −1 ) respectively. C losses from trees were greater than soils, where trees lost 55% (moist riparian ecosystem) and about 60% (drier valley site) of C during combustion. Using post-fire multi-spectral airborne lidar data, we found that increased proportion of charred soils were significantly related to enhanced reflectivity in SWIR, resulted in more negative active normalised burn ratio (aNBR) results, indicating enhanced burn severity. Increased proportional cover of regenerating vegetation resulted in less negative aNBR both at the drier site, though no significant relationships between aNBR and charred vs. vegetated results were observed at the moist riparian site. No significant relationship was observed between depth of burn/soil C loss and aNBR derived from lidar data, indicating potential limitations when using burn indices for below canopy burn severity. The use of multi-spectral lidar may improve understanding of below canopy fire fuels and C losses in optical imagery, which often occludes these important components of fire ecology. The results of this research improve understanding of C losses associated with wildland fire in montane ecosystems that have undergone fire suppression and management by Euro-American colonizers for over 100 years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".