Surface fuel loads following a coastal–transitional fire of unprecedented severity: Boulder Creek fire case study
Bibliographic record
Abstract
British Columbia experienced three years with notably large and severe wildfires since 2015. Multiple stand-replacing wildfires occurred in coastal–transitional forests, where large fires are typically rare, and thus, information on post-fire carbon is lacking. Because of their carbon storage potential, coastal–transitional forests are important in the global carbon cycle. We examined differences in surface fuel carbon among fire severity classes in 2016, one year after the Boulder Creek fire, which burned 6 735 ha of coastal–transitional forests in 2015. Using remotely sensed indices (dNBR), we partitioned the fire area into unburned (control), low-, moderate-, and high-severity classes. Field plots were randomly located in each class. At each plot, surface fuel carbon was quantified by type, namely coarse, small, and fine woody material, duff, and litter, and carbon mass by fuel type was compared among severity classes. Total surface fuel carbon did not differ significantly between burned and unburned plots; however, there was significantly less duff and litter carbon in burned plots. Remotely sensed severity classes did not properly capture wildfire impacts on surface fuels, especially at lower severities. Pre-fire stand characteristics are also important drivers of surface fuel loads. This case study provides baseline data for examining post-fire fuel carbon dynamics in coastal–transitional British Columbia.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".