Evaluation of the composite burn index for assessing fire severity in Alaskan black spruce forests
Bibliographic record
Abstract
We evaluated the utility of the composite burn index (CBI) for estimating fire severity in Alaskan black spruce forests by comparing data from 81 plots located in 2004 and 2005 fire events. We collected data to estimate the CBI and quantify crown damage, percent of trees standing after the fire, depth of the organic layer remaining after the fire, depth of burning in the surface organic layer (absolute and relative), and the substrate layer exposed by the fire. To estimate pre-fire organic layer depth, we collected data in 15 unburned stands to develop relationships between total organic layer depth and measures of the adventitious root depth above mineral soil and below the surface of the organic layer. We validated this algorithm using data collected in 17 burned stands where pre-fire organic layer depth had been measured. The average total CBI value in the black spruce stands was 2.46, with most of the variation a result of differences in the CBI observed for the substrate layer. While a quadratic equation using the substrate component of CBI was a relatively strong predictor of mineral soil exposure as a result of fire (R2 = 0.61, P < 0.0001, F = 60.3), low correlations were found between the other measures of fire severity and the CBI (R2 = 0.00–0.37). These results indicate that the CBI approach has limited potential for quantifying fire severity in these ecosystems, in particular organic layer consumption, which is an important factor to understand how ecosystems will respond to changing climate and fire regimes in northern regions.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".