Mixed-severity fire regimes: How well are they represented by existing fire-regime classification systems?
Bibliographic record
Abstract
Maps depicting historic fire regimes provide critical baselines for sustainable forest management and wildfire risk assessments. However, given our poor understanding of mixed-severity fire regimes, we asked if there may be considerable errors in fire-regime classification systems used to create landscape-level maps. We used dendrochronological field data (fire scars and tree establishment dates) from 20 randomly selected sites in southern British Columbia to evaluate two classification systems (Natural Disturbance Type (NDT) and Historical Natural Fire Regime (HNFR)) used by managers to map fire regimes. We found evidence of mixed-severity fires at 55% of sites. Each classification system made considerable and contrasting errors predicting mixed-severity regimes (relative to field data), and the discrepancies varied with elevation. The NDT system underrepresented low-to-moderate-severity fires at lower elevations, whereas the HNFR system overpredicted their occurrence at higher elevations. Errors are attributed to underlying assumptions about disturbances in the two classification systems, as well as limitations of the research methods used to estimate fire frequency in mixed-severity regimes (i.e., methods more relevant to high- versus low-severity regimes). Ecological heterogeneity created by mixed-severity regimes potentially influences decisions related to conservation, silviculture, wildfire, and fuel mitigation. Thus, understanding underlying assumptions and errors in mapping fire regimes is critical.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".