Spatial interpolation and mean fire interval analyses quantify historical mixed-severity fire regimes
Bibliographic record
Abstract
Tree-age data in combination with fire scars improved inverse-distance-weighted spatial modelling of historical fire boundaries and intervals for the Darkwoods, British Columbia, Canada. Fire-scarred trees provided direct evidence of fire. The presence of fire-sensitive trees at sites with no fire scars indicated fire-free periods over their lifespan. Sensitivity analyses showed: (1) tree ages used in combination with fire-scar dates refined fire boundaries without biasing mean fire return intervals; and (2) compared with derived conservative, moderate and liberal thresholds (i.e. minimum burn likelihood cut-off values), fixed thresholds generated area burned estimates that were most consistent with estimates based on the proportion of plots that recorded historical fires. Unweighted and weighted spatial mean fire intervals (50–56 and 58–68 years respectively) exceeded dendrochronological plot-level (38-year) estimates based on fire scars only. Including tree-age data from fire-sensitive trees to calculate landscape-level fire interval metrics lengthened the mean return intervals, better representing historical high-severity fires. Supplementing fire scars with tree ages better reflects the spatiotemporal diversity of fire frequencies and severities inherent to mixed-severity fire regimes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".