Temporal and spatial patterns of fire regime disruption in conifer forests of western North America
Bibliographic record
Abstract
Temporal and spatial patterns of fire regime disruption were reconstructed in conifer forests of western North America from information on pre-disruption and disrupted mean fire intervals (MFIs) of 498 dendrochronology-based fire chronologies. We identified the conifer forest types most affected by MFI shift and the influence of land category designation on MFI change. We also mapped the years of the MFI shift, the last fire recorded, and disrupted/pre-disruption MFI ratios. Fire cessation and longer MFIs predominated in most fire chronologies and conifer forest types. MFI was significantly higher in most conifer forest types, with most differences in dry conifer forests. MFI shift occurred mainly before the designation of protected, federal, social-property, and private areas. MFI shift began in 1829 in the United States of America Southwest, a region subjected to prolonged fire exclusion and with the highest disrupted/pre-disruption MFI ratios. Fire regime disruption moved gradually into the Pacific Northwest and the Sierra Nevada until reaching the northern conifer forests of Canada and Alaska. In contrast, one-third of fire chronologies in Mexican conifer forests retained pre-disruption MFIs. Our findings allowed us to identify areas with MFIs outside of their natural variability, with prolonged fire exclusion, or with intact 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".