BIOTIC AND ABIOTIC REGULATION OF LIGHTNING FIRE INITIATION IN THE MIXEDWOOD BOREAL FOREST
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lightning fire is the dominant natural disturbance of the western mixedwood boreal forest of North America. We quantified the independent effects of weather and forest composition on lightning fire initiation (a detected and recorded fire start) patterns in Alberta, Canada, to demonstrate how these biotic and abiotic components contribute to ecosystem dynamics in the mixedwood boreal forest. We used logistic regression to describe variation in annual initiation occurrence among 10,000-ha landscape units (voxels) covering a 9 million-ha study region over 11 years. At a voxel scale, forest composition explained more variation in annual initiation than did weather indices. Initiations occurred more frequently in landscapes with more conifer fuels (Picea spp.), and less in aspen-dominated (Populus spp.) ones. Initiations were less frequent in landscapes that had recently burned. Variation in initiation was also influenced by joint weather-lightning indices, but to a lesser degree. For each voxel, these indices quantified the number of days in the fire season when moisture levels were low and lightning was detected. Regional indices of fire weather severity explained substantial interannual variation of initiation, and the effect of forest composition was stronger in years with more severe fire weather. Our study is a conclusive demonstration of biotic and abiotic regulation of lightning fire initiation in the mixedwood boreal forest. The independent effects of forest composition emphasize that vegetation feedbacks strongly regulate disturbance dynamics in the region.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it