Conifer regeneration in stand-replacement portions of a large mixed-severity wildfire in the Klamath–Siskiyou Mountains
Bibliographic record
Abstract
Large-scale wildfires (∼10 4 –10 6 ha) have the potential to eliminate seed sources over broad areas and thus may lead to qualitatively different regeneration dynamics than in small burns; however, regeneration after such events has received little study in temperate forests. Following a 200 000 ha mixed-severity wildfire in Oregon, USA, we quantified (1) conifer and broadleaf regeneration in stand-replacement patches 2 and 4 years postfire; and (2) the relative importance of isolation from seed sources (live trees) versus local site conditions in controlling regeneration. Patch-scale conifer regeneration density (72%–80% Douglas-fir ( Pseudotsuga menziesii (Mirb). Franco)) varied widely, from 127 to 6494 stems·ha –1 . Median densities were 1721 and 1603 stems·ha –1 2 and 4 years postfire, respectively, i.e., ∼12 times prefire overstory densities (134 stems·ha –1 ). Because of the complex burn mosaic, ∼58% of stand-replacement area was ≤200 m from a live-tree edge (seed source), and ∼81% was ≤400 m. Median conifer density exceeded 1000 stems·ha –1 out to a distance of 400 m from an edge before declining farther away. The strongest controls on regeneration were distance to live trees and soil parent material, with skeletal coarse-grained soils supporting lower densities (133 stems·ha –1 ) than fine-grained soils (729–1492 stems·ha –1 ). Other site factors (e.g., topography, broadleaf cover) had little association with conifer regeneration. The mixed-severity fire pattern strongly influenced the regeneration process by providing seed sources throughout much of the burned landscape.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".