Patterns of pine regeneration following a large, severe wildfire in the mountains of southern California
Bibliographic record
Abstract
We examined establishment patterns of pines following a large, severe wildfire in the Peninsular Ranges of southern California, USA. The October 2003 Cedar Fire caused 98% pine mortality. In this study, we asked (i) where did seedlings establish and survive in formerly forested areas of the Cuyamaca Mountains 5 years following the high severity fire and (ii) what factors were associated with the spatial pattern of seedling establishment? Factors analyzed were pre-fire vegetation type, fire severity, post-fire vegetation characteristics, topography (slope, aspect, and elevation), and mapped soil type. We used a unique belt-transect survey method following the existing trail network that resulted in a representative sample of post-fire environments. Almost 1300 100 m × 20 m quadrats were searched in 2008–2009, one third of which supported juvenile pines. Regeneration primarily consisted of Coulter pine ( Pinus coulteri D. Don), a weakly serotinous pine that was establishing at densities of 5–2320/ha on half of the quadrats where it had occurred pre-fire. Pinus coulteri regenerated in areas burned at high severity where pre-fire pine cover was high and its abundance was positively associated with higher elevation and cover of bare soil. In contrast, minimal regeneration of nonserotinous pines occurred patchily in areas that were not severely burned.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".