The effects of seed source health on whitebark pine (<i>Pinus albicaulis</i>) regeneration density after wildfire
Bibliographic record
Abstract
Whitebark pine (Pinus albicaulis Engelm.) populations are declining nearly rangewide from a combination of factors, including mountain pine beetle (Dendroctonus ponderosae Hopkins, 1902) outbreaks, the exotic pathogen Cronartium ribicola J.C. Fisch. 1872, which causes the disease white pine blister rust, and successional replacement due to historical fire exclusion practices. With high mortality in cone-bearing whitebark pine, seed production may not be sufficient to support natural regeneration after disturbance such as wildfire. Our objective was to examine the relationship between whitebark pine seed source health and whitebark pine regeneration density in adjacent burns. We sampled regeneration and seed source health in 15 burns within six national forests and three Wilderness Areas in Montana, ranging from 5 to 23 years old. We found a significant, positive relationship between seed source health and seedling density in adjacent burns. Natural regeneration was sparse when the proportion of damaged or dead whitebark pine in the seed source exceeded 50%. Factors that influenced the presence of whitebark pine regeneration within a burn included both vegetation cover and potential solar radiation. Sites closer to seed sources had higher probabilities of seedling occurrence, but seedlings were present throughout most burns. Our results suggest that managers can prioritize restoration plantings of whitebark pine seedlings after wildfire based on the health status of the nearest seed sources.
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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 | 0.000 | 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) | 0.001 | 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".