Post-fire regeneration and survival of whitebark pine (Pinus albicaulis)
Bibliographic record
Abstract
The recruitment trends of whitebark pine (Pinus albicaulis Engel.) were investigated in 18 recently burned stands in the Canadian Rockies and the North Cascades. Whitebark pine recruitment in recently burned stands (fires < 60 years prior) was compared to that on paired control stands (no recent fire) to determine if fire was necessary for successful recruitment. Recruitment in recently burned stands was compared with ecological and seed source variables. Along a chronosequence, whitebark pine recruitment was compared with precipitation and with Pacific Decadal Oscillation (PDO). Field sampling consisted of systematic plots established in sampling grids in both burned and control stands. In each plot ecological data was collected as was data regarding whitebark pine age, height, and disease status. Recent fire was not found to be a requirement for recruitment. Both distance to and size of seed source were important predictors of whitebark pine recruitment. Whitebark pine regeneration densities were low on warm steep rocky sites. High whitebark pine recruitment on cooler aspects suggested that whitebark pine could establish on cooler sites when competition was removed by fire. Most stands were composed of mixed conifers, but only lodgepole pine appeared to limit the growth of whitebark pine. Whitebark pine recruitment was episodic on all stands, and recruitment years were correlated among many stands separated by large distances. Episodic recruitment may be due to more than cone masting as recruitment in several stands was also correlated with growing season precipitation and positive PDO values, which may increase the length of growing season. A logistic regression model suggested that infection by white pine blister rust is most likely on older seedlings. The percentage of whitebark pine trees infected by white pine blister rust on a site increased with time since fire. Prescribed fires and wildfires should address retention of whitebark pine seed trees on site. Fires that remove competition from mesic-submesic stands may result in the most rapid recruitment of whitebark pine. With the exception of stands heavily stocked with lodgepole pine, all recently burned stands in this study would benefit from increased stocking of whitebark pine by planting.
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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 | 0.000 | 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".