Topographic influences on the distribution of white pine blister rust in <i>Pinus albicaulis</i> treeline communities
Bibliographic record
Abstract
The exotic disease white pine blister rust (caused by Cronartium ribicola) damages and kills whitebark pine (Pinus albicaulis), even in the extreme environments of alpine treeline communities. We surveyed P. albicaulis trees and tree islands for blister rust in 2 distinct alpine treeline communities in Montana, USA, and examined meso- and microtopographic factors potentially related to the climatic requirements for blister rust infection. For each of 60 sampling plots, we created high-resolution digital elevation models, derived microtopography variables, and compared these and distance to water feature variables with blister rust occurrence and intensity (number of cankers per infected tree) for every sampled P. albicaulis tree. Infection rates were 19% (of 328 sampled trees) and 24% (of 585 sampled trees) at the 2 sites. Tree island P. albicaulis had higher infection percentages than solitary trees. Using Bayesian analysis and a zero-inflated Poisson regression model, we determined that solar radiation and moisture-related variables correlated with both presence and number of blister rust cankers on P. albicaulis. Site factors that influence moisture, such as local topography, hydrology, and climate, differed between the 2 treeline study areas, which may account for the model variability.
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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 | 0.000 | 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 | 0.000 |
| 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 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".