Landscape analysis of risk factors for white pine blister rust in the Mixed Forest Province of Minnesota, U.S.A.
Bibliographic record
Abstract
The abundance of eastern white pine (Pinus strobus L.) has been significantly reduced in northeastern Minnesota over the past 120 years. White pine blister rust (WPBR), a commonly lethal fungal disease of white pine, was introduced in Minnesota in approximately 1914 and now, along with other factors such as herbivore browsing, poses a major challenge to attempts to reestablish white pines in the region. A map delineating broad WPBR hazard zones for the Lake States region was prepared in 1964. We created a higher resolution map that estimates the spatial variability of WPBR hazard in the Laurentian Mixed Forest Province of Minnesota using modern geographic information system techniques and readily available spatial databases. The new map has significantly higher resolution than the old and demonstrates that even within areas previously classified as "high hazard", there are significant acreages of "low-hazard" areas where white pine regeneration may be possible. Our analyses are consistent with previous work in the Lake States region, showing that climate, topographic characteristics, and distance from water bodies and wetlands have a strong influence on WPBR infection hazard. We also present methods for analyzing forest conditions at regional scales using commonly available spatial data sets.
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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 | 0.001 | 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) | 0.001 | 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".