White pines,<i>Ribes</i>, and blister rust: a review and synthesis
Bibliographic record
Abstract
Summary For over a century, white pine blister rust (Cronartium ribicola) has linked white pines (Strobus) with currants and gooseberries (Ribes) in a complex and serious disease epidemic in Asia, Europe, and North America. Because of ongoing changes in climate, societal demands for forests and their amenities, and scientific advances in genetics and proteomics, our current understanding and management of the white pine blister rust pathosystem has become outdated. Here, we present a review and synthesis of international scope on the biology and management of blister rust, white pines,Ribes, and other hosts. In this article, we provide a geographical and historical background, describe the taxonomy and life cycle of the rust, discuss pathology and ecology, and introduce a series of invited papers. These review articles summarize the literature on white pines,Ribes, and blister rust with respect to their status, threats, and management through genetics and silviculture. Although the principal focus is on North America, the different epidemics in Europe and Asia are also described. In the final article, we discuss several of the key observations and conclusions from the preceding review articles and identify prudent actions for research and management of white pine blister rust.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".