White pines,<i>Ribes</i>, and blister rust: integration and action
Bibliographic record
Abstract
Summary The preceding articles in this series review the history, biology and management of white pine blister rust in North America, Europe and eastern Asia. In this integration, we connect and discuss seven recurring themes important for understanding and managing epidemics ofCronartium ribicolain the white pines (five‐needle pines in subgenusStrobus). Information and action priorities for research and management of the pathogen, telial and aecial hosts, and their interactions are listed in a detailed Appendix. Syntheses focused on genetics, plant disease, invasive species or forest management have provided alternative but knowledgeable lessons on the white pine blister rust pathosystem. Two critical issues for the conservation of white pines are to sustain ecosystems affected by blister rust and to maintain genetic diversity for adaptive traits such as disease resistance. Forest genetics includes tree improvement and molecular techniques for research; their application can increase rust resistance by artificial and natural selection. Silviculture augments genetics with methods to deploy and enhance resistance as well as to regenerate and tend white pine stands. Although cultivated or wildRibesmight serve as inoculum sources, silviculture and horticulture can reduce the risk of serious impacts from blister rust using genetics for breeding and epidemiology for hazard assessment and disease control. Climate change threatens to cause major alterations in temperature and precipitation regimes, resulting in maladapted conifers succumbing to various diseases and insect outbreaks. In contrast, many white pine species have broad ecological ranges and are tolerant of harsh environments—traits that permit successful establishment and growth over wide geographic and altitudinal zones. Given appropriate management, white pines could thrive as valuable commercial and ecologically important keystone species. In an uncertain environment, adaptive management provides a learning and participatory approach for sustaining resilient ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".