Assessing host specialization among aecial and telial hosts of the white pine blister rust fungus,<i>Cronartium ribicola</i>
Bibliographic record
Abstract
The white-pine blister rust fungus, Cronartium ribicola Fisch. in Rabenh., continues to spread in North America, utilizing various aecial (primary) and telial (alternate) hosts, some of which have only recently been discovered. This introduced pathogen has been characterized as having low genetic diversity in North America, yet it has demonstrated a capacity to invade diverse environments. The recent discovery of this rust fungus on the telial host Pedicularis racemosa Dougl. ex Benth., raises questions of whether this host association represents a recent acquisition by C. ribicola or a long-standing host association that was overlooked. Here we explore two questions: (i) is host specialization detectable at a local scale and (ii) is the capacity to infect Pedicularis racemosa local or widespread? Genetic analysis of C. ribicola isolates from different aecial and telial hosts provided no evidence for genetic differentiation and showed similar levels of expected heterozygosity within a geographic population. An inoculation test showed that diverse C. ribicola sources from across North America had the capacity to infect Pedicularis racemosa. These results support a hypothesis that ability to infect Pedicularis racemosa is common in C. ribicola from North America. Utilization of Pedicularis racemosa by C. ribicola may be dependent on the co-occurrence of this host, inoculum, and favorable environments.
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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".