Cryptic Species Diversity and Phylogenetic Relationship in the Rust Genus Chrysomyxa from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chrysomyxa rusts are fungal pathogens widely distributed in the Northern hemisphere, causing spruce needle and cone rust diseases, and they are responsible for significant economic losses in China. Taxonomic delimitation and precise species identification are difficult within this genus because some characters often overlap in several species. Adequate species delimitation, enhanced by the use of DNA-based methodologies, will help to establish well-supported species boundaries and enable the identification of cryptic species. Here, we explore the cryptic species diversity in the rust genus Chrysomyxa from China. Species delimitation analyses are conducted using a distance-based method (ABGD) and three tree-based methods (GMYC, bPTP, and mPTP) based on combined LSU and ITS sequences of over 60 specimens. Although there is some incongruence among species delimitation methods, two new species and three putative cryptic species are identified. The key to 20 Chrysomyxa species distributed in China is presented. These results suggest that a significant level of undiscovered cryptic diversity is likely to be found in Chrysomyxa from China. Future studies should consider multiple analytical methods when dealing with multi-locus datasets.
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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 | 0.000 |
| 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) | 0.000 | 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 it