Subspecies of Rosa nutkana and R. woodsii (Rosaceae) in Western North America
Bibliographic record
Abstract
Three subspecies of Rosa nutkana C. Presl and five of R. woodsii Lindley are recognized in western Canada and the United States, including four changes in combination and/or status: Rosa nutkana subsp. melina (Greene) W. H. Lewis & Ertter, R. woodsii subsp. arizonica (Rydberg) W. H. Lewis & Ertter, R. woodsii subsp. manca (Greene) W. H. Lewis & Ertter, and R. woodsii subsp. gratissima (Greene) W. H. Lewis & Ertter. Rosa nutkana subsp. melina and R. woodsii subsp. manca occur at high elevations of the southern Rocky Mountains and Colorado Plateau of Colorado and Utah with outlying populations in Arizona, Idaho, New Mexico, and Wyoming; R. woodsii subsp. arizonica is found at lower elevations of Arizona and the Colorado Plateau; and R. woodsii subsp. gratissima occurs in the mountains surrounding the Mojave Desert and southern Great Basin of California and Nevada, with its variety glabrata (Parish) D. Cole confined to the San Bernardino Mountains of California. Synonyms are provided for appropriate subspecies, 14 lectotypes and one neotype are designated here, and selected exsiccatae are given for newly recognized subspecies. The following names are lectotypified: Rosa bakeri Rydberg, nom. illeg., R. californica Chamisso & Schlechtendal var. ultramontana S. Watson, R. deserta Lunell, R. fendleri Crépin, R. macounii Greene, R. maximiliani Nees, R. megalantha G. N. Jones, R. neomexicana Cockerell, R. nutkana var. alta Suksdorf, R. nutkana var. hispida Fernald, R. nutkana var. pallida Suksdorf, R. rainierensis G. N. Jones, R. spaldingii Crépin ex Rydberg, and R. subnuda Lunell. One neotype is designated: Rosa woodsii Lindley.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".