Distribution and diagnostic characters of <i>Nassella</i> (<i>Poaceae: Stipeae</i>)
Bibliographic record
Abstract
Summary Barkworth, M. E. & Torres, M. A.: Distribution and diagnostic characters of Nassella (Poaceae: Stipeae). – Taxon 50: 439–468. 2001. – ISSN 0040‐0262. Nassella sensu lato includes 116 species, making it one of the largest genera in tribe Stipeae. Argentina has the largest number of species, 72, with the greatest concentration being in the northwestern part of the country. Bolivia, Chile, and Uruguay have 26, 27, and 27 species, respectively. Other South American countries in which the genus is present are Brazil (18 species), Colombia (8), Ecuador (9), Paraguay (4), Peru (18), and Venezuela (2). Guatemala has two species, but Costa Rica only one. Mexico has eight native species, five of which also grow in the United States. One additional species grows in both the United States and Canada. Sixty species are known only from one country; one species, N. mexicana, grows in eight countries. Several new distribution records are documented: N. caespitosa, N. elata, N. leptothera and N. punensis for Bolivia, N. pauciciliata and N. spegazzinii for Brazil, N. airoides, N. argentinensis, N. spegazzinii for Paraguay, and N. tucumana (= N. asperifolia) for Peru. Three new combinations are presented: N. burkartii, N. ligularis, and N. quinqueciliata. Two recently transferred species, N. barrancaensis and N. brachychaeta, are excluded from the genus and N. asperifolia, N. bonariensis, and N. amethystina are placed in synonymy. Tables summarising the distribution of Nassella and its morphological variation are presented.
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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 | 0.002 | 0.002 |
| 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) | 0.008 | 0.002 |
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".