Noccaea klimesii (Coluteocarpeae; Brassicaceae), a new species from Ladakh, India
Bibliographic record
Abstract
Noccaea Moench (1802: 89) is a large genus the vast majority of its species were originally described in Thlaspi Linnaeus (1753: 645). Meyer (1973) divided the latter genus into 12 segregates, including Noccaea, to which he placed 67 Eurasian and NW African species in four sections (Meyer: 2006). Experts on the Brassicaceae differ in the delimitation of Noccaea and some (e.g., Al-Shehbaz: 2014; Firat et al.: 2014; Güzel et al.: 2018; Özgişi et al.: 2018a, 2018b; Özüdoğru: 2018; Özüdoğru et al.: 2019; Özgişi: 2020a, 2020b) broadly delimit the genus to include most of Meyer’s dozen segregates and accept some 136 species, including the more recent novelties and nomenclatural adjustments by Bartolucii, Galasso & Peruzzi in Peruzzi et al. (2015), German (2016: 2017, 2018), Güzel et al. (2018), Özgişi et al. (2018b), and Özüdoğru et al. (2019). By contrast, a narrower generic concept for Noccaea was recognized in BrassiBase (https://brassibase.cos.uni-heidelberg.de/), Ali et al. (2016), and Karaismailoğlu & Erol (2018). As currently delimited, all except six species of Noccaea are Eurasian. Two species are endemic to the United States and one each in Algeria, Mexico, Patagonian South America, and Arctic Canada and Alaska (Al-Shehbaz: 2014).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".