A framework infrageneric classification of<i>Carex</i>(Cyperaceae) and its organizing principles
Bibliographic record
Abstract
Abstract Phylogenetic studies of Carex L. (Cyperaceae) have consistently demonstrated that most subgenera and sections are para‐ or polyphyletic. Yet, taxonomists continue to use subgenera and sections in Carex classification. Why? The Global Carex Group (GCG) here takes the position that the historical and continued use of subgenera and sections serves to (i) organize our understanding of lineages in Carex , (ii) create an identification mechanism to break the ~2000 species of Carex into manageable groups and stimulate its study, and (iii) provide a framework to recognize morphologically diagnosable lineages within Carex . Unfortunately, the current understanding of phylogenetic relationships in Carex is not yet sufficient for a global reclassification of the genus within a Linnean infrageneric (sectional) framework. Rather than leaving Carex classification in its current state, which is misleading and confusing, we here take the intermediate steps of implementing the recently revised subgeneric classification and using a combination of informally named clades and formally named sections to reflect the current state of our knowledge. This hybrid classification framework is presented in an order corresponding to a linear arrangement of the clades on a ladderized phylogeny, largely based on the recent phylogenies published by the GCG. It organizes Carex into six subgenera, which are, in turn, subdivided into 62 formally named Linnean sections plus 49 informal groups. This framework will serve as a roadmap for research on Carex phylogeny, enabling further development of a complete reclassification by presenting relevant morphological and geographical information on clades where possible and standardizing the use of formal sectional names.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".