A framework infrageneric classification of<i>Carex</i>(Cyperaceae) and its organizing principles
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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