Complex taxonomy in Opuntioideae: is floral morphometry essential to identify <i>Opuntia</i> species?
Bibliographic record
Abstract
Correct species identification is critical for studies on biodiversity, ecology, and conservation. Determining Opuntia s.s. species is difficult because they have similar traits and are phenotypically plastic. Taxonomic keys are based on vegetative traits rather than reproductive ones such as flowers, because they are assumed to be too similar. We analyzed morphometric characteristics of flowers and cladodes over 6 years to determine which of these is most useful for differentiating Opuntia species from the Chihuahuan Desert. For each species ( Opuntia robusta H.L. Wendl. ex Pfeiff., O. cantabrigiensis Lynch, O. tomentosa Salm-Dyck, and O. streptacantha Lem.), we tagged 20 hermaphroditic and 40 dioecious plants (totaling 100) from 2014 to 2020 to complete the sample size of flowers and cladodes. Seventeen morphometric characters were measured for new cladodes and 15 for flowers, and discriminant analysis was applied to determine which traits enabled species delimitation. Six of the 17 cladode characteristics combined explained 89% of the variation, while 9 floral characteristics combined explained 94% of the variation. Floral morphometrics proved to be very useful to accurately differentiate species and should be included, in addition to cladodes, in future taxonomic studies. Here, we provide the first taxonomic key that includes floral traits to identify Opuntia and a new description of each studied species.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".