Disentangling parasitic vines in the tropics: taxonomic notes for an accurate identification of Cuscuta (Convolvulaceae) and Cassytha (Lauraceae)
Bibliographic record
Abstract
Abstract Parasitic plants are often associated with agricultural, forestry and grassland economic losses, but they are also keystone species in their natural ecosystems. Cuscuta (Convolvulaceae) and Cassytha (Lauraceae) are parasitic plants which have evolved similar stem habit and morphology, rendering them remarkably similar during the vegetative stage. Since both genera are common in the tropics, misidentifications are frequent, which is detrimental for understanding their geographical distribution, biology and ecology, as well as to the development of adequate control or conservation practices. We here present a practical identification guide for a clear and accurate distinction between Cuscuta and Cassytha, using stems and reproductive structures of both fresh plants and herbarium specimens, aimed at taxonomists and agricultural experts. An identification key, a comparative table, detailed descriptions and illustrations are included to facilitate genus recognition. The current practice of macroscopic observation of the filiform stems, on which many professionals rely, may not be enough to distinguish the two genera. The analysis of stem micromorphology, and/or of the flower or fruit morphology, are necessary for a conclusive identification.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".