Diversity of the green macroalgal genus <i>Ulva</i> (Ulvophyceae, Chlorophyta) from the east and gulf coast of the United States based on molecular data
Bibliographic record
Abstract
Ulva (Ulvophyceae, Chlorophyta) is common in intertidal environments and can also be found in freshwater ecosystems. The difficulty to morphologically identify Ulva species due to cryptic diversity and morphological plasticity has caused a taxonomic conundrum. Fortunately, molecular data have begun to unravel a better understanding of its diversity. Here, we present a molecular analysis with 247 samples of Ulva from the Gulf of Mexico and Atlantic USA based on chloroplast (rbcL and tufA) and nuclear (ITS1-5.8S-ITS2) molecular markers. Twenty-four Ulva taxa had previously been reported for this area based on morphology and earlier molecular studies mostly from Northeastern USA and Canada. In this study, sixteen Ulva clades were identified representing 13 named clades and putatively three undescribed species. Only nine of the 24 taxa previously reported for the Western Atlantic were confirmed. Four species were identified for the first time in the U.S. East and Gulf Coast (U. aragoënsis, U. californica, U. meridionalis, and U. tepida). This study provides a foundation for future research on Ulva in this area and reiterates the necessity of using molecular-assisted identifications for this group.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".