Kelp transcriptomes provide robust support for interfamilial relationships and revision of the little known Arthrothamnaceae (Laminariales)
Bibliographic record
Abstract
If ever there were "charismatic megaflora" of the sea, the Laminariales (kelp) would undoubtedly meet that designation. From the Northeast Pacific kelp forests to the less diverse, but nonetheless dense, kelp beds ranging from the Arctic to the cold temperate waters of the Southern Hemisphere, kelp provide habitat structure and food for a variety of productive marine systems. Consequently, kelp are well represented in the literature, however, understanding their evolution has proven challenging. We used a 152-gene phylogenomics approach to better resolve the phylogeny of the "derived" kelp families (viz., Agaraceae, Alariaceae, Laminariaceae, and Lessoniaceae). The formerly unresolved Egregia menziesii firmly joined a significantly expanded Arthrothamnaceae including Arthrothamnus, Cymathaere, Ecklonia, Macrocystis, Nereocystis, Pelagophycus, Postelsia, Pseudolessonia, Saccharina, and Streptophyllopsis, which rendered both the Laminariaceae and Lessoniaceae monogeneric. A published eight-gene alignment, the most marker-rich prior to this study, was expanded and analyzed to facilitate inclusion of Aureophycus. Although the topology was unchanged at the family level between the transcriptome data set relative to eight-gene analyses, the superior resolving power of the former was clearly established.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".