A synopsis of <i>Raspailia</i> Nardo, 1833 (Porifera: Demospongiae: Axinellida) from the tropical and warm-temperate Southwestern Atlantic, with the description of four new species
Bibliographic record
Abstract
The tropical and warm-temperate Southwestern Atlantic, off the coastline of Brazil, has received substantial biodiversity inventory in the last decades. Sponges are a prominent part in this effort, which has led to the exploration of undersampled areas and taxonomic revisions. Raspailia comprises 79 species worldwide, 25 of which from the Atlantic, and up to now, only four from the tropical and warm-temperate Southwestern Atlantic. To conduct a much-needed systematic revision, we analyze materials collected from several localities spanning this vast area. Here, we describe four new species: R. ( Raspaxilla) bonsai sp. nov., R. ( Raspaxilla) konika sp. nov., R. ( Raspaxilla) estilingue sp. nov., and Raspailia ( Raspaxilla) leblanci sp. nov. Besides the new species, we redescribed R. ( Raspaxilla) tenuis Ridley & Dendy, 1886 based on revision of the holotype, described new records of R. ( Raspaxilla) muricyana Moraes, 2011 from the NE Brazilian coast, previously known only from the Fernando de Noronha Archipelago, and described new specimens of R. ( Raspaxilla) bouryesnaultae Lerner et al. 2006. Seven species of Raspailia are now known in this large sector of the Atlantic, almost double what was known before. An identification key for these species is proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".