Microscopic marine invertebrates are reservoirs for cryptic and diverse protists and fungi
Bibliographic record
Abstract
BACKGROUND: Microbial symbioses in marine invertebrates are commonplace. However, characterizations of invertebrate microbiomes are vastly outnumbered by those of vertebrates. Protists and fungi run the gamut of symbiosis, yet eukaryotic microbiome sequencing is rarely undertaken, with much of the focus on bacteria. To explore the importance of microscopic marine invertebrates as potential symbiont reservoirs, we used a phylogenetic-focused approach to analyze the host-associated eukaryotic microbiomes of 220 animal specimens spanning nine different animal phyla. RESULTS: Our data expanded the traditional host range of several microbial taxa and identified numerous undescribed lineages. A lack of comparable reference sequences resulted in several cryptic clades within the Apicomplexa and Ciliophora and emphasized the potential for microbial invertebrates to harbor novel protistan and fungal diversity. CONCLUSIONS: Microscopic marine invertebrates, spanning a wide range of animal phyla, host various protist and fungal sequences and may therefore serve as a useful resource in the detection and characterization of undescribed symbioses. Video Abstract.
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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".