Beyond the Symbiodiniaceae: diversity and role of microeukaryotic coral symbionts
Bibliographic record
Abstract
Abstract Many corals form intimate symbioses with photosynthetic dinoflagellates in the family Symbiodiniaceae. These symbioses have been deeply studied, particularly in reef-forming corals. The complex microbial community that is associated with corals contains other members that have also been well characterized such as bacteria. However, our understanding of the coral holobiont and subsequently coral reef ecosystems is not complete if we do not take into consideration the microeukaryotes like protists and fungi. Microeukaryotes are currently the greatest enigma within the coral microbiome. Only a handful of them have been characterized, very few have been cultured and even less have genomes available. This is a reflection of a smaller community of scientists working on this particular group of organisms when compared with bacteria or Symbiodiniaceae, but also of the many technical challenges that we face when trying to study microeukaryotes. Recent advances in the use of metabarcoding are revealing the importance of microeukaryotes in corals in terms of abundance and presence, with notable examples being the green algaeOstreobiumand the apicomplexans Corallicolidae. We believe that it is timely and necessary to present what we know so far about coral microeukaryotes before the expected flow of high-throughput metabarcoding studies exploring the microeukaryotic fraction of the coral microbiome.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".