Emergent “core communities” of microbes, meiofauna and macrofauna at hydrothermal vents
Bibliographic record
Abstract
Assessment of ecosystem health entails consideration of species interactions within and between size classes to determine their contributions to ecosystem function. Elucidating microbial involvement in these interactions requires tools to distil diverse microbial information down to relevant, manageable elements. We used covariance ratios (proportionality) between pairs of species and patterns of enrichment to identify "core communities" of likely interacting microbial (<64 µm), meiofaunal (64 µm to 1 mm) and macrofaunal (>1 mm) taxa within assemblages hosted by a foundation species, the hydrothermal vent tubeworm Ridgeia piscesae. Compared with samples from co-located hydrothermal fluids, microbial communities within R. piscesae assemblages are hotspots of taxonomic richness and are high in novelty (unclassified OTUs) and in relative abundance of Bacteroidetes. We also observed a robust temperature-driven distinction in assemblage composition above and below ~25 °C that spanned micro to macro size classes. The core high-temperature community included eight macro- and meiofaunal taxa and members of the Bacteroidetes and Epsilonbacteraeota, particularly the genera Carboxylicivirga, Nitratifractor and Arcobacter. The core low-temperature community included more meiofaunal species in addition to Alpha- and Gammaproteobacteria, and Actinobacteria. Inferred associations among high-temperature core community taxa suggest increased reliance on species interactions under more severe hydrothermal conditions. We propose refinement of species diversity to "core communities" as a tool to simplify investigations of relationships between taxonomic and functional diversity across domains and scales by narrowing the taxonomic scope.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".