Eukaryotic and cyanobacterial communities associated with marine snow particles in the oligotrophic Sargasso Sea
Bibliographic record
Abstract
Marine snow aggregates represent heterogeneous agglomerates of dead and living organic matter. Composition is decisive for their sinking rates, and thereby for carbon flux to the deep sea. For oligotrophic oceans, information on aggregate composition is particularly sparse. To address this, the taxonomic composition of aggregates collected from the subtropical and oligotrophic Sargasso Sea (Atlantic Ocean) was characterized by 16S and 18S rRNA gene sequencing. Taxonomy assignment was aided by a collection of the contemporary plankton community consisting of 75 morphologically and genetically identified plankton specimens. The diverse rRNA gene reads of marine snow aggregates, not considering Trichodesmium puffs, were dominated by copepods (52%), cnidarians (21%), radiolarians (11%), and alveolates (8%), with sporadic contributions by cyanobacteria, suggesting a different aggregate composition than in eutrophic regions. Composition linked significantly with sampling location but not to any measured environmental parameters or plankton biomass composition. Nevertheless, indicator and network analyses identified key roles of a few rare taxa. This points to complex regulation of aggregate composition, conceivably affected by the environment and plankton characteristics. The extent to which this has implications for particle densities, and consequently for sinking rates and carbon sequestration in oligotrophic waters, needs further interrogation.
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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 | 0.001 |
| 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) | 0.000 | 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".