Water mass and depth determine the distribution and diversity of<i>Rhodobacterales</i>in an Arctic marine system
Bibliographic record
Abstract
Marine Rhodobacterales are recognized as a widespread, abundant, and metabolically versatile bacterial group in the world's oceans. They also show a nearly universal conservation of the genes for production of gene transfer agents (GTAs), virus-like particles that mediate genetic exchange between cells. It is not yet clear what factors determine the distribution of the various taxonomic subgroups of this order. To address this question, we analyzed the Rhodobacterales communities in 10 seawater samples from northern Baffin Bay collected during September 2008. A conserved gene from the GTA gene cluster was used to characterize the Rhodobacterales community structure. A total of 320 clones from 10 clone libraries were sequenced, and 22 operational taxonomic units representing putative species and 13 clusters representing putative genera were identified. A cluster related to Octadecabacter comprised 59% of total clones from the northern Baffin Bay. Phylogenetic analysis of the clones showed that the Rhodobacterales communities had distinct compositions in the different water masses that were sampled. A change in community structure related to depth was also observed. Therefore, in northern Baffin Bay where two ocean currents meet and mix, the Rhodobacterales community structures were primarily determined by water mass and depth.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".