Effects of environmental variation and spatial distance on <i>Bacteria</i>, <i>Archaea</i> and viruses in sub-polar and arctic waters
Bibliographic record
Abstract
We investigated the influence of environmental parameters and spatial distance on bacterial, archaeal and viral community composition from 13 sites along a 3200-km long voyage from Halifax to Kugluktuk (Canada) through the Labrador Sea, Baffin Bay and the Arctic Archipelago. Variation partitioning was used to disentangle the effects of environmental parameters, spatial distance and spatially correlated environmental parameters on prokaryotic and viral communities. Viral and prokaryotic community composition were related in the Labrador Sea, but were independent of each other in Baffin Bay and the Arctic Archipelago. In oceans, the dominant dispersal mechanism for prokaryotes and viruses is the movement of water masses, thus, dispersal for both groups is passive and similar. Nevertheless, spatial distance explained 7-19% of the variation in viral community composition in the Arctic Archipelago, but was not a significant predictor of bacterial or archaeal community composition in either sampling area, suggesting a decoupling of the processes regulating community composition within these taxonomic groups. According to the metacommunity theory, patterns in bacterial and archaeal community composition suggest a role for species sorting, while patterns of virus community composition are consistent with species sorting in the Labrador Sea and suggest a potential role of mass effects in the Arctic Archipelago. Given that, a specific prokaryotic taxon may be infected by multiple viruses with high reproductive potential, our results suggest that viral community composition was subject to a high turnover relative to prokaryotic community composition in the Arctic Archipelago.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".