Evidence that viral abundance across oceans and lakes is driven by different biological factors
Bibliographic record
Abstract
Summary 1. Samples from 16 lakes in central (n = 145) and western (n = 12) North America, the coastal northeast Pacific (n = 302) and the western Canadian Arctic Oceans (n = 142) were collected and analysed for viral, bacterial and cyanobacterial abundances and chlorophyll‐a concentration. 2. Viral abundance was significantly different among the environments. It was highest in the coastal Pacific Ocean and lowest in the coastal Arctic Ocean. The abundances of bacteria and cyanobacteria as well as chlorophyll‐a concentrations also differed significantly among the environments, with both bacterial abundance and chlorophyll‐a concentration highest in lakes. As a consequence, the association of these variables with viral abundance varied among the environments. 3. Discriminant analyses with the abundance data indicated that the marine and freshwater environments were predictably different from each other. Multiple‐regression analysis included bacterial and cyanobacterial abundances, and chlorophyll‐a concentration as significant variables in explaining viral abundance in lakes. In regression models for the coastal Pacific Ocean, bacterial and cyanobacterial abundances were significant variables, and for the coastal Arctic Ocean viral abundance was predicted by bacterial abundance and chlorophyll‐a concentration. 4. The relationship of viral and bacterial abundance differed between the investigated freshwater and marine environments, probably because of differences in viral production and loss rates. However, freshwaters had fewer viruses compared to bacteria, despite previously documented higher burst sizes and frequencies of infected cells, suggesting that loss rates may be more important in lakes. 5. Together, these findings suggest that there are different drivers of viral abundance in different aquatic environments, including lakes and oceans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".