Evidence that viral abundance across oceans and lakes is driven by different biological factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it