Marine viruses and global climate change
Bibliographic record
Abstract
Sea-surface warming, sea-ice melting and related freshening, changes in circulation and mixing regimes, and ocean acidification induced by the present climate changes are modifying marine ecosystem structure and function and have the potential to alter the cycling of carbon and nutrients in surface oceans. Changing climate has direct and indirect consequences on marine viruses, including cascading effects on biogeochemical cycles, food webs, and the metabolic balance of the ocean. We discuss here a range of case studies of climate change and the potential consequences on virus function, viral assemblages and virus-host interactions. In turn, marine viruses influence directly and indirectly biogeochemical cycles, carbon sequestration capacity of the oceans and the gas exchange between the ocean surface and the atmosphere. We cannot yet predict whether the viruses will exacerbate or attenuate the magnitude of climate changes on marine ecosystems, but we provide evidence that marine viruses interact actively with the present climate change and are a key biotic component that is able to influence the oceans' feedback on climate change. Long-term and wide spatial-scale studies, and improved knowledge of host-virus dynamics in the world's oceans will permit the incorporation of the viral component into future ocean climate models and increase the accuracy of the predictions of the climate change impacts on the function of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".