Roles of food web and heterotrophic microbial processes in upper ocean biogeochemistry: Global patterns and processes
Bibliographic record
Abstract
The growth and dynamics of plankton in the ocean vary with natural cycles, global climate change and the long‐term evolution of ecosystems. The ocean is a large reservoir for CO 2 and the food webs in the upper ocean play critical roles in regulating the global carbon cycle, changes in atmospheric CO 2 and associated global warming. Microheterotrophs are a key component of the upper ocean food webs. Here, we report on the results of an analysis of the distribution of bacteria and related properties in the World Ocean. We found that, for the data set as a whole, there is a significant latitudinal gradient in all field‐measured and computed bacterial properties, except growth rate. Gradients were, for the most part, driven by an equator‐ward increase in the Southern Hemisphere. The biomass, rates of production and respiration and dissolved organic carbon concentrations were significantly higher in the Northern than the Southern hemispheres. In contrast, growth rates were the same in the two hemispheres. We conclude that the lower biomass and production in the Southern Hemisphere reflects greater top‐down control by microbial grazers, which would be due to a lower abundance or activity of omnivorous zooplankton in the Southern than Northern Hemispheres. These large spatial differences in dynamics, structure and activity of the bacterial community and the microbial food web will be reflected in different patterns of carbon cycling, export and air–sea exchange of CO 2 and the potential ability of the ocean to sequester carbon.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".