Biogeochemical fluxes through microzooplankton
Bibliographic record
Abstract
Microzooplankton ingest a significant fraction of primary production in the ocean and thus remineralize nutrients and stimulate regenerated primary production. We synthesized observations on microzooplankton carbon‐specific grazing rate, partitioning of grazed material, respiration rate, microzooplankton biomass, microzooplankton‐mediated phytoplankton mortality rate, and phytoplankton growth rate. We used these observations to parameterize and evaluate the microzooplankton compartment in a global biogeochemical model that represents five plankton functional types. Microzooplankton biomasses predicted in this simulation are closer to the independently derived evaluation data than in the previous model version. Most rates, including primary production, microzooplankton grazing, and export of sinking detritus are within observational constraints. However, the model underestimates microzooplankton and mesozooplankton biomasses and chlorophyll concentrations. Thus, we propose that sufficient carbon enters the model ecosystem, but insufficient carbon is retained. For microzooplankton, the low retention of carbon could be improved by parameterizing the model with ciliate gross growth efficiency only, indicating that ciliates may contribute more to microzooplankton activity than their biomass contribution suggests. By taking into account the model underestimation of biomass, we estimate that the ocean inventory of microzooplankton biomass is 0.24 Pg C (a range of 0.14–0.33 Pg C), which is similar to the biomass of mesozooplankton.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".