Integrating functional diversity, food web processes, and biogeochemical carbon fluxes into a conceptual approach for modeling the upper ocean in a high‐CO<sub>2</sub> world
Bibliographic record
Abstract
Marine food webs influence climate by channeling carbon below the permanent pycnocline, where it can be sequestered. Because most of the organic matter exported from the euphotic zone is remineralized within the “upper ocean” (i.e., the water column above the depth of sequestration), the resulting CO 2 would potentially return to the atmosphere on decadal timescales. Thus ocean‐climate models must consider the cycling of carbon within and from the upper ocean down to the depth of sequestration, instead of only to the base of the euphotic zone. Climate‐related changes in the upper ocean will influence the diversity and functioning of plankton functional types. In order to predict the interactions between the changing climate and the ocean's biology, relevant models must take into account the roles of functional biodiversity and pelagic ecosystem functioning in determining the biogeochemical fluxes of carbon. We propose the development of a class of models that consider the interactions, in the upper ocean, of functional types of plankton organisms (e.g., phytoplankton, heterotrophic bacteria, microzooplankton, large zooplankton, and microphagous macrozooplankton), food web processes that affect organic matter (e.g., synthesis, transformation, and remineralization), and biogeochemical carbon fluxes (e.g., photosynthesis, calcification, respiration, and deep transfer). Herein we develop a framework for this class of models, and we use it to make preliminary predictions for the upper ocean in a high‐CO 2 world, without and with iron fertilization. Finally, we suggest a general approach for implementing our proposed class of models.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".