A new look at ocean carbon remineralization for estimating deepwater sequestration
Bibliographic record
Abstract
Abstract The “biological carbon pump” causes carbon sequestration in deep waters by downward transfer of organic matter, mostly as particles. This mechanism depends to a great extent on the uptake of CO2 by marine plankton in surface waters and subsequent sinking of particulate organic carbon (POC) through the water column. Most of the sinking POC is remineralized during its downward transit, and modest changes in remineralization have substantial feedback on atmospheric CO2 concentrations, but little is known about global variability in remineralization. Here we assess this variability based on modern underwater particle imaging combined with field POC flux data and discuss the potential sources of variations. We show a significant relationship between remineralization and the size structure of the phytoplankton assemblage. We obtain the first regionalized estimates of remineralization in biogeochemical provinces, where these estimates range between −50 and +100% of the commonly used globally uniform remineralization value. We apply the regionalized values to satellite‐derived estimates of upper ocean POC export to calculate regionalized and ocean‐wide deep carbon fluxes and sequestration. The resulting value of global organic carbon sequestration at 2000 m is 0.33 Pg C yr−1, and 0.72 Pg C yr−1 at the depth of the top of the permanent pycnocline, which is up to 3 times higher than the value resulting from the commonly used approach based on uniform remineralization and constant sequestration depth. These results stress that variable remineralization and sequestration depth should be used to model ocean carbon sequestration and feedback on the atmosphere.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".