Oyster farming acts as a marine carbon dioxide removal (mCDR) hotspot for climate change mitigation
Bibliographic record
Abstract
Bivalve farming, a vital component of global aquaculture, has been proposed as a potential marine carbon dioxide removal (mCDR) strategy, yet its role remains contentious. Using field mesocosms, we demonstrate that oyster filter-feeding enhances mCDR by accelerating the formation of particulate and dissolved organic carbon in the water column and promoting organic carbon deposition in sediments. This process shifts the water column toward a more autotrophic and alkaline state, effectively sequestering CO 2 from the atmosphere. Over the full culture period, the net carbon sequestered by oyster-driven organic carbon production is 2.39 times greater than the CO 2 sequestered in oyster shells. These findings position oyster farming as a scalable, nature-based solution for climate change mitigation, offering dual benefits of carbon sequestration and enhanced food security. Our results underscore the potential of oyster farming to address global challenges such as rising food demand and ocean acidification, making it a critical component of sustainable marine resource management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".