Carbon export from seaweed forests to deep ocean sinks
Bibliographic record
Abstract
The coastal ocean represents an important global carbon sink and is a focus for interventions to mitigate climate change and meet the Paris Agreement targets while supporting biodiversity and other ecosystem functions. However, the fate of the flux of carbon exported from seaweed forests—the world’s largest coastal vegetated ecosystem—is a key unknown in marine carbon budgets. Here we provide national and global estimates for seaweed-derived particulate carbon export below 200 m depth, which totalled 3–4% of the ocean carbon sink capacity. We characterized export using models of seaweed forest extent, production and decomposition, as well as shelf–open ocean water exchange. On average, 15% of seaweed production is estimated to be exported across the continental shelf, which equates to 56 TgC yr−1 (range: 10–170 TgC yr−1). Using modelled sequestration timescales below 200 m depth, we estimated that each year, 4–44 Tg seaweed-derived carbon could be sequestered for 100 years. Determining the full extent of seaweed carbon sequestration remains challenging, but critical to guide efforts to conserve seaweed forests, which are in decline globally. Our estimate does not include shelf burial and dissolved and refractory carbon pathways; still it highlights a relevant potential contribution of seaweed to natural carbon sinks. Coastal seaweed transported to the open ocean contributes up to 3–4% of the particulate organic carbon sinking into the deeper ocean, according to combined ecological and biogeochemical modelling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".