Carbon export from seaweed forests to deep ocean sinks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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) | 0.001 | 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 it