How can blue carbon burial in seagrass meadows increase long-term, net sequestration of carbon? A critical review
Bibliographic record
Abstract
Abstract Blue carbon sequestration in seagrass meadows has been proposed as a low-risk, nature-based solution to offset carbon emissions and reduce the effects of climate change. Although the timescale of seagrass carbon burial is too short to offset emissions of ancient fossil fuel carbon, it has a role to play in reaching net zero within the modern carbon cycle. This review documents and discusses recent advances (from 2015 onwards) in the field of seagrass blue carbon. The net burial of carbon is affected by seagrass species, meadow connectivity, sediment bioturbation, grainsize, the energy of the local environment, and calcium carbonate formation. The burial rate of organic carbon can be calculated as the product of the sediment accumulation rate below the mixed layer and the burial concentration of organic carbon attributable to seagrass. A combination of biomarkers can identify seagrass material more precisely than bulk isotopes alone. The main threats related to climate change are sea-level rise, leading to a shoreline squeeze, and temperature rise, particularly during extreme events such as heat domes. In conclusion, some of the disagreement in the literature over methodology and the main controls on organic carbon burial likely results from real, regional differences in seagrasses and their habitat. Inter-regional collaboration could help to resolve the methodological differences and provide a more robust understanding of the global role of blue carbon sequestration in seagrass meadows.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".