Carbon dynamics under loss and restoration scenarios in the world’s largest seagrass meadow
Bibliographic record
Abstract
Abstract Seagrass sediments accumulate high amounts of organic carbon, but they are threatened by human activities and their global extent continues to shrink. Simultaneously, there is interest in including seagrass carbon accumulation in countries’ Nationally Determined Contributions (NDCs). We used the InVEST Coastal Blue Carbon Model to estimate sediment organic carbon (SOC) accumulation over 100 years in seagrass of the Bahama Banks, the world’s largest seagrass meadow. Using seagrass maps and sediment core measurements, we modeled SOC accumulation in two scenarios: (1) 1% seagrass area loss per year, the Business As Usual scenario (BAU); (2) restoration of seagrass extent to that of 30 years prior by 2120, meeting the goals of the Kunming-Montreal global biodiversity framework. With a conservative initial seagrass extent, by 2120, the SOC accumulation was 90.6 Mt CO 2 eq (24.0 autochthonous Mt CO 2 eq) in the BAU and 703.7 Mt CO 2 eq (186.5 autochthonous Mt CO 2 eq) in the restoration scenario, and average additional SOC accumulation was 611.0 Mt CO 2 eq (161.9 autochthonous Mt CO 2 eq). Using a high estimate of initial seagrass extent, by 2120, the net SOC accumulation was 155.4 Mt CO 2 eq (41.2 autochthonous Mt CO 2 eq) in the BAU and 1058.2 Mt CO 2 eq (280.4 autochthonous Mt CO 2 eq) in the restoration scenario, and additional SOC accumulation was 902.8 Mt CO 2 eq (239.2 autochthonous Mt CO 2 eq). The potential for either SOC accumulation or losses to occur if seagrass extent continues to decline highlights uncertainty around whether Bahamian seagrass meadows will remain a net carbon sink. The additional accumulation of autochthonous carbon if seagrasses were restored was comparable in scale to the annual greenhouse gas emissions of The Bahamas, suggesting potential for seagrass restoration to contribute to the country’s NDCs and broader climate mitigation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".