A Synthesis of Blue Carbon Stocks, Sources, and Accumulation Rates in Eelgrass (<i>Zostera marina</i>) Meadows in the Northeast Pacific
Bibliographic record
Abstract
Abstract There is increasing urgency to implement climate change mitigation strategies that enhance greenhouse gas removal from the atmosphere and reduce carbon dioxide (CO 2 ) emissions. Recently, coastal “blue carbon” habitats—mangroves, salt marshes, and seagrass meadows—have received attention for their ability to capture CO 2 and store organic carbon (OC), primarily in their sediments. Across habitat types and regions, however, information about the sequestration rates and sources of carbon to local sediments remains sparse. Here we compiled recently obtained estimates of sediment OC stocks and sequestration rates from 139 cores collected from temperate seagrass ( Zostera marina ) meadows in Alaska, British Columbia, Washington, and Oregon. Across all cores sediment OC content averaged 0.75%. Organic carbon stocks in the top 25 cm and 1 m of the sediment averaged 1,846 and 7,168 g OC m −2 , respectively. Carbon sequestration rates ranged from 4.6 to 93.0 g OC m −2 yr −1 and averaged 24.8 g OC m −2 yr −1 . Isotopic data from this region suggest that OC in the sediments is largely from noneelgrass sources. In general, these values are comparable to those from other temperate Z. marina meadows, but significantly lower than previously reported values for seagrasses globally. These results further highlight the need for local and species‐level quantification of blue carbon parameters. While temperate eelgrass meadows may not sequester and store as much carbon as seagrass meadows elsewhere, climate policy incentives should still be implemented to protect existing sediment carbon stocks and the other critical ecosystem services associated with eelgrass habitats.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".