Beyond habitat boundaries: Organic matter cycling requires a system‐wide approach for accurate blue carbon accounting
Bibliographic record
Abstract
Abstract As coastal ecosystems become widely recognized for their capacity to sequester carbon (blue carbon), standard accounting methodologies for the generation of carbon credits are being developed. To ensure the applicability of these standards across blue carbon ecosystems, we investigated organic carbon provenance and burial in salt marshes and seagrass meadows of an arid, upwelling‐dominated Eastern Pacific lagoon. We found low carbon density in benthic sediments of Bahía de San Quintín (5.9 ± 0.5 mg C cm −3 ), only marginally higher in Zostera marina beds (6.9 ± 0.5 mg C cm −3 ), likely due to remineralization and hydrodynamically driven export of seagrass material, resulting in low carbon burial rates (4.5 ± 2.5 g C m −2 yr −1 ). Sediment organic carbon is mainly controlled by the fraction of fine sediment and its source is largely allochthonous, although sources differ spatially. Salt marshes at San Quintín derive 40% of their organic matter from autochthonous material and exhibit higher carbon burial rates (up to 414.7 ± 28.6 g C m −2 yr −1 ) and sediment carbon densities (32.0 ± 0.7 mg C cm −3 ) compared to benthic sediments. This study emphasizes the connectivity of blue carbon habitats with marsh plant detritus supplementing benthic carbon burial and incorporation of detrital eelgrass in marsh sediments. Our findings highlight the importance of allochthonous organic matter for carbon sequestration in blue carbon habitats, suggesting standard accounting practices that deduct allochthonous organic matter would miss the full potential for carbon burial.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".