Geoengineering with seagrasses: is credit due where credit is given?
Bibliographic record
Abstract
Blue carbon, the carbon fixed by vegetated coastal ecosystems including seagrasses, is reported to have a large potential to sequester atmospheric carbon dioxide.Planting, expanding or protecting seagrass meadows has, accordingly, been proposed as a form of geoengineering.Seagrasses are reported to account for up to 18% of the carbon burial in the world's oceans, which is on the same order of magnitude as other proposed geoengineering techniques, including iron fertilization.International protocols have been developed to quantify carbon sequestration in seagrass meadows, with a view to awarding carbon credits under the Verified Carbon Standard.Unfortunately, because these protocols do not adequately account for post-depositional processes in marine sediment, they significantly overestimate carbon capture by seagrass beds and give an incorrect view of its distribution.Specifically, neglecting biomixing and remineralization of carbon in surface sediments biases burial rates high, while using sediment carbon inventory (soil carbon stock) over the top 1 m as a proxy for burial rate incorrectly identifies areas of high carbon burial.Seagrass beds likely provide a limited setting for geoengineering, because they generally comprise slowly-accumulating, fine to medium sand, which captures organic carbon less efficiently than fine-grained sediments or rapidly-accumulating delta deposits.While there is no question that seagrass meadows provide valuable habitat, nor that they are disappearing rapidly, their contribution to the global burial of carbon has not yet been established.The danger of geoengineering with seagrasses before reliable assessment methods have been established is that overestimated carbon offsets could lead to a net increase in emissions of carbon dioxide to the atmosphere.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.136 | 0.039 |
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".