Carbon Burial in Sediments below Seaweed Farms
Bibliographic record
Abstract
Abstract The hypothesis that seaweed farming contributes to carbon burial below the farms was tested by quantifying burial rates in 20 seaweed farms distributed globally, ranging from 2 to 300 years in operation and from 1 ha to 15,000 ha in size. This involved combining analyses of organic carbon density with sediment accumulation rate in sediments below seaweed farms relative to reference sediments beyond the farm and/or prior to the farm operation. One in every four farms sampled was set over environments that export, rather than retain materials. For the farms that were placed over depositional environments, where sediment accumulation could be quantified, the thickness of sediment layers and stocks of carbon accumulated below the farms increased with farm age, reaching 140 ton C ha -1 for the oldest farm, and tended to exceed those in reference sediments beyond the farm and/or prior to the operation of the farms. Organic carbon burial rates in the farm sediments averaged (± SE) 1.87 ± 0.73 ton CO 2 equivalent (CO 2-eq ) ha -1 year -1 (median 0.83, range 0.10 – 8.99 ton CO 2-eq ha -1 year -1 ), twice the average (± SE) burial rate in reference sediments (0.90 ± 0.27, median 0.64, range 0.10-3.00 ton CO 2-eq ha -1 year -1 ), so that the excess organic carbon burial attributable to the seaweed farms averaged 1.06 ± 0.74 ton CO 2-eq ha -1 year -1 (median 0.09, range −0.13-8.10 ton CO 2-eq ha -1 year -1 ). This first direct quantification of carbon burial in sediments below seaweed farms confirms that, when placed over depositional environments, seaweed farming tend to sequester carbon in the underlying sediments, but do so at widely variable rates, increasing with farm yield.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".