Stocks of “Blue Carbon” in Soils of Coastal Ecosystems of High-Latitude Seas of the Northern Hemisphere
Bibliographic record
Abstract
The article presents a review of data from Russian and foreign sources, as well as of our own research, concerning carbon stocks in soils of coastal zone ecosystems: marshes and seagrass meadows of the USA, Canada, Great Britain, continental Europe, Scandinavia and Greenland, as well as Russia. These soils are formed under conditions of amphibious water regime and are mainly classified as Tidalic Fluvisols. The mean values of carbon stock were 34.3 ± 21.5 t/ha in the 0–10 cm layer of marsh soils, and 7.8 ± 6.5 t/ha in the aquatic soils of seagrass meadows. Carbon stocks in soils, as a rule, directly depend on the productivity of phytocenosis. Carbon stock was shown to be positively dependent on seawater temperature. It is also shown that with increasing salinity of water, carbon stocks in the soils of marshes decrease, while in seagrass meadows they increase. On the shore, carbon stocks are maximum in soils of the rarely flooded high marsh. In the mineral soils of the marshes, carbon stocks are higher in heavy-textured soils than in coarse-textured soils. High carbon stocks in loamy–sandy and sandy soils are commonly found in the soils of sea meadows. The results of the study can be used to assess the impact of coastal ecosystems on the content, dynamics, potential for carbon absorption, and climate change, and serve as a basis for measures designed to protect and sustainably use coastal landscapes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".