Protection of seabed sediments in Canada's marine conservation network for potential climate change mitigation co-benefit
Bibliographic record
Abstract
Marine conserved areas (MCAs) can provide a range of ecological and socio-economic benefits, including climate change mitigation from the protection and enhancement of natural carbon storage. Canada's MCA network is expanding to encompass 30% of its Exclusive Economic Zone by 2030. At present, the network aims to integrate climate change mitigation by protecting coastal vegetated blue carbon ecosystems (saltmarsh, seagrass, kelp). Here, we argue that incorporating unvegetated seabed sediments could bring similar benefits. Seabed sediments can store and/or accumulate high densities of organic carbon, and due to their large spatial extent, contain carbon stores orders of magnitude larger than coastal vegetated habitats. We estimate that currently designated MCAs encompass only 10.8% of Canada's seabed sediment organic carbon stocks on the continental margin, and only 13.4% of areas with high carbon densities. Proposed MCAs would cover an additional 8.8% and 6.1% of total stocks and high carbon areas, respectively. We identify an additional set of high-priority seabed areas for future research and potential protection, ranking their importance based on carbon stocks, proxies for lability, and ecological/biological significance. The incorporation of seabed sediments into MCA networks could support climate change mitigation by preventing future releases of stored carbon.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".