Marine ecosystem restoration in a changing ocean
Bibliographic record
Abstract
Multiple human impacts on natural ecosystems cause ongoing widespread habitat loss, with consequent decline of biodiversity and ecosystem services. Seas and oceans, the largest biomes of the biosphere, show increasing numbers of degraded habitats. Ecological restoration offers a major tool to reverse this trend and recover biodiversity, along with human health and well‐being. The United Nations Decade on Ecosystem Restoration promotes global‐scale restoration of degraded habitats and we can exploit lessons learned from terrestrial restoration projects to improve and accelerate marine ecosystem restoration science, practice, and policy. Nonetheless, major differences in land and sea limit direct transfer of terrestrial approaches. Limited ecosystem baselines, greater stochasticity and connectivity, longer timescales needed for effective restoration, associated high costs and advanced technologies required to access and intervene in marine environments (especially in the deep sea), and difficulty in scaling up restoration efforts all hinder the effectiveness and expansion of marine ecosystem restoration. Pilot actions in European waters over ca 5 years in the EU‐funded MERCES consortium (Marine Ecosystem Restoration in Changing European Seas) have identified and developed new, promising tools and strategies to catalyze restoration actions, including engagement of funding organizations, governmental bodies, scientists, and citizens. We are now better positioned to implement restoration actions on a wide range of protected, vulnerable, and critical marine habitats, including seagrass meadows, algal forests, shallow rocky shore, and even some deep‐sea habitats. Outcomes from the MERCES project support future restoration initiatives by demonstrating restoration potential in the marine environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".