Achieving 30 by 30: Legal Obligations and the Role of Bst Scientific Evidence Available
Bibliographic record
Abstract
Despite global efforts, States have not met the 10% Ocean protection target set for 2020. With only 8.2% of the Ocean and a mere 2.4% of the High Seas protected today, achieving the Kunming-Montreal Global Biodiversity Framework (GBF) goal of designating 30% of the Ocean as MPAs by 2030 remains a distant prospect. A key barrier to effective marine protection lies in the lack of enforcement of obligations and the failure of States to adhere to the best scientific evidence available.In May 2024, the International Tribunal for the Law of the Sea (ITLOS) issued an Advisory Opinion on States' obligations to protect and preserve the marine environment under Art. 192 ff of the UN Convention on the Law of the Sea, specifically in relation to climate change. The Opinion’s central findings were twofold: States must adopt specific measures to meet their obligations to protect and preserve the marine environment, and these measures must be guided by the best scientific evidence available.Drawing from this Advisory Opinion and her internationally acclaimed thesis, The Designation of MPAs: A Legally Binding Obligation (Springer Nature 2023), Dr. Anna von Rebay will demonstrate in this keynote presentation how these principles apply to other significant stressors of the marine environment, particularly bottom trawling and workshop how States’ binding obligations to protect the marine environment can and should be implemented.Scientific evidence on the destructive impact of bottom trawling on the marine environment is undeniable, yet it remains underrepresented in global decision-making. Findings include that deep-sea coral and seamount ecosystems are widespread throughout the world’s Ocean; bottom trawl fishing thereby poses a major threat to the biodiversity of vulnerable deep-sea habitats and ecosystems. Losses of up to 95-98% of the coral cover of seamounts as a result of deep-sea bottom trawl fishing have been documented. High seas bottom trawl fishing led to the serial or sequential depletion of targeted deep-sea fish stocks, while producing a large amount of untargeted bycatch.This keynote will provide a vital impetus for OOS 2025, urging the international community to fully acknowledge and enforce the binding legal framework governing Ocean protection. The proposed outcome of the town hall is to inspire ideas for how coastal and other communities can leverage international obligations to hold governments accountable for using the best scientific evidence to protect, restore, and sustainably use marine ecosystems. This will deliver a compelling call to action, underscoring that the 30 by 30 target is not merely an aspirational goal but a legally binding duty. Immediate, effective action is essential to avoid international liability, ensure compliance with UNCLOS, and secure meaningful protection for our Ocean. By advocating a global ban on bottom trawling, backed by the best scientific evidence available, this town hall promotes the creation of the world’s largest MPA and contributes to achieving the 30 by 30 objective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".