Assessing progress towards global marine protection targets: shortfalls in information and action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Current global marine protection targets aim to protect 10–30% of marine habitats within the next 3–5 years. However, these targets were adopted without prior assessment of their achievability. Moreover, ability to monitor progress towards such targets has been constrained by a lack of robust data on marine protected areas. Here we present the results of the first explicitly marine-focused, global assessment of protected areas in relation to global marine protection targets. Approximately 2.35 million km 2 , 0.65% of the world's oceans and 1.6% of the total marine area within Exclusive Economic Zones, are currently protected. Only 0.08% of the world's oceans, and 0.2% of the total marine area under national jurisdiction is no-take. The global distribution of protected areas is both uneven and unrepresentative at multiple scales, and only half of the world's marine protected areas are part of a coherent network. Since 1984 the spatial extent of marine area protected globally has grown at an annual rate of 4.6%, at which even the most modest target is unlikely to be met for at least several decades rather than within the coming decade. These results validate concerns over the relevance and utility of broad conservation targets. However, given the low level of protection for marine ecosystems, a more immediate global concern is the need for a rapid increase in marine protected area coverage. In this case, the process of comparing targets to their expected achievement dates may help to mobilize support for the policy shifts and increased resources needed to improve the current level of marine protection.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it