Managing marine resources sustainably – But how do we know when marine management has been successful?
Bibliographic record
Abstract
Current marine environmental management is the 'sustainable management of people and their marine activities' to be achieved through ecosystem manipulation and activity control. Marine management per se needs to define who requires and can achieve a successfully managed environment, the tools and indicators for that management, the indications of success and the means of knowing that the environment has been successfully managed. Indicators of success require policies and plans, environmental targets and regulatory standards and guidelines, i.e. output controls such as those stipulated by legislation. It should aim for sustainable outcomes based on government policy, to satisfy public demands and using the advice and assessments by natural and social scientists. The inputs, outputs and outcomes should include scientific research and advice, reporting to the government and the public as well as compliance in programme performance evaluations. In this, there are three interested bodies: (i) those requiring a successfully managed environment such as the public; (ii) those responsible to carry-out and monitor the programmes and regulate humans and their activities as mandated by government such as administrators and regulators, and (iii) those implementing the management measures. Here, examples from Europe and North America but with relevance to all maritime states are used to emphasise that management success encompasses a well-defined planning cycle with a vision achieved as the result of objectives being met leading to actions carried out leading to outputs produced leading to outcomes achieved.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".