Managing Marine Resources Sustainably – The ‘Management Response-Footprint Pyramid’ Covering Policy, Plans and Technical Measures
Bibliographic record
Abstract
The plethora of human activities and their pressures and impacts in the oceans require managing at local, national, regional and international scales. This requires management responses in a programme of measures to determine (a) the area in which the human activities take place, (b) the area covered by the pressures generated by the activities on the prevailing habitats and species in which pressures are defined as the mechanisms of change, and (c) the area over which any adverse effects (and even benefits) occur on both the natural and human systems. The spatial and temporal scales of these leads to the concepts of activity-, pressures-, effects- and management responses-footprints, defined here. These footprints cover areas from tens of m 2 to millions of km 2 , and, in the case of management responses, from a large number of local instruments to a few global instruments thereby giving rise to what is termed the management response-footprint pyramids. This may operate from either bottom-up or top-down directions, whether as the result of local societal demands for clean, healthy, productive and diverse seas or by diktat from national, supranational and global bodies such as the United Nations. These concepts are explained and illustrated using marine examples based on experience from many jurisdictions.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".