National frameworks for marine conservation — a hierarchical geophysical approach
Bibliographic record
Abstract
1. Development of environmental protected areas has been driven ‘more by opportunity than design, scenery rather than science’ (Hackman A. 1993. Preface. A protected areas gap analysis methodology: planning for the conservation of biodiversity. World Wildlife Fund Canada Discussion Paper; i–ii). If marine environments are to be protected from the adverse effects of human activities, then identification of types of marine habitats and delineation of their boundaries in a consistent classification is required. Without such a classification system, the extent and significance of representative or distinctive habitats cannot be recognized. Such recognition is a fundamental prerequisite to the determination of location and size of marine areas to be protected. 2. A hierarchical classification has been developed based on enduring/recurrent geophysical (oceanographic and physiographic) features of the marine environment, which identifies habitat types that reflect changes in biological composition. Important oceanographic features include temperature, stratification and exposure; physiographic features include bottom relief and substrate type. 3. Classifications based only on biological data are generally prohibited at larger scales, due to lack of information. Therefore, we are generally obliged to classify habitat types as surrogates for community types. The data necessary for this classification are available from mapped sources and from remote sensing. It is believed they can be used to identify representative and distinctive marine habitats supporting different communities, and will provide an ecological framework for marine conservation planning at the national level. Copyright © 2000 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".