Developing important marine mammal area criteria: learning from ecologically or biologically significant areas and key biodiversity areas
Bibliographic record
Abstract
ABSTRACT This paper explores how criteria to identify important marine mammal areas (IMMAs) could be developed, and nested in existing global criteria. This process would consider 134 species of marine mammals. Particular attention is given to two suites of global criteria to identify areas important for the persistence of marine biodiversity: Ecologically or Biologically Significant Areas (EBSAs) developed through the Convention on Biological Diversity (CBD), and Key Biodiversity Areas (KBAs) in revision through the International Union for the Conservation of Nature (IUCN). They are seen as mutually complementary in the development of IMMAs. The specificities necessary for identifying important areas at scales below the global level may vary according to the region, the biophysical requirements of distinct populations, and available data. Refining and testing the applicability of these global criteria on marine mammals at both regional and national scales will be necessary. Combining area‐based measures with non‐spatial management actions will likely be the optimal approach for ensuring marine mammal persistence given their highly migratory nature and widespread life‐history stages. Capacity to enact IMMAs is strengthened by the existence of professional marine mammal associations and networks, and the recently formed IUCN Marine Mammal Protected Areas Task Force (MMPATF). The MMPATF is planning further development of IMMA criteria through joint work with the International Committee on Marine Mammal Protected Areas (ICMMPA). Copyright © 2014 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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".