Use of aquatic protected areas in the management of large lakes
Bibliographic record
Abstract
There is considerable variation in the strategies used to manage large lakes. Management targets and resources for monitoring, assessment, planning and enforcement (e.g. personnel, equipment, policy, legislation) can differ considerably among countries and lakes. With a growing interest and body of research regarding Marine Protected Areas, assessments of similar freshwater areas are timely, especially considering current concerns over the global loss of biodiversity and increased interest in ecosystem-based management and the Precautionary Principle. This paper examines the use of various types of Aquatic Protected Areas in the management of large lakes (e.g. Marine Protected Area equivalents, fish sanctuaries, parks). Potential and actual benefits and drawbacks, relative uses in current management strategies, purposes for which different types of areas have been created, and related trends are discussed. Very few true equivalents of Marine Protected Areas, that permanently protect both species and their habitats from exploitation and development, have been created in freshwater systems. Most protected areas within lakes exist as fish sanctuaries, which limit or prevent harvest of one or more species, and aquatic or terrestrial (shoreline) parks, which protect aquatic and riparian habitats by preventing development or resource extraction (e.g. logging or mining). Because many Aquatic Protected Areas are established in legislation the retirement of established areas is relatively difficult and the number of Aquatic Protected Areas is increasing. Although Aquatic Protected Areas have considerable potential for improving large lake management, it is essential that management goals are compatible with the function of Aquatic Protected Areas and that factors affecting their success are determined to facilitate efficient use of resources and political will.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".