Using Earth Observation to Monitor Species-Specific Habitat Change in the Greater Kejimkujik National Park Region of Canada
Bibliographic record
Abstract
The World Commission on Protected Areas (WCPA) adopted a denition that describes a protected area as clearly dened geographical space, recognized, dedicated, and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values (Dudley 2008). In general, protected lands include areas such as national parks, national forests, national seashores, all levels of natural reserves, wildlife refuges and sanctuaries, and designated areas for conservation of native biological diversity and natural and cultural heritage and signicance. Protected lands also include some of the last frontiers that have unique landscape characteristics and ecosystem functions. Along the shoreline and over the ocean and sea, the International Union for the Conservation of Nature (IUCN) has dened marine-protected areas (MPAs) as any area of intertidal or subtidal terrain, together with its overlying water and associated ora, fauna, and historical and cultural features, which has been reserved by law or other effective means to protect part or the entire enclosed environment (Kelleher 1999). As reported by the World Database on CONTENTS 1.1 Introduction ....................................................................................................1 1.2 Remote Sensing of Changing Landscape of Protected Lands ................5 1.3 Remote Sensing for Inventory, Mapping, and Conservation Planning of Protected Lands and Waters ...................................................9 1.4 Remote Sensing of Frontier Lands ............................................................ 13 1.5 Remote Sensing in Decision Support for Management of Protected Lands ....................................................................................... 16 1.6 Concluding Remarks ................................................................................... 17 Acknowledgments ................................................................................................ 19 References ............................................................................................................... 20 Protected Areas (IUCN and UNEP-WCMC 2010), as of 2009, worldwide approximately 13% of the lands are designated as protected areas and about 0.8% waters along the shoreline and over the ocean are set as MPAs. In the United States, 14.81% of the terrestrial lands have been set as protected, and along the shoreline and over the ocean 24.75% of the terrestrial waters up to 12 nautical miles are set as MPAs. Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Those provide the core of efforts to protect the world’s threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources; key components in climate change mitigation strategies; and in some cases also vehicles for protecting threatened human communities or sites of great cultural and spiritual value (Dudley 2008).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".