Integrated Approaches to Polar Bear (Ursus Maritimus) Conservation: Exploring the Potential of Photogrammetric Research Techniques and Citizen Science in Tourism Settings
Bibliographic record
Abstract
Rapid environmental change can be seen in many ecosystems today. The Arctic ecosystem is especially being altered by global climate change. It is important to monitor the health of not only the Arctic ecosystem but also the wildlife within it. Wildlife research has been the foundation of many management programs and conservation initiatives. Polar bears (Ursus maritimus) are apex predators and have been extensively studied in some regions of the Arctic. However, there are some populations that have not been as well studied, and thus we know little about. The development of new research techniques can contribute to an improved understanding of less-studied populations. Photogrammetric techniques have been used to estimate morphological traits in various species. I investigated the use of digital photogrammetry for polar bears in the western Hudson Bay subpopulation, using a laser rangefinder and camera to obtain photographs with exact distances to measure seven morphological traits. I collected non-invasive morphological measurements from tourist tundra vehicles outside of Churchill, Manitoba during the months of October and November. Non-invasive methods of obtaining measurements and determining relationship to body condition are valuable for monitoring polar bear populations, as many of these populations are being adversely affected by anthropogenic global climate change. The technique I developed shows potential to become a foundation for a non-invasive polar bear body condition-monitoring program for both tourism and community based monitoring practices. In addition to ecological research, environmental education initiatives can also support and enhance conservation outcomes. Providing educational opportunities for citizens to connect to the natural world can deepen their understanding, appreciation and overall desire to protect it. Citizen science is an avenue to achieve both environmental education and research goals, and can also be conducted in tourism settings. Integrating citizen science into mainstream tourism can encourage the creation of educational ecotourism that contributes to wildlife conservation. The educational components in ecotourism may increase the knowledge of tourists and their respect for the environment, hopefully inspiring them to make more sustainable choices.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".