UAV-Based Remote Sensing for Managing Alaskan Native Heritage Landscapes in the Yukon-Kuskokwim Delta
Bibliographic record
Abstract
The Yukon-Kuskokwim (Y-K) Delta is home to the Alaskan Native Yup’ik people who have inhabited this remote, subarctic tundra for over 1500 years. Today, their ancestral lifeways and cultural landscapes are at risk from severe climate change-related threats. In turn, we propose that remote sensing technologies, particularly with sensors mounted on Unmanned Aerial Vehicle (UAV) platforms, are uniquely suited for protecting Yup’ik landscape heritage. Based on collaborative, community-based fieldwork in Quinhagak, AK, we present evidence that cultural sites—ranging from historic fishing camps to pre-contact winter villages—exhibit predictably atypical vegetation patterns based on the local ecological biome. Furthermore, these vegetation patterns can be recorded and statistically quantified through the analysis of multispectral imagery obtained from UAV-mounted sensors with three different false color composite rasters and vegetation indices depending on biome type. Finally, we suggest how the Yupiit can combine these methodologies/workflows with local knowledge to monitor the broader heritage landscape in the face of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".