A Review of Aircraft-Subsistence Harvester Conflict in Arctic Alaska + Supplementary Appendix 1
Bibliographic record
Abstract
The traditional harvest of wild resources carries significant nutritional, economic, and sociocultural values for rural residents in the Arctic, especially for Indigenous subsistence communities. Rural communities in the Alaskan Arctic have expressed concern that aircraft activity from industry, commercial hunting, research, and tourism disrupts their harvest of wildlife, particularly caribou (Rangifer tarandus). However, little research exists on how aircraft impact harvest opportunities. Our objective was to assess the extent of scientific knowledge on aircraft-harvester interaction in the Arctic through a systematic search of the available literature. We found that no peer-reviewed publications addressed the conflict between aircraft and harvesters in the region. Some literature addressed aircraft impacts to subsistence species, but did not discuss how those impacts would affect local harvesters. Most research has been directed towards studying aircraft impacts on wildlife or humans in urbanized areas rather than in rural, subsistence communities. Therefore, we expanded our review to draw from gray literature (e.g., public records, government documents) to synthesize the current state of concern and perceptions on aircraft disturbance to subsistence harvesters. Based on the gray literature, we found that harvester frustrations were primarily directed toward low-flying aircraft and non-local operations. However, an absence of quantitative information on the extent of interaction between aircraft activity and harvesters hinders an objective assessment of the conflict. Mitigating conflict will require research focused on this data gap and may begin with better cooperation among rural communities, aircraft users, and decision-makers.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.003 |
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".