Polar Bear Aerial Survey in the Eastern Chukchi Sea: A Pilot Study
Bibliographic record
Abstract
Alaska has two polar bear populations: the Southern Beaufort Sea population, shared with Canada, and the Chukchi/Bering Seas population, shared with Russia. Currently a reliable population estimate for the Chukchi/Bering Seas population does not exist. Land-based aerial and mark-recapture population surveys may not be possible in the Chukchi Sea because variable ice conditions, the limited range of helicopters, extremely large polar bear home ranges, and severe weather conditions may limit access to remote areas. Thus line-transect aerial surveys from icebreakers may be the best available tool to monitor this polar bear stock. In August 2000, a line-transect survey was conducted in the eastern Chukchi Sea and western Beaufort Sea from helicopters based on a U.S. Coast Guard icebreaker under the "Ship of Opportunity" program. The objectives of this pilot study were to estimate polar bear density in the eastern Chukchi and western Beaufort Seas and to assess the logistical feasibility of using ship-based aerial surveys to develop polar bear population estimates. Twenty-nine polar bears in 25 groups were sighted on 94 transects (8257 km). The density of bears was estimated as 1 bear per 147 km² (CV = 38%). Additional aerial surveys in late fall, using dedicated icebreakers, would be required to achieve the number of sightings, survey effort, coverage, and precision needed for more effective monitoring of population trends in the Chukchi Sea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".