Analyses of Emperor Goose Survey Data and Harvest Potential
Bibliographic record
Abstract
This report provides information for the U. S. Fish and Wildlife Service Division of Migratory Bird Management (USFWS DMBM), Alaska Migratory Bird Co-management Council (AMBCC; comprised of the USFWS, Alaska Department of Fish and Game [ADFG], and Alaska Native representatives), Pacific Flyway Council (PFC), and other entities interested in the management, monitoring, and biology of emperor geese. Sport and subsistence hunting of emperor geese was closed in 1986 and 1987, respectively, due to a population decline and conservation concerns. Since the middle 1980s, abundance has increased. During 2012 to 2015, proposals requesting a legalized harvest have been submitted by Alaska subsistence users through the AMBCC. Past proposals have not been approved because the 3-year average population management index from the spring staging aerial survey had not reached 80,000, as required in the Yukon Kuskokwim (YK) Delta Goose Management Plan and the PFC Emperor Goose Management Plan. In 2015, the management index was 81,875 and above the threshold for consideration of a legalized harvest. The management plan for emperor geese, including revision of monitoring programs, harvest strategy, and population objectives, is currently being updated. Thus, the USFWS DMBM Region 7 funded this work to: 1) evaluate emperor goose status, trends, and monitoring methods by conducting a comprehensive assessment of all available survey data; and 2) determine harvest potential of the population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".