Weather Variables Affecting Canada Goose Harvest in Nebraska
Bibliographic record
Abstract
Improved understanding of factors influencing annual variation in harvest of Canada geese ( Branta canadensis ) would assist managers in setting hunting seasons and managing subpopulations of geese. To understand the influence of weather on harvest of Canada geese in Nebraska, we tested for relationships between annual harvest of Canada geese in Nebraska and various weather variables from North Dakota and South Dakota from 1999 to 2011. We categorized harvest data into groups by year of either high (>79,442 Canada geese harvested) or low harvest (<79,442 Canada geese harvested). We compared these groups to determine if differences in weather severity, using a Weather Severity Index ( wsi ), affected Nebraska’s annual harvest. Timing and extent of greater wsi scores and snowfall in the Dakotas were associated with higher harvest in Nebraska. For years that had an earlier onset of severe weather conditions in North and South Dakota, we also found a corresponding increase in harvest of Canada geese in Nebraska. We recommend managers promulgate hunting regulations and habitat management practices that correspond with the greatest likelihood of weather conditions favorable for migration to effectively manage Canada geese in Nebraska and elsewhere in the Central Flyway.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".