Crop Damage by Resident Canada Geese in Eastern South Dakota
Bibliographic record
Abstract
Resident giant Canada geese (Branta canadensis maxima) can cause significant crop damage to soybeans especially when adult geese are molting and young geese are still flightless. I evaluated the effectiveness of a program administered by South Dakota Game Fish and Parks Department (SDGFP) designed to alleviate this crop damage. I also determined other factors that affected the amount of goose damage to soybeans. Distance of soybean field from standing water and visual obscurity by shoreline vegetation were important in determining use by geese. Geese damaged soybeans that were closer to water (p < 0.001) and had shorelines with less visual obstruction (p=0.007). The application of deterrents by SDGFP was effective in reducing crop damage (p ≤0.001), but the date of application was important (p ≤ 0.003). Fields where deterrents were applied early in the growing season had less damage than fields where deterrents were applied later. If deterrents are properly applied as soon as damage starts, Canada goose damage to soybeans can be kept to a minimum. Energized fences were the most effective deterrent for molting geese, while visual and sonic deterrents were effective for flying geese. In addition, sites must be maintained regularly and adjustments made to deterrents if goose damage continues.
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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 | 0.000 | 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".