Non-Lethal Harassment to Disperse Canada Geese in Winter Wheat Fields
Bibliographic record
Abstract
Canada goose (Branta canadensis) populations have increased dramatically in the Atlantic Flyway during the last 50 years, primarily due to large increases in nonmigratory, resident Canada goose populations. Among the associated problems with the increase in goose numbers is grazing and trampling damage to forage crops like winter wheat. Reducing or eliminating goose presence on winter wheat fields is valuable to farmers because goose damage can result in a loss of yield and increased soil erosion. The objective of our study was to evaluate the efficacy of 3, non-lethal harassment techniques (flagging, propane cannon, and combination of flagging and propane cannon) to reduce or eliminate goose presence on New Jersey winter wheat fields. The study was conducted between December 2000 and January 2001. We measured the efficacy of each treatment based on the height of winter wheat within randomly placed 1-m 2 quadrants, as compared to a control field. We also evaluated the cost of each management option. All non-lethal treatments were statistically different (P = 0.05) relative to the control field in terms of increased wheat height. The cost for purchasing and installing the treatments ranged from $6/ac for the flags to $111/ac for the propane cannon. We recommend the use of a propane cannon, where practical, as a first resort to reduce goose grazing of winter wheat. Flagging, however, can be a cost-effective non-lethal management option to reduce goose damage to winter wheat.
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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 | 0.000 |
| 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) | 0.000 | 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".