Successful Use of Alarm and Alert Calls to Reduce Emerging Crop Damage by Resident Canada Geese near Horicon Marsh, Wisconsin
Bibliographic record
Abstract
Increased populations of resident Canada geese create major crop loss problems for farmers, especially in areas that become traditional sites for brood-rearing. Such sites concentrate geese and goslings in locations where food is abundant and flightless adults and young find escape safety on adjacent lakes or rivers. Emerging corn, winter wheat, and soybeans are favorite foods, and these sustain extensive crop damage when near water and brood-rearing sites. From 16 May to 28 August 2007, alarm and alert call playbacks from GooseBuster call units were used with and without other scare reinforcement to assess efficacy of different methods at reducing crop damage at multiple sites near Horicon Marsh, Wisconsin. Test sites were recommended by USDA APHIS Wildlife Services personnel as being sites with heaviest early summer crop damage reported in prior years. Criteria for success were based upon geese/hours/month or geese/hours/week of field use before and after treatment, using frequent counts of geese on properties, weekly farmer interviews, and dropping counts in fields to estimate number and number of hours geese were present. Crop damage assessment by USDA compared current year to prior years’ assessment, or used visible signs of damage and extent. On-demand use of call units, coupled with firing screamer and banger shells, was found to be the most effective method for inducing long-term crop avoidance. Crop damage reduction was very successful, ranging from a 94.3% reduction at one site (17 bushels lost in 2007 versus 297 bushels in 2006), to several fields declared to have “no significant goose damage in 2007” by USDA crop evaluation personnel. Goose hours/month on the largest field data collection decreased from >36,000 to <200 geese/hour/month, a 99.45% reduction. No sign of habituation to reinforced “on-demand” alarm call use was found over the course of the 100 days of the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".