Effectiveness of a Motion-Activated Laser Hazing Systemfor Repelling Captive Canada Geese
Bibliographic record
Abstract
Effective management techniques are needed to disperse Canada geese (Branta canadensis) and reduce the human–wildlife conflicts associated with high population densities. We evaluated the effectiveness of a motion-activated laser hazing system for repelling captive Canada geese. The system decreased occupancy of 8 pairs of geese on the treated subplot by 83% during habituation trials. When an additional pair of geese were added to the experiment, occupancy of the treated subplot decreased .92% during each of the 20 nights of the extended habituation test. Avoidance (conditioned during the test) remained ,80% of pretreatment levels during the 2 days immediately following the habituation test but extinguished 3 days subsequent to the permanent inactivation of the laser hazing system. The motionactivated laser hazing system effectively repelled Canada geese in captivity. Additional field research is needed to determine the spatial extent of the laser hazing system and the effectiveness of the Doppler radar motion detector for repelling wild geese.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".