Repellent Surface Applications for Pest Birds
Bibliographic record
Abstract
Common pest birds in the United States include the non-native European starling (Sturnus vulgaris), house sparrow (Passer domesticus), and the pigeon (Columba livia domestica), as well as native birds including Canada geese (Branta canadensis) and gull species (Laridae). Large concentrations of pest birds can create human health hazards and monetary losses due to consumption of crops, depredation, and fecal contamination and accumulation. Fecal contamination hazards include the potential spread of zoonotic diseases including antimicrobial-resistant zoonoses and human injury due to the accumulation of fecal material on walking surfaces. Additionally, fecal accumulation causes structural and aesthetic damage due to the accelerated deterioration of building materials and increased maintenance costs. Methods to alleviate hazards and damages from aggregations of pest birds are needed. In a series of 3 experiments conducted in Fort Collins, Colorado, USA, between 2016 and 2018, we evaluated 3 surface-application repellent formulations for the reduction of fecal accumulations due to European starlings: Airepel® HC with castor oil, an anthraquinone-based repellent; Airepel HC with castor oil without anthraquinone; and MS2, a novel inert formulation with a tacky, oily texture. We compared each formulation directly to an untreated control. All 3 formulations reduced fecal accumulations beneath treated aluminum perches as compared to fecal accumulations beneath untreated aluminum perches. Interestingly, both formulations that contained no anthraquinone worked equally well or better than Airepel HC with castor oil, the anthraquinone-based formulation. The benefits of an exclusively inert formulation include less risk to applicators and non-target species. Comprehensive experimental field testing of these surface-application repellent formulations is warranted.
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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 | 0.000 | 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) | 0.002 | 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".