Effects of Mowing on Anthraquinone for Deterrence of Canada Geese
Bibliographic record
Abstract
ABSTRACT Anthraquinone (AQ)‐based repellents have been shown to reduce Canada goose ( Branta canadensis ) use of turfgrass; however, impacts of frequent mowing on efficacy of AQ have not been studied. Our objective was to determine efficacy and longevity of a rain‐fast AQ‐based avian repellent, FlightControl® PLUS (FCP), as a deterrent of free‐ranging resident Canada geese under 2 mowing frequencies. We conducted the study at 8 sites in the Triangle region (Raleigh, Durham, and Chapel Hill) of North Carolina, USA. We arranged our experiment in a randomized complete block design, with each of 8 sites containing 4 0.1‐ha treatment combinations: 1) treated with FCP and mowed every 4 days (T4), 2) treated with FCP and mowed every 8 days (T8), 3) untreated and mowed every 4 days, and 4) untreated and mowed every 8 days. We conducted 4 37‐day field sessions (Jun‐Jul 2007, Sep‐Oct 2007, Jun‐Jul 2008, and Sep‐Oct 2008), representing the summer molting phase and the full‐plumage phase. Resident goose use (measured by daily no. of droppings) was 41–70% lower on treated plots than on untreated plots, but use was similar between T4 and T8. Average FCP coverage on grass blades decreased in coverage from approximately 95% to 10% over the 30‐day posttreatment phase. Results indicate that resident Canada goose use of FCP‐treated turfgrass areas was lower than untreated areas even when chemical coverage on grass was 10%. Further, mowing frequency did not have a clear impact on the efficacy of FCP as a Canada goose repellent.
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".