Effectiveness of Spinning-Wing Decoys Varies Among Dabbling Duck Species and Locations
Bibliographic record
Abstract
Spinning-wing decoys are strong attractants to ducks and increase kill rates over traditional decoying methods. However, it is unknown whether all duck species are attracted similarly to spinning-wing decoys and whether the effectiveness of these decoys changes with latitude. We examined the effectiveness of spinning-wing decoys for 9 species of dabbling ducks during 545 experimental hunts in California (1999–2000), Minnesota (2002), Manitoba (2001–2002), Nebraska (2000–2002), Missouri (2000–2001), and Arkansas (2001–2003). During each experimental hunt, we systematically alternated between 2 paired decoy treatments every 15–30 min (depending on study site): traditional decoys only and traditional decoys with a spinning-wing decoy. Overall, 70.2% (n = 1,925) of dabbling ducks were harvested (shot and retrieved) when spinning-wing decoys were turned on, ranging from 63.6% (n = 187) in Missouri to 76.4% (n = 356) in Minnesota. Effectiveness of spinning-wing decoys increased with latitude of study sites. Proportions of ducks shot when spinning-wing decoys were turned on differed among species, from a low of 50.0% (n = 8) for cinnamon teal (Anas cyanoptera) to a high of 79.0% (n = 119) for American wigeon (A. americana). The probability of being shot when spinning-wing decoys were turned on increased with annual survival rates among species; for example, spinning-wing decoys were more effective for American wigeon and mallard (A. platyrhynchos) than they were for cinnamon teal and American green-winged teal (A. crecca). Effectiveness of spinning-wing decoys did not differ consistently by age or sex of harvested ducks. Our results indicate that the effectiveness of spinning-wing decoys differs among duck species and changes with latitude; thus, consideration of these effects may be warranted when setting harvest regulations and methods of take.
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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 | 0.000 |
| 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 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".