Quantifying moonlight and wind effects on flighted waterfowl capture success during night‐lighting
Bibliographic record
Abstract
ABSTRACT Night‐lighting is a common technique used to capture waterfowl, upland birds, and waterbirds. The method involves using bright artificial light and a steady loud noise to startle and confuse birds, allowing close approach and capture by hand or with a long‐handled dip‐net. Many researchers using this technique have noticed that moon phase (or relative brightness) and weather conditions can affect capture rates. Using 16 years (1996–2011) of night‐lighting capture data collected during the Ontario Waterfowl Airboat Banding Program, Canada, the effects of moon phase and wind speed on capture rates for flighted waterfowl were quantified. Increasing moon brightness had a negative effect on capture rates for all species analyzed (mallard [ Anas platyrhynchos ], wood duck [ Aix sponsa ], blue‐winged teal [ Anas discors ], green‐winged teal [ Anas crecca ], ring‐necked duck [ Aythya collaris ], hooded merganser [ Lophodytes cucullatus ], and American black duck [ Anas rubripes ]), with an increasing rate of decline in capture rates when the moon was >75% full (i.e., a bright night). Attempting waterfowl capture when the moon was >75% full led to 2‐fold cost increase per bird captured over the average cost per bird captured. Increasing wind speed was positively correlated with capture rate for some species, but had no significant effect on capture rate for most species analyzed. Night‐lighting capture of flighted waterfowl should be planned primarily during dark to moderately dark nights to maximize capture efficiency and reduce costs. © 2014 The Wildlife Society.
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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 | 0.001 |
| 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".