Efficacy of a radar‐activated on‐demand system for deterring waterfowl from oil sands tailings ponds
Bibliographic record
Abstract
Summary Oil sands mining is one of several industrial activities that produces effluent that is dangerous to waterfowl. Such industries require effective systems to deter birds, but current deterrents are not always successful, presumably because wildlife ignore or habituate to them. We tested a new radar‐activated on‐demand system of deterrence in the oil sands region of Alberta, Canada, by comparing the proportion of birds that landed on a tailings pond while it was activated with the proportion that landed during two other treatments: a continuous, randomly activated, deterrent system, and control periods with no deterrents. We also assessed the efficacy of different stimuli types within the on‐demand system. Across several bird guilds, only the on‐demand deterrent system significantly reduced the probability of birds landing in comparison with the control treatment. In addition to treatment effects, birds were more likely to land earlier in the spring and when they flew at lower altitudes, and shorebirds were more likely to land than ducks, geese and gulls. The comparison of stimuli revealed that cannons elicited significantly more response by birds in flight than mechanized peregrine falcon effigies with speakers broadcasting peregrine sounds. Synthesis and applications. Our results promote the use of on‐demand systems for waterfowl deterrence at tailings ponds and recommend cannons over effigies as stimuli. We suggest that oil sands deterrence efforts should (i) be operational in the early spring, when tailings ponds appear to be most attractive to migrating waterfowl, (ii) target low‐flying waterfowl and shorebirds and (iii) be effective during both day and night. These results and recommendations have potential application for problems of bird deterrence at several other industrial sites.
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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) | 0.001 | 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 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".