Predicting Bird Oiling Events at Oil Sands Tailings Ponds and Assessing the Importance of Alternate Waterbodies for Waterfowl: a Preliminary Assessment
Bibliographic record
Abstract
Tailings ponds are an integral part of oil sands mining development in northeastern Alberta, but waterfowl and shorebirds often land in these ponds during spring migration where they may become covered with oil. For decades, managers have developed and implemented methods for deterring birds from landing in these ponds, yet no deterrent strategy is fully effective. Therefore, to enhance deterrence strategies, it will be important to understand the environmental conditions that influence bird use of tailings ponds. This study quantified waterfowl flights over, and use of, tailings ponds and compared this use to waterfowl activity at natural waterbodies in the region over a single spring migration period. Results suggest that waterfowl are most likely to land on tailings ponds before lakes have thawed, after which migratory ducks appeared mainly to use natural waterbodies for migratory stopover sites. Very high numbers of waterfowl were observed on one waterbody, Kearl Lake, suggesting that this lake may be of greater importance to spring staging waterfowl than previously thought. A small sample of birds oiled at tailings ponds were examined in relation to spring weather conditions. Logistic regression analysis demonstrated that the probability of birds being oiled tended to increase with precipitation levels. Results of this study suggest that (1) preservation of natural waterbodies may play an important role in minimizing bird use of tailings ponds, and (2) future bird deterrence efforts should especially aim to deter birds during rainy weather conditions when birds may be more likely to become oiled. These results were from a small sample size, are preliminary in nature, and should be interpreted with caution. A concerted and careful effort to collect and thoroughly analyze long-term records of oiled birds may reveal important environmental effects predicting bird oiling events.
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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 | 0.001 | 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".