Deterrents intended to mitigate mining effects mostly fail to change nesting behavior of Arctic breeding birds
Bibliographic record
Abstract
Mining is an important economic driver in the Arctic and leads to land-use changes and habitat loss for Arctic breeding birds. Various techniques are used to mitigate the impacts of extractive mining on wildlife, including deterrents designed to keep wildlife away from activities that may cause harm to animals. This study assessed the efficacy of deterrents intended to prevent birds from nesting in tundra areas that were planned for flooding during mining pit expansion. We used visual and audio deterrents (flash tape and hawk effigies, as well as predator and prey distress calls) to attempt to discourage nesting by birds at a gold mine north of Baker Lake, Nunavut. We used a before-after control-impact design to determine changes in birds’ territory densities before and after deployment of deterrent treatments. We assessed whether deterrent intensity had an impact on nest survival of the most common passerines, Lapland Longspur (Calcarius lapponicus), Horned Lark (Eremophila alpestris), and shorebirds, Semipalmated Sandpiper (Calidris pusilla), and Least Sandpiper (Calidris minutilla), because of the potential for deterrents to increase disturbance to incubating birds. Using temperature probes inserted into nests, we also determined whether deterrents influenced incubation behavior of Lapland Longspurs. Deterrents did not impact territory densities except in dissuading Horned Larks. Deterrents did not result in lower nest survival of passerines or shorebirds, nor did deterrents affect the frequency of incubation recesses for incubating Lapland Longspur females. We conclude that, in general, the deterrents used in this study were not effective at preventing birds from using treatment areas, and therefore we advise against the use of these techniques for mitigating the effects of mine flooding for Arctic-nesting birds.
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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 | 0.000 |
| 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".