Determining factors affecting carcass removal and searching efficiency during the post-construction monitoring of wind farms.
Bibliographic record
Abstract
Wind energy, although desirable in the goal to slow global warming and climate change, does have the potential to create negative impacts to bird and bat populations sharing the same airspace as the turbines used to generate energy. Quantifying the effects of wind farms on migrating birds and bats is currently done by searching for collision-related fatalities beneath turbines. There are, however, two factors that hinder the accuracy of this technique: carcass removal by scavengers prior to searches and failure to detect carcasses by researches during searches. This study aims to determine which variables affect carcass removal and searcher inefficiency in an attempt to gain a better understanding of how species are being affected by turbines. Carcass removal and searcher efficiency trials were conducted at potential wind farm locations near Chetwynd, BC and variables thought to potentially contribute to these two events were recorded and subsequently analyzed. Smaller carcasses in areas of bare ground were most likely to be scavenged and smaller, less brightly coloured carcasses in areas with high amounts shrub and tall grass were the most likely to be missed during searches. My results provide predictive models that can be effectively used to predict the likelihood of a carcass being scavenged or found by searchers. Findings can also be used to quantify the risk to certain species of being missed during searches if they are colliding with turbines. Habitat modification and the use of dogs during searchers are two other potential mitigation techniques that could be administered to correctly identify the number of collisions occurring.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".