Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) Quantifying the risk of wind turbines on bats is challenging, but may assist decision makers when siting and operating wind farms or specific turbines. Research conducted before the construction of the wind farm (pre-construction) and after operations have commenced (post-construction) is used to predict risk and quantify the impact of wind turbines on bats, respectively. Several of the tools, such as acoustic detectors, can be used in either pre- or post-construction studies, whereas carcass searching is only possible after the wind farm is operational. In some cases only one method may be appropriate, but often a suite of tools, such as acoustic surveys combined with mist-netting, is necessary. Several agencies have produced guidelines to inform stakeholders on the appropriate methods to use for pre- and post-construction studies. The method or methods used will depend on the objectives and logistical constraints of the study. For example, acoustic monitoring is a relatively cost-effective method of assessing the activity patterns of bats in relation to season and weather conditions, but capturing bats allows for species identification, tissue samples and potentially radio-tagging individuals to locate roosts. New technologies advance our understanding of bat and wind turbine interactions and complement existing methods. Historically, acoustic surveys and carcass searches have been used in combination to assess potential risk, estimate fatality, and relate activity and fatality to nightly weather patterns. However, it was not until the application of infrared imaging cameras that observing bat and wind turbine interactions became possible. Video imaging of bat behaviour, combined with weather and turbine operations data, can provide additional information on the specific timing and conditions when interactions or fatalities occur that is not available with traditional methods. This chapter summarises the benefits and limitations of the common and emerging methods used in the USA, Canada and Europe.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.312 | 0.169 |
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".