Bibliographic record
Abstract
Bird collisions with glass are a substantial source of human-caused avian mortality. It has been estimated that between 100 million and 1 billion birds die in collisions with windows every year, and bird- window collisions can have a particularly serious impact on populations because glass is dangerous for strong, healthy, breeding adults. The purpose of this study are to address the bird-window collision issue and to provide suggestions for bird-safe development by reviewing precedent studies on bird collision and analyzing bird-friendly design guidelines. Typically reflections of the sky, clouds or trees on glass, green plants in lobbies, and lights attract and confuse both migrating and resident birds. Therefore birds fatally fly into the glass because they do not recognize that reflections are false and that glass is a barrier. Many cities such as Toronto, Chicago and New York have made efforts on reducing the bird collision by encouraging the creation of environmentally conscious and bird-safe buildings. The USGBC also introduced a bird-safety credit as part of its environmental certification process, called LEED. The results of the study presented that architects and builders can help reduce or prevent bird from collisions in both new construction and existing structures with creative use of design elements. The measures to reduce bird collisions include using glass with an embedded pattern, opaque or translucent films, decals, dot patterns, awnings, louvers, and grilles. Turning off lights after midnight during the spring and fall migrations can be part of the solution as well. In order to reduce bird mortality, the most important thing is to generate awareness of the issue among designers, builders, as well as the public. Also local governments need to develop bird-friendly design guidelines and planning mechanisms to encourage bird-safe development and building operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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; both teacher heads agree on what is shown here.
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".