Vision-Based Design and Deployment Criteria for Power Line Bird Diverters
Bibliographic record
Abstract
The design of bird diverters should be based upon the perception of birds, not the perception of humans, but until now it is human vision that has guided diverter design. Aspects of bird vision pertinent to diverter design are reviewed. These are applied in an example that uses Canada Geese Branta canadensis as a putative worst-case example of a collision-prone species. The proposed design uses an achromatic checkerboard pattern of high contrast whose elements match the low spatial resolution of these birds when they are active under twilight light levels. The detectability of the device will be increased by movement, and this is best achieved with a device that rotates on its own axis driven by the wind. The recommended spacing of diverters along a power line is based upon the maximum width of the bird’s binocular field and the linear distance that it subtends at a distance sufficient to allow a bird to alter its flight path before possible impact. Given the worst-case nature of this example, other bird species should detect and avoid such a device. The basic design can be modified for use with specific target species if sufficient is known about their vision. Field trials of devices based on these design criteria are now required.
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) | 0.000 | 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) | 0.005 | 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".