A Simulation Model of the Detection Patterns of Birds Using Marine Radars
Bibliographic record
Abstract
Many bird radar studies provide estimates of the number of birds flying past a given area, but very few of these actually estimate detectability.One of the challenges in radar ornithology is estimating the probability of detection of flying targets with altitude and distance.I estimated the detection patterns associated with three marine radars by using a combination of field trials and simulation modelling, and estimated the probabilities of correctly and incorrectly detecting birds in relation to altitude.Field trials were conducted between December 18 th 2015 and January 08 th 2016 in Russell, Ontario, using radars to detect an aluminum sphere suspended below weather balloons.The results indicate considerable variation in power among radar units.The nominal beam width (+/-3dB) was 4 degrees and effective beam width as measured by the blip size was 7 degrees.Models of beam shape and return echo strength from these trials were implemented into a simulation model.The results indicate detectability varies with altitude, with few birds detected in the lower altitude bands relative to the highest.Many simulated birds were classified as two different birds when crossing the beam twice.The algorithm to remove background clutter was only partially successful, as there were many false detections, especially in the lowest altitude bands near the radar.Future bird radar studies should include data on detection probabilities, using the approaches described here or other comparable approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".