A Comparison between Nocturnal Aural Counts of Passerines and Radar Reflectivity from a Canadian Weather Surveillance Radar
Bibliographic record
Abstract
Using a Canadian weather surveillance radar (CWSR), we assessed the relationship between aural passerine counts and radar reflectivity during autumn migration on 16 nights. Reflectivity was positively correlated on all but 1 night with the number of birds detected aurally, but the correlation strength varied between -0.58 and 0.93 among nights (mean ± SD = 0.69 ± 0.42). Using linear mixed-effects models with aural counts nested within nights, we found that the number of birds detected by observers increased with reflectivity. The slope of this relationship did not vary between observers, nor was it affected by time since sunset, but the number of birds detected aurally tended to be lower when ambient noise levels were high. We know that the radar was relatively sensitive to low bird densities, because the intercept was slightly positive and its 95% confidence interval marginally included zero. However, the relationship between the number of birds detected aurally and reflectivity varied significantly among nights. Such variation was likely caused by a combination of (interacting) factors, including bird species and behavior (e.g., calling rate, flight altitude), influencing bird detectability by the observers and the radar. The weather radar network of the United States (NEXRAD) is already used for bird migration studies, and we conclude that the use of CWSR can extend NEXRAD's coverage farther north by hundreds of kilometers, thereby increasing our understanding of how birds use the North American landscapes during migration.
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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) | 0.001 | 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 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".