Wind drift and the use of radar, acoustics, and Canadian Migration Monitoring Network methods for monitoring nocturnal passerine migration
Bibliographic record
Abstract
Tools to monitor migrating birds such as radar and acoustics can add valuable information towards understanding migration ecology and population trend estimates. I used a modified marine radar and acoustic sensor to monitor nocturnal migration of passerines at the Atlantic Bird Observatory in southwestern Nova Scotia, Canada. Nightly variation of migration numbers was high. High volume nights were mainly during relatively light northerly winds and consisted of a predominantly SW migratory direction, consistent with the 'expected' regional migratory pattern. Radar data confirmed that migrants typically employ a 'constant-heading' migration strategy. Also, consistent with other studies, numbers of nocturnal migrants detected by radar were significantly positively correlated with numbers of migrants detected by ground counts the following day. These findings illustrated both the importance of a multifaceted approach to migration monitoring, and the importance of incorporating environmental data of wind conditions in interpreting ground counts at migration monitoring stations.
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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 | 0.001 | 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) | 0.000 | 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".