Movement patterns of noctural avian migrants at a wind energy project in northeast British Columbia
Bibliographic record
Abstract
In North America, the migration corridors of passerine birds between breeding and non-breeding grounds are relatively well documented, and along these corridors passerines generally move in a broad-front fashion interspersed with stopover periods in which to rest and replenish fuel stores. Understanding movement patterns at individual locations along these routes is required to identify whether anthropogenic developments, such as wind energy installations, can lead to disruption or collision risk during migrations. Wind energy installations are becoming more numerous in the corridors along migration routes as they use the same wind resources exploited by migratory birds. Documenting collision risk to nocturnal migrants, particularly passerines, through the collection of accurate data on the movement patterns and flight altitudes at wind energy sites during both pre-operational and operational phases is needed to correctly assess the level of risk to these birds. Using standard marine radar units equipped with an inexpensive digital interface system, I automated the detection and extraction of radar echo signatures or target information for nocturnal migrants (Chapter 2) at a wind energy site in northeast British Columbia. Using the open source software program radR, I identified optimal values for input criteria to automatically detect and track these migrants with high accuracy from the digital radar data, when compared to known, manually-tracked targets (R²=0.94). The program was also effective in reducing the amount of insects that were detected and tracked. Use of the auto-tracking software also increased the number of detected targets by over 500% compared to the real-time collection of radar data. Using radR, I analyzed the micro-scale movements of nocturnal migrants during the pre-operational and operational periods of the wind energy project (Chapter 3). Despite variations in wind conditions between seasons, migrants showed consistent directionality and general trends of broad-front migration at a
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".