Use of an unmanned aerial vehicle and sound localization to determine bird microhabitat
Bibliographic record
Abstract
Study of bird microhabitat use is time consuming and labour intensive. Our objective was to present a proof of concept of how emerging, high-resolution bird survey methods can be combined with vegetation data collected via unmanned aerial vehicles to accurately and efficiently quantify bird microhabitat. We used sound localization to determine mourning warbler (Geothlypis philadelphia) song posts, and a hybrid light detection and ranging/digital aerial photogrammetry canopy height model to demonstrate how mourning warblers use regenerating vegetation on reclaimed well sites. We identified differences in vegetation heights at locations used by mourning warblers versus random background locations on a reclaimed well site, with sound localization and the canopy height model both providing measurements with 1-m resolution (t = −3.45, p = 0.002). These technologies have the potential to provide large numbers of accurate bird locations that can be associated with high-resolution, spatially explicit vegetation metrics and used in different ecological niche modeling frameworks.
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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) | 0.000 | 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".