Application of Unmanned Aerial Vehicles and Thermal Imaging Cameras to Conduct Duck Brood Surveys
Bibliographic record
Abstract
ABSTRACT Brood surveys are used to estimate productivity in ducks, but road‐side transects, aerial surveys, and double‐observer ground surveys have likely underestimated productivity. Duck broods are elusive and prefer wetlands with emergent vegetation where they hide at signs of disturbance, making it difficult to get accurate brood counts. Estimates of brood detection probabilities are typically below 50% and variable, which makes biological inferences about abundance tenuous. We conducted a study to evaluate the efficacy of using an unmanned aerial vehicle (UAV) equipped with a thermal imaging camera to survey duck broods in 2 study areas. In Manitoba we located 669 broods with the UAV, compared to 344 detected by double‐observer ground surveys. In Minnesota we detected 225 ducks broods with the UAV, whereas only 105 duck broods were detected by ground observers. Using a Huggins closed‐capture model in program MARK we estimated an average detection probability across both sites of 0.55 (SE = 0.02) with the UAV compared to 0.24 (SE = 0.02) for the ground crews. Although the UAV detected twice as many broods as the ground surveys, detection probability with the UAV was impacted by temperature, humidity, vegetation density, and the criteria we used to determine whether a brood could be classified as resighted. Nevertheless, using a UAV equipped with a thermal imaging camera effectively doubled the number of broods detected compared to traditional methods, and surveys were completed 3 times faster. With advancing drone and camera technology we believe UAV brood counts will become increasingly accurate and provide reliable measures of local duck productivity. © 2021 The Wildlife Society.
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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) | 0.001 | 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".