false positive
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Timothy J. Batten ◽  
Sian Gallacher ◽  
William J. Thomas ◽  
Jeffrey Kitson ◽  
Christopher D. Smith

2022 ◽  
Vol 10 (1) ◽  
K. Nebiolo ◽  
T. Castro-Santos

Abstract Introduction Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects. Methods BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s $$\kappa$$ κ . Results Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid. Conclusion As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.

2022 ◽  
David Kipping ◽  
Steve Bryson ◽  
Chris Burke ◽  
Jessie Christiansen ◽  
Kevin Hardegree-Ullman ◽  

AbstractExomoons represent a crucial missing puzzle piece in our efforts to understand extrasolar planetary systems. To address this deficiency, we here describe an exomoon survey of 70 cool, giant transiting exoplanet candidates found by Kepler. We identify only one exhibiting a moon-like signal that passes a battery of vetting tests: Kepler-1708 b. We show that Kepler-1708 b is a statistically validated Jupiter-sized planet orbiting a Sun-like quiescent star at 1.6 au. The signal of the exomoon candidate, Kepler-1708 b-i, is a 4.8σ effect and is persistent across different instrumental detrending methods, with a 1% false-positive probability via injection–recovery. Kepler-1708 b-i is ~2.6 Earth radii and is located in an approximately coplanar orbit at ~12 planetary radii from its ~1.6 au Jupiter-sized host. Future observations will be necessary to validate or reject the candidate.

2022 ◽  
Vol Publish Ahead of Print ◽  
Bahare Saidi ◽  
Babak Fallahi ◽  
Reyhaneh Manafi-Farid ◽  
Armaghan Fard-Esfahani ◽  
Mohammad Eftekhari

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 19
Mirela Kundid Vasić ◽  
Vladan Papić

Recent results in person detection using deep learning methods applied to aerial images gathered by Unmanned Aerial Vehicles (UAVs) have demonstrated the applicability of this approach in scenarios such as Search and Rescue (SAR) operations. In this paper, the continuation of our previous research is presented. The main goal is to further improve detection results, especially in terms of reducing the number of false positive detections and consequently increasing the precision value. We present a new approach that, as input to the multimodel neural network architecture, uses sequences of consecutive images instead of only one static image. Since successive images overlap, the same object of interest needs to be detected in more than one image. The correlation between successive images was calculated, and detected regions in one image were translated to other images based on the displacement vector. The assumption is that an object detected in more than one image has a higher probability of being a true positive detection because it is unlikely that the detection model will find the same false positive detections in multiple images. Based on this information, three different algorithms for rejecting detections and adding detections from one image to other images in the sequence are proposed. All of them achieved precision value about 80% which is increased by almost 20% compared to the current state-of-the-art methods.

2022 ◽  
Vol Publish Ahead of Print ◽  
James P. Walsh ◽  
Justin B. Sims ◽  
Pooya Iranpour

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