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This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). A Github repository containing code for using the SQLite database also accompanies this paper at: http://github.com/cbalantic/false-positive-mitigation
{"references": ["Balantic, C. M., & Donovan, T. M. (2019). Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring. Bioacoustics, 1-26. DOI: 10.1080/09524622.2019.1605309"]}
A Github repository containing code for using the SQLite database also accompanies this paper at: http://github.com/cbalantic/false-positive-mitigation
bioacoustics, statistical learning, machine learning, species identification, false positives, Automated acoustic monitoring
bioacoustics, statistical learning, machine learning, species identification, false positives, Automated acoustic monitoring
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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