FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2908293859> ?p ?o ?g. }

• W2908293859 endingPage "630" @default.
• W2908293859 startingPage "619" @default.
• W2908293859 abstract "The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal-borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under-acknowledged consideration in biologging is the trade-off between sampling rate and sampling duration, created by battery- (or memory-) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low-frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free-ranging red squirrels (200-300 g) that were fitted with accelerometers (2 g) recording tri-axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long-duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi-month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior." @default.
• W2908293859 created "2019-01-11" @default.
• W2908293859 creator A5009109006 @default.
• W2908293859 creator A5018853972 @default.
• W2908293859 creator A5021419374 @default.
• W2908293859 creator A5044344117 @default.
• W2908293859 creator A5045112431 @default.
• W2908293859 creator A5081580830 @default.
• W2908293859 creator A5090240700 @default.
• W2908293859 date "2018-12-27" @default.
• W2908293859 modified "2023-10-12" @default.
• W2908293859 title "Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal" @default.
• W2908293859 cites W1513626006 @default.
• W2908293859 cites W1786819714 @default.
• W2908293859 cites W1841713506 @default.
• W2908293859 cites W1922885767 @default.
• W2908293859 cites W1988195734 @default.
• W2908293859 cites W1989722781 @default.
• W2908293859 cites W1997548708 @default.
• W2908293859 cites W2005142605 @default.
• W2908293859 cites W2019147313 @default.
• W2908293859 cites W2034065618 @default.
• W2908293859 cites W2037632996 @default.
• W2908293859 cites W2039401415 @default.
• W2908293859 cites W2040523693 @default.
• W2908293859 cites W2061733446 @default.
• W2908293859 cites W2076363610 @default.
• W2908293859 cites W2089678915 @default.
• W2908293859 cites W2091640986 @default.
• W2908293859 cites W2092011964 @default.
• W2908293859 cites W2104611383 @default.
• W2908293859 cites W2104711555 @default.
• W2908293859 cites W2106999574 @default.
• W2908293859 cites W2107687774 @default.
• W2908293859 cites W2110534501 @default.
• W2908293859 cites W2112963583 @default.
• W2908293859 cites W2119826316 @default.
• W2908293859 cites W2120553757 @default.
• W2908293859 cites W2123720099 @default.
• W2908293859 cites W2132883376 @default.
• W2908293859 cites W2136375252 @default.
• W2908293859 cites W2144408192 @default.
• W2908293859 cites W2152779085 @default.
• W2908293859 cites W2155282054 @default.
• W2908293859 cites W2158369814 @default.
• W2908293859 cites W2159588491 @default.
• W2908293859 cites W2172136457 @default.
• W2908293859 cites W2173478659 @default.
• W2908293859 cites W2288601032 @default.
• W2908293859 cites W2294124004 @default.
• W2908293859 cites W2305408943 @default.
• W2908293859 cites W2322148466 @default.
• W2908293859 cites W2512914483 @default.
• W2908293859 cites W2532445215 @default.
• W2908293859 cites W2557756348 @default.
• W2908293859 cites W2911964244 @default.
• W2908293859 doi "https://doi.org/10.1002/ece3.4786" @default.
• W2908293859 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6342100" @default.
• W2908293859 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30680142" @default.
• W2908293859 hasPublicationYear "2018" @default.
• W2908293859 type Work @default.
• W2908293859 sameAs 2908293859 @default.
• W2908293859 citedByCount "28" @default.
• W2908293859 countsByYear W29082938592019 @default.
• W2908293859 countsByYear W29082938592020 @default.
• W2908293859 countsByYear W29082938592021 @default.
• W2908293859 countsByYear W29082938592022 @default.
• W2908293859 countsByYear W29082938592023 @default.
• W2908293859 crossrefType "journal-article" @default.
• W2908293859 hasAuthorship W2908293859A5009109006 @default.
• W2908293859 hasAuthorship W2908293859A5018853972 @default.
• W2908293859 hasAuthorship W2908293859A5021419374 @default.
• W2908293859 hasAuthorship W2908293859A5044344117 @default.
• W2908293859 hasAuthorship W2908293859A5045112431 @default.
• W2908293859 hasAuthorship W2908293859A5081580830 @default.
• W2908293859 hasAuthorship W2908293859A5090240700 @default.
• W2908293859 hasBestOaLocation W29082938591 @default.
• W2908293859 hasConcept C105795698 @default.
• W2908293859 hasConcept C111919701 @default.
• W2908293859 hasConcept C112758219 @default.
• W2908293859 hasConcept C115051666 @default.
• W2908293859 hasConcept C117896860 @default.
• W2908293859 hasConcept C121332964 @default.
• W2908293859 hasConcept C140779682 @default.
• W2908293859 hasConcept C154945302 @default.
• W2908293859 hasConcept C159234332 @default.
• W2908293859 hasConcept C169258074 @default.
• W2908293859 hasConcept C18903297 @default.
• W2908293859 hasConcept C24890656 @default.
• W2908293859 hasConcept C2780598303 @default.
• W2908293859 hasConcept C33923547 @default.
• W2908293859 hasConcept C41008148 @default.
• W2908293859 hasConcept C74650414 @default.
• W2908293859 hasConcept C76155785 @default.
• W2908293859 hasConcept C79403827 @default.
• W2908293859 hasConcept C84525736 @default.
• W2908293859 hasConcept C86803240 @default.
• W2908293859 hasConcept C89805583 @default.

• next