FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1618776072> ?p ?o ?g. }

• W1618776072 endingPage "e991" @default.
• W1618776072 startingPage "e991" @default.
• W1618776072 abstract "Species identification-of importance for most biological disciplines-is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond." @default.
• W1618776072 created "2016-06-24" @default.
• W1618776072 creator A5003231596 @default.
• W1618776072 creator A5012321620 @default.
• W1618776072 creator A5015076534 @default.
• W1618776072 creator A5018723344 @default.
• W1618776072 creator A5044915920 @default.
• W1618776072 creator A5069829698 @default.
• W1618776072 creator A5081306505 @default.
• W1618776072 creator A5083951361 @default.
• W1618776072 date "2015-09-15" @default.
• W1618776072 modified "2023-10-16" @default.
• W1618776072 title "A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species" @default.
• W1618776072 cites W1565404191 @default.
• W1618776072 cites W16801492 @default.
• W1618776072 cites W1988195734 @default.
• W1618776072 cites W1998053851 @default.
• W1618776072 cites W2009116080 @default.
• W1618776072 cites W2009679183 @default.
• W1618776072 cites W2022110298 @default.
• W1618776072 cites W2022947185 @default.
• W1618776072 cites W2025636153 @default.
• W1618776072 cites W2026740844 @default.
• W1618776072 cites W2029993363 @default.
• W1618776072 cites W2049519426 @default.
• W1618776072 cites W2066160636 @default.
• W1618776072 cites W2070512123 @default.
• W1618776072 cites W2071837852 @default.
• W1618776072 cites W2072725874 @default.
• W1618776072 cites W2078810805 @default.
• W1618776072 cites W2084158895 @default.
• W1618776072 cites W2095274773 @default.
• W1618776072 cites W2095649738 @default.
• W1618776072 cites W2096062275 @default.
• W1618776072 cites W2111643931 @default.
• W1618776072 cites W2116226846 @default.
• W1618776072 cites W2116562978 @default.
• W1618776072 cites W2132323289 @default.
• W1618776072 cites W2136076298 @default.
• W1618776072 cites W2138695073 @default.
• W1618776072 cites W2141733815 @default.
• W1618776072 cites W2145690951 @default.
• W1618776072 cites W2146354591 @default.
• W1618776072 cites W2156931970 @default.
• W1618776072 cites W2158833271 @default.
• W1618776072 cites W2160095362 @default.
• W1618776072 cites W2163842890 @default.
• W1618776072 cites W2164032493 @default.
• W1618776072 cites W2170556360 @default.
• W1618776072 cites W2172354621 @default.
• W1618776072 cites W2181285891 @default.
• W1618776072 cites W2183649751 @default.
• W1618776072 cites W2911964244 @default.
• W1618776072 cites W4251384959 @default.
• W1618776072 doi "https://doi.org/10.7717/peerj.991" @default.
• W1618776072 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4699785" @default.
• W1618776072 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26734510" @default.
• W1618776072 hasPublicationYear "2015" @default.
• W1618776072 type Work @default.
• W1618776072 sameAs 1618776072 @default.
• W1618776072 citedByCount "21" @default.
• W1618776072 countsByYear W16187760722016 @default.
• W1618776072 countsByYear W16187760722017 @default.
• W1618776072 countsByYear W16187760722018 @default.
• W1618776072 countsByYear W16187760722019 @default.
• W1618776072 countsByYear W16187760722020 @default.
• W1618776072 countsByYear W16187760722021 @default.
• W1618776072 countsByYear W16187760722022 @default.
• W1618776072 countsByYear W16187760722023 @default.
• W1618776072 crossrefType "journal-article" @default.
• W1618776072 hasAuthorship W1618776072A5003231596 @default.
• W1618776072 hasAuthorship W1618776072A5012321620 @default.
• W1618776072 hasAuthorship W1618776072A5015076534 @default.
• W1618776072 hasAuthorship W1618776072A5018723344 @default.
• W1618776072 hasAuthorship W1618776072A5044915920 @default.
• W1618776072 hasAuthorship W1618776072A5069829698 @default.
• W1618776072 hasAuthorship W1618776072A5081306505 @default.
• W1618776072 hasAuthorship W1618776072A5083951361 @default.
• W1618776072 hasBestOaLocation W16187760721 @default.
• W1618776072 hasConcept C104317684 @default.
• W1618776072 hasConcept C116834253 @default.
• W1618776072 hasConcept C125390029 @default.
• W1618776072 hasConcept C153180895 @default.
• W1618776072 hasConcept C154945302 @default.
• W1618776072 hasConcept C177264268 @default.
• W1618776072 hasConcept C18903297 @default.
• W1618776072 hasConcept C193252679 @default.
• W1618776072 hasConcept C199360897 @default.
• W1618776072 hasConcept C41008148 @default.
• W1618776072 hasConcept C54355233 @default.
• W1618776072 hasConcept C58489278 @default.
• W1618776072 hasConcept C86803240 @default.
• W1618776072 hasConceptScore W1618776072C104317684 @default.
• W1618776072 hasConceptScore W1618776072C116834253 @default.
• W1618776072 hasConceptScore W1618776072C125390029 @default.
• W1618776072 hasConceptScore W1618776072C153180895 @default.
• W1618776072 hasConceptScore W1618776072C154945302 @default.
• W1618776072 hasConceptScore W1618776072C177264268 @default.

• next