FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4319790970> ?p ?o ?g. }

• W4319790970 endingPage "368" @default.
• W4319790970 startingPage "356" @default.
• W4319790970 abstract "Abstract Aim Global‐scale maps of the environment are an important source of information for researchers and decision makers. Often, these maps are created by training machine learning algorithms on field‐sampled reference data using remote sensing information as predictors. Since field samples are often sparse and clustered in geographic space, model prediction requires a transfer of the trained model to regions where no reference data are available. However, recent studies question the feasibility of predictions far beyond the location of training data. Innovation We propose a novel workflow for spatial predictive mapping that leverages recent developments in this field and combines them in innovative ways with the aim of improved model transferability and performance assessment. We demonstrate, evaluate and discuss the workflow with data from recently published global environmental maps. Main conclusions Reducing predictors to those relevant for spatial prediction leads to an increase of model transferability and map accuracy without a decrease of prediction quality in areas with high sampling density. Still, reliable gap‐free global predictions were not possible, highlighting that global maps and their evaluation are hampered by limited availability of reference data." @default.
• W4319790970 created "2023-02-11" @default.
• W4319790970 creator A5002368889 @default.
• W4319790970 creator A5003590626 @default.
• W4319790970 creator A5018168789 @default.
• W4319790970 creator A5054607061 @default.
• W4319790970 creator A5057598189 @default.
• W4319790970 date "2023-01-26" @default.
• W4319790970 modified "2023-10-01" @default.
• W4319790970 title "Assessing and improving the transferability of current global spatial prediction models" @default.
• W4319790970 cites W1831050183 @default.
• W4319790970 cites W2038546254 @default.
• W4319790970 cites W2045451950 @default.
• W4319790970 cites W2106745149 @default.
• W4319790970 cites W2123998733 @default.
• W4319790970 cites W2129435498 @default.
• W4319790970 cites W2130956715 @default.
• W4319790970 cites W2156182980 @default.
• W4319790970 cites W2218047931 @default.
• W4319790970 cites W2560136348 @default.
• W4319790970 cites W2584181319 @default.
• W4319790970 cites W2588003345 @default.
• W4319790970 cites W2614464134 @default.
• W4319790970 cites W2773188111 @default.
• W4319790970 cites W2794916302 @default.
• W4319790970 cites W2888842680 @default.
• W4319790970 cites W2889489537 @default.
• W4319790970 cites W2894470772 @default.
• W4319790970 cites W2952516441 @default.
• W4319790970 cites W2955316307 @default.
• W4319790970 cites W2963407560 @default.
• W4319790970 cites W2964297133 @default.
• W4319790970 cites W2972629016 @default.
• W4319790970 cites W2974097001 @default.
• W4319790970 cites W2980684978 @default.
• W4319790970 cites W2983606184 @default.
• W4319790970 cites W2993154072 @default.
• W4319790970 cites W3013341479 @default.
• W4319790970 cites W3018911758 @default.
• W4319790970 cites W3082766779 @default.
• W4319790970 cites W3085784695 @default.
• W4319790970 cites W3102027041 @default.
• W4319790970 cites W3139263896 @default.
• W4319790970 cites W3159563937 @default.
• W4319790970 cites W3169855463 @default.
• W4319790970 cites W3175791194 @default.
• W4319790970 cites W3175987719 @default.
• W4319790970 cites W3185151041 @default.
• W4319790970 cites W3193501617 @default.
• W4319790970 cites W3193937223 @default.
• W4319790970 cites W3194508523 @default.
• W4319790970 cites W3195225336 @default.
• W4319790970 cites W3209176285 @default.
• W4319790970 cites W4220932724 @default.
• W4319790970 cites W4224247190 @default.
• W4319790970 cites W4232681064 @default.
• W4319790970 cites W4294085915 @default.
• W4319790970 cites W429766147 @default.
• W4319790970 doi "https://doi.org/10.1111/geb.13635" @default.
• W4319790970 hasPublicationYear "2023" @default.
• W4319790970 type Work @default.
• W4319790970 citedByCount "5" @default.
• W4319790970 countsByYear W43197909702023 @default.
• W4319790970 crossrefType "journal-article" @default.
• W4319790970 hasAuthorship W4319790970A5002368889 @default.
• W4319790970 hasAuthorship W4319790970A5003590626 @default.
• W4319790970 hasAuthorship W4319790970A5018168789 @default.
• W4319790970 hasAuthorship W4319790970A5054607061 @default.
• W4319790970 hasAuthorship W4319790970A5057598189 @default.
• W4319790970 hasBestOaLocation W43197909701 @default.
• W4319790970 hasConcept C106131492 @default.
• W4319790970 hasConcept C119857082 @default.
• W4319790970 hasConcept C124101348 @default.
• W4319790970 hasConcept C140331021 @default.
• W4319790970 hasConcept C140779682 @default.
• W4319790970 hasConcept C159620131 @default.
• W4319790970 hasConcept C162324750 @default.
• W4319790970 hasConcept C176217482 @default.
• W4319790970 hasConcept C177212765 @default.
• W4319790970 hasConcept C202444582 @default.
• W4319790970 hasConcept C205649164 @default.
• W4319790970 hasConcept C21547014 @default.
• W4319790970 hasConcept C24756922 @default.
• W4319790970 hasConcept C2522767166 @default.
• W4319790970 hasConcept C2778755073 @default.
• W4319790970 hasConcept C31972630 @default.
• W4319790970 hasConcept C33676613 @default.
• W4319790970 hasConcept C33923547 @default.
• W4319790970 hasConcept C41008148 @default.
• W4319790970 hasConcept C45804977 @default.
• W4319790970 hasConcept C58640448 @default.
• W4319790970 hasConcept C60478076 @default.
• W4319790970 hasConcept C61272859 @default.
• W4319790970 hasConcept C62649853 @default.
• W4319790970 hasConcept C77088390 @default.
• W4319790970 hasConcept C9652623 @default.
• W4319790970 hasConceptScore W4319790970C106131492 @default.
• W4319790970 hasConceptScore W4319790970C119857082 @default.

• next