{"id":"https://openalex.org/W4292058731","doi":"https://doi.org/10.3390/rs14163937","title":"Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets","display_name":"Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets","publication_year":2022,"publication_date":"2022-08-13","ids":{"openalex":"https://openalex.org/W4292058731","doi":"https://doi.org/10.3390/rs14163937"},"language":"en","primary_location":{"id":"doi:10.3390/rs14163937","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163937","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3937/pdf?version=1660891059","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/16/3937/pdf?version=1660891059","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017434394","display_name":"Ali Gonzalez-Perez","orcid":"https://orcid.org/0000-0001-7898-8387"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ali Gonzalez-Perez","raw_affiliation_strings":["School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"],"affiliations":[{"raw_affiliation_string":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037840997","display_name":"Amr Abd\u2010Elrahman","orcid":"https://orcid.org/0000-0002-6182-4017"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amr Abd-Elrahman","raw_affiliation_strings":["Gulf Coast Research and Education Center, 14625 CR 672, Wimauma, FL 33598, USA","School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"],"affiliations":[{"raw_affiliation_string":"Gulf Coast Research and Education Center, 14625 CR 672, Wimauma, FL 33598, USA","institution_ids":[]},{"raw_affiliation_string":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019241609","display_name":"Benjamin Wilkinson","orcid":"https://orcid.org/0000-0001-7152-8687"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Wilkinson","raw_affiliation_strings":["School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"],"affiliations":[{"raw_affiliation_string":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035878753","display_name":"Daniel J. Johnson","orcid":"https://orcid.org/0000-0002-8585-2143"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel J. Johnson","raw_affiliation_strings":["School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA"],"affiliations":[{"raw_affiliation_string":"School of Forest Fisheries and Geomatics Sciences, University of Florida, 1745 McCarty Drive, P.O. Box 110410, Gainesville, FL 32611, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012918929","display_name":"Raymond R. Carthy","orcid":"https://orcid.org/0000-0001-8978-5083"},"institutions":[{"id":"https://openalex.org/I1286329397","display_name":"United States Geological Survey","ror":"https://ror.org/035a68863","country_code":"US","type":"government","lineage":["https://openalex.org/I1286329397","https://openalex.org/I1335927249"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raymond R. Carthy","raw_affiliation_strings":["U.S. Geological Survey, Florida Cooperative Fish & Wildlife Research Unit, P.O. Box 110485, Gainesville, FL 32611, USA"],"affiliations":[{"raw_affiliation_string":"U.S. Geological Survey, Florida Cooperative Fish & Wildlife Research Unit, P.O. Box 110485, Gainesville, FL 32611, USA","institution_ids":["https://openalex.org/I1286329397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017434394"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":3.7221,"has_fulltext":true,"cited_by_count":49,"citation_normalized_percentile":{"value":0.94002789,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"14","issue":"16","first_page":"3937","last_page":"3937"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7136066555976868},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7091761231422424},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6992436051368713},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6528502702713013},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6249813437461853},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6042271852493286},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5345954298973083},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46155500411987305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37023788690567017},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3428173065185547},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14990323781967163}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7136066555976868},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7091761231422424},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6992436051368713},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6528502702713013},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6249813437461853},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6042271852493286},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5345954298973083},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46155500411987305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37023788690567017},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3428173065185547},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14990323781967163}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/rs14163937","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163937","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3937/pdf?version=1660891059","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f94c1165316a4f7a8e35292cdf65bb29","is_oa":true,"landing_page_url":"https://doaj.org/article/f94c1165316a4f7a8e35292cdf65bb29","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 16, p 3937 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/16/3937/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14163937","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 14; Issue 16; Pages: 3937","raw_type":"Text"},{"id":"pmh:oai:noaa.stacks:noaa:48219","is_oa":true,"landing_page_url":"https://repository.library.noaa.gov/view/noaa/48219","pdf_url":null,"source":{"id":"https://openalex.org/S4377196172","display_name":"NOAA Institutional Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, 14(16), 3937","raw_type":null}],"best_oa_location":{"id":"doi:10.3390/rs14163937","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163937","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3937/pdf?version=1660891059","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G3303921695","display_name":null,"funder_award_id":"NA18NOS400198","funder_id":"https://openalex.org/F4320332181","funder_display_name":"National Oceanic and Atmospheric Administration"},{"id":"https://openalex.org/G7025818214","display_name":null,"funder_award_id":"No. NA18NOS400198","funder_id":"https://openalex.org/F4320332181","funder_display_name":"National Oceanic and Atmospheric Administration"}],"funders":[{"id":"https://openalex.org/F4320306111","display_name":"U.S. Department of Commerce","ror":"https://ror.org/04chq2495"},{"id":"https://openalex.org/F4320309142","display_name":"University of Southern Mississippi","ror":"https://ror.org/0270vfa57"},{"id":"https://openalex.org/F4320332181","display_name":"National Oceanic and Atmospheric Administration","ror":"https://ror.org/02z5nhe81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292058731.pdf","grobid_xml":"https://content.openalex.org/works/W4292058731.grobid-xml"},"referenced_works_count":101,"referenced_works":["https://openalex.org/W1582401049","https://openalex.org/W1748103765","https://openalex.org/W1965711210","https://openalex.org/W1975919281","https://openalex.org/W1989304191","https://openalex.org/W2000175358","https://openalex.org/W2027254180","https://openalex.org/W2027652188","https://openalex.org/W2046736943","https://openalex.org/W2049468624","https://openalex.org/W2065407071","https://openalex.org/W2067593534","https://openalex.org/W2067804762","https://openalex.org/W2073389462","https://openalex.org/W2102662878","https://openalex.org/W2124313187","https://openalex.org/W2136251662","https://openalex.org/W2142069533","https://openalex.org/W2154636369","https://openalex.org/W2163922914","https://openalex.org/W2164500538","https://openalex.org/W2172000360","https://openalex.org/W2194775991","https://openalex.org/W2253409621","https://openalex.org/W2340703094","https://openalex.org/W2345498906","https://openalex.org/W2412782625","https://openalex.org/W2557392958","https://openalex.org/W2560023338","https://openalex.org/W2594502636","https://openalex.org/W2613180894","https://openalex.org/W2707899687","https://openalex.org/W2767106145","https://openalex.org/W2770340534","https://openalex.org/W2781910890","https://openalex.org/W2782865993","https://openalex.org/W2792247930","https://openalex.org/W2793091350","https://openalex.org/W2794295145","https://openalex.org/W2794477125","https://openalex.org/W2802420741","https://openalex.org/W2808628447","https://openalex.org/W2883925605","https://openalex.org/W2884436604","https://openalex.org/W2885667473","https://openalex.org/W2887347313","https://openalex.org/W2901578632","https://openalex.org/W2911964244","https://openalex.org/W2913950219","https://openalex.org/W2914994652","https://openalex.org/W2923200088","https://openalex.org/W2930359273","https://openalex.org/W2940726923","https://openalex.org/W2943484762","https://openalex.org/W2944366268","https://openalex.org/W2945665377","https://openalex.org/W2950123062","https://openalex.org/W2951680932","https://openalex.org/W2955442343","https://openalex.org/W2963268125","https://openalex.org/W2964513112","https://openalex.org/W2970077477","https://openalex.org/W2971419816","https://openalex.org/W2978092824","https://openalex.org/W2984592175","https://openalex.org/W2991744134","https://openalex.org/W2996130250","https://openalex.org/W2999309192","https://openalex.org/W3000567086","https://openalex.org/W3014754275","https://openalex.org/W3019494121","https://openalex.org/W3024885429","https://openalex.org/W3049157029","https://openalex.org/W3082985050","https://openalex.org/W3083900876","https://openalex.org/W3086130142","https://openalex.org/W3087438473","https://openalex.org/W3088162569","https://openalex.org/W3098235274","https://openalex.org/W3129073052","https://openalex.org/W3133932554","https://openalex.org/W3140854437","https://openalex.org/W3181141392","https://openalex.org/W3182315975","https://openalex.org/W3182496347","https://openalex.org/W3185840992","https://openalex.org/W4200371673","https://openalex.org/W4200429143","https://openalex.org/W4205978841","https://openalex.org/W4206667434","https://openalex.org/W4212905630","https://openalex.org/W4212955958","https://openalex.org/W4225765803","https://openalex.org/W4239510810","https://openalex.org/W4301802631","https://openalex.org/W6748277205","https://openalex.org/W6751139507","https://openalex.org/W6798280765","https://openalex.org/W6806372158","https://openalex.org/W6808045094","https://openalex.org/W7018413738"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W4318664220","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W2004826645"],"abstract_inverted_index":{"The":[0,121,187],"recent":[1],"developments":[2],"of":[3,19,27,34,79,123,146,172,221],"new":[4],"deep":[5,39,105,140,183,223,261],"learning":[6,47,106,119,141,175,184,224,262],"architectures":[7,42],"create":[8],"opportunities":[9],"to":[10,58,126,226,246],"accurately":[11],"classify":[12,227],"high-resolution":[13,228],"unoccupied":[14],"aerial":[15,70],"system":[16],"(UAS)":[17],"images":[18,230],"natural":[20],"coastal":[21,60,234],"systems":[22],"and":[23,37,43,53,72,82,99,114,153,166,198],"mandate":[24],"continuous":[25],"evaluation":[26],"algorithm":[28],"performance.":[29],"We":[30,236],"evaluated":[31],"the":[32,35,89,94,100,109,115,124,127,132,139,144,151,160,170,173,182,190,207,219],"performance":[33,171],"U-Net":[36,95],"DeepLabv3":[38],"convolutional":[40],"network":[41],"two":[44],"traditional":[45],"machine":[46,51,118,174],"techniques":[48,107],"(support":[49],"vector":[50],"(SVM)":[52],"random":[54],"forest":[55],"(RF))":[56],"applied":[57],"seventeen":[59],"land":[61],"cover":[62],"types":[63],"in":[64,138,189,206,231,256],"west":[65],"Florida":[66],"using":[67,88,157,195,222],"UAS":[68,229],"multispectral":[69,247],"imagery":[71,242,248],"canopy":[73],"height":[74],"models":[75],"(CHM).":[76],"Twelve":[77],"combinations":[78],"spectral":[80,90,128,162],"bands":[81,91,129,158],"CHMs":[83],"were":[84],"used.":[85],"Our":[86,216],"results":[87,217,244],"showed":[92],"that":[93,239,249],"(83.80\u201385.27%":[96],"overall":[97,103,112,133,191],"accuracy)":[98,104,113],"DeepLabV3":[101],"(75.20\u201383.50%":[102],"outperformed":[108],"SVM":[110,152],"(60.50\u201371.10%":[111],"RF":[116,154],"(57.40\u201371.0%)":[117],"algorithms.":[120],"addition":[122,145],"CHM":[125,148],"slightly":[130],"increased":[131,169],"accuracy":[134,258],"as":[135,202],"a":[136,147,253],"whole":[137],"models,":[142],"while":[143],"notably":[149],"improved":[150],"results.":[155,186],"Similarly,":[156],"outside":[159],"three":[161],"bands,":[163],"namely,":[164],"near-infrared":[165],"red":[167],"edge,":[168],"classifiers":[176],"but":[177],"had":[178],"minimal":[179,211],"impact":[180],"on":[181],"classification":[185,208,214,257],"difference":[188],"accuracies":[192],"produced":[193],"by":[194],"UAS-based":[196],"lidar":[197],"SfM":[199],"point":[200],"clouds,":[201],"supplementary":[203],"geometrical":[204],"information,":[205],"process":[209],"was":[210],"across":[212],"all":[213],"techniques.":[215],"highlight":[218],"advantage":[220],"networks":[225],"highly":[232],"diverse":[233],"landscapes.":[235],"also":[237],"found":[238],"low-cost,":[240],"three-visible-band":[241],"produces":[243],"comparable":[245],"do":[250],"not":[251],"risk":[252],"significant":[254],"reduction":[255],"when":[259],"adopting":[260],"models.":[263]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2022-08-17T00:00:00"}
