{"id":"https://openalex.org/W3206797642","doi":"https://doi.org/10.1609/aaai.v36i4.20372","title":"Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration","display_name":"Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration","publication_year":2022,"publication_date":"2022-06-28","ids":{"openalex":"https://openalex.org/W3206797642","doi":"https://doi.org/10.1609/aaai.v36i4.20372","mag":"3206797642"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v36i4.20372","doi":"https://doi.org/10.1609/aaai.v36i4.20372","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i4.20372","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v36i4.20372","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102427250","display_name":"Sijie He","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]},{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sijie He","raw_affiliation_strings":["Department of Computer Science & Engineering, University of Minnesota, Twin Cities\nDepartment of Computer Science, University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of Minnesota, Twin Cities\nDepartment of Computer Science, University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I130238516","https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100439403","display_name":"Xinyan Li","orcid":"https://orcid.org/0000-0003-3541-7976","observed_orcids":["https://orcid.org/0000-0003-3541-7976"]},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinyan Li","raw_affiliation_strings":["Department of Computer Science & Engineering, University of Minnesota, Twin Cities"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of Minnesota, Twin Cities","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019988239","display_name":"Laurie Trenary","orcid":"https://orcid.org/0000-0002-8874-3743","observed_orcids":["https://orcid.org/0000-0002-8874-3743"]},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Laurie Trenary","raw_affiliation_strings":["Department of Atmospheric, Oceanic, and Earth Science, George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Atmospheric, Oceanic, and Earth Science, George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035564665","display_name":"Benjamin A. Cash","orcid":"https://orcid.org/0000-0003-2295-3112","observed_orcids":["https://orcid.org/0000-0003-2295-3112"]},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin A Cash","raw_affiliation_strings":["Department of Atmospheric, Oceanic, and Earth Science, George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Atmospheric, Oceanic, and Earth Science, George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045680211","display_name":"Timothy DelSole","orcid":"https://orcid.org/0000-0003-2041-3024","observed_orcids":["https://orcid.org/0000-0003-2041-3024"]},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Timothy DelSole","raw_affiliation_strings":["Department of Atmospheric, Oceanic, and Earth Science, George Mason University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Atmospheric, Oceanic, and Earth Science, George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014459472","display_name":"Arindam Banerjee","orcid":"https://orcid.org/0000-0002-7856-5699","observed_orcids":["https://orcid.org/0000-0002-7856-5699"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arindam Banerjee","raw_affiliation_strings":["Department of Computer Science, University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":29.1237,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.99900464,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"36","issue":"4","first_page":"4495","last_page":"4503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.482699990272522,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.482699990272522,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.44530001282691956,"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"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.02280000038444996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning-forecasting-models","display_name":"machine learning forecasting models","score":0.6930000185966492},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.6669999957084656},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"ensemble forecasting","score":0.4830000102519989},{"id":"https://openalex.org/keywords/cold-waves","display_name":"cold waves","score":0.6480000019073486},{"id":"https://openalex.org/keywords/polar-vortex","display_name":"polar vortex","score":0.9900000095367432},{"id":"https://openalex.org/keywords/extreme-weather-events","display_name":"extreme weather events","score":0.7039999961853027},{"id":"https://openalex.org/keywords/forecast-bias","display_name":"forecast bias","score":0.5040000081062317}],"concepts":[{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.5494347810745239},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.48416072130203247},{"id":"https://openalex.org/C205537798","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Extreme weather","level":3,"score":0.45785295963287354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4490608870983124},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.42700234055519104},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.425128310918808},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.28914937376976013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2550356984138489},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17683342099189758},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11607670783996582},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1609/aaai.v36i4.20372","doi":"https://doi.org/10.1609/aaai.v36i4.20372","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i4.20372","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2110.05196","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.05196","pdf_url":"https://arxiv.org/pdf/2110.05196","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ojs.aaai.org:article/20372","doi":null,"is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/20372","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/20372/20131","source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2110.05196","doi":"https://doi.org/10.48550/arxiv.2110.05196","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.05196","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1609/aaai.v36i4.20372","doi":"https://doi.org/10.1609/aaai.v36i4.20372","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i4.20372","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9786999821662903,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1377627663","display_name":"BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data","funder_award_id":"1447566","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G157385947","display_name":"Collaborative Research: Physics-Based Machine Learning for Sub-Seasonal Climate Forecasting","funder_award_id":"1934634","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2839271977","display_name":"III: Small: Stochastic Algorithms for Large Scale Data Analysis","funder_award_id":"1908104","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4319621678","display_name":"BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data","funder_award_id":"1447574","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5521036603","display_name":"III: Medium: Collaborative Research: Bayesian Modeling and Inference for Quantifying Terrestrial Ecosystem Functions","funder_award_id":"1563950","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G590079505","display_name":"RI: Small: Finding Patterns in Complex Data with Probablistic Graphical Models","funder_award_id":"1422557","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G947792794","display_name":"EAGER: Collaborative Research: Learning Relations between Extreme Weather Events and Planet-Wide Environmental Trends","funder_award_id":"1451986","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3206797642.pdf","grobid_xml":"https://content.openalex.org/works/W3206797642.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1525701729","https://openalex.org/W1590739740","https://openalex.org/W1678356000","https://openalex.org/W1849556524","https://openalex.org/W1926186885","https://openalex.org/W1965413418","https://openalex.org/W1972568755","https://openalex.org/W1980154793","https://openalex.org/W2002521620","https://openalex.org/W2043436896","https://openalex.org/W2052797771","https://openalex.org/W2055382430","https://openalex.org/W2060172488","https://openalex.org/W2088702514","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2114688600","https://openalex.org/W2130942839","https://openalex.org/W2131070146","https://openalex.org/W2135046866","https://openalex.org/W2136848157","https://openalex.org/W2140603473","https://openalex.org/W2141394518","https://openalex.org/W2147169375","https://openalex.org/W2153529786","https://openalex.org/W2159765473","https://openalex.org/W2173251738","https://openalex.org/W2176050019","https://openalex.org/W2295598076","https://openalex.org/W2345707736","https://openalex.org/W2471266319","https://openalex.org/W2515684462","https://openalex.org/W2583306805","https://openalex.org/W2610217940","https://openalex.org/W2611772571","https://openalex.org/W2625614184","https://openalex.org/W2754944037","https://openalex.org/W2790835752","https://openalex.org/W2809789958","https://openalex.org/W2947626232","https://openalex.org/W2956255334","https://openalex.org/W2960668489","https://openalex.org/W2964178568","https://openalex.org/W2970079308","https://openalex.org/W2973731563","https://openalex.org/W2999917762","https://openalex.org/W3014027346","https://openalex.org/W3102476541","https://openalex.org/W3108142464","https://openalex.org/W3110731194","https://openalex.org/W3128630349","https://openalex.org/W3174383901","https://openalex.org/W3186390609","https://openalex.org/W4238597082","https://openalex.org/W6910681941"],"related_works":["https://openalex.org/W3174383901","https://openalex.org/W7133240682","https://openalex.org/W7126236681","https://openalex.org/W4401112323","https://openalex.org/W7155600441","https://openalex.org/W2903758338","https://openalex.org/W3201404958","https://openalex.org/W4389921600","https://openalex.org/W7165857410","https://openalex.org/W4411806408"],"abstract_inverted_index":{"Sub-seasonal":[0],"forecasting":[1,66,172,213],"(SSF)":[2],"is":[3],"the":[4,16,75,89,110,118,129,150,162,183,187,212,216,224,229,236,239,242],"prediction":[5],"of":[6,77,97,100,215,241],"key":[7],"climate":[8],"variables":[9],"such":[10,31,46],"as":[11,32,47,204],"temperature":[12],"and":[13,36,40,49,67,226],"precipitation":[14],"on":[15,142],"2-week":[17],"to":[18,62,82,127,154,161,182,206],"2-month":[19],"time":[20],"horizon.":[21],"Skillful":[22],"SSF":[23,55,116,220],"would":[24],"have":[25,73],"substantial":[26],"societal":[27,53],"value":[28],"in":[29,117],"areas":[30],"agricultural":[33],"productivity,":[34],"hydrology":[35],"water":[37],"resource":[38],"management,":[39],"emergency":[41],"planning":[42],"for":[43,88,115,189,192,223,228,238],"extreme":[44,175,193],"events":[45],"droughts":[48],"wildfires.":[50],"Despite":[51],"its":[52],"importance,":[54],"has":[56],"stayed":[57],"a":[58,94,98],"challenging":[59],"problem":[60],"compared":[61,160],"both":[63],"short-term":[64],"weather":[65,176],"long-term":[68],"seasonal":[69],"forecasting.":[70],"Recent":[71],"studies":[72],"shown":[74],"potential":[76],"machine":[78],"learning":[79],"(ML)":[80],"models":[81,103,108,131,145,148,152,170,191,208,231],"advance":[83],"SSF.":[84],"In":[85],"this":[86],"paper,":[87],"first":[90],"time,":[91],"we":[92,124,166,196],"perform":[93],"fine-grained":[95],"comparison":[96],"suite":[99],"modern":[101],"ML":[102,130,144,151,169,207,217,230],"with":[104,157,235],"start-of-the-art":[105],"physics-based":[106],"dynamical":[107,136,147,201],"from":[109,135],"Subseasonal":[111],"Experiment":[112],"(SubX)":[113],"project":[114],"western":[119],"contiguous":[120],"United":[121],"States.":[122],"Additionally,":[123],"explore":[125],"mechanisms":[126],"enhance":[128],"by":[132],"using":[133],"forecasts":[134,156,203],"models.":[137,164,218],"Empirical":[138],"results":[139],"illustrate":[140,167],"that,":[141],"average,":[143],"outperform":[146],"while":[149],"tend":[153],"generate":[155],"conservative":[158],"magnitude":[159],"SubX":[163],"Further,":[165],"that":[168,198],"make":[171],"errors":[173],"under":[174],"conditions,":[177],"e.g.,":[178],"cold":[179],"waves":[180],"due":[181],"polar":[184],"vortex,":[185],"highlighting":[186],"need":[188],"separate":[190],"events.":[194],"Finally,":[195],"show":[197],"suitably":[199],"incorporating":[200],"model":[202],"inputs":[205],"can":[209],"substantially":[210],"improve":[211],"performance":[214],"The":[219],"dataset":[221],"constructed":[222],"work":[225],"code":[227],"are":[232],"released":[233],"along":[234],"paper":[237],"benefit":[240],"artificial":[243],"intelligence":[244],"community.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-10-25T00:00:00"}
