{"id":"https://openalex.org/W7127067666","doi":"https://doi.org/10.3390/computation14020033","title":"Integrative Nutritional Assessment of Avocado Leaves Using Entropy-Weighted Spectral Indices and Fusion Learning","display_name":"Integrative Nutritional Assessment of Avocado Leaves Using Entropy-Weighted Spectral Indices and Fusion Learning","publication_year":2026,"publication_date":"2026-02-01","ids":{"openalex":"https://openalex.org/W7127067666","doi":"https://doi.org/10.3390/computation14020033"},"language":"en","primary_location":{"id":"doi:10.3390/computation14020033","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computation14020033","pdf_url":null,"source":{"id":"https://openalex.org/S2738402919","display_name":"Computation","issn_l":"2079-3197","issn":["2079-3197"],"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":"Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/computation14020033","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124832263","display_name":"Zhen Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I196934937","display_name":"Liaocheng University","ror":"https://ror.org/03yh0n709","country_code":"CN","type":"education","lineage":["https://openalex.org/I196934937"]},{"id":"https://openalex.org/I4210105641","display_name":"Peptide Protein Research (United Kingdom)","ror":"https://ror.org/01e8xap89","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210105641"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Zhen Guo","raw_affiliation_strings":["School of Pharmaceutical Science and Food Engineering, Liaocheng University, Liaocheng 252059, China","Shandong Key Laboratory of Applied Technology for Protein and Peptide Drugs, Liaocheng 252059, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Pharmaceutical Science and Food Engineering, Liaocheng University, Liaocheng 252059, China","institution_ids":["https://openalex.org/I196934937"]},{"raw_affiliation_string":"Shandong Key Laboratory of Applied Technology for Protein and Peptide Drugs, Liaocheng 252059, China","institution_ids":["https://openalex.org/I4210105641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124806058","display_name":"Juan Sebastian Estrada","orcid":null},"institutions":[{"id":"https://openalex.org/I75778554","display_name":"Federico Santa Mar\u00eda Technical University","ror":"https://ror.org/05510vn56","country_code":"CL","type":"education","lineage":["https://openalex.org/I75778554"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Juan Sebastian Estrada","raw_affiliation_strings":["Department of Electronic Engineering, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Av. Espa\u00f1a 1680, Valparaiso 2390123, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Av. Espa\u00f1a 1680, Valparaiso 2390123, Chile","institution_ids":["https://openalex.org/I75778554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077469644","display_name":"Xingfeng Guo","orcid":"https://orcid.org/0000-0002-5251-8413"},"institutions":[{"id":"https://openalex.org/I196934937","display_name":"Liaocheng University","ror":"https://ror.org/03yh0n709","country_code":"CN","type":"education","lineage":["https://openalex.org/I196934937"]},{"id":"https://openalex.org/I4210105641","display_name":"Peptide Protein Research (United Kingdom)","ror":"https://ror.org/01e8xap89","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210105641"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Xingfeng Guo","raw_affiliation_strings":["School of Pharmaceutical Science and Food Engineering, Liaocheng University, Liaocheng 252059, China","Shandong Key Laboratory of Applied Technology for Protein and Peptide Drugs, Liaocheng 252059, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Pharmaceutical Science and Food Engineering, Liaocheng University, Liaocheng 252059, China","institution_ids":["https://openalex.org/I196934937"]},{"raw_affiliation_string":"Shandong Key Laboratory of Applied Technology for Protein and Peptide Drugs, Liaocheng 252059, China","institution_ids":["https://openalex.org/I4210105641"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Redmond R. Shamshiri","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146027","display_name":"Leibniz Institute for Agricultural Engineering and Bioeconomy","ror":"https://ror.org/04d62a771","country_code":"DE","type":"facility","lineage":["https://openalex.org/I315704651","https://openalex.org/I4210146027"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Redmond R. Shamshiri","raw_affiliation_strings":["Department of Agromechatronics, Leibniz-Institut f\u00fcr Agrartechnik und Bio\u00f6konomie e.V., Max-Eyth-Allee 100, 14469 Potsdam, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Agromechatronics, Leibniz-Institut f\u00fcr Agrartechnik und Bio\u00f6konomie e.V., Max-Eyth-Allee 100, 14469 Potsdam, Germany","institution_ids":["https://openalex.org/I4210146027"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Marcelo Pereyra","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117017","display_name":"Maxwell Institute for Mathematical Sciences","ror":"https://ror.org/02tsqtg57","country_code":"GB","type":"facility","lineage":["https://openalex.org/I32062511","https://openalex.org/I4210117017","https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Marcelo Pereyra","raw_affiliation_strings":["School of Mathematical and Computer Science, Heriot-Watt University & Maxwell Institute for Mathematical Sciences, Edinburgh EH14 4AS, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical and Computer Science, Heriot-Watt University & Maxwell Institute for Mathematical Sciences, Edinburgh EH14 4AS, UK","institution_ids":["https://openalex.org/I4210117017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061931953","display_name":"Fernando Auat Cheein","orcid":"https://orcid.org/0000-0002-6347-7696"},"institutions":[{"id":"https://openalex.org/I75778554","display_name":"Federico Santa Mar\u00eda Technical University","ror":"https://ror.org/05510vn56","country_code":"CL","type":"education","lineage":["https://openalex.org/I75778554"]},{"id":"https://openalex.org/I918000572","display_name":"Harper Adams University","ror":"https://ror.org/00z20c921","country_code":"GB","type":"education","lineage":["https://openalex.org/I918000572"]}],"countries":["CL","GB"],"is_corresponding":false,"raw_author_name":"Fernando Auat Cheein","raw_affiliation_strings":["Department of Agricultural Engineering, Harper Adams University, Newport TF10 8NB, Shropshire, UK","Department of Electronic Engineering, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Av. Espa\u00f1a 1680, Valparaiso 2390123, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Agricultural Engineering, Harper Adams University, Newport TF10 8NB, Shropshire, UK","institution_ids":["https://openalex.org/I918000572"]},{"raw_affiliation_string":"Department of Electronic Engineering, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Av. Espa\u00f1a 1680, Valparaiso 2390123, Chile","institution_ids":["https://openalex.org/I75778554"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":9.9496,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94740038,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"14","issue":"2","first_page":"33","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.5684999823570251,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.5684999823570251,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.163100004196167,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.15399999916553497,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.83160001039505},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.4722000062465668},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4255000054836273},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4120999872684479},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.40459999442100525},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4027000069618225},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.3797000050544739},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.35659998655319214}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.83160001039505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48579999804496765},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46959999203681946},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4406000077724457},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3725999891757965},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33230000734329224},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.29829999804496765},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C25989453","wikidata":"https://www.wikidata.org/wiki/Q446746","display_name":"Leaf area index","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/computation14020033","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computation14020033","pdf_url":null,"source":{"id":"https://openalex.org/S2738402919","display_name":"Computation","issn_l":"2079-3197","issn":["2079-3197"],"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":"Computation","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4fd03717799947f5b706e2a9f847a6c0","is_oa":false,"landing_page_url":"https://doaj.org/article/4fd03717799947f5b706e2a9f847a6c0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computation, Vol 14, Iss 2, p 33 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/computation14020033","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computation14020033","pdf_url":null,"source":{"id":"https://openalex.org/S2738402919","display_name":"Computation","issn_l":"2079-3197","issn":["2079-3197"],"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":"Computation","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.7398383021354675,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W588489631","https://openalex.org/W1487726742","https://openalex.org/W1821991996","https://openalex.org/W1976985468","https://openalex.org/W1980252131","https://openalex.org/W1982755765","https://openalex.org/W1997270149","https://openalex.org/W2001739950","https://openalex.org/W2073503722","https://openalex.org/W2094639611","https://openalex.org/W2094780060","https://openalex.org/W2131830373","https://openalex.org/W2602504657","https://openalex.org/W2867424916","https://openalex.org/W2883393949","https://openalex.org/W2892350612","https://openalex.org/W3010807909","https://openalex.org/W3015650736","https://openalex.org/W3023313908","https://openalex.org/W3115507469","https://openalex.org/W3127883868","https://openalex.org/W3144237733","https://openalex.org/W3162413463","https://openalex.org/W3199553515","https://openalex.org/W4293353475","https://openalex.org/W4296199842","https://openalex.org/W4309776487","https://openalex.org/W4383645964","https://openalex.org/W4386798595","https://openalex.org/W4386947036","https://openalex.org/W4388115034","https://openalex.org/W4389113571","https://openalex.org/W4389580445","https://openalex.org/W4391486074","https://openalex.org/W4398248418","https://openalex.org/W4400143252","https://openalex.org/W4400269193","https://openalex.org/W4400967375","https://openalex.org/W4403263646","https://openalex.org/W4404878953","https://openalex.org/W4405484431","https://openalex.org/W4405809774","https://openalex.org/W4405922946","https://openalex.org/W4406211871","https://openalex.org/W4406738413","https://openalex.org/W4406788035","https://openalex.org/W4407962323","https://openalex.org/W4409182503","https://openalex.org/W4409450353","https://openalex.org/W4409904310","https://openalex.org/W4410610484"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,76,82,93,123,139,150,159,165,173,213,234,250],"non-destructive":[2],"assessment":[3,32,64],"of":[4,95,211],"plant":[5,83,247],"nutritional":[6,31,49],"status":[7],"remains":[8],"a":[9,242],"key":[10],"challenge":[11],"in":[12,209],"precision":[13,252],"agriculture,":[14],"particularly":[15],"under":[16],"dynamic":[17],"physiological":[18,68],"conditions":[19],"such":[20,70],"as":[21,71],"dehydration.":[22],"Therefore,":[23],"this":[24,224],"study":[25],"focused":[26,219],"on":[27,220],"developing":[28],"an":[29,57,227],"integrated":[30,66],"framework":[33,229],"for":[34,198,245],"avocado":[35],"(Persea":[36],"americana":[37],"Mill.)":[38],"leaves":[39],"across":[40],"progressive":[41],"dehydration":[42],"stages":[43],"using":[44,56,108],"spectral":[45,157,222],"analysis.":[46],"A":[47],"novel":[48],"function":[50],"index":[51],"(NFI)":[52],"was":[53],"innovatively":[54],"constructed":[55,107],"entropy-weighted":[58,231],"multi-criteria":[59],"decision-making":[60],"approach.":[61],"This":[62,201],"unified":[63],"metric":[65],"critical":[67],"indicators,":[69],"moisture":[72,138],"content,":[73,75],"nitrogen":[74],"chlorophyll":[77],"content":[78],"estimated":[79],"from":[80,115],"soil":[81],"analyzer":[84],"development":[85],"(SPAD)":[86],"readings.":[87],"To":[88],"enhance":[89],"the":[90,146,188],"prediction":[91,179],"accuracy":[92,191,212],"interpretability":[94],"NFI,":[96],"innovative":[97],"vegetation":[98],"indices":[99],"(VIs)":[100],"specifically":[101],"tailored":[102],"to":[103,128,137],"NFI":[104,199],"were":[105,126,134],"systematically":[106],"exhaustive":[109],"wavelength-combination":[110],"screening.":[111],"Optimal":[112],"wavelengths":[113,143],"identified":[114],"short-wave":[116],"infrared":[117],"regions":[118],"(1446,":[119],"1455,":[120],"1465,":[121],"1865,":[122],"1937":[124],"nm)":[125],"employed":[127],"build":[129],"physiologically":[130],"meaningful":[131],"VIs,":[132],"which":[133],"highly":[135],"sensitive":[136],"biochemical":[140],"constituents.":[141],"Feature":[142],"selected":[144,174],"via":[145],"successive":[147],"projections":[148],"algorithm":[149,183],"competitive":[151],"adaptive":[152],"reweighted":[153],"sampling":[154],"further":[155],"reduced":[156],"redundancy":[158],"improved":[160],"modeling":[161,203],"efficiency.":[162],"Both":[163],"feature-level":[164],"algorithm-level":[166],"data":[167],"fusion":[168,236],"methods":[169],"effectively":[170],"combined":[171],"VIs":[172],"feature":[175,232],"wavelengths,":[176],"significantly":[177],"enhancing":[178],"performance.":[180],"The":[181,238],"stacking":[182],"demonstrated":[184],"robust":[185],"performance,":[186],"achieving":[187],"highest":[189],"predictive":[190],"(R2V":[192],"=":[193,196],"0.986,":[194],"RMSEV":[195],"0.032)":[197],"estimation.":[200],"fusion-based":[202],"approach":[204],"outperformed":[205],"conventional":[206],"single-model":[207],"schemes":[208],"terms":[210],"robustness.":[214],"Unlike":[215],"previous":[216],"studies":[217],"that":[218],"isolated":[221],"predictors,":[223],"work":[225],"introduces":[226],"integrative":[228],"combining":[230],"synthesis":[233],"multiscale":[235],"learning.":[237],"developed":[239],"strategy":[240],"offers":[241],"powerful":[243],"tool":[244],"real-time":[246],"health":[248],"monitoring":[249],"supports":[251],"agricultural":[253],"decision-making.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-02-03T00:00:00"}
