{"id":"https://openalex.org/W4385763789","doi":"https://doi.org/10.24963/ijcai.2023/664","title":"CGS: Coupled Growth and Survival Model with Cohort Fairness","display_name":"CGS: Coupled Growth and Survival Model with Cohort Fairness","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385763789","doi":"https://doi.org/10.24963/ijcai.2023/664"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/664","is_oa":true,"landing_page_url":"http://dx.doi.org/10.24963/ijcai.2023/664","pdf_url":"https://www.ijcai.org/proceedings/2023/0664.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0664.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082048859","display_name":"Erhu He","orcid":"https://orcid.org/0000-0002-2949-6500"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erhu He","raw_affiliation_strings":["University of Pittsburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102150304","display_name":"Yue Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Wan","raw_affiliation_strings":["University of Pittsburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076551257","display_name":"Benjamin H. Letcher","orcid":"https://orcid.org/0000-0003-0191-5678"},"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":"Benjamin H. Letcher","raw_affiliation_strings":["U.S. Geological Survey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"U.S. Geological Survey","institution_ids":["https://openalex.org/I1286329397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076715086","display_name":"Jennifer H. Fair","orcid":"https://orcid.org/0000-0002-9902-1893"},"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":"Jennifer H. Fair","raw_affiliation_strings":["U.S. Geological Survey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"U.S. Geological Survey","institution_ids":["https://openalex.org/I1286329397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049041437","display_name":"Yiqun Xie","orcid":"https://orcid.org/0000-0002-6439-1333"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqun Xie","raw_affiliation_strings":["University of Maryland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Maryland","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001445783","display_name":"Xiaowei Jia","orcid":"https://orcid.org/0000-0001-8544-5233"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaowei Jia","raw_affiliation_strings":["University of Pittsburgh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5986","last_page":"5994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10302","display_name":"Fish Ecology and Management Studies","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"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/T10302","display_name":"Fish Ecology and Management Studies","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"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":0.9883000254631042,"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/T13398","display_name":"Data Analysis with R","score":0.9623000025749207,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fish-actinopterygii","display_name":"Fish <Actinopterygii>","score":0.643777072429657},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.634637713432312},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5588812232017517},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.42917120456695557},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.42427316308021545},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41173556447029114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3213803470134735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14602956175804138},{"id":"https://openalex.org/keywords/fishery","display_name":"Fishery","score":0.14378204941749573},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.08043709397315979},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.07766076922416687}],"concepts":[{"id":"https://openalex.org/C2909208804","wikidata":"https://www.wikidata.org/wiki/Q127282","display_name":"Fish <Actinopterygii>","level":2,"score":0.643777072429657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.634637713432312},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5588812232017517},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.42917120456695557},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.42427316308021545},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41173556447029114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3213803470134735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14602956175804138},{"id":"https://openalex.org/C505870484","wikidata":"https://www.wikidata.org/wiki/Q180538","display_name":"Fishery","level":1,"score":0.14378204941749573},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.08043709397315979},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.07766076922416687},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2023/664","is_oa":true,"landing_page_url":"http://dx.doi.org/10.24963/ijcai.2023/664","pdf_url":"https://www.ijcai.org/proceedings/2023/0664.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/664","is_oa":true,"landing_page_url":"http://dx.doi.org/10.24963/ijcai.2023/664","pdf_url":"https://www.ijcai.org/proceedings/2023/0664.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.4000000059604645,"display_name":"Life below water"}],"awards":[{"id":"https://openalex.org/G1242460129","display_name":null,"funder_award_id":"80NSSC22K1164","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"},{"id":"https://openalex.org/G2140261817","display_name":null,"funder_award_id":"G21AC10564","funder_id":"https://openalex.org/F4320332183","funder_display_name":"U.S. Geological Survey"},{"id":"https://openalex.org/G327174889","display_name":"FAI: Advancing Deep Learning Towards Spatial Fairness","funder_award_id":"2147195","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6418605821","display_name":"CRII: III: Discovering Complex Mixture Patterns in Spatial Data to Advance Resilience of Communities","funder_award_id":"2105133","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7889451768","display_name":"Collaborative Research: EarthCube Capabilities: ICESpark: An Open-Source Big Data Platform for Science Discoveries in the New Arctic and Beyond","funder_award_id":"2126474","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"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320310174","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305"},{"id":"https://openalex.org/F4320322037","display_name":"Nuclear Safety and Security Commission","ror":"https://ror.org/05qk3ge34"},{"id":"https://openalex.org/F4320332183","display_name":"U.S. Geological Survey","ror":"https://ror.org/035a68863"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385763789.pdf"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1800904308","https://openalex.org/W1959608418","https://openalex.org/W1979787558","https://openalex.org/W1990106629","https://openalex.org/W2018584279","https://openalex.org/W2040825624","https://openalex.org/W2081756197","https://openalex.org/W2100897971","https://openalex.org/W2125486891","https://openalex.org/W2155702894","https://openalex.org/W2169072480","https://openalex.org/W2540757487","https://openalex.org/W2547875792","https://openalex.org/W2949672904","https://openalex.org/W2972255760","https://openalex.org/W2981673074","https://openalex.org/W3013973393","https://openalex.org/W3018301625","https://openalex.org/W3135775603","https://openalex.org/W3146711999","https://openalex.org/W3175709094","https://openalex.org/W3210018437","https://openalex.org/W4206905964","https://openalex.org/W4211064163","https://openalex.org/W4224299968","https://openalex.org/W4283790515","https://openalex.org/W4288317007","https://openalex.org/W4309651993"],"related_works":["https://openalex.org/W2315118483","https://openalex.org/W4323083255","https://openalex.org/W4238085926","https://openalex.org/W3018365851","https://openalex.org/W592721966","https://openalex.org/W3207484021","https://openalex.org/W1511221085","https://openalex.org/W4240425519","https://openalex.org/W3095700829","https://openalex.org/W4236809463"],"abstract_inverted_index":{"Fish":[0],"modeling":[1,24,165],"in":[2,13,59,99,110,151,164,203],"complex":[3,96],"environments":[4],"is":[5],"critical":[6],"for":[7,23,130],"understanding":[8],"drivers":[9,43],"of":[10,40,120,136],"population":[11],"dynamics":[12],"aquatic":[14],"systems.":[15],"This":[16],"paper":[17],"proposes":[18],"a":[19,70,146],"Bayesian":[20,86],"network":[21,91],"method":[22,158],"fish":[25,34,112,121,148,187],"survival":[26,35,107,166,175],"and":[27,57,62,139,167,176,178,189,212],"growth":[28,188],"over":[29],"multiple":[30,41],"connected":[31],"rivers.":[32],"Traditional":[33],"models":[36,173],"capture":[37,95],"the":[38,76,85,89,100,117,134,156,181,194,204],"effect":[39],"environmental":[42],"(e.g.,":[44],"stream":[45,47],"temperature,":[46],"flow)":[48],"by":[49,133,193],"adding":[50],"different":[51],"variables,":[52],"which":[53],"increases":[54],"model":[55,73,92,196],"complexity":[56],"results":[58,143],"very":[60],"long":[61],"impractical":[63],"run":[64,210],"times":[65,211],"(i.e.,":[66],"weeks).":[67],"We":[68],"propose":[69,127],"coupled":[71],"survival-growth":[72],"that":[74,155],"leverages":[75],"observations":[77],"from":[78],"both":[79],"sources":[80],"simultaneously.":[81],"It":[82],"also":[83,103,198],"integrates":[84],"process":[87],"into":[88],"neural":[90],"to":[93,105,171],"efficiently":[94],"variable":[97],"relationships":[98],"system":[101],"while":[102,207],"conforming":[104],"known":[106],"processes":[108],"used":[109],"existing":[111],"models.":[113],"To":[114],"further":[115],"reduce":[116,180],"performance":[118,182],"disparity":[119,183],"body":[122,168],"length":[123,169],"across":[124,184],"cohorts,":[125],"we":[126],"two":[128],"approaches":[129],"enforcing":[131],"fairness":[132],"adjustment":[135],"training":[137],"priorities":[138],"data":[140],"augmentation.":[141],"The":[142,186],"based":[144],"on":[145,174],"real-world":[147],"dataset":[149],"collected":[150],"Massachusetts,":[152],"US":[153],"demonstrate":[154],"proposed":[157,195],"can":[159],"greatly":[160],"improve":[161],"prediction":[162],"accuracy":[163],"compared":[170],"independent":[172],"growth,":[177],"effectively":[179],"cohorts.":[185],"movement":[190],"patterns":[191],"discovered":[192],"are":[197],"consistent":[199],"with":[200],"prior":[201],"studies":[202],"same":[205],"region,":[206],"vastly":[208],"reducing":[209],"memory":[213],"requirements.":[214]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
