{"id":"W1977587097","doi":"10.1080/09658210344000260","title":"Storage and retrieval of serial‐order information","year":2004,"lang":"en","type":"review","venue":"Memory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chaining; Chunking (psychology); Computer science; Set (abstract data type); Serial learning; Associative property; Order (exchange); Argument (complex analysis); Power set; Cognitive science; Serialization; Psychology; Information retrieval; Artificial intelligence; Cognitive psychology; Recall; Programming language; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001232207,0.0001140148,0.0003632517,0.0000638993,0.00004155735,0.00005576196,0.0003407457,0.00009051934,0.000006149763],"category_scores_gemma":[0.00001189887,0.00009115628,0.00006196527,0.0003745437,0.00002746458,0.000325587,0.0001476466,0.0001198686,0.00002437869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002316161,"about_ca_system_score_gemma":0.0001355414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000402975,"about_ca_topic_score_gemma":3.015134e-7,"domain_scores_codex":[0.9993538,0.00002562015,0.000287964,0.000128024,0.000113342,0.0000912488],"domain_scores_gemma":[0.9993053,0.00003619754,0.0002295688,0.0003477902,0.00004358955,0.00003752191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[3.374852e-7,0.000005443414,2.060738e-8,0.00243772,0.000006091286,5.32102e-7,0.0000436335,0.00003146164,2.933352e-7,0.003240805,0.0002719343,0.9939618],"study_design_scores_gemma":[0.00007470459,0.00001709443,0.000001947853,0.001305654,0.000030264,0.00001718877,0.000001755505,0.0001465743,0.000004133804,0.0002990933,0.997981,0.0001206112],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000006492379,0.9765038,0.02223449,0.00004270104,0.0002311935,0.0003489813,0.000008605013,0.00004202408,0.0005816495],"genre_scores_gemma":[0.000009802737,0.9964039,0.003334727,0.00003485175,0.00006763183,0.00001091008,0.00001773034,0.000004906319,0.0001155593],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.997709,"threshold_uncertainty_score":0.3717245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02345746758940665,"score_gpt":0.2810960865822146,"score_spread":0.257638618992808,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}