{"meta":{"query_hash":"d84a9a6e7621","filters":{"venue":"Artificial Intelligence Review"},"cohort_total":66,"direct_labels_cover":0,"predictions_cover":66,"exported":66,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/d84a9a6e7621","api":"https://metacan.xera.ac/api/v1/cohort?venue=Artificial+Intelligence+Review"},"results":[{"id":"W1487234737","doi":"10.1023/a:1015023512975","title":"Explanation and Argumentation Capabilities:Towards the Creation of More Persuasive Agents","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence; Université Laval","funders":"","keywords":"Argumentative; Computer science; Argumentation theory; Argument (complex analysis); Credibility; Field (mathematics); Domain knowledge; Data science; Set (abstract data type); Management science; Knowledge management; Artificial intelligence; Epistemology","score_opus":0.13316546871981816,"score_gpt":0.34812299756977133,"score_spread":0.21495752884995317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1487234737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07012981,0.0630382,0.84125465,0.018354414,0.0009861415,0.0033348917,0.000014330649,0.00013199341,0.0027555441],"genre_scores_gemma":[0.9712537,0.027308948,0.0008500427,0.00039439977,0.00004475544,0.000056124,0.0000062171903,0.0000047239646,0.00008110593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877053,0.0001428177,0.00047364118,0.00021872594,0.00028220506,0.00011207943],"domain_scores_gemma":[0.9992014,0.00009363825,0.00024818073,0.000279801,0.00014117551,0.000035770256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074101,0.00009590142,0.00016462477,0.000049159105,0.000112470785,0.00005201955,0.00023389302,0.000030041077,0.00014620584],"category_scores_gemma":[0.00017692441,0.000068382986,0.000057119563,0.00028323138,0.00006314237,0.00038335458,0.000038405848,0.00005029317,0.000052730415],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015696488,0.00006796375,0.00017116341,0.0006836166,0.00001787578,8.460486e-7,0.009769614,0.00006128396,0.00035493175,0.06361613,0.00054973643,0.92470527],"study_design_scores_gemma":[0.0001595947,0.00042788894,0.007822318,0.005212284,0.00022249452,0.000052701525,0.0068080067,0.89788914,0.045870274,0.01710349,0.017515814,0.0009159679],"about_ca_topic_score_codex":0.00016082749,"about_ca_topic_score_gemma":0.00001870299,"teacher_disagreement_score":0.9237893,"about_ca_system_score_codex":0.000037695492,"about_ca_system_score_gemma":0.000011688856,"threshold_uncertainty_score":0.2788577},"labels":[],"label_agreement":null},{"id":"W152517204","doi":"10.1007/s10462-004-5899-8","title":"Relation Algebras and their Application in Temporal and Spatial Reasoning","year":2005,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Binary relation; Relation (database); Spatial intelligence; Relational calculus; Converse; Relation algebra; Computer science; Spatial relation; Formalism (music); Algebra over a field; Mathematics; Relational model; Calculus (dental); Relational database; Discrete mathematics; Pure mathematics; Artificial intelligence; Information retrieval; Two-element Boolean algebra","score_opus":0.028115985875042875,"score_gpt":0.28621174368934366,"score_spread":0.2580957578143008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W152517204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032761253,0.009209979,0.9836303,0.003260789,0.000044456145,0.0003118275,3.474728e-7,0.000048067068,0.00021808337],"genre_scores_gemma":[0.9677029,0.015782839,0.016005564,0.00043299838,0.00003877447,0.000022419388,0.000004250201,0.0000040041923,0.0000062045606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991837,0.00006235334,0.00032390142,0.00025229418,0.00007206196,0.00010569744],"domain_scores_gemma":[0.99961025,0.0000541454,0.0000945198,0.00015871806,0.000034805846,0.000047577578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042041362,0.000087295855,0.00013284135,0.00006457174,0.000066582776,0.00006064945,0.000091072914,0.00003725077,0.00001883628],"category_scores_gemma":[0.000079865116,0.0000779138,0.000018681018,0.00027363643,0.000041122454,0.00040069316,0.000046152778,0.00008747185,0.000027332893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.523563e-7,0.000008252811,0.0015815295,0.00003276258,0.00000105136,2.0470313e-7,0.00018185748,0.0000845002,0.000043250573,0.050810706,0.0000055135088,0.94724953],"study_design_scores_gemma":[0.000031675023,0.00003214013,0.009329994,0.00075542025,0.000007148712,0.000031943648,0.000059259684,0.9671026,0.00094007375,0.014625881,0.0068380623,0.00024583595],"about_ca_topic_score_codex":0.00014307446,"about_ca_topic_score_gemma":0.0008743235,"teacher_disagreement_score":0.9676248,"about_ca_system_score_codex":0.000026427522,"about_ca_system_score_gemma":0.000023010522,"threshold_uncertainty_score":0.31772324},"labels":[],"label_agreement":null},{"id":"W1543603119","doi":"10.1007/s10462-009-9139-0","title":"A unified framework for improving the accuracy of all holistic face identification algorithms","year":2009,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science; Identification (biology); Benchmark (surveying); Face (sociological concept); Process (computing); Machine learning; Algorithm; Artificial intelligence; Baseline (sea); Stability (learning theory); Data mining","score_opus":0.15819327592009239,"score_gpt":0.4015026794538817,"score_spread":0.2433094035337893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543603119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029645744,0.008994411,0.9838805,0.005430894,0.00026383303,0.0010395318,0.0000052258542,0.00005593758,0.000033160468],"genre_scores_gemma":[0.8784649,0.020069042,0.09586975,0.0050045582,0.00019795405,0.0003011089,0.000025561092,0.00001558712,0.00005153915],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984791,0.000092124326,0.0006515637,0.00032320982,0.00022631323,0.0002276908],"domain_scores_gemma":[0.99808943,0.00059287844,0.0003975236,0.00064248266,0.00022341394,0.000054261105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008437696,0.00012888885,0.00022668342,0.000044937464,0.0001342249,0.00012283547,0.0008857319,0.00006323392,0.000017693472],"category_scores_gemma":[0.0014775,0.00009040415,0.00013979917,0.0004676752,0.00004567077,0.00037514366,0.000058518042,0.00014008796,0.000099245335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003073587,0.000045170735,2.107118e-7,0.00023079087,0.0000037843963,3.7238064e-7,0.00021701258,0.00002327262,0.0030294464,0.12960182,0.00016907613,0.866676],"study_design_scores_gemma":[0.000026716752,0.00021303681,0.00006482997,0.0031036874,0.00009266411,0.000009036374,0.00023996102,0.08526785,0.14384353,0.76040214,0.0063587916,0.0003777353],"about_ca_topic_score_codex":0.000012892014,"about_ca_topic_score_gemma":0.000001481835,"teacher_disagreement_score":0.8880108,"about_ca_system_score_codex":0.000017527966,"about_ca_system_score_gemma":0.000047563124,"threshold_uncertainty_score":0.3686574},"labels":[],"label_agreement":null},{"id":"W1582058909","doi":"10.1023/a:1015179704819","title":"User Interfaces and Help Systems: From Helplessness to Intelligent Assistance","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science","score_opus":0.17288281652410037,"score_gpt":0.40665667862063004,"score_spread":0.23377386209652967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582058909","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069285646,0.77157474,0.10023361,0.0131273875,0.013717588,0.0035908367,0.00011631999,0.00052126177,0.02783261],"genre_scores_gemma":[0.93755937,0.052456345,0.0002889488,0.004066784,0.0005515213,0.000336538,0.000009431465,0.000052747542,0.004678341],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975217,0.00028894484,0.0008917512,0.00063458865,0.00025668342,0.00040631992],"domain_scores_gemma":[0.99851537,0.000344658,0.0002102647,0.0005108461,0.00016416196,0.00025470532],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003746506,0.00028716476,0.0006008938,0.000098084456,0.00014942134,0.00016110984,0.0003607894,0.00012044831,0.013354207],"category_scores_gemma":[0.00026665514,0.0002636452,0.00011879668,0.00046616312,0.00009783919,0.00015718034,0.00009239002,0.0002650746,0.013285296],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053909276,0.0003228173,0.00045020465,0.0004176664,0.00016020563,0.000033579105,0.005972654,0.000021993299,0.0002456856,0.061499275,0.028256778,0.90256524],"study_design_scores_gemma":[0.000056776487,0.0002889787,0.00060935493,0.008697948,0.00023848127,0.00004047781,0.022132643,0.0005552166,0.0037255497,0.002252159,0.9602315,0.0011709506],"about_ca_topic_score_codex":0.00093328033,"about_ca_topic_score_gemma":0.00046804658,"teacher_disagreement_score":0.9319747,"about_ca_system_score_codex":0.0000938133,"about_ca_system_score_gemma":0.0000104260325,"threshold_uncertainty_score":0.9999816},"labels":[],"label_agreement":null},{"id":"W1583121608","doi":"10.1023/a:1022188514489","title":"Maximum Consistency of Incomplete Data via Non-Invasive Imputation","year":2003,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Imputation (statistics); Consistency (knowledge bases); Data mining; Algorithm; Artificial intelligence; Missing data; Machine learning","score_opus":0.38878789404148356,"score_gpt":0.48909457478556645,"score_spread":0.10030668074408289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583121608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001090535,0.0065484024,0.9908084,0.00016756746,0.00013457796,0.00060193375,0.0000593867,0.000021503305,0.0015491652],"genre_scores_gemma":[0.07142357,0.015604152,0.91250235,0.00032433015,0.000033944638,0.000031717598,0.000030223528,0.000028383247,0.000021300633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99787754,0.00026822463,0.0010118652,0.00037520126,0.00022858805,0.00023856586],"domain_scores_gemma":[0.99676144,0.0017037286,0.00035720988,0.00083843386,0.00024608622,0.000093089286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013431096,0.00017038135,0.0005744361,0.000038870836,0.00007283446,0.000010454268,0.00033664575,0.000049104106,0.00024970854],"category_scores_gemma":[0.007776309,0.0001450007,0.0000858851,0.00028998408,0.00015313209,0.0001557491,0.00009162102,0.00012437682,0.000079745616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037804457,0.00007644906,0.0000026714938,0.0018944503,0.000018856525,0.000005261222,0.000047797243,0.000009624516,0.00061479874,0.5591204,0.00012576565,0.4380801],"study_design_scores_gemma":[0.000013795781,0.000060559014,0.0000011728807,0.0013866827,0.000117813994,0.00001261494,0.000059251746,0.0022211175,0.0074731684,0.9872423,0.0012504263,0.00016110751],"about_ca_topic_score_codex":0.000014653917,"about_ca_topic_score_gemma":0.000028227096,"teacher_disagreement_score":0.43791902,"about_ca_system_score_codex":0.000021314147,"about_ca_system_score_gemma":0.00010280062,"threshold_uncertainty_score":0.93095297},"labels":[],"label_agreement":null},{"id":"W1981631833","doi":"10.1007/s10462-011-9311-1","title":"Fuzzy logic and self-referential reasoning: a comparative study with some new concepts","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Artificial intelligence; Negation; Fuzzy set; Set (abstract data type); Selection (genetic algorithm); Fuzzy set operations; Metacognition; Automated reasoning; Context (archaeology); Programming language; Cognition","score_opus":0.10057134416544353,"score_gpt":0.35506182972917505,"score_spread":0.25449048556373155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981631833","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049171574,0.6748355,0.22974944,0.0042477455,0.002201762,0.008892312,0.0000048389516,0.0011931993,0.029703623],"genre_scores_gemma":[0.9905888,0.005173675,0.0031689748,0.00057826884,0.00032478874,0.00007615464,0.0000012110974,0.0000083360665,0.00007979402],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99778265,0.00031187086,0.0005389928,0.0004843838,0.0003925138,0.00048956764],"domain_scores_gemma":[0.9986603,0.00014875078,0.00022865355,0.0005278832,0.000114401286,0.00031996475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085240905,0.00027251232,0.00063225615,0.00004601137,0.00017012407,0.00016118209,0.00059785927,0.000054768032,0.000018129402],"category_scores_gemma":[0.00005550242,0.00018401249,0.00007074345,0.0004227298,0.00007870234,0.0007810637,0.00017952578,0.00019666212,0.0003476532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016246704,0.0005184412,0.0006482527,0.00016040663,0.00009026373,0.00000950245,0.0034475897,0.000006794802,0.000016378362,0.92463833,0.00029564678,0.070152126],"study_design_scores_gemma":[0.00214773,0.020313265,0.01084272,0.017914027,0.0029313322,0.0012524691,0.021286417,0.008449707,0.0032316942,0.8181658,0.083818346,0.009646531],"about_ca_topic_score_codex":0.00013968324,"about_ca_topic_score_gemma":0.00005409783,"teacher_disagreement_score":0.9414172,"about_ca_system_score_codex":0.000037448564,"about_ca_system_score_gemma":0.00011080504,"threshold_uncertainty_score":0.7503812},"labels":[],"label_agreement":null},{"id":"W2012862892","doi":"10.1007/s10462-004-2901-4","title":"Statistical, Evolutionary, and Neurocomputing Clustering Techniques: Cluster-Based vs Object-Based Approaches","year":2005,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Cluster analysis; Computer science; Artificial neural network; Set (abstract data type); Self-organizing map; Artificial intelligence; Hierarchical clustering; Data mining; Genetic algorithm; Object (grammar); Machine learning; Correlation clustering; Cluster (spacecraft); Pattern recognition (psychology)","score_opus":0.10217262922586404,"score_gpt":0.35377746189472176,"score_spread":0.2516048326688577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012862892","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006351367,0.0072027217,0.9853158,0.005748209,0.00008634227,0.0009144914,0.000005567759,0.0004060637,0.00025726375],"genre_scores_gemma":[0.11854297,0.0018886037,0.8764797,0.0026436131,0.0001943462,0.00018169191,0.000009824643,0.000039937793,0.000019298863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670565,0.00032745034,0.0008260347,0.0008966991,0.00056129746,0.0006828441],"domain_scores_gemma":[0.9978989,0.0007173406,0.00018646881,0.0007927218,0.00015487145,0.00024974375],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011211464,0.00032872954,0.00046368167,0.00019098682,0.00033976356,0.00023533351,0.000984009,0.00008381776,0.000032309465],"category_scores_gemma":[0.00045610458,0.00031649723,0.00009761372,0.0007750601,0.0002503991,0.00045456112,0.00052938855,0.0004102827,0.000118510434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011482731,0.00009703683,0.0000089564965,0.0009282416,0.000005348006,0.000012678804,0.000040016002,0.009671544,0.000080430786,0.004746498,0.00012299129,0.9842748],"study_design_scores_gemma":[0.000032669966,0.00018150228,0.000020270983,0.0013669058,0.000011975074,0.000034294488,0.000011008033,0.98275924,0.0043809838,0.0013108453,0.0095411725,0.000349139],"about_ca_topic_score_codex":0.000022800554,"about_ca_topic_score_gemma":0.000018878049,"teacher_disagreement_score":0.98392564,"about_ca_system_score_codex":0.00016191,"about_ca_system_score_gemma":0.00019826803,"threshold_uncertainty_score":0.9999287},"labels":[],"label_agreement":null},{"id":"W2015910933","doi":"10.1007/s10462-009-9141-6","title":"On the importance of relational concept knowledge in referral networks","year":2008,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Peer-to-Peer Network Technologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"McGill University","keywords":"Computer science; Oracle; Referral; Domain (mathematical analysis); Relation (database); Domain knowledge; Knowledge management; Service (business); Business; Data mining; Software engineering","score_opus":0.14861547033252231,"score_gpt":0.33990965808597645,"score_spread":0.19129418775345414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015910933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025387593,0.12732287,0.8107626,0.026650596,0.0008652357,0.0018054866,0.0000041559942,0.00039113182,0.00681034],"genre_scores_gemma":[0.97983843,0.012396474,0.0053718532,0.0021426878,0.00006435785,0.00008334066,0.000002393605,0.000010424121,0.00009001404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813694,0.00014610321,0.00074262754,0.00038212276,0.0002719765,0.00032024732],"domain_scores_gemma":[0.99816483,0.0006133549,0.00018834871,0.00084968284,0.00013154309,0.000052222036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008568537,0.00016300184,0.00033798144,0.00009139161,0.00010378589,0.000013940879,0.0013286283,0.0000791207,0.00006190792],"category_scores_gemma":[0.00063925725,0.000116994626,0.000098359335,0.0017369288,0.00022203858,0.00016093062,0.0002693216,0.0003370689,0.00020633204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032549378,0.000060929306,0.00036401505,0.000024587684,0.000005147593,0.000010858929,0.00019268975,0.002870513,0.0000055771934,0.87950945,0.0062442934,0.11070866],"study_design_scores_gemma":[0.00012362811,0.0011646501,0.010780826,0.015166612,0.000048521917,0.00018821206,0.00024934314,0.3339682,0.009264608,0.5450763,0.08172246,0.0022466225],"about_ca_topic_score_codex":0.000010330637,"about_ca_topic_score_gemma":0.00008046496,"teacher_disagreement_score":0.95445085,"about_ca_system_score_codex":0.00006451772,"about_ca_system_score_gemma":0.000088107605,"threshold_uncertainty_score":0.47709024},"labels":[],"label_agreement":null},{"id":"W2023965287","doi":"10.1007/s10462-007-9024-7","title":"Using knowledge partitioning to investigate the psychological plausibility of mixtures of experts","year":2007,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Linear model; Population; Cognitive psychology; Proposition; Artificial intelligence; Machine learning; Psychology; Medicine; Epistemology","score_opus":0.35363232498148883,"score_gpt":0.4767902129292663,"score_spread":0.12315788794777749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023965287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1409485,0.011518609,0.8446979,0.000846064,0.0002910428,0.0004916746,0.0000011264182,0.000028803826,0.0011762556],"genre_scores_gemma":[0.9859196,0.0010123069,0.011970235,0.0010394384,0.0000429708,0.000008158186,2.769691e-7,0.0000028420457,0.0000041682197],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829406,0.00014155052,0.00071086845,0.00034694246,0.00023575452,0.00027080663],"domain_scores_gemma":[0.99862665,0.00030470366,0.00019398652,0.0004755305,0.0002929388,0.00010618502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002930452,0.00010871256,0.0002646791,0.00007017372,0.00012180982,0.000026887838,0.00079411047,0.000033741377,0.000039672905],"category_scores_gemma":[0.0009896279,0.00007155682,0.00011093152,0.0013013927,0.00029556284,0.00019957592,0.00025859152,0.00008665352,0.000026546299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005765569,0.0001360282,0.00015405647,0.00024130252,0.000006471234,0.0000031789486,0.0018273477,0.0000430745,0.03472533,0.07739019,0.00010380275,0.88536346],"study_design_scores_gemma":[0.000027872806,0.00037870638,0.0018076285,0.0075379894,0.000048228703,0.000039551367,0.0006561962,0.008100523,0.8722574,0.10371489,0.0049104644,0.00052055606],"about_ca_topic_score_codex":0.000040180017,"about_ca_topic_score_gemma":0.000057517722,"teacher_disagreement_score":0.8848429,"about_ca_system_score_codex":0.000020335501,"about_ca_system_score_gemma":0.000054491356,"threshold_uncertainty_score":0.29180023},"labels":[],"label_agreement":null},{"id":"W2037570116","doi":"10.2495/air060761","title":"Canada-US border air quality strategy (BAQS): preliminary results of mortality and cancer incidence in Windsor, Canada","year":2006,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Windsor; Incidence (geometry); Lung cancer; Bronchitis; Air pollution; Environmental health; Medicine; Cancer incidence; Cancer; Demography; Population; Environmental science; Oncology; Internal medicine; Biology; Ecology","score_opus":0.11085328645533879,"score_gpt":0.4051455098647692,"score_spread":0.2942922234094304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037570116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9478963,0.03534122,0.0001420982,0.012023759,0.00019458872,0.0010867965,0.00029858213,0.000017054106,0.0029996242],"genre_scores_gemma":[0.9827609,0.012689842,0.00008190015,0.0042779897,0.00003549657,0.000029460996,0.0000138468695,0.000008770374,0.00010181567],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966847,0.00032037246,0.0014571879,0.0004421746,0.000616755,0.00047886872],"domain_scores_gemma":[0.9987113,0.00025313595,0.0004174635,0.00038097583,0.000039158193,0.00019795696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015856242,0.00020417306,0.00050091505,0.000018483355,0.000104484425,0.000008824512,0.000261445,0.00006921588,0.0004470668],"category_scores_gemma":[0.00039921366,0.00018946474,0.000034370027,0.0004983085,0.00023782745,0.00019251132,0.000107241736,0.00022478742,0.000006932372],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000431189,0.00048107083,0.27155885,0.0064217197,0.000030031646,0.00012446844,0.00041542383,0.02342711,0.00021360457,0.0046718293,0.04437056,0.64785415],"study_design_scores_gemma":[0.000060747378,0.00013152386,0.9583821,0.0016077588,0.000033908917,0.000005188367,0.00016160637,0.0009480597,0.002240441,0.0023449715,0.033652846,0.00043082723],"about_ca_topic_score_codex":0.9998302,"about_ca_topic_score_gemma":0.99991304,"teacher_disagreement_score":0.68682325,"about_ca_system_score_codex":0.0006974815,"about_ca_system_score_gemma":0.001897047,"threshold_uncertainty_score":0.7726148},"labels":[],"label_agreement":null},{"id":"W2039521808","doi":"10.1007/s10462-011-9285-z","title":"Genetic optimized artificial immune system in spam detection: a review and a model","year":2011,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Artificial Immune Systems Applications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Computer science; Artificial immune system; Artificial intelligence; The Internet; Artificial neural network; Genetic algorithm; Machine learning; Computer security; World Wide Web","score_opus":0.09789027701188328,"score_gpt":0.3246031597684292,"score_spread":0.22671288275654589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039521808","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.6102833e-7,0.94913834,0.042748265,0.000027314798,0.00036502018,0.006482451,0.00003522974,0.0004501651,0.00075287715],"genre_scores_gemma":[0.00008626488,0.9938362,0.0014975109,0.000046678953,0.00015625691,0.0040942477,0.000027308277,0.00022149914,0.000033983324],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99301267,0.00040650225,0.0046210373,0.0009353878,0.00027277056,0.0007516652],"domain_scores_gemma":[0.9972896,0.00015928123,0.00068163604,0.0014921211,0.00015494268,0.00022242671],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014253065,0.0010546949,0.0049150414,0.0003759406,0.00016668541,0.00008448485,0.00076983945,0.00046267366,0.00012417349],"category_scores_gemma":[0.00017015424,0.0009878199,0.0007791801,0.0017252322,0.00015655714,0.00018615973,0.00017953735,0.00090529653,0.0014596897],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012486424,0.000028905568,1.615455e-8,0.23768423,0.00007450175,0.000007587818,0.00002834583,0.0002118207,0.0000035217288,0.0027813627,0.000037971608,0.7591405],"study_design_scores_gemma":[0.000017739907,0.000035142468,1.2724644e-7,0.42256334,0.0036835999,0.0002983903,0.00003616963,0.028723897,0.000059454724,0.0006314395,0.5424546,0.0014961195],"about_ca_topic_score_codex":0.00013535452,"about_ca_topic_score_gemma":0.000120730045,"teacher_disagreement_score":0.75764436,"about_ca_system_score_codex":0.00042100778,"about_ca_system_score_gemma":0.00018579898,"threshold_uncertainty_score":0.99931777},"labels":[],"label_agreement":null},{"id":"W2054720697","doi":"10.1007/s10462-011-9233-y","title":"Automated camera planning to film robot operations","year":2011,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; Université de Sherbrooke","funders":"","keywords":"Computer science; Computer vision; Robot; Artificial intelligence; Computer graphics (images); Trajectory; Computer graphics; Animation; Zoom; Computer animation; Task (project management); Frame (networking)","score_opus":0.17469800194250049,"score_gpt":0.3688946238463915,"score_spread":0.194196621903891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054720697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013263781,0.007016607,0.9878025,0.0012215456,0.0005764555,0.0005402786,0.0000026161729,0.0008958686,0.0018114639],"genre_scores_gemma":[0.044384204,0.0017857219,0.94785535,0.0054609356,0.0001268102,0.0001767932,0.000009856834,0.0000307328,0.00016956973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978831,0.00014419375,0.00068320084,0.0005412915,0.00028810074,0.00046011273],"domain_scores_gemma":[0.9986014,0.00007897424,0.00008314518,0.0008106839,0.0001737007,0.00025213233],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006677821,0.00022235897,0.00036514553,0.00013013964,0.00022000734,0.0001271553,0.0012404246,0.000057828653,0.00015015846],"category_scores_gemma":[0.00039223864,0.0002024344,0.000093374176,0.0011366622,0.00004352578,0.0004292014,0.00023049084,0.00018193331,0.0031290555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006566709,0.0003339618,0.0001356524,0.00048142372,0.00007646323,0.00032864267,0.009632806,0.1004438,0.0008188859,0.099430665,0.009311214,0.7789999],"study_design_scores_gemma":[0.000013133883,0.00020123938,0.00034114698,0.0034517285,0.000036179405,0.000087185006,0.000097309545,0.9830884,0.00714696,0.0017992748,0.0031231483,0.0006143261],"about_ca_topic_score_codex":0.00016503833,"about_ca_topic_score_gemma":0.000007181668,"teacher_disagreement_score":0.8826446,"about_ca_system_score_codex":0.00004550938,"about_ca_system_score_gemma":0.0001115773,"threshold_uncertainty_score":0.9976471},"labels":[],"label_agreement":null},{"id":"W2059768731","doi":"10.1023/b:aire.0000007179.60276.39","title":"Reasoning with Numeric and Symbolic Time Information","year":2003,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Constraint satisfaction problem; Qualitative reasoning; Tabu search; Constraint satisfaction; Constraint satisfaction dual problem; Metric (unit); Constraint (computer-aided design); Scheduling (production processes); Constraint programming; Theoretical computer science; Local consistency; Mathematical optimization; Artificial intelligence; Mathematics; Probabilistic logic","score_opus":0.014527732238881166,"score_gpt":0.24809795245565738,"score_spread":0.2335702202167762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059768731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028161946,0.0039519398,0.9892372,0.000598441,0.000057180052,0.00024212987,3.716375e-7,0.00008502939,0.0055460846],"genre_scores_gemma":[0.5613497,0.110389076,0.3201238,0.0076747457,0.000060598624,0.00012505552,0.0000205719,0.000028495822,0.00022799596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999286,0.000064303735,0.00025710315,0.00013612826,0.0001275635,0.00012890702],"domain_scores_gemma":[0.9995237,0.000036261034,0.00010083908,0.00019105068,0.000084242034,0.000063890126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029804144,0.00008794913,0.00013416375,0.000053787116,0.000096017444,0.00012376432,0.000108892316,0.000021258023,0.00016468504],"category_scores_gemma":[0.00014662705,0.000071747934,0.000021559668,0.00049079186,0.000038720787,0.0008876367,0.00001872336,0.00006333699,0.0004247452],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.0746277e-7,0.0000052785967,0.000060858973,0.000067572066,0.0000036199494,6.734963e-7,0.00010548006,0.00008623574,0.000008962277,0.21669446,0.00004650941,0.78291976],"study_design_scores_gemma":[0.00023749722,0.0007800917,0.002289179,0.0128378095,0.00025903332,0.0018426316,0.0006449618,0.5057795,0.014764684,0.040485527,0.41675457,0.0033244928],"about_ca_topic_score_codex":0.000006270507,"about_ca_topic_score_gemma":0.0000025203606,"teacher_disagreement_score":0.77959526,"about_ca_system_score_codex":0.000015517566,"about_ca_system_score_gemma":0.000052853382,"threshold_uncertainty_score":0.5459382},"labels":[],"label_agreement":null},{"id":"W2082914115","doi":"10.1007/s10462-007-9055-0","title":"Just enough learning (of association rules): the TAR2 “Treatment” learner","year":2006,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; West Virginia University; National Aeronautics and Space Administration","keywords":"Pruning; Computer science; Set (abstract data type); Association rule learning; Contrast (vision); Class (philosophy); Association (psychology); Simple (philosophy); Machine learning; Domain (mathematical analysis); Artificial intelligence; Controller (irrigation); Psychology; Mathematics; Programming language","score_opus":0.06132035250675822,"score_gpt":0.3248625355230162,"score_spread":0.263542183016258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082914115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019873313,0.054586742,0.91429365,0.018580154,0.0003775077,0.0010392343,0.000023419041,0.00026508974,0.008846879],"genre_scores_gemma":[0.6140272,0.22897443,0.1396033,0.002314544,0.0016926463,0.00087824074,0.00032659984,0.000092268034,0.012090749],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870116,0.000119254655,0.00046202584,0.00026464043,0.0002349367,0.00021798303],"domain_scores_gemma":[0.99884075,0.00024568522,0.00030661564,0.00045729717,0.00012076612,0.000028874132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069156836,0.000117453455,0.00021423369,0.000028502081,0.00022901314,0.00009869505,0.000585358,0.00003931451,0.000047023703],"category_scores_gemma":[0.00015541576,0.000079031735,0.00010948902,0.0005155929,0.00004301774,0.00021010562,0.00008690767,0.00012520888,0.0006259166],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.8433374e-7,0.0000682259,0.00006787245,0.000038290265,0.000010065043,6.95977e-7,0.00009070626,0.00011874388,0.00009186808,0.21133941,0.0009954168,0.7871784],"study_design_scores_gemma":[0.000025174966,0.00016634804,0.00034100327,0.0006905498,0.000120352655,0.000009918617,0.00013142644,0.051643066,0.010557576,0.02003448,0.91592,0.00036006162],"about_ca_topic_score_codex":0.00046164522,"about_ca_topic_score_gemma":0.000031535033,"teacher_disagreement_score":0.9149246,"about_ca_system_score_codex":0.00008125895,"about_ca_system_score_gemma":0.000051475006,"threshold_uncertainty_score":0.80451},"labels":[],"label_agreement":null},{"id":"W2085103703","doi":"10.1007/s10462-010-9180-z","title":"Data mining applications in hydrocarbon exploration","year":2010,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hydrocarbon exploration; Computer science; Data exploration; Data mining; Geology; Visualization; Geomorphology","score_opus":0.16525304646565644,"score_gpt":0.356858352819086,"score_spread":0.19160530635342957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085103703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010611673,0.0076344437,0.9868676,0.0021652319,0.0001656152,0.0003982881,0.00000362801,0.00008017941,0.001623833],"genre_scores_gemma":[0.8527081,0.021727266,0.12397331,0.000795611,0.00034997458,0.0002114711,0.00014756825,0.000024199366,0.00006247799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867713,0.000038757516,0.0005149978,0.0004249668,0.00015621983,0.00018791332],"domain_scores_gemma":[0.99846697,0.00008465604,0.00013913092,0.0012032901,0.00005022,0.00005574109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008555429,0.00010074102,0.00020278966,0.00007335843,0.00009332361,0.000110278634,0.0012888053,0.00003515172,0.000051562558],"category_scores_gemma":[0.00019267108,0.00008904116,0.00004376507,0.0010329909,0.00003540157,0.0009398357,0.0003187088,0.00016300332,0.00013107125],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.4549064e-7,0.000029677505,0.000053878823,0.000057301535,0.000002780193,0.000001156974,0.00009984562,0.000027606276,0.0003350688,0.079539195,0.000045491066,0.91980773],"study_design_scores_gemma":[0.000012219526,0.00003888037,0.00006995812,0.0008699863,0.000046464927,0.000010707318,0.000290077,0.8363929,0.0028731585,0.03726824,0.12162804,0.0004993591],"about_ca_topic_score_codex":0.000046764508,"about_ca_topic_score_gemma":0.00084124383,"teacher_disagreement_score":0.9193084,"about_ca_system_score_codex":0.000008862936,"about_ca_system_score_gemma":0.00003757098,"threshold_uncertainty_score":0.36309934},"labels":[],"label_agreement":null},{"id":"W2091203323","doi":"10.1007/s10462-009-9144-3","title":"A query-based approach for test selection in diagnosis","year":2008,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Query optimization; Test (biology); Test case; Entropy (arrow of time); Computation; Minification; Machine learning; Data mining; Mathematical optimization; Algorithm; Mathematics","score_opus":0.12013476080787351,"score_gpt":0.3236326353724061,"score_spread":0.2034978745645326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091203323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027900093,0.0128575545,0.98446745,0.0012305183,0.00006936562,0.000717568,0.0000031890254,0.00012642097,0.0002489124],"genre_scores_gemma":[0.59068155,0.017021153,0.38675565,0.0035338507,0.00017338956,0.0017038424,0.000029043335,0.000032356675,0.000069196314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841726,0.000102986196,0.0005139497,0.00044266167,0.00017770991,0.00034541392],"domain_scores_gemma":[0.9987102,0.0007268287,0.00012971557,0.00024854092,0.0001083741,0.00007632659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008962445,0.00016174014,0.00029954617,0.00012127901,0.00020005324,0.00004728458,0.00048149156,0.00006558139,0.000019169354],"category_scores_gemma":[0.0006647383,0.00015084371,0.00012222394,0.0010678675,0.000043730317,0.00022943172,0.000032972574,0.00016744593,0.0000594758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013077788,0.00088016794,0.020977478,0.0027663365,0.000014955682,0.000023544055,0.0005615492,0.015543311,0.00018491248,0.027817214,0.004795956,0.9264215],"study_design_scores_gemma":[0.000056135348,0.00039490135,0.00025561274,0.0030268126,0.000026868747,0.000042526794,0.00001621827,0.956065,0.013489545,0.0051284493,0.020872338,0.0006255988],"about_ca_topic_score_codex":0.000095761236,"about_ca_topic_score_gemma":0.00002849189,"teacher_disagreement_score":0.94052166,"about_ca_system_score_codex":0.000059938295,"about_ca_system_score_gemma":0.00017096165,"threshold_uncertainty_score":0.6151228},"labels":[],"label_agreement":null},{"id":"W2180261481","doi":"10.1023/b:aire.0000006611.32608.f2","title":"Three-Dimensional Feedforward Neural Networks and Their Realization by Nano-Devices","year":2003,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hypercube; Computer science; Realization (probability); Embedding; Binary number; Feedforward neural network; Artificial neural network; Feed forward; Topology (electrical circuits); Binary tree; Theoretical computer science; Algorithm; Parallel computing; Artificial intelligence; Mathematics; Combinatorics; Arithmetic","score_opus":0.037847750030204685,"score_gpt":0.27439843928766167,"score_spread":0.23655068925745698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180261481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022303697,0.32507655,0.6508063,0.0001521117,0.00054267404,0.0004929504,0.000004340019,0.00028370967,0.00033763118],"genre_scores_gemma":[0.9759871,0.022979192,0.00028327986,0.0006086613,0.000076675184,0.0000129530545,0.000015035031,0.000028908984,0.000008201544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990607,0.000045156503,0.00036440865,0.0002084528,0.00007794864,0.00024331831],"domain_scores_gemma":[0.9995717,0.00011209326,0.000054412354,0.00014582626,0.000037608912,0.00007836567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024551974,0.00018307952,0.0002484834,0.000021604132,0.00012207736,0.000027383523,0.00008541108,0.000048155518,0.000057094174],"category_scores_gemma":[0.00006315276,0.00014871314,0.000054009288,0.000256326,0.00004183153,0.00016132425,0.000019955363,0.00014290522,0.000016577846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052611053,0.000021362115,0.000071104536,0.0007985582,0.000024481655,0.0000039561496,0.00003932679,0.17157729,0.0037848158,0.008378184,0.001098675,0.814197],"study_design_scores_gemma":[0.000028680684,0.000083501596,0.000016551421,0.0020371594,0.00005373533,0.00007648396,0.00004278721,0.89132875,0.06536729,0.010886285,0.029369948,0.00070883235],"about_ca_topic_score_codex":0.0000036884524,"about_ca_topic_score_gemma":0.00002450679,"teacher_disagreement_score":0.9536834,"about_ca_system_score_codex":0.000015261809,"about_ca_system_score_gemma":0.0000047460276,"threshold_uncertainty_score":0.6064346},"labels":[],"label_agreement":null},{"id":"W2180467047","doi":"10.1007/s10462-015-9447-5","title":"Exponential moving average based multiagent reinforcement learning algorithms","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Nash equilibrium; Computer science; Reinforcement learning; Algorithm; Convergence (economics); Q-learning; Weighted Majority Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Wake-sleep algorithm; Unsupervised learning; Generalization error","score_opus":0.0888835926429212,"score_gpt":0.3256896554819136,"score_spread":0.2368060628389924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180467047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006333844,0.010151593,0.98661745,0.0011724874,0.00056282524,0.00073059596,4.4829943e-7,0.00029023577,0.00041103593],"genre_scores_gemma":[0.8897086,0.0027875572,0.10452237,0.0021546858,0.0002536546,0.00022915327,0.000019720732,0.000038608883,0.000285653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702716,0.000306409,0.00081048126,0.00060171384,0.0006847344,0.00056947925],"domain_scores_gemma":[0.9982266,0.00014980121,0.00029647964,0.00069485,0.00031470475,0.00031755897],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018855579,0.000280401,0.0004180069,0.000107677566,0.0001805709,0.0002012646,0.0010805742,0.0000628207,0.00007196194],"category_scores_gemma":[0.0008410765,0.00026102283,0.0002074195,0.0005713238,0.00007722726,0.0004870576,0.00033694095,0.000321671,0.000792449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066571174,0.00006758144,0.000011049735,0.00012741357,0.000019731398,0.000044918328,0.00025036655,0.028605856,0.00011481164,0.019132188,0.00008666343,0.9515328],"study_design_scores_gemma":[0.00005453851,0.00016747124,0.0000029254984,0.00071240327,0.00002405546,0.00000800097,0.00004233008,0.9756024,0.0017709567,0.0012911636,0.020004546,0.0003192128],"about_ca_topic_score_codex":0.000102224265,"about_ca_topic_score_gemma":0.0000095474625,"teacher_disagreement_score":0.95121354,"about_ca_system_score_codex":0.00020465303,"about_ca_system_score_gemma":0.00022207198,"threshold_uncertainty_score":0.9999856},"labels":[],"label_agreement":null},{"id":"W2272823591","doi":"10.1007/s10462-016-9461-2","title":"Self-controlled bio-inspired extreme learning machines for scalable regression and classification: a comprehensive analysis with some recommendations","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scalability; Machine learning; Scale (ratio); Artificial intelligence; Extreme learning machine; Regression; Artificial neural network; Mathematics; Database; Statistics","score_opus":0.08129558917893297,"score_gpt":0.3368543659528098,"score_spread":0.25555877677387684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272823591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016328642,0.021839786,0.9407323,0.034000915,0.00013337412,0.0010747998,0.0000041974645,0.00033586417,0.00024588374],"genre_scores_gemma":[0.62440693,0.13349439,0.2346702,0.002980245,0.00046532747,0.001297556,0.000080170845,0.00008574856,0.0025194178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792784,0.00034477882,0.00060864695,0.00060453406,0.0002103519,0.00030386256],"domain_scores_gemma":[0.9979503,0.00072110293,0.00040331454,0.00049539347,0.0002999569,0.00012990102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007521115,0.00023298083,0.00065657747,0.00021403341,0.000456221,0.00015391817,0.00039797486,0.00005587761,0.00006928593],"category_scores_gemma":[0.00033678103,0.00013054287,0.00019464159,0.0010178097,0.000069806425,0.00047971206,0.00009387844,0.00011291,0.00007300139],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044940272,0.00008242264,0.000573431,0.0002791585,0.00021596583,0.0000014679543,0.00012615143,0.000045038978,0.0003821272,0.03803784,0.00022877686,0.9599827],"study_design_scores_gemma":[0.00074762467,0.0006380416,0.0009886893,0.0046254224,0.0014551306,0.000026534588,0.00009160046,0.81042933,0.0008689748,0.01329878,0.16585924,0.0009706123],"about_ca_topic_score_codex":0.00002319845,"about_ca_topic_score_gemma":0.000034332752,"teacher_disagreement_score":0.9590121,"about_ca_system_score_codex":0.000037155198,"about_ca_system_score_gemma":0.000054799704,"threshold_uncertainty_score":0.5323384},"labels":[],"label_agreement":null},{"id":"W2782623435","doi":"10.1007/s10462-018-9612-8","title":"Application of artificial intelligence techniques in the petroleum industry: a review","year":2018,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":203,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Petroleum industry; Fuzzy logic; Swarm intelligence; Machine learning; Applications of artificial intelligence; Engineering; Particle swarm optimization","score_opus":0.13106337670585613,"score_gpt":0.4162598069012182,"score_spread":0.2851964301953621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782623435","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.823227e-7,0.8044774,0.19242446,0.00010461978,0.00017701538,0.0021948917,0.000017507407,0.0001628184,0.00044098837],"genre_scores_gemma":[0.00002214765,0.99404377,0.004215021,0.0001358608,0.00033710862,0.0010844996,0.00006427336,0.00008798473,0.000009322777],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946394,0.0007066593,0.003122001,0.00053837895,0.00052106916,0.00047247452],"domain_scores_gemma":[0.99713415,0.00068823603,0.00051958585,0.0013917661,0.00017096702,0.00009531041],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0045609553,0.0006614414,0.0024969908,0.00032860052,0.000056986064,0.00004875614,0.0014387751,0.00058598764,0.00013477111],"category_scores_gemma":[0.00082695665,0.0004808863,0.0006683011,0.0025369502,0.00016197244,0.00013453461,0.00008250824,0.0014669385,0.0002766561],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.539348e-7,0.00003175365,1.4711317e-7,0.1320873,0.000026663423,0.0000023512057,0.000020231808,0.0007302355,5.308133e-7,0.0027595442,0.00030825703,0.8640323],"study_design_scores_gemma":[0.000001736082,0.000037776615,7.3799825e-8,0.18311045,0.00037426478,0.000019137338,0.000013303491,0.008345322,0.000082413724,0.0017555513,0.805845,0.000414965],"about_ca_topic_score_codex":0.000022116958,"about_ca_topic_score_gemma":0.00001394036,"teacher_disagreement_score":0.86361736,"about_ca_system_score_codex":0.0001381501,"about_ca_system_score_gemma":0.000112267866,"threshold_uncertainty_score":0.99976426},"labels":[],"label_agreement":null},{"id":"W2793816938","doi":"10.1007/s10462-018-9616-4","title":"A comprehensive investigation into the performance, robustness, scalability and convergence of chaos-enhanced evolutionary algorithms with boundary constraints","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chaotic; Robustness (evolution); Computer science; Scalability; Evolutionary algorithm; Algorithm; Mathematical optimization; Convergence (economics); Heuristic; Mathematics; Machine learning; Artificial intelligence","score_opus":0.05226224469424341,"score_gpt":0.31553211472671294,"score_spread":0.26326987003246954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793816938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028992869,0.010202413,0.95754945,0.0018054463,0.00023808386,0.0010121603,0.0000037144657,0.000056931847,0.00013895056],"genre_scores_gemma":[0.75907737,0.034724873,0.20519826,0.00067834434,0.00012261477,0.0001287608,0.000010686189,0.000017555401,0.000041545554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974871,0.0003582314,0.00071014435,0.0005341565,0.00061484403,0.00029549462],"domain_scores_gemma":[0.9970481,0.0003172682,0.0002824339,0.0007113337,0.0014960621,0.00014482037],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010753758,0.00020065243,0.00037496525,0.00008727092,0.00040058364,0.0000847022,0.0007506917,0.000053754913,0.00018600607],"category_scores_gemma":[0.00034808659,0.00013619974,0.000047352256,0.00138952,0.0038862512,0.00053203455,0.0002762767,0.00020683915,0.000063112406],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002170759,0.00008082291,0.00036448592,0.0015916084,0.000046052188,0.0000027676372,0.0015311864,0.0006583838,0.0004669335,0.014316217,0.00012426973,0.98079556],"study_design_scores_gemma":[0.00005835778,0.00047909183,0.0015406123,0.0017903185,0.00003853389,0.000050060993,0.00024024463,0.9688447,0.020966956,0.0045246305,0.0011256137,0.0003408737],"about_ca_topic_score_codex":0.000056584817,"about_ca_topic_score_gemma":0.000014726929,"teacher_disagreement_score":0.9804547,"about_ca_system_score_codex":0.00004793389,"about_ca_system_score_gemma":0.0004422847,"threshold_uncertainty_score":0.9988246},"labels":[],"label_agreement":null},{"id":"W2797143217","doi":"10.1007/s10462-018-9631-5","title":"Artificial intelligence test: a case study of intelligent vehicles","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Test (biology); Computer science; Artificial intelligence; Artificial intelligence, situated approach; Applications of artificial intelligence; Machine learning","score_opus":0.11650538166903347,"score_gpt":0.38820246559087357,"score_spread":0.2716970839218401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797143217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015051834,0.004355268,0.9768848,0.00042187862,0.00054162147,0.0018886393,0.0000050240665,0.000604609,0.00024635688],"genre_scores_gemma":[0.9620049,0.004812337,0.0322549,0.00043422816,0.00023278162,0.00020054255,0.0000010005023,0.000037175454,0.000022130853],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9950683,0.00032537218,0.0022176062,0.001092065,0.0006731171,0.00062355486],"domain_scores_gemma":[0.9956168,0.0006567271,0.000681017,0.0018355321,0.00097196194,0.0002379548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016360269,0.0004695091,0.00080532086,0.00036163683,0.00036210215,0.00013385066,0.0017394945,0.00012543096,0.0001872636],"category_scores_gemma":[0.0013842558,0.00043575722,0.00023543293,0.0025594425,0.0004544202,0.00071069,0.00066782767,0.0004022183,0.00055073365],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013213789,0.0010450475,0.000034194938,0.00026127542,0.000026060525,0.0005043118,0.0014639129,0.000023066064,0.0008440076,0.024012657,0.00006411065,0.9717081],"study_design_scores_gemma":[0.000019566616,0.0066937176,0.000009973635,0.0024143648,0.00015545577,0.0032394815,0.0051221866,0.023624161,0.7700476,0.18422706,0.003143736,0.0013026929],"about_ca_topic_score_codex":0.0004172357,"about_ca_topic_score_gemma":0.0006927207,"teacher_disagreement_score":0.97040546,"about_ca_system_score_codex":0.000112870715,"about_ca_system_score_gemma":0.00013023255,"threshold_uncertainty_score":0.99980944},"labels":[],"label_agreement":null},{"id":"W2883445328","doi":"10.1007/s10462-018-9646-y","title":"40 years of cognitive architectures: core cognitive abilities and practical applications","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":516,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Air Force Office of Scientific Research; Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Cognition; Cognitive architecture; Variety (cybernetics); Set (abstract data type); Cognitive science; Perception; Field (mathematics); Selection (genetic algorithm); Data science; Rational analysis; Artificial intelligence; Psychology; Neuroscience","score_opus":0.10763490228615796,"score_gpt":0.3884198155905721,"score_spread":0.28078491330441413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883445328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00838437,0.01747724,0.97118694,0.0008930709,0.00007282548,0.0007969663,0.000022956812,0.00007574125,0.0010899061],"genre_scores_gemma":[0.98430896,0.004020938,0.010438483,0.0009838203,0.000107929984,0.000101876525,0.000008153311,0.000009008994,0.000020827953],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998755,0.00011850569,0.0003912032,0.00034451892,0.0001859754,0.00020482078],"domain_scores_gemma":[0.99716574,0.002038407,0.00017082276,0.00022823874,0.00030906935,0.0000877419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062303204,0.00011676736,0.00024949925,0.000059036483,0.00012136735,0.000044332075,0.00019040302,0.000046243335,0.000057909132],"category_scores_gemma":[0.0013032265,0.00010935407,0.000056416888,0.00040360403,0.0005091046,0.000110616624,0.00011712752,0.00016818843,0.00014186118],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016404929,0.00006195303,0.00011192004,0.00042393836,0.000024576746,0.000005352186,0.0020013845,0.0000014238182,0.00003488502,0.04818442,0.00007383349,0.9490599],"study_design_scores_gemma":[0.00026474785,0.0042731813,0.0027159178,0.068614244,0.000989258,0.0007213988,0.005987074,0.037002522,0.049582683,0.80110925,0.025819141,0.0029205496],"about_ca_topic_score_codex":0.000034541914,"about_ca_topic_score_gemma":0.000016916774,"teacher_disagreement_score":0.9759246,"about_ca_system_score_codex":0.000008716725,"about_ca_system_score_gemma":0.00011843908,"threshold_uncertainty_score":0.44593295},"labels":[],"label_agreement":null},{"id":"W2890460364","doi":"10.1023/a:1006500224529","title":"The Berkeley UNIX Consultant Project","year":2000,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"RTDS Technologies (Canada)","funders":"","keywords":"Computer science; Unix; Programming language; Utterance; Natural language; Component (thermodynamics); Artificial intelligence; Knowledge representation and reasoning; Natural language processing; Human–computer interaction","score_opus":0.049952052016624185,"score_gpt":0.3552141471328705,"score_spread":0.3052620951162463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890460364","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001444565,0.7177261,0.25904715,0.014890721,0.00029776242,0.0015061677,0.0000031758868,0.0011206267,0.0052638277],"genre_scores_gemma":[0.04130697,0.7123996,0.23565857,0.008024995,0.0002615362,0.00034365224,0.000006744262,0.000045635286,0.0019522959],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983009,0.00016744295,0.00051370333,0.00036373368,0.0002932051,0.0003609763],"domain_scores_gemma":[0.9986138,0.0002659437,0.00011099337,0.00078984024,0.0001622736,0.00005717957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010183476,0.00016778107,0.00022026167,0.000033356646,0.0003509356,0.00028225302,0.0016773855,0.00004815623,0.00012095302],"category_scores_gemma":[0.00040494153,0.00010061701,0.00009502132,0.0010653588,0.00015360129,0.0003322562,0.00011964715,0.00023804736,0.0005925411],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017623772,0.000013361509,5.8798975e-7,0.000073529656,0.0000038893077,0.000007794575,0.00006480187,3.3457727e-7,0.000050710394,0.17319703,0.0011855665,0.82540065],"study_design_scores_gemma":[0.00001041791,0.00010426414,0.0000014959679,0.002573942,0.000027994138,0.00010247483,0.000027148151,0.0044448,0.028630618,0.27432856,0.68928367,0.00046459708],"about_ca_topic_score_codex":0.000069843016,"about_ca_topic_score_gemma":0.000030271563,"teacher_disagreement_score":0.82493603,"about_ca_system_score_codex":0.000035874404,"about_ca_system_score_gemma":0.00014149937,"threshold_uncertainty_score":0.76161146},"labels":[],"label_agreement":null},{"id":"W2969402372","doi":"10.1007/s10462-019-09753-0","title":"Modeling empathy: building a link between affective and cognitive processes","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Empathy; Simulation theory of empathy; Cognition; Variety (cybernetics); Computer science; Cognitive psychology; Field (mathematics); Cognitive science; Common ground; Psychology; Artificial intelligence; Social psychology; Neuroscience","score_opus":0.14660429826668675,"score_gpt":0.44942087456388996,"score_spread":0.3028165762972032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969402372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5397706,0.22534226,0.19716652,0.0044442113,0.0023076623,0.004453008,0.000037658614,0.00039304516,0.026085015],"genre_scores_gemma":[0.9900705,0.008297927,0.00012704408,0.0008017599,0.0004352397,0.00008755681,0.000007670909,0.00002425497,0.0001480228],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855125,0.00015843452,0.0004372303,0.00042780646,0.00014601243,0.0002792716],"domain_scores_gemma":[0.99876344,0.00059158326,0.0001244202,0.00015573311,0.00026853578,0.000096285075],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004416927,0.00018003196,0.0004369559,0.00007402956,0.00010900826,0.000053664535,0.00012330635,0.00009956929,0.0014464485],"category_scores_gemma":[0.0006394961,0.00016791013,0.00009084106,0.00040034813,0.000055143537,0.0001789173,0.00004688796,0.00027887576,0.0023004883],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029857652,0.00003798805,0.0011029626,0.00083149417,0.00011081525,0.0000046180758,0.003781664,0.000020939528,0.000039827384,0.013647215,0.000029553135,0.9803631],"study_design_scores_gemma":[0.0016487923,0.0069587277,0.0076319114,0.2064101,0.009086799,0.00050970213,0.18377636,0.04228624,0.038498376,0.4008629,0.08838273,0.013947369],"about_ca_topic_score_codex":0.00016201251,"about_ca_topic_score_gemma":0.000033273725,"teacher_disagreement_score":0.9664157,"about_ca_system_score_codex":0.000026693626,"about_ca_system_score_gemma":0.000052823085,"threshold_uncertainty_score":0.99946636},"labels":[],"label_agreement":null},{"id":"W3007616576","doi":"10.1007/s10462-020-09861-2","title":"Deep learning for biomedical image reconstruction: a survey","year":2020,"lang":"en","type":"preprint","venue":"Artificial Intelligence Review","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"CIHR Skin Research Training Centre; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Software deployment; Modalities; Medical imaging; Deep learning; Iterative reconstruction; Artificial intelligence; Mobile device; Data acquisition; Latency (audio); Computer vision; Machine learning; Medical physics; Data science; Medicine","score_opus":0.19182950061046364,"score_gpt":0.4351116203323829,"score_spread":0.24328211972191924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007616576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001133873,0.016986229,0.92929876,0.0493858,0.00037936264,0.0029978617,0.000033690805,0.00038016375,0.0004247253],"genre_scores_gemma":[0.034999672,0.41129804,0.5351525,0.00891753,0.0024705543,0.003425106,0.0033265033,0.0001853414,0.00022479137],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973928,0.00017008116,0.0010824245,0.00071953825,0.00032456536,0.0003106448],"domain_scores_gemma":[0.9979699,0.0003402072,0.00033825682,0.00052432757,0.00041648158,0.00041082388],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013796178,0.00028798153,0.00095492497,0.000073346695,0.000117937445,0.000052188323,0.00030759786,0.0002492331,0.00081359246],"category_scores_gemma":[0.0046982006,0.00025113774,0.00040143463,0.0004055679,0.00033826832,0.000033870278,0.00026858156,0.0011936339,0.000283491],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030018677,0.000121950885,0.000044738586,0.008977509,0.000069379756,0.000012385323,0.000033161665,0.0000012195565,0.00059580815,0.0023621223,0.010654378,0.97709733],"study_design_scores_gemma":[0.00013787753,0.00089932256,0.00019935562,0.060268346,0.0022275369,0.00052278367,0.0001414547,0.22393072,0.0059915134,0.11253608,0.5914175,0.0017274996],"about_ca_topic_score_codex":0.00007514914,"about_ca_topic_score_gemma":0.000009009523,"teacher_disagreement_score":0.9753698,"about_ca_system_score_codex":0.00007380974,"about_ca_system_score_gemma":0.0002804187,"threshold_uncertainty_score":0.9999941},"labels":[],"label_agreement":null},{"id":"W3017300133","doi":"10.1007/s10462-020-09835-4","title":"Deep learning for face image synthesis and semantic manipulations: a review and future perspectives","year":2020,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Face recognition and analysis","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Face (sociological concept); Deep learning; Artificial intelligence; Range (aeronautics); Image (mathematics); Perception; Psychology; Linguistics","score_opus":0.08388578943577576,"score_gpt":0.3615072453684832,"score_spread":0.2776214559327075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017300133","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.038735e-9,0.77483904,0.21838456,0.0051063183,0.00004374647,0.0014786146,0.0000057892,0.00008077497,0.000061167724],"genre_scores_gemma":[0.0000013294291,0.99094385,0.007873285,0.0004787548,0.00015370731,0.00046881582,0.000017445243,0.0000325554,0.000030279238],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970119,0.00046921085,0.0010249743,0.000980158,0.00020651342,0.00030721613],"domain_scores_gemma":[0.9980193,0.0006299297,0.00054065866,0.00041209697,0.00020550872,0.00019254758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007551237,0.0004952232,0.002489609,0.00014534952,0.00027559727,0.0002782548,0.0005308991,0.00012888291,0.000121829675],"category_scores_gemma":[0.0011387405,0.0003961042,0.0006627402,0.0011161905,0.00009909629,0.00035236392,0.00021247144,0.0003839473,0.00021197571],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.9454004e-7,0.0000134468255,3.3584044e-8,0.12574531,0.00009302042,0.000004262595,0.000083088235,1.8707725e-7,8.0470244e-8,0.005852076,0.000058720663,0.8681496],"study_design_scores_gemma":[0.000004806396,0.000029514187,8.84116e-8,0.09927309,0.0033334482,0.00007241875,0.0001971515,0.00389556,0.0000014551126,0.00072917517,0.8920022,0.0004610399],"about_ca_topic_score_codex":0.000003790575,"about_ca_topic_score_gemma":0.0000053374506,"teacher_disagreement_score":0.8919435,"about_ca_system_score_codex":0.000048931233,"about_ca_system_score_gemma":0.00008460866,"threshold_uncertainty_score":0.9998491},"labels":[],"label_agreement":null},{"id":"W3091336037","doi":"10.1007/s10462-020-09915-5","title":"Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Evapotranspiration; Mean squared error; Environmental science; Robustness (evolution); Echo state network; Computer science; Machine learning; Hydrology (agriculture); Artificial neural network; Recurrent neural network; Statistics; Mathematics; Geology","score_opus":0.2447111992971406,"score_gpt":0.33924680162395676,"score_spread":0.09453560232681615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091336037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030010543,0.053331904,0.93939936,0.0026951458,0.00029571197,0.00081854826,0.00011857089,0.0003029764,0.000036703037],"genre_scores_gemma":[0.6031361,0.09426695,0.29062098,0.0104362685,0.0008046406,0.00003202405,0.000522683,0.00013267207,0.000047620455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99275297,0.0002941847,0.001806737,0.0027821043,0.00093955774,0.001424473],"domain_scores_gemma":[0.9945402,0.0003505334,0.00055434275,0.0036812818,0.00019470857,0.0006789019],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0012433606,0.00079310563,0.0011986605,0.000050809365,0.00076562597,0.00057572394,0.008773411,0.00022728075,0.000027599639],"category_scores_gemma":[0.00020113298,0.0006180152,0.00023379296,0.0012728892,0.00018020121,0.001402615,0.008883213,0.0010790088,0.00007641425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023921002,0.000103414015,0.0000055498977,0.0003014329,0.000042070544,0.000018814388,0.00011973217,0.9406233,0.0009933028,0.0015533089,0.0010409112,0.055174235],"study_design_scores_gemma":[0.00002524403,0.000070412505,5.7998893e-8,0.0009360567,0.0000864412,0.000025935276,0.000009255768,0.9892637,0.0010036636,0.007261831,0.0005935795,0.0007238385],"about_ca_topic_score_codex":0.00004963667,"about_ca_topic_score_gemma":0.00006536732,"teacher_disagreement_score":0.6487784,"about_ca_system_score_codex":0.000106765714,"about_ca_system_score_gemma":0.000151101,"threshold_uncertainty_score":0.9996271},"labels":[],"label_agreement":null},{"id":"W3118820303","doi":"10.1007/s10462-020-09948-w","title":"Machine learning towards intelligent systems: applications, challenges, and opportunities","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Government of Ontario","keywords":"Process (computing); The Internet; Mechanism (biology); Work (physics); Field (mathematics); Cognition","score_opus":0.257928733914214,"score_gpt":0.32620402234636076,"score_spread":0.06827528843214675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118820303","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030281751,0.6983346,0.28790167,0.0035534315,0.000118163196,0.00025177377,0.0000065340446,0.000090434885,0.009740421],"genre_scores_gemma":[0.014874038,0.9821012,0.0015844015,0.00090766855,0.000072449235,0.00014563728,0.00004024945,0.000010164667,0.00026417885],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983847,0.00014128721,0.0005659084,0.0004951843,0.00016929775,0.00024363486],"domain_scores_gemma":[0.9988469,0.00009798896,0.00013619813,0.00057899917,0.00016873011,0.00017121156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059136393,0.0001738821,0.00034862163,0.000054343716,0.00011891805,0.00028603376,0.0004773163,0.000049377395,0.00004645366],"category_scores_gemma":[0.000105391104,0.00016276015,0.00007050484,0.00019370066,0.000070266906,0.0005814577,0.0003917874,0.00016424475,0.00025327608],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.5206623e-7,0.000020736763,8.659066e-7,0.0005417425,0.000007277314,0.000010967405,0.00004401908,0.0000038272783,0.0000013435877,0.4297808,0.000023391436,0.56956476],"study_design_scores_gemma":[0.0000062687773,0.000034239445,0.000002770192,0.0010829616,0.000017640172,0.00012998586,0.00026357852,0.0057237954,0.00043467645,0.016215494,0.97584796,0.00024065145],"about_ca_topic_score_codex":0.000027245776,"about_ca_topic_score_gemma":0.000014040738,"teacher_disagreement_score":0.97582453,"about_ca_system_score_codex":0.000029365525,"about_ca_system_score_gemma":0.00014038915,"threshold_uncertainty_score":0.6637166},"labels":[],"label_agreement":null},{"id":"W3186240982","doi":"10.1007/s10462-021-10043-x","title":"News recommender system: a review of recent progress, challenges, and opportunities","year":2021,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":203,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Recommender system; Information overload; Focus (optics); Order (exchange); Data science; Deep neural networks; State (computer science); World Wide Web; Deep learning; Artificial intelligence","score_opus":0.4596284479293151,"score_gpt":0.4188895473862157,"score_spread":0.04073890054309942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186240982","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.7435415e-10,0.9788259,0.008057712,0.0053318394,0.0005841836,0.0028799963,0.000013321544,0.00021941715,0.0040876195],"genre_scores_gemma":[5.3454357e-8,0.9937144,0.0042749364,0.000838043,0.00012375583,0.0009148095,0.000046721154,0.00005172955,0.00003556644],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99267936,0.001756154,0.003435188,0.0011060869,0.0005335798,0.0004896588],"domain_scores_gemma":[0.99496704,0.00030431125,0.0019564482,0.0019903814,0.00053814874,0.00024366632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031820333,0.00076204544,0.0052670822,0.00019955334,0.00009251833,0.00012450955,0.0015655092,0.00028000472,0.0001076818],"category_scores_gemma":[0.0001923306,0.00058153516,0.00080908224,0.00069785904,0.00013062896,0.00033906102,0.00077745,0.0004674412,0.000048608385],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.669185e-8,0.00003032647,6.376696e-9,0.38096896,0.00005908349,0.000021489017,0.000015820096,4.3385745e-10,1.7152306e-9,0.100293025,0.0028012428,0.51580995],"study_design_scores_gemma":[0.0000024795029,0.00003867259,3.0893206e-9,0.4591896,0.00028058724,0.00024043703,0.00002538237,0.000003556924,0.000001246755,0.00026734042,0.53966933,0.00028136754],"about_ca_topic_score_codex":0.000018101442,"about_ca_topic_score_gemma":0.00000950832,"teacher_disagreement_score":0.5368681,"about_ca_system_score_codex":0.00015044845,"about_ca_system_score_gemma":0.00061898626,"threshold_uncertainty_score":0.9996636},"labels":[],"label_agreement":null},{"id":"W3201077663","doi":"10.1007/s10462-021-10068-2","title":"An automated essay scoring systems: a systematic literature review","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":466,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Grading (engineering); Cohesion (chemistry); Relevance (law); Artificial intelligence; Evaluation methods; Data science","score_opus":0.05070367394269971,"score_gpt":0.34698370668237716,"score_spread":0.29628003273967746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201077663","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015450341,0.7207227,0.2751441,0.0006024461,0.0011351439,0.0011342947,0.0000025030422,0.0007914031,0.00045200315],"genre_scores_gemma":[0.024853215,0.96231437,0.006214062,0.0034840512,0.00051568466,0.00052279374,0.00003588375,0.00007307353,0.001986887],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99431646,0.0015645886,0.0018187509,0.00097135635,0.0007207641,0.0006080792],"domain_scores_gemma":[0.99600506,0.00024679588,0.0005909433,0.0018241521,0.0010717856,0.00026125237],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030693142,0.0004329942,0.0013476224,0.000120311524,0.00029032203,0.0008972443,0.001128041,0.00011574462,0.00004599305],"category_scores_gemma":[0.0011156861,0.00035460969,0.00032726646,0.0021844602,0.00003310304,0.0010922248,0.00019728545,0.00043637902,0.00090051413],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.439358e-7,0.000102571306,0.0000054144934,0.4973691,0.00006304394,0.000636528,0.00039215246,0.00031452623,0.0003927089,0.48850206,0.0003394174,0.011881725],"study_design_scores_gemma":[0.000009694628,0.00006883163,0.0000018378196,0.9402957,0.000121441124,0.0008893764,0.00011926193,0.044529777,0.001524622,0.0003853621,0.011518602,0.0005354912],"about_ca_topic_score_codex":0.000020748097,"about_ca_topic_score_gemma":0.00000476658,"teacher_disagreement_score":0.48811668,"about_ca_system_score_codex":0.00016568549,"about_ca_system_score_gemma":0.00029100134,"threshold_uncertainty_score":0.99989057},"labels":[],"label_agreement":null},{"id":"W4210542188","doi":"10.1007/s10462-021-10124-x","title":"Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Universiti Pendidikan Sultan Idris","keywords":"Multiple-criteria decision analysis; Context (archaeology); Multidisciplinary approach; Coronavirus disease 2019 (COVID-19); Management science; Computer science; Pandemic; Decision analysis; Data science; Operations research; Risk analysis (engineering); Medicine; Disease; Sociology; Infectious disease (medical specialty); Geography; Engineering; Social science; Mathematics","score_opus":0.3122358513913376,"score_gpt":0.5498448854277446,"score_spread":0.23760903403640699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210542188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007834068,0.31835032,0.67481875,0.00223197,0.00042490766,0.0035147858,0.00044610622,0.00007177755,0.0000630345],"genre_scores_gemma":[0.40281025,0.3367572,0.20503044,0.038885895,0.0006000675,0.014849149,0.0003623286,0.00021330272,0.0004913593],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9894183,0.0014162371,0.003819515,0.0018242806,0.0028252525,0.0006963593],"domain_scores_gemma":[0.9850236,0.009432456,0.0010968522,0.0029034577,0.0010944317,0.0004491658],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.015275025,0.0004715608,0.0016715789,0.0006884348,0.0010261126,0.000435042,0.003210335,0.0000629317,0.028433353],"category_scores_gemma":[0.022281796,0.00038127945,0.0015572219,0.0065355827,0.00034201617,0.0003670409,0.0011304859,0.00037310296,0.0017043138],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010706267,0.00028422137,0.00006354267,0.00050208083,0.00008061381,0.000015612619,0.000042503572,0.00033181673,0.00003111642,0.03896614,0.004741811,0.95483345],"study_design_scores_gemma":[0.000074129886,0.000093282586,0.00022481916,0.0056266114,0.0018960303,0.000023202483,0.00020516627,0.10859937,0.000019207557,0.32653362,0.555823,0.0008815188],"about_ca_topic_score_codex":0.000007524665,"about_ca_topic_score_gemma":0.000016878921,"teacher_disagreement_score":0.95395195,"about_ca_system_score_codex":0.00019246762,"about_ca_system_score_gemma":0.00024251406,"threshold_uncertainty_score":0.9998639},"labels":[],"label_agreement":null},{"id":"W4226322152","doi":"10.1007/s10462-022-10177-6","title":"A trilevel analysis of uncertainty measuresin partition-based granular computing","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Granular computing; Computer science; Partition (number theory); Categorization; Uncertainty analysis; Granular material; Measure (data warehouse); Data mining; Mathematics; Artificial intelligence; Rough set; Simulation; Engineering","score_opus":0.10901092078283182,"score_gpt":0.33075098356987365,"score_spread":0.22174006278704184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226322152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012783102,0.022040779,0.97332585,0.0020876247,0.00025944895,0.0004735465,0.000027911146,0.000096958676,0.00040959482],"genre_scores_gemma":[0.9895176,0.001391314,0.00700807,0.001964589,0.000026081632,0.00004787148,0.000033099215,0.0000074671666,0.0000038762228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972087,0.0004966792,0.00088865566,0.00045620493,0.0006429571,0.00030677085],"domain_scores_gemma":[0.99837637,0.00028371345,0.0003624357,0.00071859214,0.00017216818,0.00008670049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021229656,0.00016362793,0.0006282371,0.0002699489,0.00033515066,0.00005037172,0.0010374882,0.00002574966,0.0003720399],"category_scores_gemma":[0.00019847712,0.00014720572,0.00046305658,0.0043781404,0.00007499491,0.00009705139,0.00020909045,0.0001833021,0.000026634181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010909846,0.00031491715,0.0002227487,0.00031737852,0.00018106544,0.000019424984,0.00026849232,0.25784892,0.00008570521,0.09878904,0.00035095788,0.6415904],"study_design_scores_gemma":[0.000021722797,0.00013247589,0.00021897741,0.00023400813,0.00043650353,0.0000027824453,0.000054201948,0.9823224,0.00044383228,0.00967882,0.006187767,0.0002664879],"about_ca_topic_score_codex":0.00019066568,"about_ca_topic_score_gemma":0.00004293182,"teacher_disagreement_score":0.98823935,"about_ca_system_score_codex":0.00007586912,"about_ca_system_score_gemma":0.00014952644,"threshold_uncertainty_score":0.6002875},"labels":[],"label_agreement":null},{"id":"W4280491585","doi":"10.1007/s10462-022-10201-9","title":"On the comparative performance of recent swarm intelligence based algorithms for optimization of real-life Sterling cycle operated refrigeration/liquefaction system","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadore College","funders":"","keywords":"Computer science; Algorithm; Swarm intelligence; Constraint (computer-aided design); Convergence (economics); Metaheuristic; Mathematical optimization; Particle swarm optimization; Mathematics","score_opus":0.11597474790925093,"score_gpt":0.33113485188959596,"score_spread":0.21516010398034502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280491585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04790485,0.0020593484,0.94586784,0.00073188765,0.0004816639,0.0019574682,0.00006177143,0.00033873023,0.00059641764],"genre_scores_gemma":[0.9872941,0.008889306,0.0031170459,0.000095781776,0.000018096645,0.0004850356,0.000078384815,0.000016635831,0.0000056570675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843353,0.00013946275,0.0008477685,0.00018910719,0.00024421889,0.00014589267],"domain_scores_gemma":[0.9988472,0.00026016665,0.0002560134,0.0002922667,0.00031689875,0.000027493677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007381962,0.00015292315,0.00033727096,0.00010852944,0.00029462128,0.000025977866,0.00024692086,0.00004216584,0.00023221753],"category_scores_gemma":[0.0001476409,0.00012373448,0.000080581,0.00064435264,0.00006615183,0.00012183964,0.000024622075,0.00017698151,0.000008748139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024607465,0.000036435977,0.0000012221045,0.0006158287,0.000020303893,1.4283174e-7,0.00012879352,0.9566845,0.00041305515,0.02423125,0.000068511916,0.01777533],"study_design_scores_gemma":[0.000014152558,0.00020536214,0.0000017151514,0.00065875106,0.000020707135,9.04525e-7,0.0007278858,0.85048056,0.14735873,0.0001725727,0.00024990187,0.000108767556],"about_ca_topic_score_codex":0.000010569393,"about_ca_topic_score_gemma":0.0000031101733,"teacher_disagreement_score":0.9427508,"about_ca_system_score_codex":0.00015605707,"about_ca_system_score_gemma":0.000070783324,"threshold_uncertainty_score":0.5045746},"labels":[],"label_agreement":null},{"id":"W4283764803","doi":"10.1007/s10462-022-10226-0","title":"Deep learning in the stock market—a systematic survey of practice, backtesting, and applications","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Portfolio; Volatility (finance); Mainstream; Deep learning; Stock (firearms); Stock market; Financial market; Artificial intelligence; Machine learning; Data science; Econometrics; Context (archaeology); Finance; Economics","score_opus":0.28705499634278425,"score_gpt":0.4776395364419016,"score_spread":0.19058454009911735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283764803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034471073,0.38338903,0.5689219,0.0031918196,0.00052333274,0.012150048,0.000022690123,0.00010474327,0.028249303],"genre_scores_gemma":[0.95041054,0.021867968,0.020665416,0.002217797,0.0001034221,0.0038690027,0.000014216643,0.000066625624,0.0007850187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97818494,0.017286954,0.0021695157,0.00053752284,0.0015432365,0.0002778149],"domain_scores_gemma":[0.9404888,0.056708407,0.0013570882,0.0008395493,0.00054788805,0.000058257705],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08631636,0.00014414368,0.000635701,0.00019177281,0.00036616463,0.00010327478,0.0011193374,0.000026399737,0.00057079596],"category_scores_gemma":[0.15232605,0.00009753762,0.00008712385,0.0036489135,0.00012832381,0.00015986613,0.00033280742,0.0004436655,0.00004705036],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020667416,0.00011492445,0.004289667,0.0035166668,0.0000127590865,0.0000046556443,0.00065287994,0.0006400089,0.0000055910423,0.003881069,0.00030963513,0.98655146],"study_design_scores_gemma":[0.00023693782,0.0021187877,0.120580696,0.036845855,0.0014678144,0.0023910091,0.05254572,0.40809846,0.00012641173,0.20175575,0.17068067,0.0031518906],"about_ca_topic_score_codex":0.00021021038,"about_ca_topic_score_gemma":0.00017670753,"teacher_disagreement_score":0.98339957,"about_ca_system_score_codex":0.000040748702,"about_ca_system_score_gemma":0.00008435893,"threshold_uncertainty_score":0.9408296},"labels":[],"label_agreement":null},{"id":"W4304698333","doi":"10.1007/s10462-022-10265-7","title":"Deep learning, graph-based text representation and classification: a survey, perspectives and challenges","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Topic Modeling","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Feature engineering; Recurrent neural network; Feature learning; Graph; Artificial neural network; Representation (politics); Machine learning; Natural language processing; Theoretical computer science","score_opus":0.22418225187240653,"score_gpt":0.3617897368252568,"score_spread":0.13760748495285025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304698333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020635398,0.41242468,0.57527906,0.009338006,0.0001175719,0.00038453433,7.180534e-7,0.00009333744,0.00029854022],"genre_scores_gemma":[0.7728261,0.22275579,0.004019874,0.00023038,0.000027686667,0.00010572843,0.000004077229,0.000008005104,0.00002235194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808574,0.00064950006,0.00031092155,0.00055585307,0.00024258115,0.00015541885],"domain_scores_gemma":[0.99905103,0.0002736074,0.00013831911,0.00037482567,0.00009891521,0.00006327328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013209585,0.00010820042,0.00019514402,0.00008459642,0.00031784587,0.00008219693,0.0003121247,0.000020736828,0.00006177491],"category_scores_gemma":[0.00034807163,0.00010911895,0.000042853095,0.00043140858,0.00008085425,0.00019322547,0.00019214404,0.00019133948,0.0000137528195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029446785,0.000036167316,0.00035818468,0.00016834949,0.000006362917,0.0000028679767,0.0016510943,0.00043572386,0.000020016681,0.12095915,0.000010264605,0.87634885],"study_design_scores_gemma":[0.0000527722,0.00032185361,0.010965145,0.00042389543,0.000040788167,0.000065425724,0.006332056,0.93381786,0.00020278488,0.03312711,0.014053005,0.000597285],"about_ca_topic_score_codex":0.00006545572,"about_ca_topic_score_gemma":0.000072299525,"teacher_disagreement_score":0.93338215,"about_ca_system_score_codex":0.000030963212,"about_ca_system_score_gemma":0.000034529738,"threshold_uncertainty_score":0.44497415},"labels":[],"label_agreement":null},{"id":"W4309349667","doi":"10.1007/s10462-022-10305-2","title":"Image denoising in the deep learning era","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Noise reduction; Artificial intelligence; Deep learning; Benchmark (surveying); Noise (video); Machine learning; Deep neural networks; Artificial neural network; Image (mathematics); Image denoising; Pattern recognition (psychology); Computer vision; Data science","score_opus":0.07066406562607672,"score_gpt":0.3549113393011013,"score_spread":0.28424727367502456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309349667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00049047003,0.05264043,0.939886,0.004297902,0.00026357954,0.00036284202,2.8142554e-7,0.000070779744,0.0019877267],"genre_scores_gemma":[0.6460613,0.04741599,0.2719892,0.033022884,0.00046701246,0.00060926424,0.000014232926,0.0000785727,0.00034156046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647224,0.0018107085,0.0005190409,0.00036755868,0.00047427643,0.00035617573],"domain_scores_gemma":[0.99877155,0.0004858166,0.00013011266,0.0005160939,0.000056971185,0.00003945774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005220937,0.00013954032,0.00025059062,0.00010165275,0.0005615803,0.00024006526,0.00159387,0.000018167693,0.0002315214],"category_scores_gemma":[0.00048283354,0.00010908953,0.00012810368,0.001543977,0.000052489864,0.000366368,0.00037348515,0.0007039691,0.00019679092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002873574,0.000050370123,0.000010047737,0.00009419894,0.0000029263122,0.00011558443,0.0012838641,0.00034933776,0.0008078327,0.02916043,0.00011718951,0.96800536],"study_design_scores_gemma":[0.00016358298,0.0009288825,0.00040878664,0.0025745763,0.00013601017,0.001329888,0.004096571,0.3310641,0.012709621,0.3203256,0.32407352,0.0021888495],"about_ca_topic_score_codex":0.00005878063,"about_ca_topic_score_gemma":0.0000113790975,"teacher_disagreement_score":0.9658165,"about_ca_system_score_codex":0.00006310382,"about_ca_system_score_gemma":0.000055352568,"threshold_uncertainty_score":0.4448542},"labels":[],"label_agreement":null},{"id":"W4310193697","doi":"10.1007/s10462-022-10339-6","title":"A novel prospect-theory-based three-way decision methodology in multi-scale information systems","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Weighting; Computer science; Prospect theory; Function (biology); Bellman equation; Object (grammar); Scope (computer science); Scale (ratio); Artificial intelligence; Point (geometry); Value (mathematics); Data mining; Machine learning; Mathematical optimization; Mathematics","score_opus":0.23347020066594282,"score_gpt":0.3769548509421719,"score_spread":0.1434846502762291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310193697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018358816,0.012091664,0.9849051,0.00058370247,0.00076434156,0.001109335,0.000008754289,0.00008896004,0.00026452355],"genre_scores_gemma":[0.16070673,0.0039675427,0.8293918,0.004486859,0.00007603294,0.0012901848,0.000033207918,0.000025659321,0.000022002536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725586,0.000528109,0.001028053,0.00039122478,0.00042785573,0.00036886777],"domain_scores_gemma":[0.9982623,0.0005885544,0.000297075,0.0006688519,0.00010471411,0.000078542005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051501654,0.0001869284,0.00045750214,0.00021335059,0.00023398284,0.00013441827,0.0011321553,0.000054903754,0.000085743995],"category_scores_gemma":[0.00046015962,0.00015809275,0.00012943894,0.0013993527,0.000055332268,0.00051958923,0.00036051628,0.00031384814,0.00023950695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018250636,0.00017139467,0.00006809973,0.0002603664,0.00000434879,0.000006283367,0.00030833576,0.010160486,0.000085612475,0.23686832,0.000049007194,0.7519995],"study_design_scores_gemma":[0.00012220776,0.00033056078,0.0003011274,0.0009793827,0.000022946577,0.00007179095,0.00021737028,0.92061377,0.00045019088,0.0475581,0.028804274,0.0005282669],"about_ca_topic_score_codex":0.00019229254,"about_ca_topic_score_gemma":0.00014611207,"teacher_disagreement_score":0.9104533,"about_ca_system_score_codex":0.00014987655,"about_ca_system_score_gemma":0.00011657679,"threshold_uncertainty_score":0.6446835},"labels":[],"label_agreement":null},{"id":"W4320490631","doi":"10.1007/s10462-023-10395-6","title":"Games of GANs: game-theoretical models for generative adversarial networks","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Alberta","funders":"","keywords":"Computer science; Generative grammar; Adversarial system; Artificial intelligence; Game theory; Nash equilibrium; Field (mathematics); Machine learning; Zero-sum game; Theoretical computer science; Mathematical economics; Mathematics","score_opus":0.06814399205353684,"score_gpt":0.32021645808620025,"score_spread":0.2520724660326634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320490631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000033715987,0.011353433,0.9842187,0.0023041621,0.00071826257,0.0008772005,0.000011245661,0.00012198237,0.00036131064],"genre_scores_gemma":[0.816223,0.096676685,0.08323742,0.0019666038,0.0012411152,0.00042664463,0.000044171775,0.00005822833,0.00012610068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975083,0.0002553575,0.0008418297,0.0005817846,0.0002971356,0.00051557797],"domain_scores_gemma":[0.99802977,0.0006476684,0.00022902386,0.0006063338,0.00035766003,0.00012953977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012733694,0.00025539534,0.00061992084,0.00010171303,0.00012796145,0.00008223234,0.0008548515,0.00009888703,0.00008903019],"category_scores_gemma":[0.0004562601,0.00021580569,0.00035922322,0.0011500615,0.000312551,0.00045680712,0.00021182936,0.0001360902,0.00008084964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013389976,0.000035970403,6.4823143e-7,0.00014204145,0.000035833225,0.000002741563,0.00014559363,0.096961096,0.00017479407,0.624015,0.0019038949,0.27656904],"study_design_scores_gemma":[0.000024772371,0.000107819455,0.0000011159793,0.00050579756,0.000044378878,0.000001673109,0.000031309453,0.80915254,0.007783051,0.18005553,0.0020889263,0.00020307947],"about_ca_topic_score_codex":0.0000106743555,"about_ca_topic_score_gemma":0.000006922333,"teacher_disagreement_score":0.90098125,"about_ca_system_score_codex":0.000027215998,"about_ca_system_score_gemma":0.00009771687,"threshold_uncertainty_score":0.8800301},"labels":[],"label_agreement":null},{"id":"W4366091323","doi":"10.1007/s10462-023-10466-8","title":"Deep learning modelling techniques: current progress, applications, advantages, and challenges","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":960,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"University of Technology Sydney","keywords":"Computer science; Deep learning; Artificial intelligence; Machine learning; Field (mathematics); Convolutional neural network; Benchmark (surveying); Feature learning; Data science","score_opus":0.11413637978421688,"score_gpt":0.36394770500349455,"score_spread":0.24981132521927768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366091323","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004377478,0.27009067,0.72591656,0.0021157959,0.000023144692,0.0006864924,4.3419323e-7,0.0009924603,0.00017003663],"genre_scores_gemma":[0.010716596,0.9462494,0.041159242,0.00008063165,0.000074826465,0.0016770351,0.00000766037,0.000014718309,0.000019878778],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857545,0.00006646493,0.00042853385,0.00049629365,0.00017836304,0.00025488678],"domain_scores_gemma":[0.9991385,0.000070489965,0.00014869444,0.0004489784,0.00010326641,0.00009008481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006228644,0.00015354769,0.00021440213,0.00011827633,0.00027964683,0.0000853495,0.0004783586,0.000049318718,0.000008188908],"category_scores_gemma":[0.000020694042,0.0001444347,0.00006943429,0.00090732466,0.00008482414,0.00025065165,0.00021535317,0.00022444212,0.00024540222],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.2871714e-7,0.000017977989,0.0000037317848,0.0004132155,0.0000020578238,5.420251e-7,0.000035755736,0.00004231491,0.0000067451438,0.18884255,0.000020105506,0.81061476],"study_design_scores_gemma":[0.000004073539,0.000051528164,0.0000054789716,0.00089746475,0.000015848516,0.000013684678,0.000063330626,0.17766248,0.0019627642,0.06740922,0.75163805,0.00027608263],"about_ca_topic_score_codex":0.000003087965,"about_ca_topic_score_gemma":0.0000017063118,"teacher_disagreement_score":0.8103387,"about_ca_system_score_codex":0.00002277638,"about_ca_system_score_gemma":0.000020352123,"threshold_uncertainty_score":0.5889876},"labels":[],"label_agreement":null},{"id":"W4379519548","doi":"10.1007/s10462-023-10518-z","title":"Uncertainty measurement of partially labeled categorical data with application to semi-supervised attribute reduction","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Categorical variable; Relation (database); Missing data; Computer science; Artificial intelligence; Reduction (mathematics); Data mining; Basis (linear algebra); Function (biology); Pattern recognition (psychology); Mathematics; Machine learning","score_opus":0.18641381719846198,"score_gpt":0.34866534760879564,"score_spread":0.16225153041033366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379519548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007287848,0.0036920712,0.9855251,0.008377739,0.00019404896,0.0011728911,0.0000122877955,0.00019771473,0.00009936586],"genre_scores_gemma":[0.97426987,0.014489989,0.010177509,0.00052535505,0.00015603835,0.00022910559,0.00012304678,0.000018146813,0.00001092817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975272,0.0001374176,0.0006537757,0.0006655319,0.0006831348,0.00033295032],"domain_scores_gemma":[0.99768126,0.000049133057,0.00016640873,0.0015534709,0.000408219,0.00014152039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019955204,0.0001674935,0.00036885648,0.000079537705,0.000107189575,0.000057362304,0.0014408885,0.000044902434,0.00001514989],"category_scores_gemma":[0.00021492263,0.00012496061,0.00005264345,0.0023841402,0.000048839735,0.00027623755,0.00037180705,0.00010664068,0.00046078107],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019039804,0.00012785092,0.000025075013,0.0004561602,0.000032424006,0.00000617465,0.00018091084,0.0055809785,0.00263147,0.025208075,0.0027101517,0.9630217],"study_design_scores_gemma":[0.00018558087,0.0015804463,0.0005235818,0.004216782,0.00038412324,0.00006902505,0.00038185672,0.79114497,0.04166524,0.03142882,0.12648243,0.0019371278],"about_ca_topic_score_codex":0.00015960725,"about_ca_topic_score_gemma":0.00011078319,"teacher_disagreement_score":0.9753476,"about_ca_system_score_codex":0.000074042066,"about_ca_system_score_gemma":0.00016198034,"threshold_uncertainty_score":0.5922562},"labels":[],"label_agreement":null},{"id":"W4383721094","doi":"10.1007/s10462-023-10542-z","title":"AFOX: a new adaptive nature-inspired optimization algorithm","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Metaheuristic; Meta-optimization; Computer science; Mathematical optimization; Multi-swarm optimization; Derivative-free optimization; Particle swarm optimization; Imperialist competitive algorithm; Convergence (economics); Optimization problem; Algorithm; Local optimum; Parallel metaheuristic; Benchmark (surveying); Continuous optimization; Engineering optimization; Optimization algorithm; Mathematics","score_opus":0.09369246705703665,"score_gpt":0.37165255071036646,"score_spread":0.2779600836533298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383721094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.136472e-7,0.013599266,0.9786362,0.0045496924,0.00066091324,0.0008211643,0.0000059024605,0.0005835581,0.0011427024],"genre_scores_gemma":[0.00024751358,0.07459037,0.9217798,0.0016337526,0.00028035234,0.00009820984,0.000043021166,0.000037460788,0.0012895031],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966167,0.00030285597,0.00080642605,0.0007675606,0.00090700417,0.00059940474],"domain_scores_gemma":[0.9977174,0.00030525393,0.00020719378,0.0009507828,0.00046966242,0.00034973794],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014316447,0.00026619754,0.00044810944,0.00034026228,0.00021076089,0.00026131005,0.0014913182,0.0001544835,0.00054749224],"category_scores_gemma":[0.0011051301,0.0002461681,0.00017082153,0.005122401,0.00007962032,0.00060730433,0.00042339627,0.00044979932,0.0037278603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001952474,0.000032683347,6.711312e-7,0.00007310216,0.000018766028,0.000032365846,0.000062414714,0.024539823,0.000004551267,0.053443313,0.004889334,0.91690105],"study_design_scores_gemma":[0.000020380276,0.000069695445,0.000003176741,0.00050914485,0.000016834445,0.000012322021,0.000018501014,0.97730863,0.0006483011,0.010448052,0.010680372,0.00026456147],"about_ca_topic_score_codex":0.00004275079,"about_ca_topic_score_gemma":0.000004661005,"teacher_disagreement_score":0.95276886,"about_ca_system_score_codex":0.000083425424,"about_ca_system_score_gemma":0.00039709228,"threshold_uncertainty_score":0.99999905},"labels":[],"label_agreement":null},{"id":"W4385700994","doi":"10.1007/s10462-023-10568-3","title":"RNN-AFOX: adaptive FOX-inspired-based technique for automated tuning of recurrent neural network hyper-parameters","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Regina","funders":"","keywords":"Recurrent neural network; Computer science; Closing (real estate); Artificial neural network; Artificial intelligence; Machine learning; Economics","score_opus":0.14520438954012696,"score_gpt":0.32589332435967633,"score_spread":0.18068893481954937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385700994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043248627,0.07911605,0.85550123,0.003084567,0.003025311,0.010313229,0.001092042,0.0013605369,0.0032583792],"genre_scores_gemma":[0.9779064,0.00975223,0.011066713,0.00038137197,0.00006957698,0.0006256058,0.00011259338,0.000051579,0.000033933175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975062,0.00007171718,0.0014049748,0.00049816293,0.00006351057,0.00045544162],"domain_scores_gemma":[0.9983272,0.00039556794,0.0006267073,0.00045324018,0.000119716315,0.00007757086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002393526,0.00022483454,0.0007839872,0.00016785998,0.00012323767,0.000031437306,0.0003354596,0.00010672531,0.00010123412],"category_scores_gemma":[0.0006608751,0.00024308581,0.000359594,0.0011578272,0.00009948521,0.000105751664,0.00006570158,0.00016454645,0.00007731161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002773165,0.00044633003,0.01032839,0.007075937,0.00021121216,0.000009346287,0.00020262718,0.017578924,0.00024305355,0.2392539,0.004278066,0.7200949],"study_design_scores_gemma":[0.000025865149,0.00017986221,0.00024228632,0.0010351947,0.000023763721,7.650962e-7,0.000013015897,0.95982045,0.00030135992,0.031557884,0.0065334025,0.00026612458],"about_ca_topic_score_codex":0.00009515418,"about_ca_topic_score_gemma":0.000029036328,"teacher_disagreement_score":0.94224155,"about_ca_system_score_codex":0.000075109,"about_ca_system_score_gemma":0.000042507814,"threshold_uncertainty_score":0.9912752},"labels":[],"label_agreement":null},{"id":"W4391576353","doi":"10.1007/s10462-023-10647-5","title":"Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Division of Graduate Education; Centre Scientifique et Technique du Bâtiment; Natural Science Foundation of Chongqing; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Management science; Flexibility (engineering); Fuzzy set; Strengths and weaknesses; Set (abstract data type); Fuzzy logic; Boosting (machine learning); Context (archaeology); Artificial intelligence; Operations research; Mathematics","score_opus":0.5391893118409208,"score_gpt":0.4773155601423373,"score_spread":0.0618737516985835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391576353","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009170836,0.76532364,0.21700591,0.005066124,0.0014135444,0.0010771386,0.00006936314,0.000082463615,0.0007909646],"genre_scores_gemma":[0.37024808,0.6251088,0.0037349802,0.0005397341,0.0001249203,0.000097116696,0.000014571457,0.00003767897,0.00009412905],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99381673,0.0009360625,0.00215506,0.0012514921,0.0013889455,0.00045168918],"domain_scores_gemma":[0.9907021,0.007751296,0.0001929084,0.0010151735,0.00012370419,0.0002148528],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.012001517,0.00032511924,0.0008077346,0.00052194064,0.00017443596,0.0007123171,0.0008793105,0.00012524331,0.0015802705],"category_scores_gemma":[0.015466686,0.0002426367,0.00017969149,0.0015330107,0.0002550056,0.00048245987,0.0003930277,0.00031369427,0.00407573],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001273531,0.000059103633,0.00013981671,0.00008279286,0.000009755791,0.00004460041,0.00012787059,0.000030375,0.00013083604,0.04069509,0.00051813654,0.9581489],"study_design_scores_gemma":[0.00004504095,0.00008903625,0.008203797,0.005317996,0.00003874975,0.00005395249,0.00019980752,0.015786491,0.00013623187,0.7621886,0.20734718,0.0005930974],"about_ca_topic_score_codex":0.0001385005,"about_ca_topic_score_gemma":0.002923946,"teacher_disagreement_score":0.9575558,"about_ca_system_score_codex":0.00009264166,"about_ca_system_score_gemma":0.000070215676,"threshold_uncertainty_score":0.9993324},"labels":[],"label_agreement":null},{"id":"W4391654576","doi":"10.1007/s10462-023-10670-6","title":"Learning team-based navigation: a review of deep reinforcement learning techniques for multi-agent pathfinding","year":2024,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Victoria","funders":"","keywords":"Pathfinding; Reinforcement learning; Computer science; Artificial intelligence; Human–computer interaction; Shortest path problem","score_opus":0.16325681667852845,"score_gpt":0.42313002362440405,"score_spread":0.25987320694587557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391654576","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.1589466e-9,0.5089546,0.4878573,0.000073034964,0.0003015164,0.0025040868,0.000002456371,0.00024914884,0.00005788095],"genre_scores_gemma":[6.0113683e-7,0.82216865,0.17566457,0.0002394492,0.00014492394,0.0014136342,0.00018175383,0.00007606493,0.0001103819],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9930317,0.00076384784,0.0034895032,0.0012373959,0.0007728909,0.00070470833],"domain_scores_gemma":[0.9955437,0.0006598028,0.002072654,0.001052663,0.00048036268,0.00019077164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0049836584,0.000869264,0.0033259448,0.0003672539,0.000270933,0.00017183574,0.0018270095,0.00028774698,0.00003759674],"category_scores_gemma":[0.0019508653,0.00070771284,0.0015991979,0.0020274585,0.00010488635,0.0002546534,0.00041503969,0.0012614516,0.00037309856],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.4226738e-7,0.000024165436,7.080504e-8,0.3637782,0.00005274355,0.000012944432,0.000047373866,0.00023347024,3.2588844e-7,0.0012166704,0.0001001932,0.6345335],"study_design_scores_gemma":[0.000006132587,0.00016331128,3.4255845e-9,0.49280316,0.0007896508,0.000028260109,0.0000063808034,0.07923237,0.000045651992,0.00006630417,0.42645425,0.00040453637],"about_ca_topic_score_codex":0.0000100464595,"about_ca_topic_score_gemma":3.9276867e-7,"teacher_disagreement_score":0.634129,"about_ca_system_score_codex":0.00035817132,"about_ca_system_score_gemma":0.0006171991,"threshold_uncertainty_score":0.9995374},"labels":[],"label_agreement":null},{"id":"W4391738567","doi":"10.1007/s10462-023-10678-y","title":"Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Science Fund of the Republic of Serbia","keywords":"Computer science; Wind power; Benchmark (surveying); Metaheuristic; Hyperparameter; Artificial neural network; Renewable energy; Decomposition; Artificial intelligence; Machine learning","score_opus":0.11953714791841913,"score_gpt":0.3208487149796302,"score_spread":0.20131156706121106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391738567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017883085,0.12071827,0.8748609,0.000049438917,0.0012766134,0.00029555228,0.0000135075425,0.00032232082,0.00067508727],"genre_scores_gemma":[0.94142735,0.036745526,0.019880796,0.0001458653,0.0012345916,0.00013394908,0.00026585456,0.00012005717,0.000046019784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816424,0.000046447913,0.00083193654,0.00037030893,0.0001845708,0.00040250018],"domain_scores_gemma":[0.99930274,0.00020153848,0.00005088586,0.00023024098,0.0001052805,0.00010928431],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004940691,0.00030063107,0.00039563785,0.00012630073,0.00016742092,0.00017653196,0.00017298432,0.00010443099,0.000052830725],"category_scores_gemma":[0.000049791128,0.00029706577,0.00024368086,0.00034904122,0.00003237559,0.00044783327,0.000031495212,0.00016679364,0.000019737054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023587963,0.000010310705,3.198783e-7,0.0015538717,0.00005836959,0.00001580211,0.00011113261,0.36899924,0.0044544213,0.0057316795,0.00015449515,0.618908],"study_design_scores_gemma":[0.000007730261,0.00003121777,1.099132e-7,0.0029902589,0.00018343993,0.0000440017,0.000008938464,0.9309246,0.06027467,0.0041314084,0.0010906535,0.00031295928],"about_ca_topic_score_codex":0.00001255245,"about_ca_topic_score_gemma":0.000068161346,"teacher_disagreement_score":0.93963903,"about_ca_system_score_codex":0.000096792486,"about_ca_system_score_gemma":0.000029404664,"threshold_uncertainty_score":0.99994814},"labels":[],"label_agreement":null},{"id":"W4391953812","doi":"10.1007/s10462-024-10703-8","title":"Fashion intelligence in the Metaverse: promise and future prospects","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Fashion and Cultural Textiles","field":"Arts and Humanities","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Metaverse; Extant taxon; Computer science; Possible world; Data science; Virtual reality; Human–computer interaction; Epistemology; Philosophy","score_opus":0.08534012757455763,"score_gpt":0.31681939374471946,"score_spread":0.23147926617016185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391953812","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02530672,0.7269085,0.0005859635,0.08864837,0.0053232345,0.0068637133,0.00004902972,0.00073106517,0.14558342],"genre_scores_gemma":[0.76052356,0.23389295,0.000057376423,0.0026860477,0.001202697,0.0001710992,0.000012794607,0.000020996362,0.0014324597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986783,0.00012371366,0.00043666203,0.00031690995,0.00021857946,0.00022578532],"domain_scores_gemma":[0.99955,0.00009547174,0.00004579571,0.00020649891,0.000055168035,0.000047071688],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006555699,0.00018797106,0.00023890114,0.00005828389,0.00017483323,0.00047855475,0.0002569485,0.000037782644,0.0028885778],"category_scores_gemma":[0.000057267807,0.000102255115,0.0001089355,0.00023070513,0.00021739559,0.0003178993,0.000035036068,0.00027858125,0.0010249679],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001367227,0.000021076932,5.759479e-7,0.0006627207,0.0000062705003,0.000016271484,0.01467952,3.0195247e-7,0.000008216654,0.6123763,0.0008194552,0.37140793],"study_design_scores_gemma":[0.0000053131066,0.0000707196,0.000008835906,0.0021628751,0.00006736602,0.000028661132,0.031616654,0.0003699129,0.00015749905,0.023635147,0.94162667,0.0002503744],"about_ca_topic_score_codex":0.000021525331,"about_ca_topic_score_gemma":0.0007539592,"teacher_disagreement_score":0.94080716,"about_ca_system_score_codex":0.000021088146,"about_ca_system_score_gemma":0.00002687047,"threshold_uncertainty_score":0.9997529},"labels":[],"label_agreement":null},{"id":"W4392401953","doi":"10.1007/s10462-024-10724-3","title":"Resampling strategies for imbalanced regression: a survey and empirical analysis","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; École de technologie supérieure","keywords":"Resampling; Computer science; Regression; Regression analysis; Statistics; Machine learning; Artificial intelligence; Econometrics; Mathematics","score_opus":0.2207092403690384,"score_gpt":0.4567738095203853,"score_spread":0.2360645691513469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392401953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013965185,0.045531474,0.9506298,0.002584207,0.000146265,0.00044814014,0.000035803485,0.00038756194,0.000097151256],"genre_scores_gemma":[0.6116623,0.1294052,0.25538516,0.0022261622,0.00020856944,0.0006893437,0.00026264114,0.000045715722,0.0001148997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803674,0.00017713419,0.0006100533,0.0006927018,0.00021610533,0.00026726598],"domain_scores_gemma":[0.9981327,0.00076457334,0.00010594684,0.00071859616,0.00018903987,0.00008916243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018733485,0.0001806226,0.0004238994,0.00020204294,0.00012879058,0.00060738804,0.00064651633,0.00006890229,0.000023724117],"category_scores_gemma":[0.00046470723,0.0001390574,0.00015691845,0.0022246004,0.00008936986,0.0006572227,0.00014653958,0.00014634406,0.00003937116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006178026,0.000027675995,0.0002897538,0.0010379379,0.0001048733,0.0000056839112,0.00017033842,0.0000137259285,0.0002938242,0.36362383,0.0030310433,0.6313951],"study_design_scores_gemma":[0.000026642298,0.00025403057,0.002922574,0.0062803132,0.00051156204,0.000032798984,0.00016339008,0.4896026,0.009760795,0.39761835,0.09158839,0.0012385525],"about_ca_topic_score_codex":0.000029250648,"about_ca_topic_score_gemma":0.00007204912,"teacher_disagreement_score":0.69524455,"about_ca_system_score_codex":0.000037155565,"about_ca_system_score_gemma":0.00016239856,"threshold_uncertainty_score":0.5857057},"labels":[],"label_agreement":null},{"id":"W4400895761","doi":"10.1007/s10462-024-10865-5","title":"A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions","year":2024,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Production (economics); Robustness (evolution); Mean absolute percentage error; Predictive modelling; Big data; Data mining; Machine learning; Econometrics; Artificial neural network; Mathematics","score_opus":0.5533894886058799,"score_gpt":0.4615328802120601,"score_spread":0.09185660839381976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400895761","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.8338564e-7,0.9865695,0.008937858,0.00030699762,0.0011565711,0.0023203194,0.0005057798,0.00013193404,0.00007024993],"genre_scores_gemma":[2.6488107e-7,0.96826684,0.028550487,0.0000123995615,0.0013414994,0.0004608159,0.0012454866,0.00008485325,0.000037325135],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969655,0.00027487238,0.0015590101,0.0007025419,0.00026529134,0.00023276497],"domain_scores_gemma":[0.99730814,0.0009028717,0.0006342625,0.00084221916,0.00022597503,0.00008651194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011666142,0.00046112086,0.0018345721,0.00016028695,0.000084185274,0.00006717554,0.00046291618,0.00018600038,0.00004790498],"category_scores_gemma":[0.0011836289,0.00037595766,0.00040625155,0.0008303152,0.00006550817,0.0004000865,0.00015260714,0.00049358973,0.000029794022],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.052882e-7,0.000012248988,2.3120217e-8,0.4110439,0.00019516498,7.532576e-7,0.000013139485,0.00081621646,1.2865065e-7,0.0001504548,0.00062118686,0.587146],"study_design_scores_gemma":[0.0000047321646,0.000015042534,1.5184666e-8,0.27377716,0.0021686465,0.000017061546,0.000026202926,0.037791613,0.0000014773218,0.00024601436,0.68573266,0.00021938041],"about_ca_topic_score_codex":0.000021121057,"about_ca_topic_score_gemma":0.00001317488,"teacher_disagreement_score":0.68511146,"about_ca_system_score_codex":0.00006189838,"about_ca_system_score_gemma":0.000101611,"threshold_uncertainty_score":0.9998692},"labels":[],"label_agreement":null},{"id":"W4401009456","doi":"10.1007/s10462-024-10853-9","title":"Knowledge transfer in lifelong machine learning: a systematic literature review","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lifelong learning; Knowledge transfer; Transfer of learning; Artificial intelligence; Systematic review; Machine learning; Knowledge management; Psychology; MEDLINE; Pedagogy; Chemistry","score_opus":0.06351313169488046,"score_gpt":0.34313097841094625,"score_spread":0.2796178467160658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401009456","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004627769,0.7517581,0.24241811,0.0030909118,0.0003304839,0.0008848049,8.8148306e-7,0.00022558561,0.0012864895],"genre_scores_gemma":[0.025515465,0.97008586,0.0011655932,0.002262675,0.00007683557,0.00019444543,0.000011129838,0.000025781941,0.0006622347],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99671763,0.00081536116,0.0011820336,0.0005983935,0.00032055168,0.00036603466],"domain_scores_gemma":[0.998774,0.00041219653,0.000069073525,0.00048160047,0.00012562927,0.00013748882],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002796634,0.00028085147,0.0007469316,0.00023670442,0.00009520277,0.00040553848,0.00075858354,0.00007447114,0.0001630519],"category_scores_gemma":[0.00076652627,0.00021841541,0.0002786832,0.003164035,0.000038879447,0.0005771922,0.000083376464,0.00070945884,0.0019075585],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013103997,0.00006299331,0.0000042502443,0.4684098,0.000024198125,0.00018304333,0.0018541632,0.000033531036,0.000030626394,0.19243592,0.00031163052,0.33664855],"study_design_scores_gemma":[0.000012251372,0.000056707075,0.0000022133015,0.8213158,0.00007006727,0.00012822024,0.000028941182,0.09915583,0.00009606726,0.0014852452,0.07729587,0.0003527859],"about_ca_topic_score_codex":0.000005387808,"about_ca_topic_score_gemma":0.000031214786,"teacher_disagreement_score":0.35290602,"about_ca_system_score_codex":0.000068852096,"about_ca_system_score_gemma":0.00011417718,"threshold_uncertainty_score":0.9988696},"labels":[],"label_agreement":null},{"id":"W4402692326","doi":"10.1007/s10462-024-10932-x","title":"Review of medical image processing using quantum-enabled algorithms","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Jilin Scientific and Technological Development Program; Department of Science and Technology of Jilin Province","keywords":"Computer science; Image processing; Algorithm; Quantum; Image (mathematics); Computer vision; Artificial intelligence","score_opus":0.06344783206658251,"score_gpt":0.37514444593264296,"score_spread":0.31169661386606046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402692326","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000043948818,0.4877762,0.5065685,0.004768768,0.00036324596,0.00025200847,0.000001646913,0.00015480173,0.00007089105],"genre_scores_gemma":[0.0041724625,0.89076674,0.09976189,0.0047391127,0.00047083315,0.000024569534,0.000005682551,0.00004554068,0.000013189771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632823,0.0002409173,0.0012691495,0.0006851015,0.0010142737,0.00046234723],"domain_scores_gemma":[0.9984628,0.000223268,0.0002251498,0.0006280101,0.0002542149,0.0002065778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032904746,0.00028764008,0.0007150635,0.00013804609,0.00014299054,0.00022457,0.0015763332,0.0000923685,0.00020997196],"category_scores_gemma":[0.0006089815,0.00021865944,0.00031225572,0.0020600252,0.00016454112,0.00041263827,0.00042288427,0.00048463297,0.00014194877],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.3330996e-7,0.000042288964,2.0238653e-7,0.051016312,0.0000152364,0.000082009916,0.00009082728,0.000036881902,0.00009600462,0.01942389,0.0005475485,0.92864835],"study_design_scores_gemma":[0.000005505538,0.000038178943,2.7424062e-7,0.21993527,0.000046441335,0.00020016976,0.000007039311,0.75440156,0.0012213279,0.008792667,0.015128271,0.00022326627],"about_ca_topic_score_codex":0.000052875715,"about_ca_topic_score_gemma":0.0000016598365,"teacher_disagreement_score":0.9284251,"about_ca_system_score_codex":0.00004214191,"about_ca_system_score_gemma":0.000628183,"threshold_uncertainty_score":0.89166725},"labels":[],"label_agreement":null},{"id":"W4403291952","doi":"10.1007/s10462-024-10978-x","title":"A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions","year":2024,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Science Fund of the Republic of Serbia","keywords":"Compliance (psychology); Personal protective equipment; Computer science; Risk analysis (engineering); Medicine; Psychology; Coronavirus disease 2019 (COVID-19); Pathology","score_opus":0.11885685547743512,"score_gpt":0.3976492832622018,"score_spread":0.27879242778476665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403291952","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.599748e-8,0.99039215,0.00083754625,0.0001985538,0.0011533576,0.007079384,0.000036018533,0.000103513106,0.00019940274],"genre_scores_gemma":[0.0000042203023,0.9974352,0.00016335968,0.00010324609,0.00040562786,0.0017996641,0.000014961318,0.00006502875,0.00000867712],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9954323,0.0007373243,0.0024835602,0.00057793304,0.0004651716,0.000303701],"domain_scores_gemma":[0.99814326,0.0004327648,0.0006713729,0.00043563222,0.00021300415,0.00010397841],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020906148,0.00059964834,0.0034264724,0.00030159924,0.000059137856,0.000048312213,0.00021148963,0.0004952593,0.000059619317],"category_scores_gemma":[0.00029844113,0.0004472989,0.0004977088,0.0010682575,0.00005607005,0.00018200996,0.00006535112,0.0013806246,0.0001332931],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012013543,0.000025871666,1.274139e-9,0.5136818,0.000088274996,0.0000071850263,0.000033090022,0.0000017644434,5.2012307e-8,0.00008723008,0.000052038446,0.4860215],"study_design_scores_gemma":[0.000009447118,0.000090030095,1.618994e-8,0.65351075,0.0011310539,0.000058203335,0.000064380845,0.0005504602,0.0000010876971,0.000018653169,0.3443178,0.0002481129],"about_ca_topic_score_codex":0.000014095037,"about_ca_topic_score_gemma":0.000010527168,"teacher_disagreement_score":0.48577335,"about_ca_system_score_codex":0.00035379294,"about_ca_system_score_gemma":0.0001521416,"threshold_uncertainty_score":0.9997979},"labels":[],"label_agreement":null},{"id":"W4403656793","doi":"10.1007/s10462-024-10996-9","title":"Counterfactuals in fuzzy relational models","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Sultan Qaboos University","keywords":"Counterfactual conditional; Computer science; Fuzzy logic; Artificial intelligence; Econometrics; Mathematics; Counterfactual thinking; Epistemology; Philosophy","score_opus":0.16407664457364365,"score_gpt":0.35601444126017767,"score_spread":0.19193779668653402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403656793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004203538,0.22160397,0.76525134,0.006214312,0.00060087786,0.00025728514,0.000001402287,0.00020602842,0.0054444307],"genre_scores_gemma":[0.89207673,0.09346036,0.012379662,0.0017545994,0.00010417833,0.000057280813,0.0000034929164,0.000012626397,0.00015106537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985615,0.00007179656,0.00051298883,0.00037718058,0.00024068494,0.00023586728],"domain_scores_gemma":[0.9992389,0.0002911626,0.00003730936,0.00034243605,0.000047587095,0.000042570304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00077533594,0.00012302528,0.00023106793,0.00010819806,0.00003836333,0.00014161725,0.00051717734,0.000048621707,0.000086567205],"category_scores_gemma":[0.00013531203,0.00009971322,0.000086953034,0.0006778864,0.00004762908,0.0007587352,0.00010374509,0.00016138886,0.0011642508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.562348e-7,0.000012871726,0.0000059816084,0.00022731454,0.0000038119208,0.00002292495,0.00016176091,0.00031107382,0.000014801356,0.70160335,0.00038161047,0.29725406],"study_design_scores_gemma":[0.000004360954,0.000024731211,0.000034859502,0.0034658115,0.000009373752,0.000027838698,0.000030296971,0.26320308,0.00060129183,0.71967536,0.01273246,0.00019054697],"about_ca_topic_score_codex":0.00004078117,"about_ca_topic_score_gemma":0.000041361756,"teacher_disagreement_score":0.8916564,"about_ca_system_score_codex":0.00004750269,"about_ca_system_score_gemma":0.00010636591,"threshold_uncertainty_score":0.99961346},"labels":[],"label_agreement":null},{"id":"W4403775493","doi":"10.1007/s10462-024-10911-2","title":"$$p,q,r-$$Fractional fuzzy sets and their aggregation operators and applications","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Applied mathematics; Mathematics; Artificial intelligence","score_opus":0.25773871148196315,"score_gpt":0.4798106051713636,"score_spread":0.22207189368940045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403775493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010154256,0.4648148,0.5102444,0.009154378,0.0010724849,0.0018673155,0.00007947669,0.0002104499,0.0024023806],"genre_scores_gemma":[0.90012777,0.093552634,0.003504442,0.0018335738,0.00040679018,0.00026680608,0.000014733181,0.000034843866,0.00025838966],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973828,0.00020532889,0.0009600528,0.0007096123,0.0005507851,0.00019139439],"domain_scores_gemma":[0.99711835,0.0018949748,0.00012493216,0.00044374127,0.00027210076,0.0001459168],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030519832,0.00018647559,0.00033444003,0.00023521265,0.00024969116,0.000882537,0.0003139696,0.00006628276,0.0006919388],"category_scores_gemma":[0.0016601426,0.00012476927,0.00009193323,0.0011451795,0.00016566011,0.0005777296,0.00015424844,0.00018099968,0.0012687222],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002155167,0.000012444806,0.000027798913,0.00010273901,0.0000070306023,0.000002514526,0.00012629422,0.000011672325,0.00020632146,0.04904997,0.0007318754,0.9497192],"study_design_scores_gemma":[0.000011649502,0.000029324983,0.0001401449,0.0018362869,0.00003105353,0.00011715738,0.00049035763,0.026612367,0.0012843665,0.40944213,0.5597091,0.00029608523],"about_ca_topic_score_codex":0.000012356317,"about_ca_topic_score_gemma":0.000022841752,"teacher_disagreement_score":0.9494231,"about_ca_system_score_codex":0.000028752649,"about_ca_system_score_gemma":0.00007177875,"threshold_uncertainty_score":0.9995089},"labels":[],"label_agreement":null},{"id":"W4406814688","doi":"10.1007/s10462-025-11111-2","title":"HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"People's Government of Jilin Province; National Natural Science Foundation of China; Jiangsu University; Jilin Province Development and Reform Commission","keywords":"Computer science; Cloud computing; Feature (linguistics); Scale (ratio); Net (polyhedron); Segmentation; Semantic feature; Point cloud; Artificial intelligence; Pattern recognition (psychology); Cartography; Operating system; Mathematics","score_opus":0.02559056250704876,"score_gpt":0.3005442089437189,"score_spread":0.27495364643667014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406814688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005053786,0.075006686,0.92175746,0.001082474,0.0006416668,0.00052540714,0.000023895827,0.00019076768,0.00026629123],"genre_scores_gemma":[0.81306225,0.16725,0.012972615,0.003436161,0.0012075914,0.00045171494,0.00040737304,0.00010538843,0.0011069182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986659,0.000042871874,0.00051363045,0.00027363913,0.00012527395,0.00037867425],"domain_scores_gemma":[0.9993964,0.00009133897,0.000056813285,0.00029370087,0.00010145023,0.00006028704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005576408,0.00020056474,0.00041940567,0.000064456995,0.00015149702,0.000055521206,0.00018398136,0.000054340206,0.00009500817],"category_scores_gemma":[0.000055515924,0.00018964197,0.00027071402,0.0005027194,0.00001557564,0.00008137943,0.00002690261,0.00015185782,0.00014181947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014984245,0.00009031748,0.00007287724,0.0073629953,0.00029840128,0.0000060140846,0.00019343095,0.26300174,0.00027287673,0.005182715,0.07855447,0.6449492],"study_design_scores_gemma":[0.0000265713,0.000022848079,0.0000031395084,0.0039355177,0.0004083578,0.0000023965874,0.00013845116,0.95196056,0.006382198,0.011118502,0.025689907,0.00031154964],"about_ca_topic_score_codex":0.000010294689,"about_ca_topic_score_gemma":0.00009949597,"teacher_disagreement_score":0.9087848,"about_ca_system_score_codex":0.000059044432,"about_ca_system_score_gemma":0.000023262357,"threshold_uncertainty_score":0.77333754},"labels":[],"label_agreement":null},{"id":"W4408707508","doi":"10.1007/s10462-025-11167-0","title":"Bibliometric analysis of artificial intelligence cyberattack detection models","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.1828336125369347,"score_gpt":0.3797865332793412,"score_spread":0.1969529207424065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408707508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077715875,0.031507213,0.9492779,0.0012088282,0.0012505623,0.0009669632,0.00002567042,0.00020318823,0.0077881184],"genre_scores_gemma":[0.97146904,0.025636517,0.00046340787,0.00179179,0.00033671263,0.000086192566,0.000092052665,0.000029665822,0.000094643634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958067,0.00005521952,0.0020542943,0.00083755015,0.00067502854,0.00057122187],"domain_scores_gemma":[0.99671185,0.00026884206,0.00073607656,0.0010019602,0.0012455429,0.00003574388],"candidate_categories":["metaepi_narrow","bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0016669476,0.0004424613,0.0011253374,0.047562625,0.0002749615,0.0003686138,0.0010947637,0.00018786751,0.0014437946],"category_scores_gemma":[0.001183411,0.0004054655,0.00059697195,0.29103497,0.0002815627,0.0018503929,0.00037115335,0.00032075745,0.0007304403],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031784675,0.00021362756,0.0001442965,0.0015277748,0.0002884833,0.0000028319675,0.000011010844,0.0038704518,0.0004148642,0.19061223,0.00025789134,0.80262476],"study_design_scores_gemma":[0.000020101372,0.00004801854,0.0009982799,0.0043507745,0.0071643796,0.0000032466503,0.00034602603,0.66456604,0.031800043,0.25870684,0.030651012,0.0013452173],"about_ca_topic_score_codex":0.0010893409,"about_ca_topic_score_gemma":0.000904456,"teacher_disagreement_score":0.96369743,"about_ca_system_score_codex":0.00007013034,"about_ca_system_score_gemma":0.000081729435,"threshold_uncertainty_score":0.9998397},"labels":[],"label_agreement":null},{"id":"W4410076467","doi":"10.1007/s10462-025-11214-w","title":"Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Computer science; Interpretability; Software deployment; Psychological intervention; Artificial intelligence; Data science; Process management; Medicine; Software engineering","score_opus":0.07471890793677392,"score_gpt":0.3971085548490244,"score_spread":0.32238964691225047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410076467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43842962,0.18507841,0.21710935,0.04817433,0.0070531275,0.020449968,0.00012559537,0.001681768,0.0818978],"genre_scores_gemma":[0.95710164,0.039864425,0.00057141436,0.001860336,0.000061966326,0.00010984956,0.00026698146,0.000013321489,0.00015005302],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99841523,0.00028298784,0.0006493707,0.0002797047,0.00019604425,0.0001766468],"domain_scores_gemma":[0.9987291,0.0004549743,0.00012460245,0.00021703159,0.00043823713,0.00003603143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083642354,0.00014181313,0.0004056242,0.00034332645,0.00009324553,0.000019895117,0.00005842054,0.00007272677,0.000037763926],"category_scores_gemma":[0.0018247649,0.00012418204,0.00008219094,0.0020194966,0.00006922441,0.00006636601,0.000026533806,0.00035154968,0.00009708195],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029297432,0.00025063194,0.007028978,0.0018877144,0.00005679238,0.00001556085,0.0002475215,0.00012925257,0.0127923405,0.023908023,0.00092974695,0.95246047],"study_design_scores_gemma":[0.0005489168,0.0013472729,0.43138212,0.10663627,0.00056465145,0.000008741064,0.0010552474,0.004497162,0.28934294,0.038919687,0.12398526,0.0017117303],"about_ca_topic_score_codex":0.00032174343,"about_ca_topic_score_gemma":0.000392563,"teacher_disagreement_score":0.95074874,"about_ca_system_score_codex":0.00010425584,"about_ca_system_score_gemma":0.00011139441,"threshold_uncertainty_score":0.5063997},"labels":[],"label_agreement":null},{"id":"W4411041682","doi":"10.1007/s10462-025-11203-z","title":"Web Intelligence (WI) 3.0: in search of a better-connected world to create a future intelligent society","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; York University","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; American Indian Graduate Center","keywords":"Computer science; World Wide Web; Information retrieval","score_opus":0.05722965990088423,"score_gpt":0.3493319640979139,"score_spread":0.29210230419702965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411041682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011245284,0.035839748,0.924918,0.01844003,0.005307033,0.001869047,0.0000024243054,0.00025007522,0.0021283396],"genre_scores_gemma":[0.6608993,0.11047178,0.18585397,0.035771847,0.00462644,0.00058802764,0.000035977522,0.0001623871,0.0015902993],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956188,0.00031539606,0.0017261257,0.0009787327,0.0004984681,0.0008624764],"domain_scores_gemma":[0.99751365,0.00049688976,0.0002101985,0.0011485855,0.00042517352,0.0002055024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022004743,0.0004125857,0.0008942505,0.00058741425,0.0001638164,0.0001576355,0.002209609,0.00012284018,0.000055965975],"category_scores_gemma":[0.00029413335,0.00038465814,0.0004263439,0.007516163,0.0001348079,0.00030274,0.0009647358,0.0006055756,0.00032181913],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013132199,0.0001826998,0.00031915575,0.0012778976,0.000040884275,0.000014108395,0.0014510446,0.00018097888,0.0004966549,0.033763994,0.0036097283,0.9586497],"study_design_scores_gemma":[0.00014625813,0.00066709303,0.0009128241,0.050546415,0.00020108966,0.000036384943,0.0013833654,0.26936287,0.28704706,0.08634352,0.3003693,0.0029838318],"about_ca_topic_score_codex":0.00015307276,"about_ca_topic_score_gemma":0.00019373758,"teacher_disagreement_score":0.9556659,"about_ca_system_score_codex":0.00020350692,"about_ca_system_score_gemma":0.00039941652,"threshold_uncertainty_score":0.9998605},"labels":[],"label_agreement":null},{"id":"W4411535114","doi":"10.1007/s10462-025-11268-w","title":"Comprehensive review of reinforcement learning for medical ultrasound imaging","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; École de Technologie Supérieure; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Khalifa University of Science, Technology and Research; Concordia University","keywords":"Computer science; Reinforcement learning; Software portability; Artificial intelligence; Process (computing); Field (mathematics); Modalities; Data science; Human–computer interaction; Risk analysis (engineering); Machine learning","score_opus":0.03399191280276161,"score_gpt":0.39362435900706383,"score_spread":0.35963244620430224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411535114","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013509036,0.72843134,0.2363795,0.029172529,0.0003428659,0.0016104428,9.1486623e-7,0.00006020336,0.0038671214],"genre_scores_gemma":[0.026977256,0.93712413,0.0018905948,0.033388916,0.00012198248,0.00010220168,0.000061007606,0.000020833433,0.00031304485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975551,0.00011768458,0.0012271027,0.0003107154,0.00048582756,0.0003035729],"domain_scores_gemma":[0.99797624,0.00082805986,0.00025870773,0.00031575185,0.00045098332,0.00017028062],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0016463763,0.00018689658,0.00086706853,0.00009895778,0.000106848085,0.000012504584,0.00022265142,0.000050340474,0.0008202167],"category_scores_gemma":[0.009283472,0.00015266406,0.0003336059,0.00048674096,0.00021372034,0.000050715116,0.00006532903,0.00046621056,0.000032962933],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025790787,0.00007241333,0.00030847496,0.12230997,0.00009342284,0.000012882858,0.000028318602,0.00005475449,0.0005088004,0.011607666,0.0077210222,0.8572565],"study_design_scores_gemma":[0.00012220473,0.000131388,0.00005567754,0.3269839,0.00063369516,0.00009754848,0.00008829,0.019550575,0.002174751,0.0018170379,0.6481221,0.00022288853],"about_ca_topic_score_codex":0.000031316747,"about_ca_topic_score_gemma":5.816974e-7,"teacher_disagreement_score":0.8570336,"about_ca_system_score_codex":0.00005594327,"about_ca_system_score_gemma":0.0003016512,"threshold_uncertainty_score":0.99906176},"labels":[],"label_agreement":null},{"id":"W4412732533","doi":"10.1007/s10462-025-11291-x","title":"Cuckoo catfish optimizer: a new meta-heuristic optimization algorithm","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Meta heuristic; Computer science; Cuckoo; Cuckoo search; Catfish; Heuristic; Optimization algorithm; Mathematical optimization; Algorithm; Metaheuristic; Artificial intelligence; Fish <Actinopterygii>; Mathematics; Fishery; Biology; Zoology; Particle swarm optimization","score_opus":0.10654809245438929,"score_gpt":0.3755850223123636,"score_spread":0.26903692985797434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412732533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.545373e-8,0.061212674,0.9235848,0.009202515,0.0006696659,0.0010629689,0.0000056743957,0.00027453425,0.003987049],"genre_scores_gemma":[0.000060790026,0.07784916,0.91501176,0.0031982912,0.000113185306,0.0001866509,0.000024930478,0.000026452202,0.0035287964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99563724,0.0004990611,0.0013991538,0.0010144867,0.0007945903,0.00065544184],"domain_scores_gemma":[0.99657995,0.0005245829,0.0002907662,0.0015358945,0.0007129545,0.00035587922],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001777265,0.00041417536,0.0009981695,0.0004075426,0.00027818055,0.00050905306,0.0021466184,0.000121503246,0.0018564808],"category_scores_gemma":[0.0021148913,0.00036286487,0.00042181398,0.003712572,0.00012969617,0.00064993434,0.00054432737,0.00038044728,0.00071491016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027833155,0.00011536557,7.835028e-7,0.0003358035,0.0002935863,0.000019238338,0.000044776603,0.05226994,0.0000023272946,0.09854915,0.010632363,0.83773386],"study_design_scores_gemma":[0.00003746519,0.00004409551,7.7716e-7,0.000502784,0.00054624025,0.000014697756,0.000009149119,0.95045716,0.00058018946,0.018378714,0.029082812,0.00034594815],"about_ca_topic_score_codex":0.00013099238,"about_ca_topic_score_gemma":0.000006040799,"teacher_disagreement_score":0.89818716,"about_ca_system_score_codex":0.000121501165,"about_ca_system_score_gemma":0.0007652857,"threshold_uncertainty_score":0.99988234},"labels":[],"label_agreement":null},{"id":"W4413358968","doi":"10.1007/s10462-025-11346-z","title":"Deep learning for intrusion detection in emerging technologies: a comprehensive survey and new perspectives","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; National Research Council Canada; Research and Productivity Council","funders":"National Research Council Canada","keywords":"Computer science; Intrusion detection system; Deep learning; Data science; Emerging technologies; Artificial intelligence","score_opus":0.06294716237207329,"score_gpt":0.335509420769786,"score_spread":0.27256225839771275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413358968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00444882,0.10581827,0.88714385,0.0014998158,0.00026924847,0.0005946929,1.6139624e-7,0.00018370435,0.000041459618],"genre_scores_gemma":[0.7812727,0.21336535,0.004956536,0.00025084676,0.000040369596,0.00007613141,0.0000018138643,0.000008093352,0.000028134411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985642,0.00019784048,0.00042951078,0.00046739195,0.00010334648,0.00023775108],"domain_scores_gemma":[0.99901974,0.00041976737,0.00011769121,0.00023856279,0.00017088215,0.000033337186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007014321,0.00014447898,0.000291719,0.0002539429,0.00023263154,0.000095090996,0.0003141403,0.00009245861,0.000008256214],"category_scores_gemma":[0.0010651163,0.0001398797,0.00006309506,0.0015278595,0.00006401718,0.00030106056,0.00022162512,0.00028712256,0.000010521574],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014205294,0.000018388868,0.000049494563,0.00020171789,0.0000061762444,6.6599165e-7,0.00035581933,0.00024544823,0.0003194422,0.013888447,0.000026287193,0.9848739],"study_design_scores_gemma":[0.00012164374,0.00048276928,0.0015399149,0.005010544,0.000034102646,0.000017248778,0.003514578,0.8016733,0.020734346,0.13104892,0.035150554,0.00067210774],"about_ca_topic_score_codex":0.00026449145,"about_ca_topic_score_gemma":0.0011678487,"teacher_disagreement_score":0.9842018,"about_ca_system_score_codex":0.000074313924,"about_ca_system_score_gemma":0.000041349816,"threshold_uncertainty_score":0.5704129},"labels":[],"label_agreement":null},{"id":"W4413408754","doi":"10.1007/s10462-025-11333-4","title":"Privacy and security in recommenders: an analytical review","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Assiniboine Community College","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Computer security; Internet privacy","score_opus":0.13232938753366644,"score_gpt":0.4062132593583857,"score_spread":0.2738838718247193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413408754","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003235489,0.5068537,0.18285686,0.30593365,0.00027426597,0.00120429,0.000004516719,0.00045875055,0.0020903996],"genre_scores_gemma":[0.019602101,0.93016773,0.03924947,0.010864817,0.000017503051,0.000073525734,0.000006932851,0.000008615847,0.000009294973],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99738,0.0003200026,0.00086995645,0.000781889,0.0002536716,0.0003944768],"domain_scores_gemma":[0.9936685,0.00027731425,0.00012748296,0.005743635,0.00008871991,0.000094379146],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0023013058,0.00021526379,0.00056998007,0.0001798154,0.00007873054,0.0001150567,0.011675515,0.00009675288,0.00006623476],"category_scores_gemma":[0.023981357,0.00019587389,0.00007422293,0.0019713605,0.00015065254,0.0008248392,0.019185614,0.00042635188,0.000047036825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013056184,0.00009012868,0.0001229829,0.0031693063,0.0000074056898,0.000012591133,0.000021007949,1.3711885e-7,0.000003082945,0.10399813,0.024708956,0.86786497],"study_design_scores_gemma":[0.000015883086,0.000046693498,0.000059295682,0.025357326,0.000035284047,0.0000126608065,0.0000230047,0.043069195,0.00033029582,0.8909869,0.03978029,0.00028314773],"about_ca_topic_score_codex":0.000035581168,"about_ca_topic_score_gemma":0.000036566955,"teacher_disagreement_score":0.8675818,"about_ca_system_score_codex":0.00008143104,"about_ca_system_score_gemma":0.00010376645,"threshold_uncertainty_score":0.9936718},"labels":[],"label_agreement":null},{"id":"W4416135351","doi":"10.1007/s10462-025-11378-5","title":"Heuristics for the direct aperture optimisation in intensity modulated radiotion therapy: a systematic literature review","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agencia Nacional de Investigación y Desarrollo","keywords":"Heuristics; Context (archaeology); Heuristic; Set (abstract data type); Aperture (computer memory); Systematic review","score_opus":0.031896910084971454,"score_gpt":0.34376152731201864,"score_spread":0.31186461722704717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416135351","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013299694,0.60453916,0.3891787,0.002359988,0.000115996656,0.0036378654,0.000014465802,0.00004657876,0.000093940966],"genre_scores_gemma":[0.015739972,0.97461385,0.003478044,0.004643743,0.00012009788,0.0011835912,0.00009275613,0.000017169077,0.00011080687],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.998471,0.00018642897,0.000745011,0.00028609496,0.000115401475,0.00019605042],"domain_scores_gemma":[0.99842,0.00065046595,0.00023676215,0.00040779897,0.00025928934,0.000025664966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088322756,0.00022371009,0.00068587635,0.000049042945,0.00011414437,0.00005120859,0.0002704587,0.000054643406,0.000055975193],"category_scores_gemma":[0.00030858812,0.00012923322,0.0002293954,0.0008535526,0.000038831975,0.00011073321,0.000020196676,0.00022184382,0.0000032425546],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005576433,0.00023708108,0.00010406349,0.13206784,0.00026805585,0.0000023594928,0.00021124241,0.00027182983,0.00097099954,0.026627544,0.0029975504,0.83618563],"study_design_scores_gemma":[0.00008031275,0.000110497145,0.00003288331,0.87551856,0.0004456923,0.0000061885253,0.000072595634,0.05044243,0.005175607,0.021045627,0.04643635,0.0006332544],"about_ca_topic_score_codex":0.00001837417,"about_ca_topic_score_gemma":0.0000041609514,"teacher_disagreement_score":0.8355524,"about_ca_system_score_codex":0.000072235955,"about_ca_system_score_gemma":0.00003368754,"threshold_uncertainty_score":0.52699775},"labels":[],"label_agreement":null},{"id":"W4416673024","doi":"10.1007/s10462-025-11421-5","title":"Exploring unanswerability in machine reading comprehension: approaches, benchmarks, and open challenges","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Ted Rogers Centre for Heart Research; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Key (lock); Comprehension; Reading (process); Work (physics); Reading comprehension","score_opus":0.5294704198234306,"score_gpt":0.3733258316109949,"score_spread":0.15614458821243565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416673024","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042299116,0.53575104,0.41889468,0.027089259,0.0006900133,0.0020819386,0.0000013696359,0.0001270862,0.011134693],"genre_scores_gemma":[0.5579834,0.41030967,0.03066378,0.00074684154,0.000035940982,0.00022283017,0.0000036696547,0.000008975128,0.00002489173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790204,0.000249007,0.000683463,0.0007314636,0.00015158353,0.00028246202],"domain_scores_gemma":[0.99873406,0.00024207984,0.00008460493,0.0008226696,0.000044930588,0.00007165697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018308595,0.00017858934,0.00047918552,0.000109598135,0.000111454945,0.00015584676,0.0011872221,0.000042531283,0.000019712725],"category_scores_gemma":[0.00022112555,0.00016275891,0.00004944491,0.0005926876,0.000053471358,0.00085562136,0.0010902787,0.00023536154,0.000010961291],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001632376,0.000033222874,0.00007846443,0.00058544084,0.0000037021287,0.0000033112215,0.00022455175,0.000045946785,0.0000072036405,0.32725188,0.00000474911,0.6717599],"study_design_scores_gemma":[0.00012958053,0.00012929169,0.0021694936,0.026605634,0.00006300833,0.00003584692,0.0009017567,0.5147109,0.0025657325,0.4019082,0.049509823,0.0012707375],"about_ca_topic_score_codex":0.00025907316,"about_ca_topic_score_gemma":0.0002668568,"teacher_disagreement_score":0.67048913,"about_ca_system_score_codex":0.00006401924,"about_ca_system_score_gemma":0.00006444008,"threshold_uncertainty_score":0.66371155},"labels":[],"label_agreement":null},{"id":"W4417344491","doi":"10.1007/s10462-025-11400-w","title":"A review of network delay prediction and advances in large language models for air traffic","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Jiangsu Province; China Scholarship Council; Government of Jiangsu Province; Nanjing University of Aeronautics and Astronautics; China Postdoctoral Science Foundation","keywords":"Causal inference; Causality (physics); Deep learning; Artificial neural network; Inference; Air traffic control; Network topology; Graph; Learning network","score_opus":0.01670916189376542,"score_gpt":0.2886140109507852,"score_spread":0.2719048490570198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417344491","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000067837645,0.638755,0.3595581,0.00011310455,0.00009377343,0.00068795757,0.0000059914123,0.000050601255,0.0006676142],"genre_scores_gemma":[0.042375278,0.95538265,0.00160713,0.0004295515,0.000028834276,0.00011973942,0.00003015208,0.000008470811,0.000018224335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999157,0.000025349382,0.00047844404,0.00013162624,0.000054888882,0.00015265096],"domain_scores_gemma":[0.9997274,0.000054959266,0.000047171805,0.000117011354,0.00003592008,0.00001753583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005032339,0.000096382064,0.0002767973,0.000051335504,0.000024544826,0.0000045578627,0.00007641785,0.000031428866,0.000020590389],"category_scores_gemma":[0.000053116488,0.000091272195,0.000054742613,0.0004483472,0.000015663167,0.00017704538,0.000014363615,0.000055141783,0.0000018965995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032897738,0.000016341683,0.000004470592,0.040495206,0.0000096547155,2.8745694e-7,0.000046614547,0.4185961,6.860824e-7,0.012057916,0.0011439223,0.5276255],"study_design_scores_gemma":[0.00002393281,0.000020432784,0.000002989798,0.04911045,0.00008175837,3.1885102e-7,0.00004806309,0.9199264,0.000029570727,0.0014404989,0.029220544,0.0000950409],"about_ca_topic_score_codex":8.894923e-7,"about_ca_topic_score_gemma":0.000052187435,"teacher_disagreement_score":0.5275305,"about_ca_system_score_codex":0.000017186345,"about_ca_system_score_gemma":0.000008524214,"threshold_uncertainty_score":0.3721972},"labels":[],"label_agreement":null},{"id":"W7114780943","doi":"10.1007/s10462-025-11433-1","title":"Reinforcement learning and the Metaverse: a symbiotic collaboration","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Metaverse; Reinforcement learning; Context (archaeology); Variety (cybernetics); Key (lock); Categorization; Architecture","score_opus":0.03430058131461977,"score_gpt":0.3492977334825877,"score_spread":0.3149971521679679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114780943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000074251606,0.028027402,0.94231224,0.023463555,0.0000902728,0.0006914935,1.9874388e-7,0.000047817997,0.005292776],"genre_scores_gemma":[0.86334896,0.12997206,0.0016124294,0.004237561,0.000021349852,0.0001039748,0.0000027143751,0.0000036241863,0.00069734914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906707,0.00014486375,0.00034008984,0.00018680813,0.00013190087,0.00012929035],"domain_scores_gemma":[0.99924713,0.00020856842,0.00009279001,0.00030097886,0.000112200185,0.000038315357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010257058,0.00007990346,0.00017750377,0.000040291725,0.0002324264,0.00020011413,0.0002999103,0.000021820651,0.000022172508],"category_scores_gemma":[0.00058799365,0.00005219666,0.000043662523,0.0009660004,0.000104992905,0.00020740926,0.00012760423,0.00010011461,0.00009672427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019646873,0.0000057404964,9.752889e-7,0.00008947599,0.00000833011,1.5158118e-7,0.00010918373,0.000117597476,0.000023988403,0.7290606,0.00016663823,0.27041534],"study_design_scores_gemma":[0.0001380855,0.00015549091,0.000028953727,0.003118775,0.0002354827,0.000010811769,0.00081928534,0.30164546,0.009378416,0.24195895,0.4420853,0.0004249863],"about_ca_topic_score_codex":0.00004053364,"about_ca_topic_score_gemma":0.000014435743,"teacher_disagreement_score":0.9406998,"about_ca_system_score_codex":0.000028679271,"about_ca_system_score_gemma":0.000085538784,"threshold_uncertainty_score":0.21285181},"labels":[],"label_agreement":null}]}