{"meta":{"query_hash":"d726784c8c01","filters":{"venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings"},"cohort_total":50,"direct_labels_cover":0,"predictions_cover":50,"exported":50,"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/d726784c8c01","api":"https://metacan.xera.ac/api/v1/cohort?venue=The+2006+IEEE+International+Joint+Conference+on+Neural+Network+Proceedings"},"results":[{"id":"W2005890246","doi":"10.1109/ijcnn.2006.246739","title":"A Heuristic for Free Parameter Optimization with Support Vector Machines","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Maxima and minima; Computer science; Hyperparameter optimization; Support vector machine; Heuristic; Simulated annealing; Generalization; Mathematical optimization; Machine learning; Gradient descent; Selection (genetic algorithm); Incremental heuristic search; Artificial intelligence; Algorithm; Search algorithm; Mathematics; Beam search; Artificial neural network","score_opus":0.03147208949691202,"score_gpt":0.2770154457785389,"score_spread":0.2455433562816269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005890246","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":[0.85823286,0.00006571732,0.033511434,0.06657301,0.0011561555,0.0018115897,0.000095065756,0.00044327063,0.038110882],"genre_scores_gemma":[0.986865,0.000014937533,0.005719333,0.0013215857,0.0014059725,0.00009650893,0.00009716759,0.000033908043,0.0044455854],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998584,0.0000074821032,0.00033930672,0.0003350771,0.00043368564,0.0003004813],"domain_scores_gemma":[0.99886274,0.000074705815,0.00022937015,0.00015225785,0.0006147874,0.000066136374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018679988,0.00023249551,0.00029517576,0.000098480494,0.00013281229,0.00017739086,0.00026346053,0.000048364484,0.00012627312],"category_scores_gemma":[0.000083173625,0.00014000824,0.00014179619,0.00019151691,0.0001184294,0.00012631105,0.00002842242,0.00022121519,0.00001310965],"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":[0.009282026,0.0011413224,0.07239671,0.00037950007,0.0012383838,0.00012926065,0.00040521298,0.40569842,0.006686652,0.072664574,0.4211286,0.008849336],"study_design_scores_gemma":[0.0015186812,0.00074997934,0.005443939,0.00028429052,0.00029826042,0.00021916187,0.000033514996,0.9854423,0.0007093782,0.0037771505,0.0012361787,0.00028720195],"about_ca_topic_score_codex":0.00007955042,"about_ca_topic_score_gemma":0.000008555594,"teacher_disagreement_score":0.57974386,"about_ca_system_score_codex":0.000050011226,"about_ca_system_score_gemma":0.00003892304,"threshold_uncertainty_score":0.57093704},"labels":[],"label_agreement":null},{"id":"W2033483180","doi":"10.1109/ijcnn.2006.246752","title":"Neural and Statistical Classification to Families of Bio-sequences","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","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 Guelph","funders":"","keywords":"Generalization; Computer science; Property (philosophy); Entropy (arrow of time); Artificial intelligence; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Feature vector; String (physics); Machine learning; Mathematics","score_opus":0.027900896080192715,"score_gpt":0.2816318045052348,"score_spread":0.2537309084250421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033483180","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":[0.98493,0.00002372047,0.0012849106,0.003671526,0.00029852882,0.0002147638,0.00003393218,0.000020602974,0.009522059],"genre_scores_gemma":[0.99688363,0.000037932983,0.0016318355,0.0005555025,0.00044839614,0.00001842374,0.00005602941,0.000010814532,0.00035741745],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989806,0.000018376557,0.00033532892,0.00021675894,0.00025808878,0.00019085653],"domain_scores_gemma":[0.99940515,0.000026299587,0.00019263256,0.00009727756,0.00023324908,0.0000454151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019663511,0.00015174391,0.0001301759,0.000052365438,0.00008013644,0.00007825201,0.00027684105,0.00006493688,0.000019764084],"category_scores_gemma":[0.00008742868,0.000110122346,0.000034619745,0.00008798034,0.00020881534,0.00001316674,0.00007768463,0.00014409621,0.000007972083],"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":[0.0007865092,0.00015535773,0.051362168,0.0001068407,0.000113989576,0.0000029513014,0.00041495904,0.023201928,0.57647073,0.25457042,0.07095627,0.021857895],"study_design_scores_gemma":[0.0013117824,0.0022860256,0.28840414,0.00027282798,0.000076545206,0.00016642753,0.00080636353,0.62351453,0.05127065,0.011210156,0.019549698,0.0011308152],"about_ca_topic_score_codex":0.00005651544,"about_ca_topic_score_gemma":0.000019522327,"teacher_disagreement_score":0.60031265,"about_ca_system_score_codex":0.000012001942,"about_ca_system_score_gemma":0.000020132802,"threshold_uncertainty_score":0.4490659},"labels":[],"label_agreement":null},{"id":"W2098216970","doi":"10.1109/ijcnn.2006.246986","title":"Combining Diversity and Classification Accuracy for Ensemble Selection in Random Subspaces","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Random subspace method; Classifier (UML); Linear subspace; Artificial intelligence; Ensemble learning; Computer science; Pattern recognition (psychology); Correlation; Machine learning; Random forest; Statistical classification; Data mining; Mathematics","score_opus":0.0688004532114124,"score_gpt":0.27433051043084883,"score_spread":0.20553005721943643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098216970","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":[0.9652731,0.000019124862,0.023184657,0.006818089,0.00052942394,0.0004704574,0.0000029900714,0.00010482719,0.0035973703],"genre_scores_gemma":[0.9980404,0.00004676381,0.0009741422,0.00036033738,0.0002480227,0.000059660608,0.0000069924886,0.000005924198,0.00025775953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989371,0.000023501827,0.00023743541,0.00031787888,0.00025806748,0.00022606317],"domain_scores_gemma":[0.99925387,0.00019021778,0.00020775977,0.000054410346,0.00026173485,0.000032015592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037076164,0.00013576378,0.0001375145,0.00010203149,0.00046405362,0.00025567634,0.0003712332,0.00005998091,0.000005869654],"category_scores_gemma":[0.000050457118,0.000105272964,0.000044546556,0.00019350988,0.000044958655,0.0006599641,0.00016594,0.00017618065,0.000007137621],"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":[0.0018607691,0.0004906035,0.06702075,0.00008808881,0.000082324725,0.0000061247974,0.0033911134,0.015437046,0.14018191,0.62713397,0.08472354,0.059583783],"study_design_scores_gemma":[0.0017390351,0.0001298294,0.041592192,0.00016050425,0.00000932663,0.00001773473,0.0001042156,0.9050768,0.004872437,0.045495413,0.0005725389,0.00022997173],"about_ca_topic_score_codex":0.00010943278,"about_ca_topic_score_gemma":0.000059609876,"teacher_disagreement_score":0.88963974,"about_ca_system_score_codex":0.000046870922,"about_ca_system_score_gemma":0.000018292922,"threshold_uncertainty_score":0.42929068},"labels":[],"label_agreement":null},{"id":"W2100666605","doi":"10.1109/ijcnn.2006.247367","title":"On Classification Models of Gene Expression Microarrays: The Simpler the Better","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Feature selection; Classifier (UML); Computer science; DNA microarray; Artificial intelligence; Gene selection; Microarray analysis techniques; Pattern recognition (psychology); Microarray; Selection (genetic algorithm); Machine learning; Data mining; Feature (linguistics); Gene expression; Gene; Biology; Genetics","score_opus":0.050658340474977644,"score_gpt":0.26879089910378495,"score_spread":0.21813255862880732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100666605","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":[0.9667389,0.000097818964,0.0023737603,0.017254397,0.0007053854,0.00048167142,0.00002284633,0.000023795194,0.012301421],"genre_scores_gemma":[0.9950733,0.00006960455,0.000114366725,0.002242139,0.0011215819,0.000105915955,0.000054312077,0.000019956311,0.0011988343],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986428,0.000050238956,0.00035315653,0.0003417898,0.0003973952,0.00021460577],"domain_scores_gemma":[0.9989417,0.000035950554,0.0003583807,0.00031105604,0.00032471892,0.00002817576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028151376,0.00019224576,0.00011497583,0.000036077763,0.00021934239,0.00009197884,0.0006793771,0.00009824234,0.000037991504],"category_scores_gemma":[0.00002067509,0.000096085045,0.00011145569,0.000112169946,0.00017590582,0.000015730695,0.000059663864,0.00022162308,0.0000108394825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001849998,0.00004714841,0.00012121769,0.0000030612816,0.00001718318,1.2208098e-7,0.000067872104,0.008730022,0.85655934,0.013414835,0.1199308,0.0009233914],"study_design_scores_gemma":[0.0006675182,0.0002523451,0.006526534,0.00012943316,0.00003126233,0.000017599668,0.0003283615,0.05194973,0.9039094,0.022440368,0.013407876,0.00033961158],"about_ca_topic_score_codex":0.000018962011,"about_ca_topic_score_gemma":0.000007566419,"teacher_disagreement_score":0.106522925,"about_ca_system_score_codex":0.00002504217,"about_ca_system_score_gemma":0.000036125115,"threshold_uncertainty_score":0.39182344},"labels":[],"label_agreement":null},{"id":"W2112021728","doi":"10.1109/ijcnn.2006.246928","title":"Is High Resolution Representation More Effective for Content Based Image Classification?","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Image Retrieval and Classification Techniques","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 Northern British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Matching (statistics); Principal component analysis; Representation (politics); Image resolution; Artificial neural network; Image (mathematics); Contextual image classification; Resolution (logic); Process (computing); Simple (philosophy); Feature extraction; Computer vision; Mathematics","score_opus":0.08518976754621325,"score_gpt":0.31459671361429986,"score_spread":0.2294069460680866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112021728","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":[0.030265465,0.000027916487,0.8922449,0.06894631,0.0011403058,0.0021413404,0.000055177832,0.00060518924,0.0045734346],"genre_scores_gemma":[0.9785889,0.000014464118,0.016729597,0.0020391117,0.0007286844,0.00063846813,0.00005650291,0.000019984553,0.0011843011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979015,0.000043571796,0.0004940215,0.0006100943,0.00061097223,0.0003398552],"domain_scores_gemma":[0.9975372,0.00018651401,0.00048080808,0.0002953102,0.0014452382,0.000054921966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004664188,0.0002500798,0.00020972594,0.00012949282,0.00031069055,0.00052234833,0.0009331628,0.00009524532,0.000024137247],"category_scores_gemma":[0.000089614114,0.00018825808,0.0001659669,0.0003083663,0.00016940222,0.00067153724,0.000068208894,0.00023668921,0.000031299063],"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":[0.00032253916,0.00021359324,0.0005709769,0.000028765422,0.000046244673,0.0000021925637,0.00013466395,0.0003861944,0.1426632,0.7913066,0.04924592,0.015079082],"study_design_scores_gemma":[0.00073831953,0.00022485277,0.028961934,0.00010110545,0.000020966327,0.000010261188,0.00005761895,0.7443933,0.1919219,0.031739045,0.0015187728,0.00031191067],"about_ca_topic_score_codex":0.000102116326,"about_ca_topic_score_gemma":0.0000027849917,"teacher_disagreement_score":0.9483234,"about_ca_system_score_codex":0.00017876862,"about_ca_system_score_gemma":0.000048931717,"threshold_uncertainty_score":0.7676942},"labels":[],"label_agreement":null},{"id":"W2115615930","doi":"10.1109/ijcnn.2006.247204","title":"Extend Single-agent Reinforcement Learning Approach to a Multi-robot Cooperative Task in an Unknown Dynamic Environment","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":7,"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","funders":"","keywords":"Reinforcement learning; Computer science; Robot; Markov decision process; Robustness (evolution); Robot learning; Artificial intelligence; Q-learning; Obstacle; Mobile robot; Markov process; Task (project management); Machine learning; Engineering; Mathematics","score_opus":0.052808010837956824,"score_gpt":0.26725047299880855,"score_spread":0.21444246216085172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115615930","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":[0.067667834,0.000019686711,0.9159742,0.002201477,0.00074060523,0.0011018761,0.0000015936059,0.00021534521,0.012077357],"genre_scores_gemma":[0.9810828,0.000021954529,0.013600375,0.0007853197,0.00025805205,0.00014972907,0.000033589116,0.000032892076,0.004035269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666405,0.00008569179,0.0007584609,0.0008330412,0.00094199117,0.0007167914],"domain_scores_gemma":[0.9988455,0.00005687819,0.00037880847,0.0003196872,0.0002503576,0.00014873884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005769551,0.00044344392,0.00032439447,0.00023291873,0.0002863924,0.00074229547,0.0015815032,0.00009628271,0.000028377763],"category_scores_gemma":[0.000042386102,0.00035561388,0.00009152617,0.00039810373,0.00012006726,0.00070896605,0.0004049499,0.000708204,0.00011136324],"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":[0.00004735575,0.00018848953,0.00027097235,0.000006588221,0.0000189907,0.0000060970806,0.00073517265,0.96673936,0.0053500235,0.024646673,0.00033655262,0.0016537299],"study_design_scores_gemma":[0.0005874382,0.00057527435,0.0023105403,0.000110238885,0.000007925537,0.000019597792,0.00008739922,0.99426544,0.0004431905,0.0002070225,0.00097357837,0.00041237785],"about_ca_topic_score_codex":0.000066733104,"about_ca_topic_score_gemma":0.000018231536,"teacher_disagreement_score":0.91341496,"about_ca_system_score_codex":0.000523723,"about_ca_system_score_gemma":0.000044570632,"threshold_uncertainty_score":0.9998896},"labels":[],"label_agreement":null},{"id":"W2115813140","doi":"10.1109/ijcnn.2006.246815","title":"Rough Set Theory based Neural Network Architecture","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","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 Regina","funders":"","keywords":"Artificial neural network; Computer science; Boundary (topology); Backpropagation; Rough set; Convergence (economics); Computation; Process (computing); Set (abstract data type); Artificial intelligence; Division (mathematics); Network architecture; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.03739502074110487,"score_gpt":0.2514734797662133,"score_spread":0.21407845902510844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115813140","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":[0.3307012,0.00057750393,0.20961133,0.14175542,0.018603435,0.0030439002,0.00011415068,0.0028289086,0.29276416],"genre_scores_gemma":[0.98205537,0.000014577546,0.005114548,0.0077614137,0.0038697661,0.00006384596,0.000027486585,0.000033702992,0.0010593059],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967524,0.00010876797,0.0005894257,0.0007768275,0.00087628333,0.00089630595],"domain_scores_gemma":[0.9984573,0.00025340758,0.00040450745,0.000415968,0.00034508895,0.00012369783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078515406,0.0004899243,0.00035738698,0.000108461325,0.00045139992,0.00089393527,0.0024214282,0.00014183334,0.00011129285],"category_scores_gemma":[0.000037953658,0.00032693872,0.00024005589,0.0005584259,0.00021381098,0.00045515219,0.00027922651,0.0007900279,0.000085354295],"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":[0.00015814079,0.00007537667,0.00057789555,0.000009578444,0.000036352092,0.000028576687,0.00012552697,0.4062548,0.00017477873,0.5041766,0.07482749,0.013554891],"study_design_scores_gemma":[0.00051749166,0.0002283048,0.003788189,0.00010715689,0.000017135118,0.00009007757,0.0000131688075,0.7711912,0.00014049877,0.21575749,0.007663263,0.0004860154],"about_ca_topic_score_codex":0.00006181408,"about_ca_topic_score_gemma":0.000018542914,"teacher_disagreement_score":0.65135413,"about_ca_system_score_codex":0.00008052259,"about_ca_system_score_gemma":0.00006654296,"threshold_uncertainty_score":0.9999183},"labels":[],"label_agreement":null},{"id":"W2120763784","doi":"10.1109/ijcnn.2006.246725","title":"A Reinforcement Learning Framework for Medical Image Segmentation","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":71,"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 Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Thresholding; Artificial intelligence; Segmentation; Image segmentation; Structuring element; Computer vision; Machine learning; Image (mathematics); Image processing; Mathematical morphology","score_opus":0.03457277230092933,"score_gpt":0.31471739117421715,"score_spread":0.28014461887328784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120763784","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":[0.0033522197,0.000011734638,0.97399604,0.014556429,0.00095740246,0.0006487091,0.000002036275,0.00041501314,0.0060604224],"genre_scores_gemma":[0.79533327,0.000068059984,0.19438355,0.005843139,0.0020555442,0.0004826109,0.000044886037,0.000032713564,0.0017562234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971264,0.0000391994,0.00062145956,0.00046289936,0.0013095466,0.00044048435],"domain_scores_gemma":[0.9985015,0.0002738554,0.0004042608,0.00016752271,0.00052906515,0.00012381344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088103744,0.00024813545,0.0002062854,0.00010839757,0.0002851238,0.00057337596,0.0012759565,0.0001217947,0.00027734824],"category_scores_gemma":[0.0003314121,0.0001858597,0.00012185624,0.00026370614,0.00015038613,0.0006534164,0.00017311434,0.00054469035,0.00005304599],"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":[0.00012176678,0.00013481772,0.00021580522,0.00004107379,0.00006396564,0.00001436536,0.0004036417,0.0055965353,0.012837136,0.8238743,0.10184432,0.054852284],"study_design_scores_gemma":[0.0007680348,0.00040531252,0.00034994324,0.00035257437,0.000016173375,0.000044472938,0.00008613698,0.8437667,0.036021244,0.11642869,0.0013543458,0.00040638942],"about_ca_topic_score_codex":0.000036582027,"about_ca_topic_score_gemma":0.0000029779285,"teacher_disagreement_score":0.8381702,"about_ca_system_score_codex":0.00012208421,"about_ca_system_score_gemma":0.00007133925,"threshold_uncertainty_score":0.7579138},"labels":[],"label_agreement":null},{"id":"W2121030950","doi":"10.1109/ijcnn.2006.247069","title":"Appearance-based Pain Recognition from Video Sequences","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Face recognition and analysis","field":"Computer Science","cited_by":8,"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 Northern British Columbia","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Face (sociological concept); Feature (linguistics); Facial recognition system; Biometrics; Pattern recognition (psychology); Feature vector; Feature extraction; Face detection; Three-dimensional face recognition","score_opus":0.048427626659507325,"score_gpt":0.24547934325919485,"score_spread":0.1970517165996875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121030950","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":[0.5352463,0.00025968006,0.30318835,0.10682539,0.004136753,0.0010572617,0.00013647716,0.001454517,0.04769526],"genre_scores_gemma":[0.9906745,0.00003418372,0.0029564996,0.0047510145,0.0010920132,0.000060813763,0.000053400367,0.0000138236455,0.00036377777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784267,0.000089762085,0.0004538575,0.0005541423,0.00067968445,0.00037987754],"domain_scores_gemma":[0.99875796,0.00015409323,0.00032708797,0.0001802421,0.0005040882,0.000076551405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006825112,0.0002671915,0.00023401664,0.00013877153,0.00024292267,0.00069635967,0.001026779,0.00008372026,0.000204289],"category_scores_gemma":[0.00006330229,0.00019853759,0.00018357013,0.00046516475,0.00012310175,0.00054534484,0.00006131058,0.0003220425,0.00030464382],"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":[0.00045059618,0.00085887825,0.006205356,0.00007139726,0.00043589598,0.000087971704,0.0005100377,0.078107394,0.039607376,0.11117283,0.18929161,0.57320064],"study_design_scores_gemma":[0.00044221742,0.00010052107,0.0012163627,0.00033519513,0.000020665007,0.000007782692,0.000037393373,0.9145385,0.006165415,0.07518671,0.0015768579,0.0003723841],"about_ca_topic_score_codex":0.0003603408,"about_ca_topic_score_gemma":0.0000619331,"teacher_disagreement_score":0.8364311,"about_ca_system_score_codex":0.00007522285,"about_ca_system_score_gemma":0.000054152308,"threshold_uncertainty_score":0.80961275},"labels":[],"label_agreement":null},{"id":"W2125191947","doi":"10.1109/ijcnn.2006.247103","title":"A Cooperative Recurrent Neural Network Algorithm for Parameter Estimation of Autoregressive Signals","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Blind Source Separation Techniques","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":"University of Waterloo","funders":"","keywords":"Autoregressive model; Computer science; Algorithm; Weighting; Artificial neural network; Convergence (economics); Gaussian; Gaussian noise; Recurrent neural network; Noise (video); Standard deviation; Mathematics; Artificial intelligence; Statistics","score_opus":0.04575464100919267,"score_gpt":0.3025317251679997,"score_spread":0.256777084158807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125191947","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":[0.040056556,0.000075376185,0.94571376,0.007974821,0.001653349,0.0015991116,0.000046619047,0.00040245373,0.0024779248],"genre_scores_gemma":[0.91721624,0.000017251514,0.07982006,0.0012301414,0.0009114563,0.00031684156,0.000040181512,0.000024745914,0.00042309565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759454,0.000080540696,0.0007328253,0.00052574504,0.000626642,0.00043968952],"domain_scores_gemma":[0.99745655,0.00036318714,0.0007612532,0.00022073775,0.0011333611,0.00006492309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000668136,0.00033475613,0.00036559298,0.00012116712,0.00022193162,0.0004570251,0.0010782861,0.00011333229,0.00002151535],"category_scores_gemma":[0.00008318899,0.00024639178,0.00018072345,0.0003331442,0.00016394469,0.000714347,0.00014054615,0.00035093105,0.000008743161],"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":[0.00013164856,0.00020522517,0.00008863892,0.000019376714,0.00009342136,0.0000035411736,0.0006328501,0.40624613,0.0009142342,0.3863076,0.08050882,0.12484851],"study_design_scores_gemma":[0.00035900585,0.00041306764,0.0003719681,0.0001698493,0.0000151685845,0.00001781837,0.000013670691,0.9456318,0.005032378,0.047277715,0.0004460625,0.0002515205],"about_ca_topic_score_codex":0.000030625968,"about_ca_topic_score_gemma":0.0000067123683,"teacher_disagreement_score":0.87715966,"about_ca_system_score_codex":0.00007990107,"about_ca_system_score_gemma":0.000077054516,"threshold_uncertainty_score":0.9999988},"labels":[],"label_agreement":null},{"id":"W2125258143","doi":"10.1109/ijcnn.2006.246873","title":"Self-Organizing Feature Map (SOFM) based Deformable CAD Models","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":2,"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":"Polygon mesh; Computer science; Feature (linguistics); Hexahedron; Point (geometry); Artificial intelligence; Surface (topology); Computer vision; Object (grammar); Topology (electrical circuits); Solid modeling; Algorithm; Geometry; Computer graphics (images); Finite element method; Mathematics; Engineering","score_opus":0.024596419988793413,"score_gpt":0.21305262203639663,"score_spread":0.18845620204760322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125258143","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":[0.7080946,0.00072273216,0.057380464,0.025364911,0.00695603,0.0011371775,0.00012523466,0.0043810816,0.19583775],"genre_scores_gemma":[0.9938613,0.00004672472,0.001431378,0.00061461533,0.0024885526,0.000026871461,0.000033542732,0.000054576485,0.0014423992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983726,0.000010104392,0.0003488848,0.00029912198,0.000490262,0.00047900755],"domain_scores_gemma":[0.9993433,0.00003126491,0.000110395595,0.0001344642,0.00031809954,0.00006250349],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022013372,0.00032603927,0.0002570083,0.00012429134,0.00020427437,0.00030090046,0.0004982662,0.00012310734,0.000078515644],"category_scores_gemma":[0.000006143266,0.0002473956,0.00015180136,0.00026978858,0.000037055623,0.0003401571,0.000036989924,0.0005205094,0.00008952695],"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":[0.000016148457,0.000023113345,0.00009628061,0.00002753023,0.000056560486,0.0000035067053,0.00007094391,0.9268959,0.00090644835,0.004242596,0.06733987,0.0003211144],"study_design_scores_gemma":[0.00024693267,0.000024239325,0.000043236043,0.00011690868,0.000043890726,0.0000115952025,0.000033736364,0.9906005,0.0015467153,0.005255445,0.0017873675,0.00028940864],"about_ca_topic_score_codex":0.000047000536,"about_ca_topic_score_gemma":0.000019727275,"teacher_disagreement_score":0.28576672,"about_ca_system_score_codex":0.00013867875,"about_ca_system_score_gemma":0.000025929232,"threshold_uncertainty_score":0.99999785},"labels":[],"label_agreement":null},{"id":"W2129903536","doi":"10.1109/ijcnn.2006.246954","title":"Fuzzy Clustering of Open-Source Software Quality Data: A Case Study of Mozilla","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Software Engineering Research","field":"Computer Science","cited_by":11,"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; Data mining; Software quality; Fuzzy logic; Software; Cluster analysis; Software metric; Ranking (information retrieval); Quality (philosophy); Software sizing; Metric (unit); Artificial intelligence; Software construction; Software system; Software development; Operating system; Engineering","score_opus":0.13971397060377239,"score_gpt":0.3598691079223464,"score_spread":0.220155137318574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129903536","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":[0.9671449,0.00002536178,0.029682525,0.0006024634,0.0006847787,0.00072202034,0.000024901228,0.00017820994,0.00093485264],"genre_scores_gemma":[0.99501,0.0000057530174,0.0042242454,0.000070403345,0.00032981587,0.00003858698,0.0000068010686,0.000023640745,0.0002907564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713665,0.00006992749,0.0007867632,0.00063987344,0.0009847834,0.00038199144],"domain_scores_gemma":[0.997338,0.00057342695,0.000480214,0.0007872073,0.0007500223,0.00007110708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013711957,0.00025052406,0.00038970343,0.00015415637,0.00014100307,0.0004313194,0.0048080436,0.000059757916,0.0000118723465],"category_scores_gemma":[0.00034737954,0.00019593268,0.00006020581,0.0005450491,0.00011180756,0.0008401431,0.002474696,0.00038612873,0.00000566771],"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":[0.0008503692,0.0039465744,0.15549287,0.00048393584,0.0007433538,0.00088203355,0.006586132,0.6498035,0.0066920947,0.062265534,0.046608705,0.065644875],"study_design_scores_gemma":[0.003149764,0.0013277988,0.047276612,0.0005930366,0.000047024605,0.0015908179,0.0014703021,0.9362359,0.0013343506,0.0054309717,0.00054509705,0.0009983169],"about_ca_topic_score_codex":0.0034593903,"about_ca_topic_score_gemma":0.00024271532,"teacher_disagreement_score":0.2864324,"about_ca_system_score_codex":0.000064978354,"about_ca_system_score_gemma":0.00006980447,"threshold_uncertainty_score":0.8934622},"labels":[],"label_agreement":null},{"id":"W2133342902","doi":"10.1109/ijcnn.2006.246687","title":"Learning to Coordinate Behaviors in Soft Behavior-Based Systems Using Reinforcement Learning","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Reinforcement Learning in Robotics","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":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Architecture; Task (project management); Mobile robot; Robot; Behavior-based robotics; Robotics; Mechanism (biology); Reinforcement; Engineering","score_opus":0.0449721869003996,"score_gpt":0.28035691451561023,"score_spread":0.23538472761521062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133342902","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":[0.60850996,0.000020200696,0.38212213,0.001478751,0.0020560694,0.0010870618,8.660849e-7,0.00042756856,0.0042974087],"genre_scores_gemma":[0.9957201,0.0000042225893,0.0016256125,0.00041980314,0.00042051586,0.00009573413,0.000011790662,0.000038867496,0.0016633424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966092,0.00007781665,0.0007966138,0.0006403277,0.001082846,0.0007932135],"domain_scores_gemma":[0.99849343,0.00009693786,0.00055626256,0.00023972776,0.00049855496,0.00011507882],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088409544,0.0004044279,0.0003501753,0.00031264973,0.00043955058,0.0010028386,0.0014355679,0.00012957778,0.00004034036],"category_scores_gemma":[0.00007388968,0.00035950713,0.000120159566,0.00060092215,0.0000890719,0.00052328315,0.00028513177,0.0010825408,0.00009511965],"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":[0.00004045046,0.000034719622,0.012552298,0.000010290267,0.000009000175,0.00001817085,0.00023968115,0.9772554,0.0034025714,0.005308124,0.0004295179,0.00069976953],"study_design_scores_gemma":[0.0004740586,0.00041619135,0.0038167387,0.00034020448,0.000016984979,0.00002749665,0.00012268618,0.99317753,0.00035277224,0.000085324944,0.000763271,0.0004067659],"about_ca_topic_score_codex":0.00076224963,"about_ca_topic_score_gemma":0.000015518597,"teacher_disagreement_score":0.38721016,"about_ca_system_score_codex":0.0003602024,"about_ca_system_score_gemma":0.000088063854,"threshold_uncertainty_score":0.9998857},"labels":[],"label_agreement":null},{"id":"W2137161313","doi":"10.1109/ijcnn.2006.246925","title":"A Novel Cooperative Neural Learning Algorithm for Data Fusion","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"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 Waterloo","funders":"","keywords":"Robustness (evolution); Algorithm; Computer science; Least absolute deviations; Artificial neural network; Modular design; Sensor fusion; Fusion; Image fusion; Gaussian; Variance (accounting); Artificial intelligence; Image (mathematics); Mathematics; Estimator","score_opus":0.07620864815218481,"score_gpt":0.2962242574833673,"score_spread":0.22001560933118247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137161313","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":[0.02380717,0.00007446271,0.93407476,0.031947583,0.0024847155,0.0015643924,0.00016087005,0.00057040015,0.00531566],"genre_scores_gemma":[0.9645476,0.000051001924,0.025985876,0.0021333592,0.0035398346,0.0002433014,0.00025025973,0.000040272047,0.0032085485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976433,0.000023315803,0.0004813782,0.0008204297,0.00050797896,0.00052359153],"domain_scores_gemma":[0.99837375,0.00018796227,0.00034370995,0.0004056238,0.00060477195,0.0000841753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042176247,0.00032004784,0.0002494795,0.00006080728,0.0006274701,0.00077778735,0.0026025574,0.00007984227,0.000021952043],"category_scores_gemma":[0.000034131575,0.00023126187,0.00010024362,0.0004087519,0.00011244487,0.0008291787,0.00056403776,0.0005051676,0.00002698371],"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":[0.0000973927,0.0003708863,0.00016651006,0.000014033111,0.00008424754,0.000007123492,0.00017889185,0.08716592,0.019484121,0.41576883,0.27059042,0.20607163],"study_design_scores_gemma":[0.0004605832,0.00014984715,0.00039725564,0.000052026702,0.0000121256835,0.00005384264,0.00002092797,0.9813271,0.0004039508,0.0035274785,0.013317005,0.00027786696],"about_ca_topic_score_codex":0.00007300472,"about_ca_topic_score_gemma":0.000022103835,"teacher_disagreement_score":0.94074035,"about_ca_system_score_codex":0.000050146373,"about_ca_system_score_gemma":0.000044374552,"threshold_uncertainty_score":0.94305855},"labels":[],"label_agreement":null},{"id":"W2139263136","doi":"10.1109/ijcnn.2006.247153","title":"Improving the Convergence of Backpropagation by Opposite Transfer Functions","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":87,"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 Waterloo","funders":"","keywords":"Backpropagation; Computer science; Convergence (economics); Benchmark (surveying); Perceptron; Artificial neural network; Rate of convergence; Activation function; Artificial intelligence; Multilayer perceptron; Transfer function; Machine learning; Algorithm; Key (lock); Engineering","score_opus":0.024682205241403854,"score_gpt":0.22773344666735623,"score_spread":0.20305124142595238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139263136","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":[0.4971744,0.00016905028,0.43936926,0.046621986,0.002923742,0.0014797142,0.000073366304,0.00037249338,0.011815979],"genre_scores_gemma":[0.99609923,0.00003484998,0.0002851802,0.0009043532,0.0006143141,0.000104082974,0.000015839436,0.000013986744,0.0019281367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822396,0.000029985647,0.00049197505,0.00041032233,0.0005139315,0.00032980007],"domain_scores_gemma":[0.9989027,0.000103167215,0.00023584053,0.00026981893,0.00043687853,0.00005164346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031223372,0.00022158881,0.00017007043,0.00004759729,0.00037408152,0.00029022244,0.0013222115,0.000059904603,0.00004801756],"category_scores_gemma":[0.000009972127,0.0001369071,0.00012239028,0.0004562628,0.00017525096,0.0004654498,0.00009994604,0.00033552668,0.000036051442],"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":[0.00007325195,0.00019289771,0.0008096562,0.000019650548,0.00005867063,0.0000016428494,0.00018641024,0.022410698,0.18343337,0.60248363,0.17309412,0.01723598],"study_design_scores_gemma":[0.00045006373,0.00019676868,0.0031084334,0.00009791837,0.00003597843,0.000044223372,0.000057388665,0.95144826,0.027913282,0.010611685,0.005628863,0.00040711855],"about_ca_topic_score_codex":0.00014846466,"about_ca_topic_score_gemma":0.000019273779,"teacher_disagreement_score":0.9290376,"about_ca_system_score_codex":0.000038274335,"about_ca_system_score_gemma":0.00003367364,"threshold_uncertainty_score":0.55829096},"labels":[],"label_agreement":null},{"id":"W2143680741","doi":"10.1109/ijcnn.2006.246661","title":"Aggregation of Reinforcement Learning Algorithms","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":9,"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 Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Learning classifier system; Instance-based learning; Machine learning; Robustness (evolution); Robot learning; Online machine learning; Unsupervised learning; Algorithm; Proactive learning; Computational learning theory; Robot","score_opus":0.036895722843877536,"score_gpt":0.2604671221146379,"score_spread":0.22357139927076036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143680741","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":[0.049716312,0.000036774738,0.8484924,0.0061775516,0.0026239974,0.0006552082,0.000001426797,0.00041113418,0.09188523],"genre_scores_gemma":[0.99110377,0.000034237986,0.0032452012,0.00032554189,0.00065769954,0.000024228499,0.000011354395,0.000015620122,0.00458232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768275,0.000029492874,0.0006294886,0.0003500003,0.00092555146,0.00038273496],"domain_scores_gemma":[0.9984043,0.000085741856,0.00066753506,0.00020430614,0.0005890745,0.000049022787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049041276,0.00023734091,0.00021681201,0.0001323221,0.00020442689,0.00030435913,0.0011973488,0.000071207505,0.000054143627],"category_scores_gemma":[0.000059877733,0.00018217634,0.00011189255,0.00034906348,0.00011276374,0.00053710106,0.00019811337,0.0004310876,0.000053676704],"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":[0.000017510598,0.00001557444,0.00047587266,0.000007445853,0.000020591255,0.0000017095564,0.00010403849,0.8216796,0.0009959466,0.16973372,0.0033513186,0.0035966951],"study_design_scores_gemma":[0.00030718718,0.00022563328,0.0011962372,0.00013543834,0.000008588564,0.00001684128,0.000024905827,0.9881991,0.0042676604,0.0037824446,0.0016386384,0.00019733423],"about_ca_topic_score_codex":0.00006982416,"about_ca_topic_score_gemma":0.0000019158256,"teacher_disagreement_score":0.9413875,"about_ca_system_score_codex":0.00009312642,"about_ca_system_score_gemma":0.00004103505,"threshold_uncertainty_score":0.7428936},"labels":[],"label_agreement":null},{"id":"W2158026689","doi":"10.1109/ijcnn.2006.247280","title":"Multi-view ANNs for Multi-relational Classification","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Machine Learning and Data Classification","field":"Computer Science","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 Ottawa","funders":"","keywords":"Computer science; Artificial neural network; Relation (database); Artificial intelligence; Relational database; Exploit; Bridging (networking); Construct (python library); Machine learning; Statistical relational learning; Data mining","score_opus":0.14638829148945168,"score_gpt":0.32609371611177446,"score_spread":0.17970542462232278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158026689","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":[0.0151946265,0.00009397858,0.9393938,0.034619,0.0022546854,0.0010719359,0.000062875064,0.00052707794,0.0067819813],"genre_scores_gemma":[0.9320907,0.000037349502,0.06216084,0.0010377605,0.0011465413,0.00022193529,0.00018074455,0.000023438844,0.0031006667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794364,0.00004191068,0.00053276704,0.0006107041,0.00049937895,0.00037157547],"domain_scores_gemma":[0.9983886,0.00013158619,0.00046575122,0.00028167953,0.000664317,0.00006809711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006213231,0.00026175528,0.00019400548,0.00011765041,0.00039446208,0.00046974496,0.0011621525,0.000100996585,0.000027336484],"category_scores_gemma":[0.00011399549,0.00019833402,0.00012618504,0.00028731045,0.00009806544,0.000652942,0.0000969326,0.00035941132,0.00011139413],"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":[0.000050889386,0.00022609622,0.0029589883,0.0000183532,0.000030703213,8.790798e-7,0.00010988715,0.007613396,0.0049063656,0.9337408,0.03256079,0.017782854],"study_design_scores_gemma":[0.00058522576,0.000063984764,0.061852135,0.00006103585,0.000010051971,0.000014444141,0.0000148892805,0.9163963,0.00014310278,0.0038190198,0.01681356,0.00022623884],"about_ca_topic_score_codex":0.00005558648,"about_ca_topic_score_gemma":0.000027575832,"teacher_disagreement_score":0.9299218,"about_ca_system_score_codex":0.00009401293,"about_ca_system_score_gemma":0.00005923281,"threshold_uncertainty_score":0.8087827},"labels":[],"label_agreement":null},{"id":"W2162500908","doi":"10.1109/ijcnn.2006.247217","title":"A Neuro - Fuzzy Approach for the Motion Planning of Redundant Manipulators","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":3,"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 Regina","funders":"","keywords":"Obstacle avoidance; Motion planning; Computer science; Control theory (sociology); Robot; Kinematics; Inverse kinematics; Gravitational singularity; Artificial intelligence; Adaptive neuro fuzzy inference system; Fuzzy logic; Control engineering; Fuzzy control system; Mathematics; Mobile robot; Engineering","score_opus":0.04858257020278005,"score_gpt":0.24434867696582332,"score_spread":0.1957661067630433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162500908","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":[0.20976746,0.00015344388,0.74259764,0.0024709385,0.004329581,0.0017122929,0.00004669629,0.00045199634,0.038469967],"genre_scores_gemma":[0.99393106,0.000018625438,0.0048713214,0.00014067607,0.00068674644,0.00006819731,0.000018947634,0.00003381626,0.00023063575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988525,0.000006817728,0.00036941053,0.00019668833,0.00030537974,0.00026919704],"domain_scores_gemma":[0.9994466,0.00009863842,0.00014971003,0.000113299844,0.00016211053,0.000029681354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024532314,0.0001947976,0.00017723125,0.000054891618,0.00013088905,0.00010708471,0.0003996385,0.000062447885,0.0000113036995],"category_scores_gemma":[0.000020786209,0.00012369781,0.00010609836,0.00012455572,0.000058995087,0.00010981669,0.000031126114,0.00023072417,0.000002258574],"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":[0.00003423834,0.00001702564,0.00008654974,0.000021600963,0.000025737076,4.7452073e-7,0.000045657354,0.8205408,0.0016595087,0.17139278,0.005464256,0.00071135774],"study_design_scores_gemma":[0.00020161843,0.000054970944,0.0010183114,0.000052099564,0.000024193685,0.000019616027,0.000060980205,0.9800054,0.00043028017,0.0178891,0.00011287571,0.00013058593],"about_ca_topic_score_codex":0.00002914855,"about_ca_topic_score_gemma":0.0000017131475,"teacher_disagreement_score":0.7841636,"about_ca_system_score_codex":0.00003976035,"about_ca_system_score_gemma":0.000008427132,"threshold_uncertainty_score":0.504425},"labels":[],"label_agreement":null},{"id":"W2162541874","doi":"10.1109/ijcnn.2006.246960","title":"Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Anomaly Detection Techniques and Applications","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","funders":"","keywords":"Cluster analysis; Computer science; Hidden Markov model; Heuristic; Markov chain; Data mining; Traffic analysis; Markov process; Machine learning; Artificial intelligence; Computer security","score_opus":0.01987366171158034,"score_gpt":0.24700673331215373,"score_spread":0.2271330716005734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162541874","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":[0.16518204,0.000007024016,0.8197406,0.0067069298,0.00013956193,0.00064324593,0.000015510026,0.0014392775,0.0061258087],"genre_scores_gemma":[0.9832015,0.000008648681,0.0152138565,0.000502719,0.00030445022,0.00027196333,0.00001612326,0.000017187185,0.00046357082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982271,0.000017897351,0.0004134858,0.0005435901,0.0004720716,0.00032580775],"domain_scores_gemma":[0.99895,0.000040080162,0.00024035067,0.00028651557,0.00040275746,0.00008031542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023739808,0.00023860896,0.00023132315,0.00020101435,0.00032237975,0.00040713488,0.001001097,0.000066525135,0.0000151856275],"category_scores_gemma":[0.0000037703853,0.00017576572,0.00010931347,0.0013674325,0.000063651976,0.00044930849,0.000111997564,0.0002084966,0.000020354635],"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":[0.0001435513,0.00009556849,0.00028513052,0.0000071892655,0.00014185924,0.0000018930747,0.0002283257,0.87344116,0.0027192277,0.08992083,0.0050528967,0.027962385],"study_design_scores_gemma":[0.00014543216,0.00009941203,0.0041301656,0.000024366973,0.00003881826,0.0000137251145,0.000022876682,0.99189144,0.0007904983,0.0021849107,0.0004309436,0.0002274275],"about_ca_topic_score_codex":0.00015632044,"about_ca_topic_score_gemma":0.0001277334,"teacher_disagreement_score":0.81801945,"about_ca_system_score_codex":0.00012359828,"about_ca_system_score_gemma":0.000029567394,"threshold_uncertainty_score":0.7167518},"labels":[],"label_agreement":null},{"id":"W2164582339","doi":"10.1109/ijcnn.2006.247041","title":"PARTCAT: A Subspace Clustering Algorithm for High Dimensional Categorical Data","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Categorical variable; Cluster analysis; Computer science; Subspace topology; Data mining; Dimension (graph theory); Similarity (geometry); Clustering high-dimensional data; Artificial intelligence; Artificial neural network; Cluster (spacecraft); Pattern recognition (psychology); Algorithm; Mathematics; Machine learning","score_opus":0.08700548172302576,"score_gpt":0.3200420799469654,"score_spread":0.23303659822393963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164582339","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":[0.0073307464,0.00004794984,0.96847945,0.018740296,0.002694737,0.00081398664,0.000087559194,0.00036375478,0.0014415061],"genre_scores_gemma":[0.88534874,0.000025162153,0.10774924,0.0007499927,0.0033028265,0.00020598012,0.00012379423,0.000052551186,0.0024417283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644053,0.000035674446,0.0005390928,0.001045252,0.0010900458,0.0008493939],"domain_scores_gemma":[0.9979443,0.0002657383,0.00028706994,0.0006574048,0.000706267,0.00013923302],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007108852,0.00037339143,0.00032135847,0.00013084877,0.00042036356,0.0006787482,0.0035965487,0.00010504021,0.000023259312],"category_scores_gemma":[0.00007272784,0.00028466334,0.000093789495,0.00040797298,0.00016870102,0.0009756375,0.0013278272,0.0005292122,0.000053766365],"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":[0.00036278623,0.000545853,0.00018976117,0.00006492181,0.00023009411,0.00008751153,0.000254928,0.16231738,0.004513971,0.3456495,0.1495404,0.33624288],"study_design_scores_gemma":[0.00058951654,0.00015744794,0.00053537183,0.000055928736,0.000009000386,0.000122053774,0.0000105483105,0.9707219,0.0005703593,0.024962252,0.0019371912,0.00032840198],"about_ca_topic_score_codex":0.00017037982,"about_ca_topic_score_gemma":0.000036947797,"teacher_disagreement_score":0.87801796,"about_ca_system_score_codex":0.00016951462,"about_ca_system_score_gemma":0.00009322597,"threshold_uncertainty_score":0.99996054},"labels":[],"label_agreement":null},{"id":"W2164629576","doi":"10.1109/ijcnn.2006.246749","title":"Synthetic Biometrics: A Survey","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":17,"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 Calgary","funders":"","keywords":"Biometrics; Computer science; Replicate; Variety (cybernetics); Biometric data; Artificial intelligence; Data mining; Machine learning; Mathematics","score_opus":0.050110178718847175,"score_gpt":0.2626810110835333,"score_spread":0.21257083236468613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164629576","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":[0.6880578,0.00034593552,0.15120253,0.02487665,0.015275176,0.00092081336,0.0000470582,0.001492712,0.11778137],"genre_scores_gemma":[0.99441725,0.00002578982,0.0024049578,0.00051557185,0.00086357037,0.000022965092,0.000007690767,0.00001528632,0.0017269163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802214,0.000046082336,0.00041078837,0.0005080074,0.0005929699,0.0004200032],"domain_scores_gemma":[0.9986268,0.00013386669,0.00031042084,0.00020735819,0.0006608437,0.000060663275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007902409,0.00024099027,0.00019931584,0.00028209068,0.00029133767,0.0009325507,0.0012737352,0.00007794399,0.000033233024],"category_scores_gemma":[0.00010031687,0.00017085508,0.000092196155,0.0010041449,0.00016351638,0.0005742191,0.00013039191,0.00033300702,0.00012651006],"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":[0.0002482497,0.0005154983,0.017542036,0.000066651824,0.00016766497,0.000036758214,0.00039724016,0.010212709,0.009520684,0.5048793,0.13711806,0.31929517],"study_design_scores_gemma":[0.00068291294,0.00022054625,0.031244833,0.0002640849,0.000019321107,0.00044800653,0.000029752015,0.8957035,0.0067531248,0.060720097,0.0031429627,0.0007708663],"about_ca_topic_score_codex":0.00018791034,"about_ca_topic_score_gemma":0.00001878731,"teacher_disagreement_score":0.8854908,"about_ca_system_score_codex":0.00006886757,"about_ca_system_score_gemma":0.00006616918,"threshold_uncertainty_score":0.89926076},"labels":[],"label_agreement":null},{"id":"W2164665696","doi":"10.1109/ijcnn.2006.246962","title":"Refining Spherical K-Means for Clustering Documents","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Convexity; Cluster analysis; Mathematical optimization; Constraint (computer-aided design); Set (abstract data type); Computer science; Maximization; Algorithm; Linear programming; Mathematics; Function (biology); Optimization problem; Artificial intelligence","score_opus":0.06047978020347221,"score_gpt":0.318466311927053,"score_spread":0.25798653172358077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164665696","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":[0.022102125,0.000030997984,0.9342531,0.015306575,0.002779726,0.00061980734,0.000013699482,0.000500158,0.024393793],"genre_scores_gemma":[0.8932073,0.00001791967,0.09591331,0.0011631261,0.002349334,0.00025254153,0.000010980603,0.000049041475,0.0070364345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973721,0.000023309229,0.0004733776,0.00062345533,0.0007923574,0.00071541814],"domain_scores_gemma":[0.9987541,0.00016419617,0.0002432478,0.00025625108,0.0004868872,0.00009535819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047101168,0.0002860482,0.00024043336,0.0000855447,0.00034446863,0.00071349146,0.0017870131,0.00007565182,0.000027584043],"category_scores_gemma":[0.000065490705,0.00022095146,0.000125975,0.00032066068,0.000109603425,0.0006908954,0.0004235298,0.0004111863,0.000048112037],"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":[0.0005448988,0.000302798,0.0011586113,0.00008817424,0.00016405304,0.000049034148,0.00046585884,0.48981214,0.018080447,0.2769196,0.06153065,0.15088372],"study_design_scores_gemma":[0.0005521818,0.00019856267,0.0006032663,0.000119710494,0.0000049675623,0.000054624736,0.000019965308,0.9748298,0.00088251807,0.014516045,0.00793141,0.0002869593],"about_ca_topic_score_codex":0.00007648235,"about_ca_topic_score_gemma":0.000021169271,"teacher_disagreement_score":0.8711052,"about_ca_system_score_codex":0.00018250955,"about_ca_system_score_gemma":0.000043242737,"threshold_uncertainty_score":0.9010139},"labels":[],"label_agreement":null},{"id":"W2169183587","doi":"10.1109/ijcnn.2006.246689","title":"Opposition-Based Q(&amp;amp;#955;) Algorithm","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":48,"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 Waterloo","funders":"","keywords":"Opposition (politics); Reinforcement learning; Computer science; Lambda; Algorithm; Artificial intelligence; Law; Political science; Physics","score_opus":0.053300843179456,"score_gpt":0.27574545304177817,"score_spread":0.22244460986232217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169183587","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":[0.010230401,0.00001825438,0.9393387,0.014087876,0.0024104894,0.0004209532,0.000009088803,0.00048055607,0.033003684],"genre_scores_gemma":[0.9345991,0.000015801994,0.052198704,0.0037611006,0.001992154,0.00007740696,0.0000636151,0.000038748367,0.007253394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720216,0.000038738915,0.0005726708,0.0005489487,0.0010506554,0.00058680214],"domain_scores_gemma":[0.998314,0.00014691848,0.00041301094,0.0003652552,0.00065961236,0.000101169106],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045076004,0.00035442712,0.0002446093,0.00016192287,0.00036235148,0.00096860237,0.001863323,0.00010379084,0.00015914299],"category_scores_gemma":[0.00004268459,0.00027644544,0.00016133244,0.00042892818,0.00015648223,0.0005539669,0.00017998519,0.00053911534,0.0005508347],"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":[0.000024371931,0.000072523035,0.00033683947,0.000008853822,0.000034920555,0.0000059843833,0.000071293834,0.7986826,0.0019313842,0.122110605,0.0715099,0.0052107307],"study_design_scores_gemma":[0.00039678745,0.000090233974,0.0007526577,0.00011810915,0.000012706832,0.000036811554,0.0000059250087,0.9732943,0.00080397347,0.0070708776,0.017053185,0.00036438464],"about_ca_topic_score_codex":0.00008342553,"about_ca_topic_score_gemma":0.000011568774,"teacher_disagreement_score":0.9243687,"about_ca_system_score_codex":0.00014832616,"about_ca_system_score_gemma":0.00008576233,"threshold_uncertainty_score":0.99996877},"labels":[],"label_agreement":null},{"id":"W2170184326","doi":"10.1109/ijcnn.2006.247076","title":"Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Benchmark (surveying); Recurrent neural network; Stochastic neural network; Artificial neural network; Inverted pendulum; Artificial intelligence; Construct (python library); Spiking neural network; Outcome (game theory); Machine learning; Mathematics","score_opus":0.026099613010765927,"score_gpt":0.25352625071448004,"score_spread":0.2274266377037141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170184326","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":[0.991664,0.00017768226,0.0019744597,0.0005434543,0.0006426327,0.0002239308,0.000009157475,0.00018308831,0.0045816093],"genre_scores_gemma":[0.9986439,0.000065376116,0.0001578898,0.000104137514,0.00074908684,0.00000791844,0.0000171805,0.000028274822,0.00022624063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983742,0.000024152605,0.00047803635,0.00032243147,0.00036639493,0.0004347869],"domain_scores_gemma":[0.99926084,0.00014394349,0.00017336733,0.00011889646,0.00023446957,0.00006849603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029437832,0.0002828276,0.00041897126,0.00019618247,0.00013706647,0.00012505898,0.00033588655,0.000073302144,0.00004985088],"category_scores_gemma":[0.00001738234,0.00022595933,0.00014873905,0.00055399217,0.00012884758,0.0002056498,0.000084718675,0.0004878397,0.0000042508336],"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":[0.00007804003,0.000019167992,0.001919503,0.000015484407,0.00016603683,0.0000048952506,0.00006717901,0.97687,0.009824263,0.004737857,0.0010093449,0.00528822],"study_design_scores_gemma":[0.00020279331,0.00008494243,0.006113934,0.00009510698,0.00011447036,0.00001623207,0.000031433457,0.9893694,0.0031460882,0.0004945377,0.0001050872,0.00022601102],"about_ca_topic_score_codex":0.000021584343,"about_ca_topic_score_gemma":0.000008303657,"teacher_disagreement_score":0.012499355,"about_ca_system_score_codex":0.00004908033,"about_ca_system_score_gemma":0.0000058115293,"threshold_uncertainty_score":0.9214354},"labels":[],"label_agreement":null},{"id":"W4205661532","doi":"10.1109/ijcnn.2006.1716671","title":"Refining Spherical K-Means for Clustering Documents","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Clustering Algorithms Research","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":"McMaster University","funders":"","keywords":"Convexity; Cluster analysis; Mathematical optimization; Constraint (computer-aided design); Set (abstract data type); Maximization; Mathematics; Algorithm; Computer science; Function (biology); Linear programming; Optimization problem; Artificial intelligence","score_opus":0.06047978020347221,"score_gpt":0.318466311927053,"score_spread":0.25798653172358077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205661532","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":[0.022102125,0.000030997984,0.9342531,0.015306575,0.002779726,0.00061980734,0.000013699482,0.000500158,0.024393793],"genre_scores_gemma":[0.8932073,0.00001791967,0.09591331,0.0011631261,0.002349334,0.00025254153,0.000010980603,0.000049041475,0.0070364345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973721,0.000023309229,0.0004733776,0.00062345533,0.0007923574,0.00071541814],"domain_scores_gemma":[0.9987541,0.00016419617,0.0002432478,0.00025625108,0.0004868872,0.00009535819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047101168,0.0002860482,0.00024043336,0.0000855447,0.00034446863,0.00071349146,0.0017870131,0.00007565182,0.000027584043],"category_scores_gemma":[0.000065490705,0.00022095146,0.000125975,0.00032066068,0.000109603425,0.0006908954,0.0004235298,0.0004111863,0.000048112037],"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":[0.0005448988,0.000302798,0.0011586113,0.00008817424,0.00016405304,0.000049034148,0.00046585884,0.48981214,0.018080447,0.2769196,0.06153065,0.15088372],"study_design_scores_gemma":[0.0005521818,0.00019856267,0.0006032663,0.000119710494,0.0000049675623,0.000054624736,0.000019965308,0.9748298,0.00088251807,0.014516045,0.00793141,0.0002869593],"about_ca_topic_score_codex":0.00007648235,"about_ca_topic_score_gemma":0.000021169271,"teacher_disagreement_score":0.8711052,"about_ca_system_score_codex":0.00018250955,"about_ca_system_score_gemma":0.000043242737,"threshold_uncertainty_score":0.9010139},"labels":[],"label_agreement":null},{"id":"W4205904304","doi":"10.1109/ijcnn.2006.1716831","title":"Multi-view ANNs for Multi-relational Classification","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Data Mining Algorithms and Applications","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 Ottawa","funders":"","keywords":"Computer science; Artificial neural network; Relation (database); Artificial intelligence; Bridging (networking); Construct (python library); Exploit; Relational database; Machine learning; Data mining; Statistical relational learning","score_opus":0.15919875916176615,"score_gpt":0.3239139567962549,"score_spread":0.16471519763448875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205904304","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":[0.010163329,0.00006089639,0.9635747,0.01989611,0.0014557694,0.0009801891,0.00013190653,0.00034922012,0.0033878456],"genre_scores_gemma":[0.7113921,0.00004412683,0.2813519,0.0013831764,0.001595148,0.00059505284,0.00018437277,0.00003081675,0.0034233013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816775,0.000016328047,0.0004808566,0.0005623155,0.00041322637,0.00035954578],"domain_scores_gemma":[0.99860084,0.00010140819,0.00034319208,0.00025843506,0.00062955625,0.000066591296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004092378,0.00023651353,0.00017791982,0.00008686447,0.00039048123,0.0004610833,0.0012400149,0.00007999549,0.000019911073],"category_scores_gemma":[0.000044337517,0.00018153328,0.000115616895,0.00029744513,0.000104838124,0.0006013109,0.00011456615,0.00024758794,0.00009145278],"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":[0.000019577108,0.0002584319,0.00049100805,0.000010837589,0.0000328013,7.966083e-7,0.000101961785,0.0032623424,0.0038143224,0.910616,0.054250296,0.02714165],"study_design_scores_gemma":[0.00050009304,0.000051434465,0.015220064,0.000060373473,0.000010145083,0.000015239698,0.00001750364,0.9630868,0.00026948002,0.005351336,0.0151950745,0.00022246939],"about_ca_topic_score_codex":0.00004454116,"about_ca_topic_score_gemma":0.00001842252,"teacher_disagreement_score":0.95982444,"about_ca_system_score_codex":0.00007761133,"about_ca_system_score_gemma":0.000055709064,"threshold_uncertainty_score":0.7402712},"labels":[],"label_agreement":null},{"id":"W4231833091","doi":"10.1109/ijcnn.2006.1716663","title":"Fuzzy Clustering of Open-Source Software Quality Data: A Case Study of Mozilla","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","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":"Mozilla Foundation","keywords":"Computer science; Data mining; Software quality; Fuzzy logic; Software; Cluster analysis; Software metric; Ranking (information retrieval); Artificial intelligence; Software development; Operating system","score_opus":0.16967533549687067,"score_gpt":0.38844686424841907,"score_spread":0.2187715287515484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231833091","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":[0.8832261,0.000029868337,0.1105867,0.0010974853,0.0007831111,0.0011162163,0.000044624514,0.00016667717,0.0029492395],"genre_scores_gemma":[0.98699075,0.000009022757,0.011944523,0.000096112875,0.00034834113,0.00004334859,0.0000072412918,0.000024676836,0.0005360004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967443,0.00009941207,0.00091070076,0.0007528268,0.0010587013,0.00043406474],"domain_scores_gemma":[0.99720347,0.00027311806,0.00070591987,0.00085153634,0.00088803045,0.00007790699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012432397,0.00027521775,0.00044051267,0.00014296653,0.00021042289,0.0004444922,0.0052120415,0.000060340608,0.000010515954],"category_scores_gemma":[0.00014320573,0.00021371047,0.00006279637,0.00053932605,0.00016587172,0.0011345367,0.0036927538,0.00041851684,0.000005236235],"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":[0.001036784,0.003464135,0.012248135,0.00034546747,0.00052045146,0.00085108343,0.0054002968,0.74292797,0.0078216875,0.041732613,0.012509981,0.17114139],"study_design_scores_gemma":[0.0017248936,0.0007507158,0.003165535,0.0002640251,0.000019456049,0.0009646516,0.0013294447,0.9844977,0.0007201344,0.0058248895,0.00026948043,0.00046904833],"about_ca_topic_score_codex":0.003370122,"about_ca_topic_score_gemma":0.00038196638,"teacher_disagreement_score":0.24156974,"about_ca_system_score_codex":0.00007876809,"about_ca_system_score_gemma":0.00007187416,"threshold_uncertainty_score":0.9685357},"labels":[],"label_agreement":null},{"id":"W4233539997","doi":"10.1109/ijcnn.2006.1716136","title":"A Reinforcement Learning Framework for Medical Image Segmentation","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Reinforcement Learning in Robotics","field":"Computer Science","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 Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Thresholding; Artificial intelligence; Segmentation; Image segmentation; Structuring element; Computer vision; Machine learning; Image (mathematics); Image processing; Mathematical morphology","score_opus":0.03411624730538505,"score_gpt":0.2948383576014699,"score_spread":0.2607221102960849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233539997","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":[0.005449948,0.0000101434825,0.9668142,0.012492955,0.0015891439,0.00058256096,0.0000010520447,0.00028328755,0.012776714],"genre_scores_gemma":[0.9558555,0.000038566817,0.037058115,0.0020592138,0.0018154324,0.0001729822,0.000029777788,0.000029857118,0.0029405942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996981,0.000033083477,0.0006460291,0.0004708091,0.0013187397,0.0005503016],"domain_scores_gemma":[0.9983706,0.00029460128,0.0004949169,0.00019986641,0.0005372813,0.00010275269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008024382,0.00028992392,0.00022497709,0.00011067683,0.00039004293,0.000756777,0.0014662269,0.0001344424,0.00015448543],"category_scores_gemma":[0.00029430527,0.00022277652,0.00014392742,0.00027754277,0.00012090253,0.0006074935,0.00021823765,0.000667858,0.000086045235],"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":[0.000044799675,0.000021429647,0.00019390577,0.000012634937,0.00002883292,0.0000033810115,0.00013638099,0.5141384,0.0006096106,0.47172147,0.010906313,0.0021828255],"study_design_scores_gemma":[0.0004693365,0.00026662793,0.0002969017,0.00018899435,0.000011300983,0.000023491568,0.000044162887,0.9701421,0.0012333079,0.024806803,0.0022578316,0.0002590908],"about_ca_topic_score_codex":0.000028430995,"about_ca_topic_score_gemma":0.0000022540269,"teacher_disagreement_score":0.95040554,"about_ca_system_score_codex":0.00014079353,"about_ca_system_score_gemma":0.00007655358,"threshold_uncertainty_score":0.90845627},"labels":[],"label_agreement":null},{"id":"W4236921545","doi":"10.1109/ijcnn.2006.1716710","title":"PARTCAT: A Subspace Clustering Algorithm for High Dimensional Categorical Data","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Categorical variable; Cluster analysis; Computer science; Subspace topology; Dimension (graph theory); Data mining; Similarity (geometry); Algorithm; Clustering high-dimensional data; Artificial neural network; Cluster (spacecraft); Artificial intelligence; Pattern recognition (psychology); Mathematics; Machine learning","score_opus":0.08700548172302576,"score_gpt":0.3200420799469654,"score_spread":0.23303659822393963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236921545","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":[0.0073307464,0.00004794984,0.96847945,0.018740296,0.002694737,0.00081398664,0.000087559194,0.00036375478,0.0014415061],"genre_scores_gemma":[0.88534874,0.000025162153,0.10774924,0.0007499927,0.0033028265,0.00020598012,0.00012379423,0.000052551186,0.0024417283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644053,0.000035674446,0.0005390928,0.001045252,0.0010900458,0.0008493939],"domain_scores_gemma":[0.9979443,0.0002657383,0.00028706994,0.0006574048,0.000706267,0.00013923302],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007108852,0.00037339143,0.00032135847,0.00013084877,0.00042036356,0.0006787482,0.0035965487,0.00010504021,0.000023259312],"category_scores_gemma":[0.00007272784,0.00028466334,0.000093789495,0.00040797298,0.00016870102,0.0009756375,0.0013278272,0.0005292122,0.000053766365],"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":[0.00036278623,0.000545853,0.00018976117,0.00006492181,0.00023009411,0.00008751153,0.000254928,0.16231738,0.004513971,0.3456495,0.1495404,0.33624288],"study_design_scores_gemma":[0.00058951654,0.00015744794,0.00053537183,0.000055928736,0.000009000386,0.000122053774,0.0000105483105,0.9707219,0.0005703593,0.024962252,0.0019371912,0.00032840198],"about_ca_topic_score_codex":0.00017037982,"about_ca_topic_score_gemma":0.000036947797,"teacher_disagreement_score":0.87801796,"about_ca_system_score_codex":0.00016951462,"about_ca_system_score_gemma":0.00009322597,"threshold_uncertainty_score":0.99996054},"labels":[],"label_agreement":null},{"id":"W4239436589","doi":"10.1109/ijcnn.2006.1716725","title":"Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","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":"Brock University; University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Recurrent neural network; Benchmark (surveying); Stochastic neural network; Artificial neural network; Inverted pendulum; Construct (python library); Artificial intelligence; Spiking neural network; Outcome (game theory); Machine learning; Mathematics","score_opus":0.029138410603138598,"score_gpt":0.2659151546236827,"score_spread":0.2367767440205441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239436589","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":[0.9429782,0.00022558257,0.030799795,0.015937919,0.00091195165,0.0007081657,0.000018877712,0.00025505648,0.008164469],"genre_scores_gemma":[0.9974088,0.000063307285,0.0006998263,0.00045005776,0.00070968334,0.000029515695,0.000017738383,0.00001654154,0.0006045021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763703,0.00004682364,0.00058726646,0.00061245175,0.000602932,0.00051347684],"domain_scores_gemma":[0.9985289,0.00018385777,0.00041028473,0.00029438475,0.00048018733,0.00010239204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047106398,0.00030262774,0.00044738394,0.00023343255,0.0002522802,0.00045603787,0.001160711,0.00008390844,0.000039806677],"category_scores_gemma":[0.000013749653,0.0002267144,0.00020352044,0.0012005545,0.00019741809,0.00036755504,0.0002994682,0.00045001885,0.000008295345],"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":[0.000115231465,0.0001878242,0.00891674,0.000013903263,0.00039734648,0.000009340874,0.00019523838,0.5051597,0.0040152445,0.4406416,0.018891944,0.021455891],"study_design_scores_gemma":[0.00019729379,0.000113784256,0.014599135,0.00005432268,0.000091143935,0.00001699099,0.000015988575,0.9816902,0.0003223019,0.0023266438,0.00034081787,0.00023134041],"about_ca_topic_score_codex":0.00011445508,"about_ca_topic_score_gemma":0.000023615774,"teacher_disagreement_score":0.47653055,"about_ca_system_score_codex":0.00004362598,"about_ca_system_score_gemma":0.000018984603,"threshold_uncertainty_score":0.92451453},"labels":[],"label_agreement":null},{"id":"W4240815580","doi":"10.1109/ijcnn.2006.1716163","title":"Neural and Statistical Classification to Families of Bio-sequences","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","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 Guelph","funders":"Family Process Institute","keywords":"Computer science; Generalization; Property (philosophy); Artificial intelligence; Artificial neural network; Entropy (arrow of time); Feature (linguistics); Pattern recognition (psychology); Feature vector; String (physics); Machine learning; Mathematics","score_opus":0.027900896080192715,"score_gpt":0.2816318045052348,"score_spread":0.2537309084250421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240815580","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":[0.98493,0.00002372047,0.0012849106,0.003671526,0.00029852882,0.0002147638,0.00003393218,0.000020602974,0.009522059],"genre_scores_gemma":[0.99688363,0.000037932983,0.0016318355,0.0005555025,0.00044839614,0.00001842374,0.00005602941,0.000010814532,0.00035741745],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989806,0.000018376557,0.00033532892,0.00021675894,0.00025808878,0.00019085653],"domain_scores_gemma":[0.99940515,0.000026299587,0.00019263256,0.00009727756,0.00023324908,0.0000454151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019663511,0.00015174391,0.0001301759,0.000052365438,0.00008013644,0.00007825201,0.00027684105,0.00006493688,0.000019764084],"category_scores_gemma":[0.00008742868,0.000110122346,0.000034619745,0.00008798034,0.00020881534,0.00001316674,0.00007768463,0.00014409621,0.000007972083],"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":[0.0007865092,0.00015535773,0.051362168,0.0001068407,0.000113989576,0.0000029513014,0.00041495904,0.023201928,0.57647073,0.25457042,0.07095627,0.021857895],"study_design_scores_gemma":[0.0013117824,0.0022860256,0.28840414,0.00027282798,0.000076545206,0.00016642753,0.00080636353,0.62351453,0.05127065,0.011210156,0.019549698,0.0011308152],"about_ca_topic_score_codex":0.00005651544,"about_ca_topic_score_gemma":0.000019522327,"teacher_disagreement_score":0.60031265,"about_ca_system_score_codex":0.000012001942,"about_ca_system_score_gemma":0.000020132802,"threshold_uncertainty_score":0.4490659},"labels":[],"label_agreement":null},{"id":"W4242297044","doi":"10.1109/ijcnn.2006.1716433","title":"A Cooperative Recurrent Neural Network Algorithm for Parameter Estimation of Autoregressive Signals","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","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 Waterloo","funders":"","keywords":"Autoregressive model; Computer science; Algorithm; Weighting; Artificial neural network; Convergence (economics); Gaussian; Recurrent neural network; Gaussian noise; Noise (video); Standard deviation; Mathematics; Artificial intelligence; Statistics","score_opus":0.042923676088890084,"score_gpt":0.2922119532105307,"score_spread":0.2492882771216406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242297044","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":[0.09242725,0.00021572139,0.8795239,0.017663876,0.003914626,0.0031050835,0.00012947239,0.00042542614,0.0025946386],"genre_scores_gemma":[0.9667813,0.000028199805,0.029121734,0.00095865654,0.0019320025,0.0004826197,0.000051328712,0.000028310084,0.00061582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974439,0.00004871893,0.00075473933,0.0006214026,0.0005676707,0.00056357996],"domain_scores_gemma":[0.99762654,0.00040301177,0.0007299585,0.00026599265,0.00088607887,0.0000883871],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038690702,0.00037204227,0.000390248,0.000083092884,0.0003480979,0.00041077659,0.0012398483,0.00010024535,0.000024052408],"category_scores_gemma":[0.000040843952,0.00026606067,0.00021207269,0.00042679385,0.00017827567,0.0005409734,0.00016236695,0.00035421818,0.000012784011],"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":[0.00007277492,0.00015801794,0.000073637195,0.000013660536,0.000067680696,0.000002785658,0.00013261993,0.59367085,0.00065542484,0.19932188,0.091648564,0.11418209],"study_design_scores_gemma":[0.00040415683,0.00029779633,0.0005715487,0.00016191977,0.000022609353,0.00002199296,0.000010994093,0.96198344,0.0013581462,0.034131847,0.00075511035,0.00028045804],"about_ca_topic_score_codex":0.000028650555,"about_ca_topic_score_gemma":0.0000083439,"teacher_disagreement_score":0.87435406,"about_ca_system_score_codex":0.0000681043,"about_ca_system_score_gemma":0.000054770633,"threshold_uncertainty_score":0.99997914},"labels":[],"label_agreement":null},{"id":"W4242964546","doi":"10.1109/ijcnn.2006.1716754","title":"On Stability of Nonlinear Observers Based on Neural Networks","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","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":"Western University; Concordia University","funders":"","keywords":"Artificial neural network; Control theory (sociology); Nonlinear system; Stability (learning theory); Sigmoid function; Computer science; Bounded function; Identifier; Mathematics; Artificial intelligence; Machine learning","score_opus":0.038600903594590244,"score_gpt":0.23761266157289038,"score_spread":0.19901175797830012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242964546","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":[0.96973747,0.000045195582,0.0027317943,0.0020981333,0.0032053066,0.0008104358,0.000077216166,0.0004017417,0.020892698],"genre_scores_gemma":[0.9970801,0.0000068059335,0.00020380743,0.0005545678,0.0018843708,0.000040991126,0.000033888093,0.00006161391,0.00013384986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975931,0.00004468549,0.00071026746,0.00040978726,0.0007480563,0.0004941055],"domain_scores_gemma":[0.99859697,0.00030893422,0.00028363764,0.00026317764,0.0004587043,0.00008859507],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042420824,0.00043102802,0.00043774192,0.00011644065,0.00009859219,0.00010945962,0.0006407866,0.00013985537,0.0001080892],"category_scores_gemma":[0.00006991476,0.0003299161,0.00022428672,0.00025162744,0.00015306922,0.00016967596,0.000036995098,0.00065784354,0.000029491204],"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":[0.00040700068,0.00010528909,0.0012296794,0.00002371868,0.00004343018,0.0000055373694,0.000019776411,0.9834245,0.0012673691,0.005947368,0.0067334855,0.0007928654],"study_design_scores_gemma":[0.00072412594,0.00034360445,0.004189695,0.00019643432,0.000018416273,0.000004323477,0.000021309705,0.992393,0.0010638474,0.00037677825,0.00038001168,0.00028845354],"about_ca_topic_score_codex":0.00006311954,"about_ca_topic_score_gemma":0.000032164087,"teacher_disagreement_score":0.027342627,"about_ca_system_score_codex":0.00018546204,"about_ca_system_score_gemma":0.000025277637,"threshold_uncertainty_score":0.9999153},"labels":[],"label_agreement":null},{"id":"W4243384362","doi":"10.1109/ijcnn.2006.1716150","title":"A Heuristic for Free Parameter Optimization with Support Vector Machines","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Maxima and minima; Computer science; Support vector machine; Simulated annealing; Heuristic; Hyperparameter optimization; Mathematical optimization; Generalization; Gradient descent; Machine learning; Artificial intelligence; Algorithm; Mathematics; Artificial neural network","score_opus":0.03147208949691202,"score_gpt":0.2770154457785389,"score_spread":0.2455433562816269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243384362","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":[0.85823286,0.00006571732,0.033511434,0.06657301,0.0011561555,0.0018115897,0.000095065756,0.00044327063,0.038110882],"genre_scores_gemma":[0.986865,0.000014937533,0.005719333,0.0013215857,0.0014059725,0.00009650893,0.00009716759,0.000033908043,0.0044455854],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998584,0.0000074821032,0.00033930672,0.0003350771,0.00043368564,0.0003004813],"domain_scores_gemma":[0.99886274,0.000074705815,0.00022937015,0.00015225785,0.0006147874,0.000066136374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018679988,0.00023249551,0.00029517576,0.000098480494,0.00013281229,0.00017739086,0.00026346053,0.000048364484,0.00012627312],"category_scores_gemma":[0.000083173625,0.00014000824,0.00014179619,0.00019151691,0.0001184294,0.00012631105,0.00002842242,0.00022121519,0.00001310965],"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":[0.009282026,0.0011413224,0.07239671,0.00037950007,0.0012383838,0.00012926065,0.00040521298,0.40569842,0.006686652,0.072664574,0.4211286,0.008849336],"study_design_scores_gemma":[0.0015186812,0.00074997934,0.005443939,0.00028429052,0.00029826042,0.00021916187,0.000033514996,0.9854423,0.0007093782,0.0037771505,0.0012361787,0.00028720195],"about_ca_topic_score_codex":0.00007955042,"about_ca_topic_score_gemma":0.000008555594,"teacher_disagreement_score":0.57974386,"about_ca_system_score_codex":0.000050011226,"about_ca_system_score_gemma":0.00003892304,"threshold_uncertainty_score":0.57093704},"labels":[],"label_agreement":null},{"id":"W4244620182","doi":"10.1109/ijcnn.2006.1716365","title":"Particle Swarm Optimization of Fuzzy ARTMAP Parameters","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Generalization; Particle swarm optimization; Overtraining; Set (abstract data type); Computer science; Fuzzy set; Fuzzy logic; Artificial intelligence; Training set; Mathematical optimization; Function (biology); Mathematics; Machine learning","score_opus":0.03697638489600584,"score_gpt":0.23674471789950755,"score_spread":0.19976833300350172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244620182","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":[0.6953685,0.00020661246,0.10140133,0.02661068,0.004832925,0.0012768776,0.00001680882,0.00059619517,0.16969003],"genre_scores_gemma":[0.99559075,0.000017395638,0.0026706434,0.00062486815,0.0004633858,0.00004450964,0.0000037889079,0.000010554682,0.0005740951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818623,0.000034692242,0.000513297,0.00035659238,0.00056218466,0.00034702956],"domain_scores_gemma":[0.9988678,0.00008221949,0.00038544586,0.00020521822,0.0004022203,0.00005708746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003704117,0.0001989793,0.0002348649,0.000055188808,0.00012113197,0.0002583714,0.0009994223,0.00006234183,0.000010941355],"category_scores_gemma":[0.000028869206,0.00014016479,0.0001140975,0.00029282246,0.00011079099,0.00041450458,0.00009798819,0.00017765535,0.00003397894],"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":[0.000054983095,0.0000750271,0.00074721687,0.0000072844136,0.00002780612,0.0000031455472,0.000090811205,0.492169,0.0014028584,0.49788693,0.0057856,0.0017493351],"study_design_scores_gemma":[0.00052618445,0.0001902895,0.001396763,0.00008475274,0.000012145118,0.000026264988,0.00003639757,0.9438435,0.0030177983,0.05042806,0.00020930746,0.00022854915],"about_ca_topic_score_codex":0.000111900554,"about_ca_topic_score_gemma":0.0000064295295,"teacher_disagreement_score":0.4516745,"about_ca_system_score_codex":0.00004787135,"about_ca_system_score_gemma":0.000030920248,"threshold_uncertainty_score":0.5715754},"labels":[],"label_agreement":null},{"id":"W4245122632","doi":"10.1109/ijcnn.2006.1716226","title":"Rough Set Theory based Neural Network Architecture","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Rough Sets and Fuzzy Logic","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 Regina","funders":"","keywords":"Artificial neural network; Computer science; Boundary (topology); Backpropagation; Rough set; Convergence (economics); Computation; Process (computing); Set (abstract data type); Artificial intelligence; Division (mathematics); Network architecture; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.03739502074110487,"score_gpt":0.2514734797662133,"score_spread":0.21407845902510844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245122632","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":[0.3307012,0.00057750393,0.20961133,0.14175542,0.018603435,0.0030439002,0.00011415068,0.0028289086,0.29276416],"genre_scores_gemma":[0.98205537,0.000014577546,0.005114548,0.0077614137,0.0038697661,0.00006384596,0.000027486585,0.000033702992,0.0010593059],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967524,0.00010876797,0.0005894257,0.0007768275,0.00087628333,0.00089630595],"domain_scores_gemma":[0.9984573,0.00025340758,0.00040450745,0.000415968,0.00034508895,0.00012369783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078515406,0.0004899243,0.00035738698,0.000108461325,0.00045139992,0.00089393527,0.0024214282,0.00014183334,0.00011129285],"category_scores_gemma":[0.000037953658,0.00032693872,0.00024005589,0.0005584259,0.00021381098,0.00045515219,0.00027922651,0.0007900279,0.000085354295],"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":[0.00015814079,0.00007537667,0.00057789555,0.000009578444,0.000036352092,0.000028576687,0.00012552697,0.4062548,0.00017477873,0.5041766,0.07482749,0.013554891],"study_design_scores_gemma":[0.00051749166,0.0002283048,0.003788189,0.00010715689,0.000017135118,0.00009007757,0.0000131688075,0.7711912,0.00014049877,0.21575749,0.007663263,0.0004860154],"about_ca_topic_score_codex":0.00006181408,"about_ca_topic_score_gemma":0.000018542914,"teacher_disagreement_score":0.65135413,"about_ca_system_score_codex":0.00008052259,"about_ca_system_score_gemma":0.00006654296,"threshold_uncertainty_score":0.9999183},"labels":[],"label_agreement":null},{"id":"W4245519553","doi":"10.1109/ijcnn.2006.1716621","title":"Self-Organizing Feature Map (SOFM) based Deformable CAD Models","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","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":"Western University","funders":"","keywords":"Polygon mesh; Computer science; Feature (linguistics); Hexahedron; Point (geometry); Artificial intelligence; Surface (topology); Computer vision; Object (grammar); Solid modeling; Topology (electrical circuits); Algorithm; Geometry; Computer graphics (images); Finite element method; Mathematics","score_opus":0.03749992494722998,"score_gpt":0.21723163137615734,"score_spread":0.17973170642892736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245519553","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":[0.8607633,0.00031538986,0.00028599118,0.014469924,0.0029172252,0.00057783147,0.00014806067,0.0006109566,0.11991133],"genre_scores_gemma":[0.9929335,0.000026409287,0.0009214971,0.0015349636,0.0011620307,0.0000053373415,0.00012564287,0.000011978111,0.003278628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815893,0.000034662335,0.00030546394,0.00037148688,0.00059399783,0.0005354848],"domain_scores_gemma":[0.9992455,0.000066898785,0.00019457132,0.0001112122,0.00029865312,0.00008311582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039671073,0.00029531374,0.00021631784,0.00005743609,0.00041614455,0.00041892982,0.00058630644,0.00011459138,0.00048003142],"category_scores_gemma":[0.000011300431,0.00018834733,0.0001086146,0.00024203048,0.000074892014,0.00054902065,0.000019772417,0.00047713929,0.00025576766],"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":[0.00045804723,0.00013500983,0.06805837,0.00010207554,0.00011976829,0.00004651299,0.00055115053,0.6263182,0.0011695508,0.015700191,0.28059033,0.0067508],"study_design_scores_gemma":[0.00037589826,0.0001220426,0.016873213,0.00012069983,0.000020386538,0.000041426916,0.00010986873,0.9691445,0.0004965407,0.0063654506,0.00593963,0.00039031202],"about_ca_topic_score_codex":0.0007093618,"about_ca_topic_score_gemma":0.0004887417,"teacher_disagreement_score":0.34282634,"about_ca_system_score_codex":0.000027061584,"about_ca_system_score_gemma":0.00004524511,"threshold_uncertainty_score":0.7680581},"labels":[],"label_agreement":null},{"id":"W4246177449","doi":"10.1109/ijcnn.2006.1716100","title":"Opposition-Based Q(λ) Algorithm","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"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 Waterloo","funders":"","keywords":"Opposition (politics); Reinforcement learning; Computer science; Lambda; Reinforcement; Artificial intelligence; Algorithm; Theoretical computer science; Engineering; Political science; Law; Physics; Structural engineering","score_opus":0.030114229558463834,"score_gpt":0.25399593957540195,"score_spread":0.22388171001693813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246177449","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":[0.016128954,0.000110206085,0.82693756,0.09186126,0.002702842,0.00089921313,0.00006160646,0.0008904346,0.0604079],"genre_scores_gemma":[0.945357,0.000019429628,0.04764436,0.0029681197,0.0021182883,0.00018657108,0.000040621682,0.00002042599,0.0016451284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812967,0.000020289473,0.00038175672,0.00047466386,0.0006047906,0.00038883855],"domain_scores_gemma":[0.99888015,0.000083394196,0.00022578714,0.0002449518,0.000488562,0.00007715015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002605692,0.0002372481,0.00015793247,0.00009542852,0.00040776999,0.00044395664,0.0013308675,0.00006838709,0.00006667419],"category_scores_gemma":[0.0000072511352,0.00018062322,0.00012165419,0.00040451254,0.00012618116,0.00044106515,0.00010539498,0.0003055568,0.00015120834],"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":[0.000013786578,0.00022982623,0.00015199388,0.0000046343653,0.00002530801,0.000008065711,0.000037001933,0.019406777,0.0019235893,0.84808016,0.09378171,0.036337163],"study_design_scores_gemma":[0.000275898,0.0000759263,0.0019178353,0.00004586487,0.0000066780344,0.00003559818,0.0000079704205,0.93878967,0.0011542828,0.050954703,0.006502491,0.00023306714],"about_ca_topic_score_codex":0.00007423906,"about_ca_topic_score_gemma":0.0000048301545,"teacher_disagreement_score":0.9292281,"about_ca_system_score_codex":0.00009183737,"about_ca_system_score_gemma":0.000069917565,"threshold_uncertainty_score":0.7365601},"labels":[],"label_agreement":null},{"id":"W4246584857","doi":"10.1109/ijcnn.2006.1716072","title":"Aggregation of Reinforcement Learning Algorithms","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Evolutionary Algorithms and Applications","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 Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Learning classifier system; Instance-based learning; Machine learning; Robustness (evolution); Robot learning; Algorithm; Online machine learning; Unsupervised learning; Computational learning theory; Proactive learning; Robot; Mobile robot","score_opus":0.031508803972247715,"score_gpt":0.25756897615265123,"score_spread":0.22606017218040353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246584857","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":[0.17516537,0.0001640201,0.66645783,0.03264513,0.0028313263,0.0013757247,0.000012233576,0.00071696367,0.12063142],"genre_scores_gemma":[0.99107695,0.00004104628,0.005392569,0.00024739062,0.00074491964,0.00006174168,0.000015398295,0.000009640066,0.0024103487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983744,0.000017244769,0.00045506866,0.00031749165,0.0005681744,0.0002676395],"domain_scores_gemma":[0.9988362,0.000057094905,0.00039680462,0.0001633344,0.000504512,0.000042022482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030334716,0.00017209638,0.00015279082,0.000086970205,0.00024420803,0.00015710978,0.00086636026,0.000051461888,0.000038684317],"category_scores_gemma":[0.000017698543,0.00013176308,0.000091130474,0.00034712572,0.00009887029,0.0004268622,0.00012784656,0.00027104196,0.000038085018],"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":[0.0000152442235,0.000072006405,0.0003722174,0.0000064543246,0.000022680551,0.0000013255088,0.00010432679,0.18468256,0.0023641612,0.7891507,0.0100264065,0.013181923],"study_design_scores_gemma":[0.00024512742,0.00012290511,0.0029315527,0.00007349474,0.0000065120407,0.000022358894,0.000025230735,0.96156293,0.0030950438,0.028723914,0.0030232312,0.00016772134],"about_ca_topic_score_codex":0.00008951294,"about_ca_topic_score_gemma":0.0000030506374,"teacher_disagreement_score":0.8159116,"about_ca_system_score_codex":0.00006412333,"about_ca_system_score_gemma":0.00003651695,"threshold_uncertainty_score":0.5373143},"labels":[],"label_agreement":null},{"id":"W4247204284","doi":"10.1109/ijcnn.2006.1716669","title":"Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Anomaly Detection Techniques and Applications","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":"University of British Columbia","funders":"University of British Columbia; Clemson University","keywords":"Cluster analysis; Computer science; Hidden Markov model; Heuristic; Markov chain; Data mining; Traffic analysis; Machine learning; Artificial intelligence; Computer security","score_opus":0.01987366171158034,"score_gpt":0.24700673331215373,"score_spread":0.2271330716005734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247204284","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":[0.16518204,0.000007024016,0.8197406,0.0067069298,0.00013956193,0.00064324593,0.000015510026,0.0014392775,0.0061258087],"genre_scores_gemma":[0.9832015,0.000008648681,0.0152138565,0.000502719,0.00030445022,0.00027196333,0.00001612326,0.000017187185,0.00046357082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982271,0.000017897351,0.0004134858,0.0005435901,0.0004720716,0.00032580775],"domain_scores_gemma":[0.99895,0.000040080162,0.00024035067,0.00028651557,0.00040275746,0.00008031542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023739808,0.00023860896,0.00023132315,0.00020101435,0.00032237975,0.00040713488,0.001001097,0.000066525135,0.0000151856275],"category_scores_gemma":[0.0000037703853,0.00017576572,0.00010931347,0.0013674325,0.000063651976,0.00044930849,0.000111997564,0.0002084966,0.000020354635],"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":[0.0001435513,0.00009556849,0.00028513052,0.0000071892655,0.00014185924,0.0000018930747,0.0002283257,0.87344116,0.0027192277,0.08992083,0.0050528967,0.027962385],"study_design_scores_gemma":[0.00014543216,0.00009941203,0.0041301656,0.000024366973,0.00003881826,0.0000137251145,0.000022876682,0.99189144,0.0007904983,0.0021849107,0.0004309436,0.0002274275],"about_ca_topic_score_codex":0.00015632044,"about_ca_topic_score_gemma":0.0001277334,"teacher_disagreement_score":0.81801945,"about_ca_system_score_codex":0.00012359828,"about_ca_system_score_gemma":0.000029567394,"threshold_uncertainty_score":0.7167518},"labels":[],"label_agreement":null},{"id":"W4247419420","doi":"10.1109/ijcnn.2006.1716419","title":"Appearance-based Pain Recognition from Video Sequences","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":2,"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 Northern British Columbia","funders":"University of British Columbia; University of Northern British Columbia","keywords":"Artificial intelligence; Computer vision; Computer science; Face (sociological concept); Feature (linguistics); Facial recognition system; Biometrics; Pattern recognition (psychology); Feature vector; Feature extraction; Face detection; Three-dimensional face recognition","score_opus":0.035962303090103884,"score_gpt":0.2314801203264261,"score_spread":0.19551781723632222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247419420","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":[0.93477064,0.00027647585,0.017052788,0.0133884605,0.0021218697,0.00030661194,0.00007264251,0.0009182409,0.031092254],"genre_scores_gemma":[0.9958745,0.000045424837,0.00043702,0.0013981935,0.0018875739,0.000041309104,0.00007115354,0.000023427943,0.00022139569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856395,0.000031987205,0.00035844592,0.0002658874,0.0004725503,0.0003071751],"domain_scores_gemma":[0.999447,0.00010633603,0.000104510334,0.000092376256,0.00018337755,0.00006639904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043169918,0.00021952976,0.00020212258,0.0000780844,0.00010654719,0.00021338419,0.00034833304,0.00006792222,0.00028120968],"category_scores_gemma":[0.00005606002,0.00016227874,0.000113725306,0.00021470971,0.000112263566,0.00016412747,0.000013565786,0.00035282876,0.00013658586],"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":[0.00012016063,0.00017038241,0.007455935,0.000098861485,0.00033284846,0.000040508494,0.00019052294,0.4960966,0.03839173,0.003663591,0.28242552,0.17101334],"study_design_scores_gemma":[0.00026311327,0.000026177182,0.00094182533,0.00040765156,0.000030070109,0.0000031150012,0.000031296793,0.977849,0.0038894084,0.014518374,0.0017890007,0.0002509455],"about_ca_topic_score_codex":0.00026770306,"about_ca_topic_score_gemma":0.0000208691,"teacher_disagreement_score":0.48175243,"about_ca_system_score_codex":0.00006136022,"about_ca_system_score_gemma":0.000016188964,"threshold_uncertainty_score":0.6617535},"labels":[],"label_agreement":null},{"id":"W4248290806","doi":"10.1109/ijcnn.2006.1716763","title":"Improving the Convergence of Backpropagation by Opposite Transfer Functions","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":32,"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 Waterloo","funders":"","keywords":"Backpropagation; Computer science; Convergence (economics); Benchmark (surveying); Perceptron; Artificial neural network; Rate of convergence; Artificial intelligence; Activation function; Multilayer perceptron; Machine learning; Algorithm; Key (lock)","score_opus":0.024682205241403854,"score_gpt":0.22773344666735623,"score_spread":0.20305124142595238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248290806","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":[0.4971744,0.00016905028,0.43936926,0.046621986,0.002923742,0.0014797142,0.000073366304,0.00037249338,0.011815979],"genre_scores_gemma":[0.99609923,0.00003484998,0.0002851802,0.0009043532,0.0006143141,0.000104082974,0.000015839436,0.000013986744,0.0019281367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822396,0.000029985647,0.00049197505,0.00041032233,0.0005139315,0.00032980007],"domain_scores_gemma":[0.9989027,0.000103167215,0.00023584053,0.00026981893,0.00043687853,0.00005164346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031223372,0.00022158881,0.00017007043,0.00004759729,0.00037408152,0.00029022244,0.0013222115,0.000059904603,0.00004801756],"category_scores_gemma":[0.000009972127,0.0001369071,0.00012239028,0.0004562628,0.00017525096,0.0004654498,0.00009994604,0.00033552668,0.000036051442],"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":[0.00007325195,0.00019289771,0.0008096562,0.000019650548,0.00005867063,0.0000016428494,0.00018641024,0.022410698,0.18343337,0.60248363,0.17309412,0.01723598],"study_design_scores_gemma":[0.00045006373,0.00019676868,0.0031084334,0.00009791837,0.00003597843,0.000044223372,0.000057388665,0.95144826,0.027913282,0.010611685,0.005628863,0.00040711855],"about_ca_topic_score_codex":0.00014846466,"about_ca_topic_score_gemma":0.000019273779,"teacher_disagreement_score":0.9290376,"about_ca_system_score_codex":0.000038274335,"about_ca_system_score_gemma":0.00003367364,"threshold_uncertainty_score":0.55829096},"labels":[],"label_agreement":null},{"id":"W4248360727","doi":"10.1109/ijcnn.2006.1716775","title":"Virtual Reality Visual Data Mining via Neural Networks obtained from Multi-objective Evolutionary Optimization: Application to Geophysical Prospecting","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Artificial neural network; Artificial intelligence; Principal component analysis; Set (abstract data type); Genetic algorithm; Multi-objective optimization; Linear programming; Pattern recognition (psychology); Data mining; Machine learning; Algorithm","score_opus":0.05396050701975837,"score_gpt":0.31641636383682803,"score_spread":0.2624558568170697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248360727","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":[0.008016547,0.000019149962,0.9828083,0.0056297807,0.0011845822,0.0010351308,0.00004560136,0.0003610126,0.00089992164],"genre_scores_gemma":[0.91385615,0.0000096042695,0.08125915,0.0007355268,0.0031768414,0.00016230522,0.0003851487,0.00004323394,0.00037201776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545217,0.00015149373,0.0008711898,0.0014599521,0.0013445535,0.0007206558],"domain_scores_gemma":[0.99678653,0.0003763955,0.0005295502,0.0007178957,0.0013767561,0.00021286825],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082251354,0.000451224,0.00040278782,0.00018966277,0.0006041195,0.00083463895,0.0029859305,0.00014282392,0.00005394213],"category_scores_gemma":[0.00027210033,0.0003846283,0.00009932881,0.0011359204,0.00017738128,0.0012015245,0.001206443,0.00065733155,0.000044467895],"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":[0.000121798126,0.00020376513,0.00067185273,0.0000030028452,0.000047674006,0.0000060531634,0.00012842959,0.97545165,0.00022331595,0.007949972,0.00509492,0.010097565],"study_design_scores_gemma":[0.0005349201,0.00016495031,0.0067319665,0.000046912115,0.000017707676,0.000021997492,0.000051667717,0.99088585,0.00006994809,0.0010067923,0.00007417485,0.00039308213],"about_ca_topic_score_codex":0.00055950246,"about_ca_topic_score_gemma":0.00003655492,"teacher_disagreement_score":0.9058396,"about_ca_system_score_codex":0.00027133437,"about_ca_system_score_gemma":0.00010517902,"threshold_uncertainty_score":0.9998606},"labels":[],"label_agreement":null},{"id":"W4248539564","doi":"10.1109/ijcnn.2006.1716619","title":"Shape Morphing and Reconstruction Using A Self-Organizing Feature Map","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Image Processing and 3D Reconstruction","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":"McGill University; Western University","funders":"","keywords":"Morphing; Computer science; Artificial intelligence; Process (computing); Computer vision; Feature (linguistics); Pattern recognition (psychology); Computer graphics (images)","score_opus":0.027630513745008498,"score_gpt":0.23810980592669245,"score_spread":0.21047929218168396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248539564","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":[0.9624202,0.00018155115,0.0155393025,0.011002619,0.0042009843,0.00029263398,0.0000034687266,0.0005848391,0.005774395],"genre_scores_gemma":[0.96643525,0.000028710418,0.031120002,0.00044024255,0.001604154,0.000008681635,0.000002259561,0.00001910894,0.0003415617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984318,0.000024552879,0.00031444777,0.000496793,0.0003731474,0.00035927215],"domain_scores_gemma":[0.9989589,0.000037074376,0.0004249538,0.00012393322,0.00039744333,0.000057689642],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00031184478,0.00025345627,0.00019119409,0.00013679123,0.00050312834,0.0010496682,0.0005332595,0.000104520244,0.000018597588],"category_scores_gemma":[0.000021642716,0.00019638437,0.00006118251,0.0002973398,0.00011874557,0.0010825215,0.00013176141,0.00046190497,0.000012761007],"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":[0.00022422393,0.00027909523,0.018885002,0.00027198266,0.0003245812,0.00006746812,0.0023095491,0.013315052,0.14848334,0.2769702,0.03129443,0.5075751],"study_design_scores_gemma":[0.00029729478,0.000045490342,0.0015085803,0.0002810124,0.000019530253,0.0014458317,0.000072062605,0.97587436,0.0032410538,0.016616559,0.00029758553,0.00030066146],"about_ca_topic_score_codex":0.0000471876,"about_ca_topic_score_gemma":0.000004487551,"teacher_disagreement_score":0.9625593,"about_ca_system_score_codex":0.000093558265,"about_ca_system_score_gemma":0.00006371019,"threshold_uncertainty_score":0.99998736},"labels":[],"label_agreement":null},{"id":"W4250455569","doi":"10.1109/ijcnn.2006.1716795","title":"Extend Single-agent Reinforcement Learning Approach to a Multi-robot Cooperative Task in an Unknown Dynamic Environment","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Reinforcement Learning in Robotics","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":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Computer science; Robot; Markov decision process; Robustness (evolution); Robot learning; Artificial intelligence; Q-learning; Obstacle; Mobile robot; Markov process; Machine learning; Mathematics","score_opus":0.052808010837956824,"score_gpt":0.26725047299880855,"score_spread":0.21444246216085172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250455569","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":[0.067667834,0.000019686711,0.9159742,0.002201477,0.00074060523,0.0011018761,0.0000015936059,0.00021534521,0.012077357],"genre_scores_gemma":[0.9810828,0.000021954529,0.013600375,0.0007853197,0.00025805205,0.00014972907,0.000033589116,0.000032892076,0.004035269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666405,0.00008569179,0.0007584609,0.0008330412,0.00094199117,0.0007167914],"domain_scores_gemma":[0.9988455,0.00005687819,0.00037880847,0.0003196872,0.0002503576,0.00014873884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005769551,0.00044344392,0.00032439447,0.00023291873,0.0002863924,0.00074229547,0.0015815032,0.00009628271,0.000028377763],"category_scores_gemma":[0.000042386102,0.00035561388,0.00009152617,0.00039810373,0.00012006726,0.00070896605,0.0004049499,0.000708204,0.00011136324],"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":[0.00004735575,0.00018848953,0.00027097235,0.000006588221,0.0000189907,0.0000060970806,0.00073517265,0.96673936,0.0053500235,0.024646673,0.00033655262,0.0016537299],"study_design_scores_gemma":[0.0005874382,0.00057527435,0.0023105403,0.000110238885,0.000007925537,0.000019597792,0.00008739922,0.99426544,0.0004431905,0.0002070225,0.00097357837,0.00041237785],"about_ca_topic_score_codex":0.000066733104,"about_ca_topic_score_gemma":0.000018231536,"teacher_disagreement_score":0.91341496,"about_ca_system_score_codex":0.000523723,"about_ca_system_score_gemma":0.000044570632,"threshold_uncertainty_score":0.9998896},"labels":[],"label_agreement":null},{"id":"W4250870185","doi":"10.1109/ijcnn.2006.1716487","title":"A Neuro—Fuzzy Approach for the Motion Planning of Redundant Manipulators","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Robotic Path Planning Algorithms","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":"University of Regina","funders":"","keywords":"Obstacle avoidance; Computer science; Motion planning; Control theory (sociology); Kinematics; Robot; Inverse kinematics; Adaptive neuro fuzzy inference system; Gravitational singularity; Artificial intelligence; Control engineering; Fuzzy logic; Fuzzy control system; Mathematics; Mobile robot; Engineering","score_opus":0.07641989096458972,"score_gpt":0.2787647750373605,"score_spread":0.2023448840727708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250870185","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":[0.04254331,0.000096259806,0.9326367,0.008077009,0.0029733905,0.0011684236,0.000015886073,0.00028664866,0.01220239],"genre_scores_gemma":[0.9738962,0.000006100317,0.024074716,0.00047162198,0.0010530413,0.000094400195,0.000011130465,0.000021209456,0.00037158543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997909,0.000029643003,0.0005231024,0.00047098714,0.00064629177,0.0004209935],"domain_scores_gemma":[0.99851054,0.00027667804,0.0004918583,0.00027169278,0.00040217114,0.0000470884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006552577,0.00025849827,0.00024150671,0.00010261725,0.0002962232,0.00031056337,0.0016653488,0.00007215049,0.0000033541744],"category_scores_gemma":[0.00007422498,0.0001614273,0.00014398256,0.00031968026,0.00013935956,0.00032902154,0.0001505429,0.0003373249,0.0000049588566],"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":[0.000082156956,0.00010471582,0.0011905025,0.000025443727,0.000060070844,0.0000046471396,0.00035641287,0.72609687,0.0017299413,0.24127378,0.024769133,0.004306325],"study_design_scores_gemma":[0.00029952987,0.00012103561,0.00669877,0.000084937354,0.000018094044,0.00006924306,0.000045917805,0.98027474,0.00070639065,0.011284792,0.00021862336,0.00017794104],"about_ca_topic_score_codex":0.00007132459,"about_ca_topic_score_gemma":4.8665385e-7,"teacher_disagreement_score":0.9313529,"about_ca_system_score_codex":0.000053572898,"about_ca_system_score_gemma":0.000035901816,"threshold_uncertainty_score":0.65828145},"labels":[],"label_agreement":null},{"id":"W4252338385","doi":"10.1109/ijcnn.2006.1716418","title":"A New Facial Expression Recognition Technique using 2-D DCT and Neural Networks Based Decision Tree","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Face and Expression Recognition","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":"Computer science; Decision tree; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; Tree (set theory); Facial expression; Discrete cosine transform; Feedforward neural network; Support vector machine; Template matching; Facial recognition system; Image (mathematics); Mathematics","score_opus":0.049604429795636056,"score_gpt":0.26884666354888215,"score_spread":0.21924223375324609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252338385","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":[0.2600855,0.000048266298,0.7332294,0.0022877755,0.0015117878,0.00069076574,0.000009914025,0.00030743162,0.0018291591],"genre_scores_gemma":[0.96136236,0.000022177457,0.03642938,0.00080325524,0.0011482124,0.00006195186,0.000024482046,0.000024428038,0.00012376222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976734,0.00005555424,0.0005243651,0.00064384774,0.0006404041,0.00046242386],"domain_scores_gemma":[0.9988365,0.00014619887,0.0003595107,0.00019757646,0.0003238764,0.0001363094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039235194,0.0003549167,0.0002594622,0.00019436356,0.00037122553,0.00067782967,0.00073937606,0.00018344712,0.00006798437],"category_scores_gemma":[0.000041493524,0.0002620991,0.00012340312,0.00035836117,0.00007944797,0.00091447233,0.00019676374,0.00048221604,0.000014101896],"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":[0.000915922,0.00023193781,0.0007445897,0.000026298652,0.000030926396,0.000044578457,0.00015024375,0.19721456,0.09496277,0.0042230035,0.062384862,0.63907033],"study_design_scores_gemma":[0.0006345191,0.00013961364,0.0006242214,0.00046548506,0.000012300938,0.000085716965,0.000011867325,0.9649628,0.013404104,0.019126529,0.00020148806,0.00033137307],"about_ca_topic_score_codex":0.00011877302,"about_ca_topic_score_gemma":0.00001490441,"teacher_disagreement_score":0.76774824,"about_ca_system_score_codex":0.00007195147,"about_ca_system_score_gemma":0.00005111546,"threshold_uncertainty_score":0.99998313},"labels":[],"label_agreement":null},{"id":"W4254079413","doi":"10.1109/ijcnn.2006.1716653","title":"A Novel Cooperative Neural Learning Algorithm for Data Fusion","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","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":"University of Waterloo","funders":"","keywords":"Algorithm; Robustness (evolution); Computer science; Least absolute deviations; Artificial neural network; Modular design; Fusion; Sensor fusion; Gaussian; Image fusion; Variance (accounting); Artificial intelligence; Image (mathematics); Mathematics; Estimator","score_opus":0.07620864815218481,"score_gpt":0.2962242574833673,"score_spread":0.22001560933118247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254079413","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":[0.02380717,0.00007446271,0.93407476,0.031947583,0.0024847155,0.0015643924,0.00016087005,0.00057040015,0.00531566],"genre_scores_gemma":[0.9645476,0.000051001924,0.025985876,0.0021333592,0.0035398346,0.0002433014,0.00025025973,0.000040272047,0.0032085485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976433,0.000023315803,0.0004813782,0.0008204297,0.00050797896,0.00052359153],"domain_scores_gemma":[0.99837375,0.00018796227,0.00034370995,0.0004056238,0.00060477195,0.0000841753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042176247,0.00032004784,0.0002494795,0.00006080728,0.0006274701,0.00077778735,0.0026025574,0.00007984227,0.000021952043],"category_scores_gemma":[0.000034131575,0.00023126187,0.00010024362,0.0004087519,0.00011244487,0.0008291787,0.00056403776,0.0005051676,0.00002698371],"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":[0.0000973927,0.0003708863,0.00016651006,0.000014033111,0.00008424754,0.000007123492,0.00017889185,0.08716592,0.019484121,0.41576883,0.27059042,0.20607163],"study_design_scores_gemma":[0.0004605832,0.00014984715,0.00039725564,0.000052026702,0.0000121256835,0.00005384264,0.00002092797,0.9813271,0.0004039508,0.0035274785,0.013317005,0.00027786696],"about_ca_topic_score_codex":0.00007300472,"about_ca_topic_score_gemma":0.000022103835,"teacher_disagreement_score":0.94074035,"about_ca_system_score_codex":0.000050146373,"about_ca_system_score_gemma":0.000044374552,"threshold_uncertainty_score":0.94305855},"labels":[],"label_agreement":null},{"id":"W4255988759","doi":"10.1109/ijcnn.2006.1716617","title":"The Self-Organising Hierarchical Variance Map","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","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":"Toronto Metropolitan University","funders":"","keywords":"Hebbian theory; Self-organizing map; Computer science; Cluster analysis; Variance (accounting); Artificial neural network; Artificial intelligence; Unsupervised learning; Competitive learning; Neural gas; Network topology; Topology (electrical circuits); Pattern recognition (psychology); Mathematics; Recurrent neural network","score_opus":0.02392029913870507,"score_gpt":0.24832798346208407,"score_spread":0.224407684323379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255988759","genre_codex":"commentary","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":[0.18805994,0.00038584255,0.1266667,0.5743663,0.012146755,0.0025737202,0.00002716856,0.0026012082,0.0931724],"genre_scores_gemma":[0.98962766,0.00006885243,0.0030299942,0.0020540494,0.0027586326,0.00008528567,0.00000430666,0.000022498216,0.00234873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771225,0.000043013064,0.0004739775,0.00053627,0.0006487591,0.0005857179],"domain_scores_gemma":[0.9985738,0.000268349,0.0003724259,0.00035940108,0.000338534,0.00008752855],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004842641,0.00027890617,0.00017805408,0.000043314107,0.0009872854,0.001261425,0.0023174451,0.00007317575,0.000023223214],"category_scores_gemma":[0.000022569106,0.00016767826,0.000117460964,0.0004159254,0.00016442023,0.00040479947,0.00028333624,0.0005976386,0.00013496714],"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":[0.000017606348,0.00005482215,0.00012894823,0.0000027656238,0.000020711743,0.000004208582,0.00005115305,0.0032297305,0.0011675087,0.91199166,0.078066036,0.0052648783],"study_design_scores_gemma":[0.00035975507,0.00011189624,0.005530468,0.000092880735,0.000015838172,0.00011116944,0.000022142052,0.73389035,0.001110735,0.20781472,0.050487682,0.00045236066],"about_ca_topic_score_codex":0.000032060263,"about_ca_topic_score_gemma":0.00001729241,"teacher_disagreement_score":0.80156773,"about_ca_system_score_codex":0.000075123324,"about_ca_system_score_gemma":0.000057132183,"threshold_uncertainty_score":0.99977535},"labels":[],"label_agreement":null},{"id":"W4256653491","doi":"10.1109/ijcnn.2006.1716631","title":"Noise resistance and enhancement of neural performance by using spike signals","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"stochastic dynamics and bifurcation","field":"Physics and Astronomy","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":"University of Saskatchewan","funders":"","keywords":"Robustness (evolution); Computer science; Spike (software development); Artificial neural network; Noise (video); Amplitude; Reliability (semiconductor); Biological system; Control theory (sociology); Artificial intelligence; Physics","score_opus":0.022885805823756867,"score_gpt":0.24675904940332527,"score_spread":0.2238732435795684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256653491","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":[0.9867257,0.000035194123,0.0030669034,0.00068788644,0.00032567375,0.00021106742,0.00003148745,0.000012995245,0.008903087],"genre_scores_gemma":[0.9978664,0.000008576257,0.00020812116,0.00010241149,0.0006959893,0.000019518837,0.000023077531,0.000012103107,0.0010638218],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887025,0.000008098493,0.00035956473,0.00023489038,0.00030226103,0.0002249157],"domain_scores_gemma":[0.9992676,0.000030579464,0.0003410804,0.00007422052,0.00025303892,0.000033472803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015497858,0.00017422538,0.00017263753,0.000036780468,0.00012640141,0.00009430164,0.00021217961,0.000026466058,0.00007196423],"category_scores_gemma":[0.0000029168032,0.00013266254,0.000049702336,0.000095926844,0.000106649546,0.000158857,0.000042178486,0.00014522136,0.0000034810041],"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":[0.0009184951,0.00071074127,0.048227526,0.00014364699,0.00027032636,0.0000014417157,0.0005097249,0.059959978,0.55826485,0.25800118,0.0465376,0.02645448],"study_design_scores_gemma":[0.0005988401,0.0001444728,0.0042532086,0.00029663576,0.000040420928,0.0000033360861,0.00007825156,0.9661105,0.018488169,0.00920698,0.00044558354,0.00033358307],"about_ca_topic_score_codex":0.00011302488,"about_ca_topic_score_gemma":0.0000025306051,"teacher_disagreement_score":0.9061505,"about_ca_system_score_codex":0.000028319213,"about_ca_system_score_gemma":0.00001897073,"threshold_uncertainty_score":0.5409821},"labels":[],"label_agreement":null}]}