{"meta":{"query_hash":"8f9c8a7e256b","filters":{"venue":"IEEE Transactions on Neural Networks"},"cohort_total":114,"direct_labels_cover":0,"predictions_cover":114,"exported":114,"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/8f9c8a7e256b","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Neural+Networks"},"results":[{"id":"W1500616748","doi":"10.1109/tnn.2003.811562","title":"A Kohonen-like decomposition method for the euclidean traveling salesman problem - KNIES_DECOMPOSE","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Travelling salesman problem; Bottleneck traveling salesman problem; Euclidean geometry; 2-opt; Self-organizing map; Heuristic; Euclidean distance; Partition (number theory); Mathematical optimization; Computer science; Artificial neural network; Traveling purchaser problem; Mathematics; Decomposition; Algorithm; Artificial intelligence; Combinatorics","score_opus":0.020380633936201206,"score_gpt":0.28915941299335646,"score_spread":0.26877877905715525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500616748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021962505,0.00020287593,0.9950119,0.00008685518,0.00005281757,0.00004277166,0.00003801572,0.00015745495,0.0022111088],"genre_scores_gemma":[0.069279425,0.00036806893,0.92542905,0.00010798722,0.000046078498,0.00016208731,0.00019185337,0.00009472988,0.004320772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968433,0.00006626294,0.000021585136,0.000056991255,0.00013718143,0.000033647073],"domain_scores_gemma":[0.99982125,0.000055917233,0.0000146043085,0.000021087642,0.00007044737,0.000016640844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005164829,0.00068329234,0.0006110552,0.0005500981,0.0005070606,0.00078674173,0.0011649672,0.0008958516,0.0036298505],"category_scores_gemma":[0.0010282383,0.0004125546,0.0009865236,0.0007814555,0.0003972154,0.0013835442,0.0010443019,0.0011568812,0.0008129377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009512797,0.00010790139,0.00047715416,0.00034516805,0.00012484033,0.00009872217,0.00015489687,0.528926,0.008142227,0.05682757,0.007817183,0.39688322],"study_design_scores_gemma":[0.000012829515,0.000023964874,0.000089341585,0.000013691781,0.000013045067,0.000043781245,0.00003079508,0.9797426,0.0013411365,0.013226996,0.0054491423,0.000012737047],"about_ca_topic_score_codex":0.004968675,"about_ca_topic_score_gemma":0.0073186057,"teacher_disagreement_score":0.004968675,"about_ca_system_score_codex":0.00047941535,"about_ca_system_score_gemma":0.0010548503,"threshold_uncertainty_score":0.0121430755},"labels":[],"label_agreement":null},{"id":"W1971944463","doi":"10.1109/tnn.2011.2169809","title":"Parallel Programmable Asynchronous Neighborhood Mechanism for Kohonen SOM Implemented in CMOS Technology","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":41,"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":"Self-organizing map; Computer science; Asynchronous communication; Artificial neural network; Chip; CMOS; Realization (probability); Topology (electrical circuits); Artificial intelligence; Electronic engineering; Electrical engineering; Mathematics; Engineering","score_opus":0.025429751763777934,"score_gpt":0.24847249950286876,"score_spread":0.22304274773909083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971944463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17107445,0.0006598402,0.815811,0.000115393355,0.00019877788,0.000074444666,0.000106018706,0.0019313225,0.010028814],"genre_scores_gemma":[0.8095333,0.0001870277,0.18712662,0.00004723487,0.000025620242,0.00006794482,0.000056827714,0.000038742604,0.0029167454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991465,0.000010027176,0.0000062331624,0.000020621495,0.00003493519,0.000013605885],"domain_scores_gemma":[0.99992096,0.000019142697,0.000012017252,0.000015318437,0.000024305074,0.0000082598945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223861,0.00018662767,0.00019247252,0.00019640986,0.0002301683,0.00034637444,0.0010511732,0.0002845342,0.0014905113],"category_scores_gemma":[0.00021210022,0.00011097218,0.00022166163,0.00017490386,0.00017810192,0.00047786982,0.0002955136,0.00020331371,0.00025518762],"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":[0.0004894761,0.0001673427,0.0015003199,0.000541532,0.00011407928,0.0005578632,0.0002497877,0.08354507,0.51368207,0.047431268,0.0027559325,0.34896523],"study_design_scores_gemma":[0.000108378415,0.0006314027,0.0017128524,0.000034478464,0.00008018616,0.00067731034,0.00006793393,0.71199185,0.24965796,0.015331304,0.019648362,0.00005794936],"about_ca_topic_score_codex":0.00045826007,"about_ca_topic_score_gemma":0.00086088834,"teacher_disagreement_score":0.0014905113,"about_ca_system_score_codex":0.00019359545,"about_ca_system_score_gemma":0.00019735562,"threshold_uncertainty_score":0.0049862266},"labels":[],"label_agreement":null},{"id":"W1984461808","doi":"10.1109/72.950141","title":"Neural-network control of mobile manipulators","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Control and Dynamics of Mobile Robots","field":"Engineering","cited_by":216,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Control theory (sociology); Artificial neural network; Computer science; Convergence (economics); Controller (irrigation); Estimator; Kinematics; Mobile manipulator; Motion control; Stability (learning theory); Bounded function; Mobile robot; Tracking (education); Process (computing); Control engineering; Artificial intelligence; Control (management); Robot; Mathematics; Engineering; Machine learning","score_opus":0.0068421050308280235,"score_gpt":0.1984100618241816,"score_spread":0.1915679567933536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984461808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021456491,0.0028403336,0.96707803,0.0001821257,0.00023923925,0.00004202804,0.0000367242,0.00032678837,0.0077981497],"genre_scores_gemma":[0.92622286,0.0017690753,0.06438433,0.000106164916,0.00016866795,0.00017740067,0.00008199854,0.000023745797,0.0070657916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984634,0.00002902706,0.000009504576,0.000037394526,0.000058757072,0.000018972036],"domain_scores_gemma":[0.9998795,0.000040340175,0.00002980993,0.0000065126546,0.000037610793,0.0000062156864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031901253,0.0005634272,0.0004130475,0.00022191877,0.00026025492,0.00044547164,0.00055302895,0.0006413131,0.0011166289],"category_scores_gemma":[0.0006638498,0.00015945043,0.00020863925,0.00030701954,0.0005083813,0.00037915568,0.00046913594,0.0005248142,0.00024408243],"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":[0.00007684368,0.000026350202,0.0002318536,0.00021099401,0.00003451205,0.00014942465,0.000053237098,0.88281214,0.012495464,0.021288322,0.00084632874,0.08177455],"study_design_scores_gemma":[0.0000110382325,0.00004631386,0.00013741502,0.000010697214,0.0000059390272,0.000020696729,0.000004216693,0.9931371,0.001067927,0.0034601314,0.0020925694,0.0000060387524],"about_ca_topic_score_codex":0.0039850986,"about_ca_topic_score_gemma":0.0035042942,"teacher_disagreement_score":0.0039850986,"about_ca_system_score_codex":0.0003665387,"about_ca_system_score_gemma":0.00031853997,"threshold_uncertainty_score":0.0079238415},"labels":[],"label_agreement":null},{"id":"W1988196484","doi":"10.1109/tnn.2011.2169808","title":"Bioinspired Neural Network for Real-Time Cooperative Hunting by Multirobots in Unknown Environments","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":126,"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":"Robot; Computer science; Artificial neural network; Artificial intelligence; Robotics; Motion planning; Alliance; Path (computing); Collision avoidance; Order (exchange); Collision; Computer security","score_opus":0.026455500938740276,"score_gpt":0.238045988728049,"score_spread":0.21159048778930872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988196484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055792157,0.00094891345,0.9392302,0.00032500306,0.00012051812,0.000038648323,0.00002415913,0.00048560213,0.0030347053],"genre_scores_gemma":[0.858046,0.0005368699,0.13726515,0.00018533789,0.000040733878,0.00018405981,0.0000895435,0.000027625445,0.0036246432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998053,0.00005078085,0.000013375087,0.000052429692,0.00005685157,0.000021352915],"domain_scores_gemma":[0.99964833,0.00015374631,0.000051675233,0.000026851078,0.000100174,0.000019126112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070720277,0.00048075552,0.00039674668,0.0002617777,0.00032602556,0.0004628307,0.0009978913,0.0010206773,0.0009419812],"category_scores_gemma":[0.001470249,0.00023705416,0.00034002957,0.00027959692,0.0004645541,0.0008002479,0.0005704968,0.00088304834,0.00015681062],"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":[0.00006869181,0.00006413352,0.0005618009,0.00005429483,0.00003803915,0.000099965044,0.000064662476,0.92208314,0.004101175,0.0038598373,0.0005958206,0.06840844],"study_design_scores_gemma":[0.0000036301635,0.000014755638,0.00004941311,0.000002173839,0.000003041736,0.00000779628,0.0000024002682,0.99875236,0.00032231782,0.0007069915,0.00013294318,0.000002281094],"about_ca_topic_score_codex":0.0041799806,"about_ca_topic_score_gemma":0.0032115348,"teacher_disagreement_score":0.0041799806,"about_ca_system_score_codex":0.00059077406,"about_ca_system_score_gemma":0.0006269654,"threshold_uncertainty_score":0.008311331},"labels":[],"label_agreement":null},{"id":"W1991360560","doi":"10.1109/tnn.2010.2050601","title":"Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":340,"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":"Computer science; Artificial neural network; Multi-agent system; Control theory (sociology); Constraint (computer-aided design); Tracking error; Adaptive control; State (computer science); Control (management); Artificial intelligence; Algorithm; Mathematics","score_opus":0.01990881461671435,"score_gpt":0.2318356880729288,"score_spread":0.21192687345621447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991360560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026968982,0.0006804476,0.96850264,0.00017657221,0.00010045336,0.000041367046,0.000021443264,0.00027885567,0.003229193],"genre_scores_gemma":[0.95605683,0.00033667844,0.040656123,0.000078170946,0.00006475657,0.00012148887,0.000037487913,0.000015672793,0.0026328291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974555,0.00006600544,0.000018803943,0.000060407554,0.00007334012,0.00003591242],"domain_scores_gemma":[0.9996346,0.00014405765,0.00008451985,0.00001836831,0.00010066275,0.000017775455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066570455,0.00058204145,0.0005998825,0.00024522727,0.00045341108,0.0005561175,0.0011357486,0.0009010797,0.0008429085],"category_scores_gemma":[0.0010764807,0.00024518894,0.0003270301,0.00033338426,0.00048971287,0.00063969725,0.0006436546,0.00073223846,0.0001524001],"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":[0.000063779495,0.000036512385,0.0002895338,0.00006571252,0.000034629447,0.00010130584,0.00007714994,0.9512359,0.0031018676,0.0048288805,0.0005174201,0.039647255],"study_design_scores_gemma":[0.0000056785534,0.000020148429,0.000044036893,0.0000022090799,0.0000034090738,0.000006434797,0.0000024967565,0.99898726,0.0002279145,0.0005611125,0.00013647764,0.0000028155173],"about_ca_topic_score_codex":0.00611627,"about_ca_topic_score_gemma":0.0048418334,"teacher_disagreement_score":0.00611627,"about_ca_system_score_codex":0.000508869,"about_ca_system_score_gemma":0.0005767665,"threshold_uncertainty_score":0.0121613145},"labels":[],"label_agreement":null},{"id":"W1995349519","doi":"10.1109/tnn.2003.810608","title":"Bias learning, knowledge sharing","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":45,"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 de Montréal","funders":"","keywords":"Computer science; Generalization; Artificial intelligence; Machine learning; Inductive bias; Class (philosophy); Multi-task learning; Domain (mathematical analysis); Space (punctuation); Domain knowledge; Variety (cybernetics); Task (project management); Mathematics","score_opus":0.027695966266377804,"score_gpt":0.26385655350726067,"score_spread":0.23616058724088287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995349519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025654156,0.0034369393,0.95986086,0.0017915149,0.00015561603,0.0002511174,0.00019863811,0.0010101085,0.0076410486],"genre_scores_gemma":[0.68957454,0.0029087388,0.29564902,0.0010554477,0.00060325925,0.0009147538,0.0007157939,0.00028348956,0.008294962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9917441,0.0038710455,0.00036419858,0.0018887346,0.0017564218,0.00037534971],"domain_scores_gemma":[0.9721196,0.015324573,0.002131585,0.007470756,0.0023169182,0.0006364954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011099946,0.001426588,0.0019521002,0.001840462,0.001350639,0.002976061,0.0029963467,0.0030569548,0.004854894],"category_scores_gemma":[0.055887327,0.0006043579,0.0010720519,0.0023159888,0.0033466665,0.0075759827,0.0058764904,0.0024238233,0.0019204115],"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":[0.0005807933,0.00039899643,0.005247223,0.0007811527,0.0004468935,0.000246683,0.0006403848,0.13923468,0.004580093,0.22279826,0.008715311,0.61632955],"study_design_scores_gemma":[0.00010528736,0.00019756731,0.0010526583,0.00010532522,0.00009492419,0.00027066385,0.00010601594,0.30884826,0.004568281,0.67491907,0.009682346,0.000049546714],"about_ca_topic_score_codex":0.001624909,"about_ca_topic_score_gemma":0.0011227684,"teacher_disagreement_score":0.011099946,"about_ca_system_score_codex":0.0020127515,"about_ca_system_score_gemma":0.0021531938,"threshold_uncertainty_score":0.058702826},"labels":[],"label_agreement":null},{"id":"W2003228681","doi":"10.1109/tnn.2011.2176541","title":"Neural Network-Based Multiple Robot Simultaneous Localization and Mapping","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of New Brunswick","funders":"","keywords":"Artificial intelligence; Computer science; Simultaneous localization and mapping; Occupancy grid mapping; Computer vision; Cluster analysis; Robot; Pattern recognition (psychology); Self-organizing map; Mobile robot","score_opus":0.02040240018987655,"score_gpt":0.19345935783708074,"score_spread":0.1730569576472042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003228681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01943948,0.00039192388,0.9756941,0.0001203248,0.000093018825,0.00003666425,0.000040233906,0.0011778456,0.0030064269],"genre_scores_gemma":[0.76541865,0.0004153945,0.22477187,0.00017175991,0.00009440954,0.00022008311,0.00021324922,0.00009595007,0.008598612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996716,0.000051499814,0.000012364247,0.000094107585,0.00012240266,0.00004803484],"domain_scores_gemma":[0.99971014,0.000100209145,0.000042884578,0.000037079863,0.00009417406,0.000015479049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003580236,0.00051892066,0.0005735975,0.00039245517,0.00033315044,0.00055370695,0.000997131,0.0007516867,0.0016039243],"category_scores_gemma":[0.001001887,0.0003007488,0.00034426633,0.00058132905,0.0003561502,0.0009849314,0.0007168186,0.0006391771,0.00053524587],"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":[0.0001449528,0.0001227132,0.00063506287,0.00009984612,0.00007997751,0.00013048819,0.00006177243,0.72528446,0.012214826,0.003952868,0.0018880428,0.25538504],"study_design_scores_gemma":[0.000011451215,0.000038041744,0.00023122971,0.000004184027,0.000008385641,0.00002631378,0.000008113718,0.9949811,0.0023815783,0.0014042397,0.0008993479,0.000006024522],"about_ca_topic_score_codex":0.0044353865,"about_ca_topic_score_gemma":0.0059564454,"teacher_disagreement_score":0.0044353865,"about_ca_system_score_codex":0.0005062413,"about_ca_system_score_gemma":0.00059330935,"threshold_uncertainty_score":0.008819163},"labels":[],"label_agreement":null},{"id":"W2010980582","doi":"10.1109/tnn.2004.828765","title":"Probabilistic Sequential Independent Components Analysis","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","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 Toronto","funders":"Royal Society of Canada","keywords":"Computer science; Independent component analysis; Artificial intelligence; Probabilistic logic; Projection (relational algebra); Sequence (biology); Feature (linguistics); Component (thermodynamics); Field (mathematics); Algorithm; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.025676999232074206,"score_gpt":0.2624316476554458,"score_spread":0.2367546484233716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010980582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001231735,0.00016941607,0.9977465,0.00005959816,0.000022808808,0.000035538713,0.000085751315,0.00013391489,0.00051470497],"genre_scores_gemma":[0.15961358,0.0015373605,0.8305415,0.00020485622,0.00034703084,0.0005741055,0.0015608812,0.00024463984,0.0053761113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625266,0.001386335,0.00020931187,0.0009039095,0.0010326544,0.00021522972],"domain_scores_gemma":[0.9947876,0.0027603037,0.00046326965,0.0010126887,0.00086769916,0.000108435765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004554801,0.0017818478,0.0021984493,0.0016437036,0.0007204263,0.0019581348,0.0021593827,0.0012338343,0.0035250862],"category_scores_gemma":[0.013542324,0.0009788522,0.0016964136,0.002619052,0.0016680253,0.0034275185,0.0022689172,0.00262641,0.0015068974],"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":[0.00023301766,0.00009471039,0.0017841174,0.00030626467,0.00030711974,0.00016757338,0.00018096589,0.42231473,0.0029633746,0.24423148,0.007401929,0.32001472],"study_design_scores_gemma":[0.000014168391,0.000033812685,0.0002975671,0.000017156151,0.000022273987,0.000081808954,0.000012854782,0.89457047,0.0008781441,0.10049468,0.003556316,0.000020665693],"about_ca_topic_score_codex":0.002837582,"about_ca_topic_score_gemma":0.0024990516,"teacher_disagreement_score":0.004554801,"about_ca_system_score_codex":0.00084693404,"about_ca_system_score_gemma":0.0021909748,"threshold_uncertainty_score":0.024088442},"labels":[],"label_agreement":null},{"id":"W2031601132","doi":"10.1109/tnn.2003.816345","title":"Kerneltron: support vector \"machine\" in silicon","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Support vector machine; Computer hardware; Massively parallel; Very-large-scale integration; Parallel computing; Artificial intelligence; Embedded system","score_opus":0.008405461851561097,"score_gpt":0.20692007297401543,"score_spread":0.19851461112245433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031601132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010039269,0.00049776083,0.95944697,0.00028051864,0.00014136227,0.00007755327,0.0004177097,0.020883221,0.008215683],"genre_scores_gemma":[0.25291637,0.00068257574,0.72017175,0.0003500827,0.00010860141,0.00035622975,0.0022375374,0.0010519382,0.02212489],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962413,0.00006667394,0.000024950328,0.00005567853,0.00019434847,0.000034282275],"domain_scores_gemma":[0.99961734,0.00012755011,0.00002983359,0.00006213456,0.00014583317,0.00001733926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041647762,0.00048382184,0.00033245655,0.00044365384,0.00017794999,0.0008503182,0.0011746683,0.0006553399,0.009731857],"category_scores_gemma":[0.0014540144,0.00028964787,0.00031600488,0.00054503453,0.0003104992,0.0012535738,0.0005729437,0.00066693587,0.004516288],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006822403,0.00014150745,0.0017420809,0.0005315796,0.000098237586,0.00037197777,0.00018826094,0.08090707,0.052988537,0.0787701,0.06367406,0.7199043],"study_design_scores_gemma":[0.00011608053,0.00027274102,0.000734486,0.00004211695,0.00002601323,0.00036145342,0.000041239906,0.8331804,0.072126746,0.020343442,0.0727149,0.000040297764],"about_ca_topic_score_codex":0.0009961374,"about_ca_topic_score_gemma":0.0011158958,"teacher_disagreement_score":0.009731857,"about_ca_system_score_codex":0.00039877626,"about_ca_system_score_gemma":0.0005879477,"threshold_uncertainty_score":0.032556295},"labels":[],"label_agreement":null},{"id":"W2033640106","doi":"10.1109/tnn.2011.2173804","title":"Application of IFT and SPSA to Servo System Control","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Control Systems and Identification","field":"Engineering","cited_by":62,"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":"Simultaneous perturbation stochastic approximation; Linear-quadratic-Gaussian control; Control theory (sociology); Servomechanism; Computer science; Integrator; Linear-quadratic regulator; Mathematical optimization; Control engineering; Optimal control; Stochastic process; Mathematics; Engineering; Control (management); Artificial intelligence; Bandwidth (computing)","score_opus":0.008363191633463907,"score_gpt":0.17722515755277923,"score_spread":0.16886196591931532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033640106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002889342,0.0001367002,0.99510443,0.00005567106,0.0000262897,0.000014307454,0.000007692147,0.00023869646,0.001526852],"genre_scores_gemma":[0.6384086,0.000412555,0.35858688,0.00012967191,0.00013809298,0.00015753695,0.000077643526,0.00016092349,0.0019280777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988381,0.00028121937,0.00006720105,0.00013888415,0.00061600737,0.000058733658],"domain_scores_gemma":[0.9988651,0.0005549506,0.00011719462,0.0001525221,0.00028024602,0.000029976321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013046886,0.0006015403,0.00092301564,0.0004917753,0.0003584503,0.0007755129,0.0007082144,0.00077823095,0.0011091258],"category_scores_gemma":[0.0041828956,0.00030766884,0.0007050638,0.00047039465,0.0007690335,0.0005962797,0.0009708127,0.00096663187,0.00031448263],"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":[0.00005716846,0.000029536446,0.00042726792,0.00014544575,0.00005336132,0.00007418326,0.00008190723,0.8150471,0.005394653,0.02620008,0.000503148,0.1519862],"study_design_scores_gemma":[0.0000041665658,0.000030930987,0.00009793327,0.0000067439337,0.000003892837,0.000040287174,0.000003561135,0.99159735,0.0011201903,0.0062460396,0.0008440746,0.0000048836005],"about_ca_topic_score_codex":0.001805841,"about_ca_topic_score_gemma":0.00057202484,"teacher_disagreement_score":0.001805841,"about_ca_system_score_codex":0.00041757405,"about_ca_system_score_gemma":0.0008263139,"threshold_uncertainty_score":0.0068998933},"labels":[],"label_agreement":null},{"id":"W2036679573","doi":"10.1109/72.935086","title":"Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":174,"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 Windsor","funders":"","keywords":"Overfitting; Early stopping; Regularization (linguistics); Bayesian probability; Econometrics; Computer science; Artificial neural network; Generalization; Standard deviation; Black–Scholes model; Derivative (finance); Baseline (sea); Artificial intelligence; Mathematics; Statistics; Volatility (finance); Economics; Financial economics","score_opus":0.03771736784902873,"score_gpt":0.3041129530411983,"score_spread":0.26639558519216955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036679573","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80765146,0.0008365859,0.18910277,0.0005867168,0.00004624088,0.000051643856,0.00007059241,0.00031712194,0.0013368978],"genre_scores_gemma":[0.9629232,0.00017246132,0.03606737,0.000101627666,0.00002970341,0.00004025139,0.00013445079,0.000026866086,0.00050402025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979746,0.001124381,0.00011768997,0.0001738108,0.0004329149,0.0001766938],"domain_scores_gemma":[0.9852496,0.010814627,0.0012995956,0.0011168614,0.0012228533,0.00029643794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012119216,0.0010598813,0.0012798737,0.0010712807,0.00042976957,0.0010125817,0.001328972,0.0013761714,0.0004080651],"category_scores_gemma":[0.028339071,0.00052058126,0.00076914526,0.0010849786,0.00095947215,0.0026372217,0.0010573101,0.0018138947,0.00012898904],"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":[0.0004111606,0.00031477254,0.010525878,0.00004715907,0.00016786363,0.000048617276,0.00008421103,0.9191339,0.0008745967,0.0038712572,0.00046602,0.06405464],"study_design_scores_gemma":[0.000013335945,0.00007049382,0.0009431649,0.0000057811785,0.000011222668,0.0000057965644,0.000005278562,0.9967501,0.0004906458,0.0016466142,0.000051164043,0.000006298253],"about_ca_topic_score_codex":0.006879271,"about_ca_topic_score_gemma":0.007897867,"teacher_disagreement_score":0.012119216,"about_ca_system_score_codex":0.0012976169,"about_ca_system_score_gemma":0.00092766195,"threshold_uncertainty_score":0.06409329},"labels":[],"label_agreement":null},{"id":"W2054999339","doi":"10.1109/tnn.2005.853337","title":"Connectionist-Based Dempster–Shafer Evidential Reasoning for Data Fusion","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":61,"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":"Dempster–Shafer theory; Computer science; Artificial intelligence; Evidential reasoning approach; Artificial neural network; Connectionism; Machine learning; A priori and a posteriori; Context (archaeology); Benchmark (surveying); Multilayer perceptron; Bayesian probability; Perceptron; Sensor fusion; Decision support system","score_opus":0.057648117375027744,"score_gpt":0.3007436376198504,"score_spread":0.24309552024482267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054999339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055519943,0.00047690258,0.9925349,0.000182029,0.000018010493,0.000026083415,0.000025951342,0.000075830416,0.0011084379],"genre_scores_gemma":[0.6650875,0.0012445232,0.33181298,0.0001441869,0.00008946512,0.00018777093,0.00015578045,0.000026691716,0.0012511612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985661,0.0005274279,0.00013980926,0.00019452066,0.0005174441,0.000054755525],"domain_scores_gemma":[0.9978436,0.001338614,0.00023082702,0.00019930421,0.000341301,0.00004631207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034114884,0.0007370594,0.0009564274,0.001661307,0.00066523615,0.0016284582,0.0015344314,0.0013944163,0.0011075542],"category_scores_gemma":[0.007948222,0.0004798641,0.00086649565,0.001702639,0.0012291581,0.0023053444,0.0016726061,0.0018678717,0.00019475278],"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":[0.00009159971,0.000049742517,0.0006407739,0.00019845687,0.00014255391,0.0001603532,0.00013385151,0.758975,0.0023640487,0.13108855,0.0008467416,0.10530823],"study_design_scores_gemma":[0.000004008807,0.000010892141,0.00009663095,0.000011563991,0.000010331491,0.000018699191,0.0000057493207,0.95247775,0.00062202103,0.046313416,0.0004196953,0.0000090678905],"about_ca_topic_score_codex":0.0028073473,"about_ca_topic_score_gemma":0.002765888,"teacher_disagreement_score":0.0034114884,"about_ca_system_score_codex":0.0017869304,"about_ca_system_score_gemma":0.0013250641,"threshold_uncertainty_score":0.018041909},"labels":[],"label_agreement":null},{"id":"W2057491769","doi":"10.1109/tnn.2005.863458","title":"A Stable Neural Network-Based Observer With Application to Flexible-Joint Manipulators","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":271,"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); Robustness (evolution); Computer science; Nonlinear system; Backpropagation; Observer (physics); Lyapunov function; A priori and a posteriori; Artificial intelligence; Control (management)","score_opus":0.015701725084008467,"score_gpt":0.20578911031865374,"score_spread":0.19008738523464527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057491769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006285692,0.00025873387,0.9915291,0.000079057005,0.000079162026,0.000028099856,0.000014641547,0.0004128208,0.0013127327],"genre_scores_gemma":[0.73804915,0.00084768975,0.25393865,0.00010135968,0.000099403755,0.00023045178,0.000099661,0.000051564428,0.006582122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975175,0.000040701187,0.00001894453,0.00005519373,0.00011244443,0.000021015712],"domain_scores_gemma":[0.9997267,0.00006184457,0.000053995944,0.000028811404,0.000115372626,0.000013255716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005147816,0.00041027807,0.00055472914,0.00024787083,0.00029133118,0.00041841457,0.0007555691,0.0008521703,0.00096896506],"category_scores_gemma":[0.0009817124,0.00026024226,0.00041493037,0.0002819534,0.0003933983,0.000620013,0.00048915093,0.00072273996,0.00034489576],"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":[0.00023350531,0.0000812622,0.001282594,0.00042845766,0.00008793983,0.0006250978,0.00024553784,0.6055043,0.09089616,0.018627517,0.0020698032,0.2799179],"study_design_scores_gemma":[0.000015083013,0.00007816666,0.00023855327,0.000008740873,0.000012522647,0.000053806893,0.000005837027,0.99198115,0.005053456,0.00077043945,0.0017694406,0.000012941208],"about_ca_topic_score_codex":0.0034759936,"about_ca_topic_score_gemma":0.0031578525,"teacher_disagreement_score":0.0034759936,"about_ca_system_score_codex":0.00037078687,"about_ca_system_score_gemma":0.00054746907,"threshold_uncertainty_score":0.006911516},"labels":[],"label_agreement":null},{"id":"W2070592416","doi":"10.1109/tnn.2011.2167685","title":"Data-Based Virtual Unmodeled Dynamics Driven Multivariable Nonlinear Adaptive Switching Control","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Control Systems and Identification","field":"Engineering","cited_by":58,"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":"State Key Laboratory of Synthetical Automation for Process Industries; Chinese Academy of Engineering; National Natural Science Foundation of China","keywords":"Control theory (sociology); Controller (irrigation); Multivariable calculus; Nonlinear system; Computer science; Convergence (economics); Control engineering; Stability (learning theory); Adaptive control; Engineering; Control (management); Artificial intelligence","score_opus":0.028713331989650433,"score_gpt":0.21492745752102882,"score_spread":0.1862141255313784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070592416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030882068,0.000113231414,0.96603376,0.000066338,0.00004984461,0.000035327663,0.000025496282,0.0004914127,0.002302624],"genre_scores_gemma":[0.95693195,0.00009252187,0.04162105,0.00005344225,0.00001950227,0.00008282526,0.00006357935,0.000024440153,0.0011106664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977845,0.000045349716,0.000016573333,0.00003841366,0.000100105004,0.000021176966],"domain_scores_gemma":[0.9996207,0.00015368855,0.000056625093,0.00005088966,0.000104116705,0.0000139320555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050760945,0.00047425987,0.00040161313,0.00034190502,0.0002636196,0.0005981486,0.0008044553,0.00034293806,0.0011411351],"category_scores_gemma":[0.0008726922,0.00018686094,0.0002993431,0.00027879677,0.00039019893,0.0005041382,0.0005805516,0.00049339846,0.00013239903],"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":[0.00036102073,0.00014680126,0.00094521284,0.00033250765,0.00009530475,0.00017509193,0.00031213142,0.7318045,0.052657463,0.02407899,0.0013663869,0.1877246],"study_design_scores_gemma":[0.000011676482,0.00005456016,0.00013818347,0.0000033620254,0.00000675841,0.000014794027,0.000005615886,0.9947338,0.003069747,0.0014533567,0.0005021584,0.000005991104],"about_ca_topic_score_codex":0.0017759537,"about_ca_topic_score_gemma":0.001910184,"teacher_disagreement_score":0.0017759537,"about_ca_system_score_codex":0.00030582206,"about_ca_system_score_gemma":0.00032003332,"threshold_uncertainty_score":0.003817439},"labels":[],"label_agreement":null},{"id":"W2072499402","doi":"10.1109/tnn.2005.860855","title":"A Sequential Dynamic Heteroassociative Memory for Multistep Pattern Recognition and One-to-Many Association","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"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":"Computer science; Backpropagation; Artificial intelligence; Bidirectional associative memory; Pattern recognition (psychology); Associative property; Content-addressable memory; Sequence learning; Artificial neural network; Feed forward; Association (psychology); Context (archaeology); Set (abstract data type); Task (project management); Content-addressable storage; Feedforward neural network; Machine learning; Mathematics","score_opus":0.0214230759761563,"score_gpt":0.24619839756790232,"score_spread":0.224775321591746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072499402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041168425,0.002000839,0.9395738,0.0005761803,0.0007726736,0.00016261742,0.00028628216,0.0026459415,0.012813187],"genre_scores_gemma":[0.6643967,0.001658217,0.3079212,0.00062374864,0.0002620788,0.00036185456,0.00063263054,0.00011714581,0.024026422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997694,0.000028652774,0.00001614169,0.0000826402,0.00007297349,0.00003024977],"domain_scores_gemma":[0.9996997,0.000060094622,0.00003105507,0.000090885405,0.00008195362,0.00003620437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003777699,0.00050386117,0.0006035359,0.00038780426,0.0004426438,0.0007419537,0.0017894828,0.0008305672,0.00696843],"category_scores_gemma":[0.0007849707,0.00025307937,0.00056230085,0.000597995,0.00055786496,0.0017553363,0.00073572877,0.0009812944,0.0016632623],"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":[0.00075176894,0.0006261265,0.0018563053,0.00070490676,0.00025548248,0.00070591515,0.00024053059,0.06694922,0.15824422,0.1450663,0.013145147,0.6114541],"study_design_scores_gemma":[0.00011237192,0.0006148337,0.0012759725,0.000062477506,0.00013274963,0.0014478333,0.00005528331,0.82850766,0.05326432,0.07305048,0.041403566,0.00007240775],"about_ca_topic_score_codex":0.0010608657,"about_ca_topic_score_gemma":0.0015914023,"teacher_disagreement_score":0.00696843,"about_ca_system_score_codex":0.00043152433,"about_ca_system_score_gemma":0.00073442765,"threshold_uncertainty_score":0.023311734},"labels":[],"label_agreement":null},{"id":"W2086608109","doi":"10.1109/tnn.2011.2168422","title":"Hierarchical Approximate Policy Iteration With Binary-Tree State Space Decomposition","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Markov decision process; Reinforcement learning; Kernel (algebra); State space; Mathematical optimization; Tree (set theory); Algorithm; Markov process; Artificial intelligence; Mathematics","score_opus":0.018154263890840724,"score_gpt":0.24563729892052735,"score_spread":0.22748303502968664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086608109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006782828,0.00011697951,0.99202675,0.00004825805,0.000018210305,0.000029924184,0.00001928075,0.0002449503,0.00071274134],"genre_scores_gemma":[0.5534014,0.0002322468,0.44307312,0.00018011832,0.000039243074,0.00043565469,0.00024099094,0.00013403971,0.0022631977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989479,0.00031576012,0.0000670562,0.00018434504,0.00036053202,0.00012438011],"domain_scores_gemma":[0.99855095,0.0007785615,0.00012844765,0.00013558223,0.0003238175,0.0000827261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013841775,0.0008674485,0.0016087267,0.00061567366,0.00045196264,0.0009069126,0.0011728096,0.0011351494,0.0019524642],"category_scores_gemma":[0.0041424474,0.0006122989,0.0008120968,0.0006928028,0.0008196833,0.0012734734,0.0013004056,0.0016706637,0.00043762548],"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":[0.00009134349,0.00004820773,0.00044119696,0.000071886876,0.000028366252,0.000043051798,0.00007172691,0.9356131,0.0014789971,0.013958419,0.00068520824,0.0474685],"study_design_scores_gemma":[0.000004664442,0.000008265047,0.000013968399,0.0000014747977,0.0000012937268,0.0000029013265,0.0000015533458,0.9985885,0.00012360117,0.0011633369,0.000088955814,0.0000014657776],"about_ca_topic_score_codex":0.007478093,"about_ca_topic_score_gemma":0.0043564257,"teacher_disagreement_score":0.007478093,"about_ca_system_score_codex":0.00093150407,"about_ca_system_score_gemma":0.0020410314,"threshold_uncertainty_score":0.014869094},"labels":[],"label_agreement":null},{"id":"W2096863150","doi":"10.1109/tnn.2005.845145","title":"Blind Equalization Using a Predictive Radial Basis Function Neural Network","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Blind equalization; Radial basis function; Computer science; Artificial neural network; Deconvolution; Mean squared error; Algorithm; Clutter; Noise (video); Blind deconvolution; Equalization (audio); Radial basis function network; Perceptron; Artificial intelligence; Radar; Mathematics; Statistics; Telecommunications","score_opus":0.03052155042257175,"score_gpt":0.2712075245042388,"score_spread":0.24068597408166703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096863150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005029266,0.00026290675,0.9935969,0.000060234117,0.000036073034,0.000012120784,0.000006919664,0.0003157972,0.00067981327],"genre_scores_gemma":[0.49555936,0.00081292295,0.49845257,0.00022055893,0.00015301889,0.00010936435,0.00007940408,0.000065243614,0.004547476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995875,0.00010909393,0.000019765637,0.000080824386,0.00016822902,0.00003460555],"domain_scores_gemma":[0.99955565,0.00019024064,0.00005006944,0.00005117445,0.00013994901,0.000012972139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000814879,0.00051150186,0.00067449926,0.00044956844,0.00028396348,0.0005257474,0.0009856364,0.0010368516,0.0009447582],"category_scores_gemma":[0.0018817085,0.000313959,0.0004804859,0.0003976693,0.00051087234,0.0012479752,0.0005092912,0.00084561604,0.00052719394],"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":[0.0004347631,0.00014020149,0.00081000564,0.00021077992,0.00011976366,0.00017538689,0.000083826955,0.49460414,0.047107078,0.016107416,0.0014088273,0.43879777],"study_design_scores_gemma":[0.000010970551,0.000040097275,0.00009475383,0.000005930208,0.00001268161,0.000044833003,0.000002476824,0.99304974,0.004713738,0.0012197897,0.0007932189,0.000011862163],"about_ca_topic_score_codex":0.0015469297,"about_ca_topic_score_gemma":0.0012807698,"teacher_disagreement_score":0.0015469297,"about_ca_system_score_codex":0.00030623257,"about_ca_system_score_gemma":0.0005506308,"threshold_uncertainty_score":0.0043095946},"labels":[],"label_agreement":null},{"id":"W2097261572","doi":"10.1109/tnn.2009.2033367","title":"Speech Enhancement Based on Nonlinear Models Using Particle Filters","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Speech and Audio Processing","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 Ottawa","funders":"","keywords":"Speech enhancement; Particle filter; Computer science; Context (archaeology); Nonlinear system; Noise (video); Algorithm; Speech recognition; Artificial neural network; Filter (signal processing); Artificial intelligence; Kalman filter; Noise reduction","score_opus":0.0319381845658925,"score_gpt":0.2625605635525427,"score_spread":0.2306223789866502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097261572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0098175695,0.0001272351,0.9891944,0.00004867009,0.000019717996,0.000012938062,0.000008689717,0.0003067008,0.00046402588],"genre_scores_gemma":[0.36486992,0.0005179798,0.629989,0.000082885934,0.00006616043,0.00006490819,0.0000966339,0.00011630047,0.0041961726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996879,0.00010444375,0.000015688787,0.00007466237,0.00009065952,0.000026623873],"domain_scores_gemma":[0.9989925,0.00070102693,0.00008878191,0.000091603644,0.00010723757,0.000018817109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013017842,0.0009494536,0.00072144286,0.00041973733,0.00022080165,0.00066291756,0.0006558082,0.0007450603,0.0009178035],"category_scores_gemma":[0.0032781463,0.00041855383,0.0006699571,0.0003422125,0.00047484293,0.0011947118,0.0008636477,0.0008468198,0.00037519942],"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":[0.0002258818,0.0000680343,0.00053994387,0.00015424028,0.00007428106,0.000098004166,0.00013561305,0.7370934,0.01932881,0.006674342,0.0006950016,0.23491238],"study_design_scores_gemma":[0.0000073303727,0.000023261131,0.00011795136,0.0000042028482,0.0000093463095,0.000021672187,0.000005147218,0.9949007,0.0034790789,0.0010934263,0.0003320107,0.0000059295835],"about_ca_topic_score_codex":0.0021105171,"about_ca_topic_score_gemma":0.0019376194,"teacher_disagreement_score":0.0021105171,"about_ca_system_score_codex":0.0003479314,"about_ca_system_score_gemma":0.0004771645,"threshold_uncertainty_score":0.006884575},"labels":[],"label_agreement":null},{"id":"W2098232303","doi":"10.1109/tnn.2006.885040","title":"A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":72,"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 Saskatchewan","funders":"University of Saskatchewan","keywords":"Subnetwork; Computer science; Artificial neural network; Recurrent neural network; Bounded function; Stability (learning theory); Decomposition; Stochastic neural network; Artificial intelligence; Mathematics; Machine learning","score_opus":0.013220099711196883,"score_gpt":0.2574756441492499,"score_spread":0.244255544438053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098232303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010080292,0.00052104925,0.9861435,0.00009864406,0.000049928243,0.00002409923,0.000037642665,0.00052658876,0.0025183025],"genre_scores_gemma":[0.782282,0.0007362688,0.21126135,0.00010799906,0.00007956231,0.00017084357,0.00019874718,0.000071855124,0.005091369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997116,0.00006900736,0.000021244787,0.00008119941,0.00008894806,0.00002802732],"domain_scores_gemma":[0.9998554,0.000046127905,0.000027108537,0.00001640308,0.000046692574,0.00000826643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055393425,0.0004950141,0.0005480478,0.00023022403,0.00023475254,0.00051255996,0.00077687873,0.0007214452,0.0015197123],"category_scores_gemma":[0.00097347517,0.00021640907,0.00047047716,0.0003327118,0.0003491975,0.00055460253,0.00051338214,0.0007550576,0.0003070251],"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":[0.00007181115,0.000034827717,0.00033691022,0.0001245568,0.000059346068,0.0001501011,0.000066175526,0.8666252,0.016176993,0.027957603,0.0013111018,0.08708531],"study_design_scores_gemma":[0.0000035552937,0.000015404541,0.000041852643,0.000003036347,0.000006669292,0.000010173617,0.0000015015562,0.9978904,0.00052267715,0.0010747757,0.0004266611,0.0000032172222],"about_ca_topic_score_codex":0.0045140805,"about_ca_topic_score_gemma":0.004427134,"teacher_disagreement_score":0.0045140805,"about_ca_system_score_codex":0.0004890299,"about_ca_system_score_gemma":0.00054837833,"threshold_uncertainty_score":0.008975565},"labels":[],"label_agreement":null},{"id":"W2098412053","doi":"10.1109/72.896800","title":"Experiments on the application of IOHMMs to model financial returns series","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Université de Montréal","keywords":"Hidden Markov model; Hidden semi-Markov model; Gaussian; Moment (physics); Variable-order Markov model; Series (stratigraphy); Conditional probability distribution; Generalization; Conditional expectation; Markov chain; Mathematics; Markov model; Forward algorithm; Econometrics; Applied mathematics; Mixture model; Computer science; Artificial intelligence; Statistics","score_opus":0.02458885608029223,"score_gpt":0.2592432467594541,"score_spread":0.23465439067916186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098412053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9509608,0.0018391418,0.03741282,0.000874769,0.00022028034,0.0002991242,0.0018392067,0.0029857568,0.003568021],"genre_scores_gemma":[0.96544325,0.00036938937,0.030695627,0.00016707725,0.000048134676,0.00019531819,0.0018609216,0.00008460171,0.0011357905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979588,0.0009833851,0.00026023886,0.00044209644,0.00019621466,0.00015931053],"domain_scores_gemma":[0.979618,0.016884474,0.0005698695,0.0013492958,0.0012251361,0.00035317754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075547565,0.0017932284,0.0012195389,0.0008278251,0.0006851661,0.0008358111,0.0013439059,0.0024577559,0.0019282942],"category_scores_gemma":[0.020043792,0.00062381354,0.0011166603,0.001094801,0.0006736119,0.0018877687,0.0009765299,0.002465269,0.00065056875],"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":[0.0017720663,0.0016570733,0.009179841,0.00050993665,0.00048673118,0.00022332651,0.0003974037,0.89162487,0.0039047953,0.0016310622,0.0032607573,0.08535218],"study_design_scores_gemma":[0.00009037426,0.00035080995,0.0019530171,0.000018981722,0.000048181446,0.00003725614,0.00005928274,0.9927273,0.0032811055,0.0010423637,0.00036596815,0.00002538887],"about_ca_topic_score_codex":0.029500563,"about_ca_topic_score_gemma":0.020901266,"teacher_disagreement_score":0.029500563,"about_ca_system_score_codex":0.0014088375,"about_ca_system_score_gemma":0.00088465016,"threshold_uncertainty_score":0.058657706},"labels":[],"label_agreement":null},{"id":"W2099691301","doi":"10.1109/72.925559","title":"Thresholding neural network for adaptive noise reduction","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":159,"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":"Thresholding; Artificial neural network; Artificial intelligence; Computer science; Adaptive filter; Noise (video); Noise reduction; Reduction (mathematics); Pattern recognition (psychology); Adaptive learning; Unsupervised learning; Algorithm; Mathematics; Image (mathematics)","score_opus":0.04049094824790187,"score_gpt":0.2826696035139611,"score_spread":0.24217865526605922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099691301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005726722,0.0016489908,0.9881009,0.00017190269,0.00015117644,0.000021098274,0.000019580679,0.00021322812,0.0039464063],"genre_scores_gemma":[0.48931515,0.0035758207,0.49263763,0.0005247281,0.00032151633,0.0002492129,0.00020648893,0.00011015489,0.013059248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967873,0.000064495915,0.000019160028,0.000072937575,0.00014431398,0.000020292953],"domain_scores_gemma":[0.9997708,0.00010117968,0.00002234354,0.000018475668,0.00007908896,0.000008110094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005358112,0.00039133715,0.00046044347,0.00031739075,0.00021435975,0.00047297365,0.00059946586,0.0007675414,0.0014680723],"category_scores_gemma":[0.0015529534,0.0001582819,0.00031051072,0.0005710503,0.00040423073,0.0007031099,0.00040697888,0.00073897094,0.00043990582],"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":[0.0001677361,0.00006943969,0.0009835428,0.00052487385,0.00011939785,0.00027522768,0.00010850346,0.37622747,0.042392556,0.09011695,0.0052967,0.4837176],"study_design_scores_gemma":[0.000008145486,0.000035044242,0.00017990259,0.00001969035,0.000022470107,0.000108408225,0.0000068910726,0.9787249,0.004371171,0.011680904,0.004833568,0.000008948635],"about_ca_topic_score_codex":0.0009982502,"about_ca_topic_score_gemma":0.0011523251,"teacher_disagreement_score":0.0014680723,"about_ca_system_score_codex":0.0003808538,"about_ca_system_score_gemma":0.00038006366,"threshold_uncertainty_score":0.0049111843},"labels":[],"label_agreement":null},{"id":"W2099759795","doi":"10.1109/tnn.2005.851786","title":"Constructive Feedforward Neural Networks Using Hermite Polynomial Activation Functions","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":142,"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":"Activation function; Constructive; Sigmoid function; Hermite polynomials; Artificial neural network; Computer science; Feedforward neural network; Orthonormal basis; Polynomial; Adaptation (eye); Function (biology); Constructive proof; Topology (electrical circuits); Mathematics; Artificial intelligence; Discrete mathematics; Pure mathematics; Combinatorics; Mathematical analysis","score_opus":0.01768520251766669,"score_gpt":0.2410274838492189,"score_spread":0.2233422813315522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099759795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004922759,0.00012585547,0.99341667,0.000044852262,0.000023087086,0.000013818639,0.000009732321,0.00025173582,0.0011915133],"genre_scores_gemma":[0.5174772,0.00056591816,0.4753587,0.00017033228,0.000068710746,0.00014275838,0.000093359144,0.00010593777,0.0060171285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994715,0.0002095742,0.000019990646,0.00007048185,0.0001886838,0.00003975152],"domain_scores_gemma":[0.9989441,0.0006172421,0.000091005546,0.00009020782,0.00022851425,0.00002893404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110199,0.0009758775,0.0004992142,0.00042290078,0.00026755527,0.0007228243,0.0015093668,0.0011148502,0.00140234],"category_scores_gemma":[0.003307554,0.00044563497,0.0005185933,0.00043500832,0.00078554876,0.0014719891,0.0010277188,0.0011022582,0.0007661518],"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":[0.00010899345,0.000051325536,0.00035156801,0.00013753067,0.000054234974,0.00019368391,0.00011393368,0.7953232,0.020004543,0.040600557,0.00093903986,0.14212143],"study_design_scores_gemma":[0.0000038590447,0.000027080876,0.000043216016,0.000007512977,0.0000074088534,0.000035952162,0.0000043255127,0.990364,0.0042492715,0.004561508,0.0006888492,0.000006973434],"about_ca_topic_score_codex":0.0009381393,"about_ca_topic_score_gemma":0.0016352892,"teacher_disagreement_score":0.0015093668,"about_ca_system_score_codex":0.0005393652,"about_ca_system_score_gemma":0.0005060744,"threshold_uncertainty_score":0.0058279037},"labels":[],"label_agreement":null},{"id":"W2100717332","doi":"10.1109/tnn.2010.2073481","title":"High-Performance Reconfigurable Hardware Architecture for Restricted Boltzmann Machines","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Speedup; Modular design; Exploit; Parallel computing; Virtex; Embedded system; Artificial neural network; Reconfigurable computing; Computer architecture; Hardware acceleration; Hardware architecture; Computer hardware; Software; Operating system; Artificial intelligence","score_opus":0.01109397436070237,"score_gpt":0.21800306020283178,"score_spread":0.20690908584212941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100717332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16488884,0.00076055777,0.80288374,0.00046464964,0.0002310097,0.000142732,0.00018987179,0.012081239,0.018357385],"genre_scores_gemma":[0.8008753,0.00016227183,0.19438308,0.000109541834,0.000029970786,0.00013693736,0.00022602663,0.00013110817,0.003945758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998235,0.000040191044,0.000013266258,0.00004251308,0.000044612378,0.00003602554],"domain_scores_gemma":[0.99980813,0.000053878888,0.000022059385,0.00006424164,0.000033788296,0.000017943355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022951284,0.00048313793,0.00035147663,0.00037340063,0.00025573344,0.0005434632,0.0017589194,0.0003561526,0.0075655784],"category_scores_gemma":[0.00057334057,0.00026295913,0.00037023763,0.00035113387,0.00027839013,0.00079678046,0.0005347048,0.0005507077,0.001560806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010782488,0.0002702147,0.0025383271,0.0003752796,0.00022977374,0.00063970155,0.00020701342,0.44956535,0.12102965,0.048233803,0.013093606,0.36273897],"study_design_scores_gemma":[0.00013031854,0.00036459442,0.0009813282,0.000027186565,0.00005632924,0.00024148809,0.000034348,0.9384489,0.03779283,0.008832417,0.013053398,0.00003684795],"about_ca_topic_score_codex":0.0012518454,"about_ca_topic_score_gemma":0.00148559,"teacher_disagreement_score":0.0075655784,"about_ca_system_score_codex":0.00056724244,"about_ca_system_score_gemma":0.00037649518,"threshold_uncertainty_score":0.025309384},"labels":[],"label_agreement":null},{"id":"W2100914167","doi":"10.1109/tnn.2003.820829","title":"Stability analysis of bidirectional associative memory networks with time delays","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bidirectional associative memory; Content-addressable memory; Computer science; Artificial neural network; Stability (learning theory); Associative property; Content-addressable storage; Variety (cybernetics); Exponential stability; State space; Recurrent neural network; Field (mathematics); Control theory (sociology); Topology (electrical circuits); Artificial intelligence; Mathematics; Nonlinear system; Machine learning; Control (management)","score_opus":0.0125718734674755,"score_gpt":0.21400634369493843,"score_spread":0.20143447022746291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100914167","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19661155,0.0009528954,0.79085743,0.00041706045,0.0000819575,0.0000339642,0.00009473413,0.00019642871,0.010753982],"genre_scores_gemma":[0.98651147,0.0004253683,0.008514919,0.000032480664,0.0000310445,0.00007989629,0.000045851226,0.00002209044,0.004336957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985135,0.000043283173,0.000007951126,0.000028254322,0.00004338376,0.00002566912],"domain_scores_gemma":[0.9996406,0.000126982,0.00009346089,0.000020892443,0.000093564784,0.000024537316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004758439,0.0005079642,0.00039068543,0.00046429114,0.0003725798,0.0005769303,0.00061101525,0.0005699079,0.001614577],"category_scores_gemma":[0.0014273634,0.00017274426,0.00037258086,0.00027136385,0.0006464204,0.0006649015,0.0010196038,0.00047853123,0.00024001616],"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":[0.00015765282,0.000035855614,0.0010874289,0.00013562685,0.00008176003,0.00025351168,0.00020377403,0.70580447,0.025866084,0.24601842,0.000856625,0.019498773],"study_design_scores_gemma":[0.0000051465076,0.000016753396,0.000087887325,0.0000043341824,0.000005618571,0.000017848546,0.000012858965,0.9772307,0.00056842377,0.021832593,0.00021269331,0.0000051765246],"about_ca_topic_score_codex":0.00248666,"about_ca_topic_score_gemma":0.0012795561,"teacher_disagreement_score":0.00248666,"about_ca_system_score_codex":0.0005429325,"about_ca_system_score_gemma":0.0005472709,"threshold_uncertainty_score":0.0054012537},"labels":[],"label_agreement":null},{"id":"W2102145388","doi":"10.1109/tnn.2006.875994","title":"A Neural Network Approach to Dynamic Task Assignment of Multirobots","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":108,"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":"Computer science; Artificial neural network; Task (project management); Artificial intelligence; Time delay neural network","score_opus":0.013378527268674747,"score_gpt":0.23105934192514752,"score_spread":0.21768081465647277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102145388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053652236,0.0006676492,0.99061245,0.00019504238,0.00006758018,0.000016481808,0.00002151013,0.000115292285,0.0029389048],"genre_scores_gemma":[0.63979584,0.0017817203,0.3455501,0.00022775392,0.0002786856,0.00027250024,0.00012376462,0.00007726785,0.0118923765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998041,0.000050245304,0.000009729486,0.000048717535,0.000060592607,0.000026699438],"domain_scores_gemma":[0.9998343,0.000073602736,0.000020541682,0.000011108664,0.000048256687,0.000012184806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003020669,0.00054863293,0.00045689763,0.0004425704,0.00034024246,0.0005462981,0.0009894948,0.00095438934,0.0017754752],"category_scores_gemma":[0.0008084167,0.0002981983,0.00038248367,0.00055925763,0.00049082557,0.0010357936,0.0005646642,0.0011098714,0.0002606336],"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":[0.000026530925,0.000026491885,0.00017535863,0.00006415554,0.000028380746,0.00005875487,0.000036155216,0.9351204,0.0021352808,0.018392151,0.0005549661,0.04338131],"study_design_scores_gemma":[0.000002499378,0.0000103056345,0.00004000485,0.000003419341,0.000003308477,0.000009616191,0.000004466553,0.9933189,0.00030001663,0.005698283,0.0006055251,0.000003680658],"about_ca_topic_score_codex":0.0048183855,"about_ca_topic_score_gemma":0.004377691,"teacher_disagreement_score":0.0048183855,"about_ca_system_score_codex":0.0007165171,"about_ca_system_score_gemma":0.00059991766,"threshold_uncertainty_score":0.009580672},"labels":[],"label_agreement":null},{"id":"W2102555533","doi":"10.1109/72.991418","title":"Neural data fusion algorithms based on a linearly constrained least square method","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Image Fusion Techniques","field":"Engineering","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 Calgary","funders":"","keywords":"Algorithm; Artificial neural network; Covariance intersection; Covariance matrix; Sensor fusion; Computer science; Covariance; Invertible matrix; Estimation of covariance matrices; Artificial intelligence; Mathematics; Statistics","score_opus":0.03510574400188336,"score_gpt":0.2758330014542319,"score_spread":0.24072725745234852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102555533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018101016,0.00013115577,0.99723464,0.00007166498,0.000025199755,0.00002169597,0.0000117883255,0.00018072002,0.00051305286],"genre_scores_gemma":[0.11997608,0.00039245645,0.8760974,0.00015450921,0.000069988586,0.00021368949,0.00009350809,0.000068750705,0.002933632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883634,0.00022262755,0.00008871201,0.00020645034,0.000576079,0.00006978475],"domain_scores_gemma":[0.9988226,0.00036852804,0.0001524426,0.00010020463,0.0005210939,0.000035201476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013314075,0.0006731036,0.0008784592,0.00080182723,0.0005307088,0.0009354415,0.0012595814,0.0012015221,0.0018759721],"category_scores_gemma":[0.0036131623,0.0003923923,0.0005725441,0.0010601978,0.0006033703,0.0015658299,0.0013562551,0.0012587515,0.0006629087],"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":[0.00016405205,0.000076586955,0.00047818854,0.00017941838,0.000106041545,0.0000908611,0.0001691036,0.33083874,0.023052897,0.024943747,0.0027094015,0.61719096],"study_design_scores_gemma":[0.000013633021,0.000027496551,0.00012655907,0.00000877065,0.0000102797385,0.00004013523,0.000009871146,0.98739314,0.007244626,0.0031012506,0.0020041128,0.000020109273],"about_ca_topic_score_codex":0.0036737497,"about_ca_topic_score_gemma":0.0029894644,"teacher_disagreement_score":0.0036737497,"about_ca_system_score_codex":0.00072884146,"about_ca_system_score_gemma":0.0010894327,"threshold_uncertainty_score":0.007304728},"labels":[],"label_agreement":null},{"id":"W2106485397","doi":"10.1109/tnn.2005.852861","title":"NDRAM: Nonlinear Dynamic Recurrent Associative Memory for Learning Bipolar and Nonbipolar Correlated Patterns","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":54,"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":"Hebbian theory; Bidirectional associative memory; Content-addressable memory; Attractor; Computer science; Artificial neural network; Content-addressable storage; Hopfield network; Learning rule; Nonlinear system; Spurious relationship; Leabra; Artificial intelligence; Recurrent neural network; Competitive learning; Machine learning; Mathematics; Wake-sleep algorithm","score_opus":0.010190180093398218,"score_gpt":0.24679394345844005,"score_spread":0.23660376336504182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106485397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09599647,0.0021797705,0.88240993,0.00028460694,0.00031687642,0.000082557766,0.00026155985,0.002720626,0.015747579],"genre_scores_gemma":[0.77332443,0.0008391734,0.2103091,0.00020778232,0.00009329432,0.00009022385,0.00044601332,0.0001031247,0.01458689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999354,0.000008761652,0.000005937652,0.000021378028,0.000020805834,0.000007641566],"domain_scores_gemma":[0.9999074,0.00002186302,0.000017703775,0.000022200025,0.000022251279,0.000008522936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019319457,0.00031913238,0.00032643141,0.00022407812,0.00013627172,0.00032867512,0.0009542125,0.00035147314,0.0025365562],"category_scores_gemma":[0.0004297792,0.00009961853,0.00021612305,0.0002638192,0.0002171604,0.0007801608,0.00045104141,0.00033323048,0.0006640017],"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":[0.00037682158,0.00019269816,0.001840323,0.00049859297,0.00015628245,0.00049611117,0.00012874385,0.102835625,0.11617463,0.055135585,0.006430067,0.7157344],"study_design_scores_gemma":[0.000036168563,0.00021418634,0.0006866484,0.000022241724,0.000052396997,0.00046140281,0.000018780027,0.9483394,0.024800302,0.015616111,0.009724695,0.00002772656],"about_ca_topic_score_codex":0.0005545338,"about_ca_topic_score_gemma":0.0013797081,"teacher_disagreement_score":0.0025365562,"about_ca_system_score_codex":0.00019939993,"about_ca_system_score_gemma":0.00017963962,"threshold_uncertainty_score":0.008485675},"labels":[],"label_agreement":null},{"id":"W2107370115","doi":"10.1109/tnn.2009.2022979","title":"A Multiscale Scheme for Approximating the Quantron's Discriminating Function","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Image and Signal Denoising Methods","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":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Maxima and minima; Function approximation; Laplace transform; Wavelet; Artificial neural network; Convergence (economics); Function (biology); Multiresolution analysis; Scheme (mathematics); Computer science; Algorithm; Mathematics; Applied mathematics; Approximation theory; Mathematical optimization; Wavelet transform; Artificial intelligence; Mathematical analysis; Discrete wavelet transform","score_opus":0.03025656581598325,"score_gpt":0.28152300473530695,"score_spread":0.2512664389193237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107370115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017009605,0.00015259327,0.98090106,0.000116835545,0.000041205745,0.000019582762,0.000018541648,0.000054977707,0.0016855224],"genre_scores_gemma":[0.37067345,0.000471833,0.62426525,0.00013173965,0.00006858749,0.00011378198,0.00005210292,0.000066949346,0.004156328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979705,0.000074309224,0.00001301483,0.000031691754,0.00006331382,0.000020715608],"domain_scores_gemma":[0.9996892,0.00010943182,0.000041143878,0.00006934497,0.00006005446,0.000030811905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011620838,0.00036177295,0.00041177293,0.00065638445,0.00032430072,0.00069189287,0.0008141773,0.00092567765,0.0016163206],"category_scores_gemma":[0.0019046175,0.00020014547,0.00046488238,0.00048439822,0.00080360455,0.0011330077,0.00091336615,0.0010361283,0.0004135043],"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":[0.000118417796,0.000047651127,0.0006657144,0.000105121464,0.000023814286,0.00015568339,0.00019933964,0.1406917,0.041678663,0.7409649,0.0013312262,0.07401775],"study_design_scores_gemma":[0.000009177155,0.000053759148,0.0001415402,0.000008732742,0.0000075855464,0.00006563326,0.000015063205,0.9611211,0.0022205415,0.03481375,0.001528641,0.000014526235],"about_ca_topic_score_codex":0.0005683765,"about_ca_topic_score_gemma":0.0006749645,"teacher_disagreement_score":0.0016163206,"about_ca_system_score_codex":0.0005720322,"about_ca_system_score_gemma":0.00041938995,"threshold_uncertainty_score":0.0061457753},"labels":[],"label_agreement":null},{"id":"W2107786698","doi":"10.1109/tnn.2005.863420","title":"A Bidirectional Heteroassociative Memory for Binary and Grey-Level Patterns","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":85,"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":"Spurious relationship; Computer science; Recall; Noise (video); Bidirectional associative memory; Content-addressable storage; Content-addressable memory; Binary number; Artificial intelligence; Function (biology); Learning rule; Algorithm; Artificial neural network; Machine learning; Mathematics; Arithmetic; Cognitive psychology","score_opus":0.020951321730597543,"score_gpt":0.2306263238409195,"score_spread":0.20967500211032195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107786698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22874032,0.0006749908,0.75484496,0.0006434558,0.00023324232,0.00006779355,0.00024932635,0.0013343296,0.013211528],"genre_scores_gemma":[0.9365019,0.00031184856,0.054384284,0.00013242054,0.000033013137,0.00009260008,0.000103587365,0.000030655196,0.008409543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999125,0.00001596773,0.000004864484,0.000027165052,0.000022590808,0.000016806904],"domain_scores_gemma":[0.99979216,0.00006904676,0.000026843802,0.000043423162,0.00004700683,0.000021665383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029068557,0.0002622233,0.00033938268,0.00018972301,0.00028942895,0.00047622592,0.0009266774,0.0007214579,0.0027969927],"category_scores_gemma":[0.00071437587,0.0001587105,0.0002996155,0.00021519509,0.00040059467,0.0012864738,0.0004179478,0.0004996065,0.0006734771],"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":[0.00088710425,0.00048673686,0.003924654,0.0005682206,0.00022099678,0.00094828475,0.00050859293,0.31162417,0.23261863,0.12403534,0.0071902876,0.316987],"study_design_scores_gemma":[0.000041554642,0.00018395892,0.000714618,0.000023814648,0.000038358965,0.000331448,0.000028167438,0.9617325,0.015911106,0.017626313,0.00334585,0.000022228664],"about_ca_topic_score_codex":0.0010221895,"about_ca_topic_score_gemma":0.0013563315,"teacher_disagreement_score":0.0027969927,"about_ca_system_score_codex":0.00025011378,"about_ca_system_score_gemma":0.00034718623,"threshold_uncertainty_score":0.009356797},"labels":[],"label_agreement":null},{"id":"W2108511354","doi":"10.1109/72.870045","title":"Signal detection using the radial basis function coupled map lattice","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Radar Systems and Signal Processing","field":"Engineering","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":"McMaster University; University of Calgary","funders":"","keywords":"Clutter; Constant false alarm rate; Radial basis function; Computer science; Radar; Chaotic; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Telecommunications","score_opus":0.013385151670210382,"score_gpt":0.20352976242740226,"score_spread":0.19014461075719188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108511354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022676358,0.00016049459,0.9757597,0.00011288285,0.00002560174,0.000020657291,0.000025011872,0.00034404857,0.0008751908],"genre_scores_gemma":[0.54862905,0.00027191822,0.44885,0.00009626011,0.000046417354,0.00008800707,0.000098074175,0.000046572903,0.0018736994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919444,0.00032642964,0.000023849929,0.000084663305,0.00032554174,0.000045165514],"domain_scores_gemma":[0.9986047,0.0006969934,0.00013043675,0.00012016212,0.0003935597,0.00005408265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010491937,0.00040132052,0.0005735308,0.0005870219,0.00021041508,0.0007498383,0.00071246637,0.0007396554,0.00069234375],"category_scores_gemma":[0.0045025027,0.000280355,0.00039652718,0.0005431434,0.00055141014,0.0009602271,0.0007958285,0.00064345374,0.00044904914],"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":[0.00044496913,0.00010128851,0.001330541,0.00012496386,0.00007397293,0.00016314314,0.00009178815,0.7093192,0.03295502,0.038678456,0.0015214685,0.21519513],"study_design_scores_gemma":[0.0000056889407,0.00001737257,0.000044215634,0.0000013189967,0.0000013892839,0.000013160132,0.000001943789,0.99667525,0.001543075,0.0015612917,0.00013041397,0.0000049078603],"about_ca_topic_score_codex":0.0012550361,"about_ca_topic_score_gemma":0.0008303282,"teacher_disagreement_score":0.0012550361,"about_ca_system_score_codex":0.00042856575,"about_ca_system_score_gemma":0.00057883165,"threshold_uncertainty_score":0.0055487156},"labels":[],"label_agreement":null},{"id":"W2108934838","doi":"10.1109/tnn.2005.860834","title":"A New Synaptic Plasticity Rule for Networks of Spiking Neurons","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":35,"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 Hospital; Alberta Hospital Edmonton","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Spiking neural network; Image segmentation; Pattern recognition (psychology); Artificial neural network; Neuroscience; Biology","score_opus":0.012342132044290879,"score_gpt":0.21872629682555184,"score_spread":0.20638416478126095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108934838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010186192,0.00023473705,0.9821967,0.0001741944,0.00017528342,0.00008126396,0.000071940216,0.00059569225,0.0062840376],"genre_scores_gemma":[0.38173014,0.0005655995,0.6089462,0.00049383304,0.0002061559,0.00046126428,0.0002220428,0.00037200615,0.007002746],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990439,0.00011600919,0.0001059497,0.00018348363,0.0004877992,0.00006284026],"domain_scores_gemma":[0.9989864,0.0003658828,0.00007606499,0.00021282885,0.00028993894,0.000068815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009875303,0.00035815433,0.0005986063,0.00046739774,0.00039094232,0.0006793296,0.0017847498,0.0008834006,0.0017295732],"category_scores_gemma":[0.0038157715,0.00025333287,0.0008832012,0.00023225971,0.0007653072,0.0014839421,0.00087172026,0.0012820557,0.00066110556],"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":[0.0002087315,0.00013327232,0.001793925,0.00035279064,0.00016179607,0.0012535338,0.00020347472,0.30250275,0.11489872,0.25502127,0.007491116,0.31597862],"study_design_scores_gemma":[0.00004994934,0.00012439833,0.0004854377,0.000035501154,0.000044700748,0.0011407624,0.000012795868,0.8904214,0.021541415,0.068111196,0.017981283,0.000051124007],"about_ca_topic_score_codex":0.0010554026,"about_ca_topic_score_gemma":0.0012599035,"teacher_disagreement_score":0.0017847498,"about_ca_system_score_codex":0.0004174779,"about_ca_system_score_gemma":0.00058320205,"threshold_uncertainty_score":0.0057860017},"labels":[],"label_agreement":null},{"id":"W2110569122","doi":"10.1109/tnn.2008.2011130","title":"Option Pricing With Modular Neural Networks","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Lakehead University","funders":"","keywords":"Nonparametric statistics; Artificial neural network; Modular design; Modular neural network; Computer science; Econometrics; Valuation of options; Moneyness; Economics; Artificial intelligence; Time delay neural network","score_opus":0.016550855628434998,"score_gpt":0.20599042909944945,"score_spread":0.18943957347101445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110569122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24005069,0.00035991438,0.7564584,0.00058994885,0.000048147558,0.00001742436,0.00006154163,0.00022943557,0.0021844958],"genre_scores_gemma":[0.9679836,0.00013401543,0.030492533,0.000052505922,0.000049767674,0.000021748274,0.000036948906,0.000018264976,0.0012106851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999624,0.00021569997,0.000013144964,0.0000529485,0.00006006389,0.000034143275],"domain_scores_gemma":[0.9986663,0.00084727927,0.00017646418,0.00010939202,0.0001374598,0.000063066036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014288913,0.0005488427,0.00048460165,0.0003826596,0.00019955951,0.00058830983,0.0008989876,0.00081529375,0.0008929665],"category_scores_gemma":[0.005687967,0.00031794075,0.00055589806,0.00043465642,0.000681213,0.0018106642,0.0008298924,0.0008029221,0.00012058442],"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":[0.00007000887,0.000028260229,0.0007923197,0.000020742458,0.000042793647,0.00005751111,0.00002381373,0.954958,0.0009977391,0.028777428,0.00023980957,0.013991543],"study_design_scores_gemma":[0.0000024300539,0.000004439692,0.00005566888,5.7556736e-7,0.0000014484327,0.0000029393445,5.6874086e-7,0.9939672,0.0000453304,0.0058963764,0.00002193779,0.0000011319319],"about_ca_topic_score_codex":0.0028498457,"about_ca_topic_score_gemma":0.0021207116,"teacher_disagreement_score":0.0028498457,"about_ca_system_score_codex":0.00073622225,"about_ca_system_score_gemma":0.00038571062,"threshold_uncertainty_score":0.007556796},"labels":[],"label_agreement":null},{"id":"W2110658950","doi":"10.1109/tnn.2011.2105888","title":"Semisupervised Learning Using Bayesian Interpretation: Application to LS-SVM","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Artificial intelligence; Support vector machine; Machine learning; Computer science; Bayesian probability; Inference; Bayesian inference; Kernel (algebra); Pattern recognition (psychology); Least squares support vector machine; Mathematics","score_opus":0.024875864552527778,"score_gpt":0.24588813345989805,"score_spread":0.22101226890737027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110658950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013195442,0.000076046956,0.99811256,0.000098302415,0.0000058419128,0.000009153094,0.000008203114,0.00007925594,0.00029116104],"genre_scores_gemma":[0.23132433,0.0004863146,0.7662005,0.00016604611,0.00011640787,0.00015922422,0.0001066184,0.00010898152,0.0013315836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812514,0.0011487422,0.00008580496,0.00017557309,0.00041932115,0.000045421417],"domain_scores_gemma":[0.9952767,0.0032156105,0.00039048324,0.00029377325,0.0007300919,0.00009333596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028743506,0.00063965644,0.00094156124,0.0009025593,0.00044620794,0.001045873,0.0011646954,0.0012590933,0.001123558],"category_scores_gemma":[0.011083067,0.00042989946,0.00057737343,0.00085265643,0.0012343598,0.0018096769,0.0014724481,0.0017625424,0.0004184078],"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":[0.00012682496,0.00009463415,0.0014131268,0.00020228222,0.000119807104,0.00020381159,0.00036636772,0.4603576,0.006113838,0.19619517,0.0035205227,0.331286],"study_design_scores_gemma":[0.0000049225077,0.000009668532,0.000082296785,0.000007079115,0.000004550114,0.00003365531,0.000009220623,0.9539844,0.0006080823,0.044444636,0.00080300204,0.000008486056],"about_ca_topic_score_codex":0.0013295783,"about_ca_topic_score_gemma":0.0016710254,"teacher_disagreement_score":0.0028743506,"about_ca_system_score_codex":0.00072011095,"about_ca_system_score_gemma":0.00082017743,"threshold_uncertainty_score":0.015201151},"labels":[],"label_agreement":null},{"id":"W2111525525","doi":"10.1109/tnn.2009.2025588","title":"Simple Artificial Neural Networks That Match Probability and Exploit and Explore When Confronting a Multiarmed Bandit","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Behavioral and Psychological Studies","field":"Psychology","cited_by":20,"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; University of Alberta","funders":"","keywords":"Perceptron; Artificial neural network; Computer science; Artificial intelligence; Reinforcement learning; Multi-armed bandit; Matching (statistics); Matching law; Exploit; Simple (philosophy); Machine learning; Contingency; Operant conditioning; Reinforcement; Mathematics; Engineering","score_opus":0.1887466762966764,"score_gpt":0.3206264415080561,"score_spread":0.13187976521137967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111525525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46206567,0.0002492785,0.5252451,0.0006367823,0.00014045429,0.0002810087,0.00015329273,0.0007585411,0.0104698455],"genre_scores_gemma":[0.88199985,0.00018430145,0.113487676,0.00028195552,0.000028721279,0.00023174699,0.00009470844,0.00003111603,0.0036599399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964404,0.00009533911,0.000023576109,0.000094158495,0.00010126936,0.000041619485],"domain_scores_gemma":[0.99892527,0.0005743745,0.00020565532,0.00015698132,0.000083581996,0.000054075095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009859158,0.00059459585,0.00040030587,0.00030169074,0.00020576634,0.00071451033,0.00083115115,0.001016215,0.0017651074],"category_scores_gemma":[0.0048774746,0.0003208545,0.00033999732,0.0003897194,0.0007586574,0.0011933902,0.0006073146,0.0008533269,0.0003053012],"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":[0.00038946455,0.0005287517,0.010448268,0.00030025927,0.0003197473,0.00031748027,0.0003340843,0.73891294,0.06060183,0.062921904,0.0017642024,0.1231611],"study_design_scores_gemma":[0.000039617218,0.00017878391,0.001577576,0.000016198203,0.00003678008,0.00007905755,0.000023518447,0.9597986,0.0066470616,0.030416537,0.0011635361,0.000022731472],"about_ca_topic_score_codex":0.0007866589,"about_ca_topic_score_gemma":0.001401995,"teacher_disagreement_score":0.0017651074,"about_ca_system_score_codex":0.00044211184,"about_ca_system_score_gemma":0.00029755195,"threshold_uncertainty_score":0.0059048533},"labels":[],"label_agreement":null},{"id":"W2112338564","doi":"10.1109/tnn.2009.2031143","title":"Semisupervised Least Squares Support Vector Machine","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":77,"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; Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Computer science; Heuristics; Generalization; Margin (machine learning); Artificial intelligence; Least squares support vector machine; Machine learning; Classifier (UML); Structured support vector machine; Maximization; Pattern recognition (psychology); Algorithm; Mathematical optimization; Mathematics","score_opus":0.013967953898349527,"score_gpt":0.2336830820277411,"score_spread":0.21971512812939156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112338564","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00900374,0.00013016335,0.9893339,0.00008983956,0.000019823428,0.000032167518,0.000058451296,0.00071672635,0.000615139],"genre_scores_gemma":[0.35211957,0.00020901808,0.64290416,0.0001833471,0.00011153436,0.00023808298,0.00086043566,0.00017697668,0.003196859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816066,0.00069535756,0.0001221715,0.00043640868,0.00049532746,0.00009004566],"domain_scores_gemma":[0.99426645,0.0021726715,0.00077976385,0.001089202,0.0015813571,0.00011058054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014340442,0.0009133266,0.0016142112,0.00066384726,0.00034326236,0.001066418,0.002037406,0.0014027768,0.0017001558],"category_scores_gemma":[0.008062693,0.00046074687,0.000689064,0.0009055445,0.00067440193,0.0015880112,0.0010574528,0.0012876831,0.0017060196],"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":[0.0003783708,0.00032918397,0.0022547664,0.000298999,0.00017600569,0.00018589337,0.00017727415,0.30363753,0.018498125,0.0163604,0.008141498,0.649562],"study_design_scores_gemma":[0.000011819259,0.000051844745,0.0002252263,0.000007776766,0.00000720081,0.000081510356,0.000017306429,0.9887581,0.0043996084,0.005453071,0.00097490527,0.000011678476],"about_ca_topic_score_codex":0.0004602837,"about_ca_topic_score_gemma":0.00073826424,"teacher_disagreement_score":0.002037406,"about_ca_system_score_codex":0.00029646797,"about_ca_system_score_gemma":0.00068157696,"threshold_uncertainty_score":0.0075840354},"labels":[],"label_agreement":null},{"id":"W2112830891","doi":"10.1109/72.883420","title":"On-line learning of dynamical systems in the presence of model mismatch and disturbances","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Computer science; Line (geometry); Nonlinear dynamical systems; Artificial intelligence; Nonlinear system; Mathematics; Physics","score_opus":0.018987192055971096,"score_gpt":0.2507532648143782,"score_spread":0.2317660727584071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112830891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074263394,0.00018355765,0.9232687,0.00011769502,0.000032150074,0.000039669692,0.0000144036285,0.0003580295,0.0017222774],"genre_scores_gemma":[0.95972556,0.00012310033,0.038411357,0.00007312424,0.000027247093,0.00005342094,0.000046631823,0.000048474165,0.0014910981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993156,0.00026079704,0.000050708273,0.00013618819,0.00016703877,0.00006964274],"domain_scores_gemma":[0.9971263,0.001795332,0.0004038661,0.00023416347,0.00037491976,0.000065384644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018605933,0.00066384696,0.00090872304,0.0002725462,0.00029023195,0.00103958,0.0011293773,0.001312327,0.0008356251],"category_scores_gemma":[0.008008409,0.00041457373,0.00039631518,0.00027626744,0.00083548547,0.0014969712,0.0011779924,0.0010589724,0.00023737802],"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":[0.00011723125,0.00006429056,0.0007812116,0.00006263995,0.00005010221,0.00020226555,0.0001258016,0.9290532,0.0033890526,0.0042399815,0.00027884924,0.061635416],"study_design_scores_gemma":[0.0000035375724,0.000025029454,0.00008096267,0.0000025034242,0.0000032268872,0.000015378317,0.0000046507475,0.9980116,0.00063222303,0.0011117161,0.000106629865,0.0000025507557],"about_ca_topic_score_codex":0.0028939794,"about_ca_topic_score_gemma":0.0022069004,"teacher_disagreement_score":0.0028939794,"about_ca_system_score_codex":0.00044269275,"about_ca_system_score_gemma":0.0004101209,"threshold_uncertainty_score":0.009839833},"labels":[],"label_agreement":null},{"id":"W2113803786","doi":"10.1109/tnn.2007.902962","title":"A Generalized Least Absolute Deviation Method for Parameter Estimation of Autoregressive Signals","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Control Systems and Identification","field":"Engineering","cited_by":63,"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; Least absolute deviations; Gaussian noise; Noise (video); Estimation theory; Gaussian; Artificial neural network; Mathematics; Standard deviation; Approximation error; Absolute deviation; Computer science; Algorithm; Applied mathematics; Statistics; Mathematical optimization; Artificial intelligence","score_opus":0.024037866569187327,"score_gpt":0.2593295341048331,"score_spread":0.2352916675356458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113803786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006333754,0.0002119919,0.9988115,0.000026030686,0.000028987837,0.0000046196014,0.000008210894,0.00010940386,0.00016591708],"genre_scores_gemma":[0.088881366,0.0008851019,0.9072607,0.00014609503,0.00016865913,0.00010470725,0.00019219935,0.00021008834,0.0021511281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982267,0.00062153273,0.00009130849,0.00038785403,0.00062160444,0.00005095924],"domain_scores_gemma":[0.9988418,0.00058179826,0.000098197845,0.00016411828,0.00028565605,0.000028347675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014596991,0.0010439955,0.0010890559,0.00090014725,0.00037132224,0.0008483838,0.0013787197,0.0012055598,0.0011735354],"category_scores_gemma":[0.003885576,0.0004318377,0.0011386556,0.0011940252,0.0008155517,0.0012038826,0.00093044684,0.0018619674,0.00092343346],"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":[0.00014283705,0.00005944932,0.000827866,0.00037655167,0.00019779867,0.00014331762,0.00017452694,0.3153843,0.03470167,0.035187576,0.003341025,0.60946316],"study_design_scores_gemma":[0.00001670091,0.000075392105,0.0003159256,0.000026398193,0.000026867992,0.00015186665,0.000015059797,0.975775,0.006969054,0.007981926,0.0085927695,0.000053074473],"about_ca_topic_score_codex":0.0015096817,"about_ca_topic_score_gemma":0.001188811,"teacher_disagreement_score":0.0015096817,"about_ca_system_score_codex":0.0003984105,"about_ca_system_score_gemma":0.0007238266,"threshold_uncertainty_score":0.007719755},"labels":[],"label_agreement":null},{"id":"W2113884043","doi":"10.1109/tnn.2004.839356","title":"Mixtures-of-Experts of Autoregressive Time Series: Asymptotic Normality and Model Specification","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":55,"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":"Autoregressive model; Estimator; Series (stratigraphy); Asymptotic distribution; Conditional probability distribution; Mathematics; Covariate; Applied mathematics; Conditional expectation; Multinomial distribution; Model selection; Time series; Linear model; Computer science; Econometrics; Statistics","score_opus":0.014191435877500097,"score_gpt":0.2465642726845874,"score_spread":0.23237283680708729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113884043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007825684,0.00016498505,0.9912042,0.0001732009,0.000010035496,0.000028837483,0.00004444054,0.00006518198,0.0004834107],"genre_scores_gemma":[0.50625104,0.0010878961,0.48659852,0.0002769535,0.00024931107,0.0005873351,0.00063887297,0.00012575656,0.004184301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99407405,0.003805144,0.00021365532,0.0008819122,0.0006949387,0.00033041002],"domain_scores_gemma":[0.9611258,0.032720823,0.0025252916,0.0018463997,0.0013947382,0.00038697952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0148274265,0.0015061378,0.0025087523,0.0017734818,0.00072038686,0.0018798269,0.0031088917,0.0029945113,0.002062456],"category_scores_gemma":[0.06602494,0.0013334303,0.0018948258,0.0016010891,0.0030026496,0.0039072614,0.0024750107,0.003749257,0.00075496413],"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":[0.00014717269,0.0000751289,0.0030511634,0.00014390267,0.00013697112,0.00022721107,0.00044991274,0.594203,0.0009546359,0.3706417,0.0012235149,0.02874574],"study_design_scores_gemma":[0.000017235445,0.000020996378,0.00027839845,0.000022800179,0.000014580791,0.000047288868,0.000029312496,0.87326056,0.00033286473,0.12531625,0.00063940237,0.00002038026],"about_ca_topic_score_codex":0.0038010692,"about_ca_topic_score_gemma":0.0030975745,"teacher_disagreement_score":0.0148274265,"about_ca_system_score_codex":0.001229246,"about_ca_system_score_gemma":0.0013925524,"threshold_uncertainty_score":0.07841587},"labels":[],"label_agreement":null},{"id":"W2114397911","doi":"10.1109/tnn.2002.1021883","title":"Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":63,"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":"University of Illinois at Urbana-Champaign","keywords":"Computer science; Image retrieval; Content-based image retrieval; Relevance feedback; Artificial intelligence; Visual Word; Relevance (law); Tree (set theory); Automatic image annotation; Similarity (geometry); Pattern recognition (psychology); Information retrieval; Data mining; Image (mathematics); Computer vision","score_opus":0.04178716581228696,"score_gpt":0.26097281638139547,"score_spread":0.2191856505691085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114397911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013891981,0.00014953186,0.98393005,0.00007281105,0.000017997028,0.000043330354,0.000018653258,0.0009843282,0.00089127274],"genre_scores_gemma":[0.43195626,0.0002528591,0.56290555,0.000121825746,0.00006372924,0.00027281023,0.00016764882,0.00015638782,0.004102949],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.00007042878,0.00001007524,0.000045633496,0.000077439014,0.000018755081],"domain_scores_gemma":[0.9996675,0.00015523307,0.000025070864,0.000044414915,0.000094916955,0.000012971735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005554069,0.0003999241,0.00039058414,0.0004576083,0.00034325302,0.0004943869,0.00097416824,0.00065695925,0.0015537292],"category_scores_gemma":[0.0014168845,0.0002404814,0.00040501432,0.00048091498,0.00035122325,0.0011055134,0.0004411064,0.00053359417,0.0008578067],"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":[0.00018914018,0.00026665564,0.0011547648,0.00022381793,0.00010341684,0.00017535115,0.0003047633,0.21192421,0.07018528,0.016574206,0.0046090223,0.6942894],"study_design_scores_gemma":[0.0000059275103,0.000048230206,0.00022195671,0.0000032660419,0.000010162059,0.00003742028,0.000012091532,0.9894338,0.0052827904,0.0037558104,0.0011810221,0.000007453236],"about_ca_topic_score_codex":0.0019705775,"about_ca_topic_score_gemma":0.0031024003,"teacher_disagreement_score":0.0019705775,"about_ca_system_score_codex":0.00040513984,"about_ca_system_score_gemma":0.00044136398,"threshold_uncertainty_score":0.0051977634},"labels":[],"label_agreement":null},{"id":"W2114467592","doi":"10.1109/tnn.2009.2023379","title":"Stability Analysis of Discrete-Time Recurrent Neural Networks With Stochastic Delay","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":34,"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":"Stability (learning theory); Probability distribution; Artificial neural network; Discrete time and continuous time; Recurrent neural network; Random variable; Control theory (sociology); Computer science; Stochastic process; Stochastic neural network; Probability density function; Mathematics; Artificial intelligence; Statistics; Machine learning","score_opus":0.012485482420344307,"score_gpt":0.23114540382400572,"score_spread":0.21865992140366142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114467592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10449784,0.00065365003,0.8916055,0.00023552065,0.000040327857,0.000018943207,0.000042107295,0.0001585247,0.0027476687],"genre_scores_gemma":[0.9874292,0.00030825144,0.0103743775,0.000023339568,0.000022218794,0.000033452478,0.00004339638,0.0000208696,0.0017449848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995389,0.00013227588,0.000028910075,0.00011931579,0.00013641673,0.000044130473],"domain_scores_gemma":[0.9989225,0.0005276159,0.00023030878,0.000051717358,0.00023657312,0.000031165757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001122075,0.00051922293,0.0005356141,0.0004006063,0.0002051473,0.00070062367,0.0006292468,0.0005808422,0.00060562906],"category_scores_gemma":[0.0029680098,0.00023166212,0.0005020746,0.0002680639,0.0007540086,0.0006744723,0.00056172523,0.00046572197,0.0001140396],"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":[0.00008631495,0.000011768025,0.0005960913,0.000058893176,0.000050487026,0.00015188208,0.00007864651,0.94938767,0.008193541,0.03234319,0.00017882178,0.008862791],"study_design_scores_gemma":[0.0000020829116,0.000007682846,0.00005089263,0.0000011738085,0.0000026467073,0.0000072460707,0.0000025758868,0.9974583,0.0003123733,0.0021091162,0.00004386663,0.000002153589],"about_ca_topic_score_codex":0.002515538,"about_ca_topic_score_gemma":0.0010936387,"teacher_disagreement_score":0.002515538,"about_ca_system_score_codex":0.0008255423,"about_ca_system_score_gemma":0.00052036246,"threshold_uncertainty_score":0.0059897304},"labels":[],"label_agreement":null},{"id":"W2115101923","doi":"10.1109/72.963787","title":"Compound binomial processes in neural integration","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","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 Winnipeg","funders":"","keywords":"Artificial neural network; Computer science; Stochastic process; Bernoulli's principle; Stochastic neural network; Bernoulli process; Event (particle physics); Multiplexing; Bernoulli trial; Algorithm; Mathematics; Artificial intelligence; Recurrent neural network; Statistics; Telecommunications","score_opus":0.02193154123387744,"score_gpt":0.2515548845564565,"score_spread":0.22962334332257908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115101923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018038273,0.0029918654,0.9620154,0.0011314417,0.00021982685,0.00004316466,0.00005554458,0.00009016291,0.015414338],"genre_scores_gemma":[0.7433452,0.0080294935,0.21634337,0.0014274879,0.0009801857,0.00027890477,0.00014601368,0.00017451402,0.029274939],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918944,0.0003294098,0.000039906725,0.00012375815,0.00021342805,0.000103988255],"domain_scores_gemma":[0.99555176,0.0037176574,0.0002313259,0.00012926395,0.00025360673,0.000116392206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003235105,0.0007031568,0.0010277991,0.0013542467,0.00075960177,0.0013795149,0.0011340259,0.0017380401,0.0039989124],"category_scores_gemma":[0.010110377,0.00052362593,0.0009353488,0.0012075182,0.0022896586,0.0033315178,0.0014091885,0.001975368,0.00058946153],"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":[0.000053931097,0.000038210685,0.0007042466,0.000065020075,0.000040774717,0.00021225192,0.0001786277,0.108333424,0.0012396808,0.86702585,0.001404169,0.0207037],"study_design_scores_gemma":[0.000018744304,0.00004745537,0.00038212552,0.000029070785,0.000017887767,0.00012201625,0.000021719547,0.53144366,0.00059798924,0.46456897,0.0027247975,0.000025586243],"about_ca_topic_score_codex":0.0028177854,"about_ca_topic_score_gemma":0.0024235547,"teacher_disagreement_score":0.0039989124,"about_ca_system_score_codex":0.001338532,"about_ca_system_score_gemma":0.00067769364,"threshold_uncertainty_score":0.017109036},"labels":[],"label_agreement":null},{"id":"W2116019577","doi":"10.1109/tnn.2002.806647","title":"Face recognition using LDA-based algorithms","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":799,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Linear discriminant analysis; Eigenface; Facial recognition system; Pattern recognition (psychology); Computer science; Artificial intelligence; Face (sociological concept); Feature (linguistics); Feature extraction; Statistical classification; Representation (politics); Principal component analysis; Machine learning","score_opus":0.04399648047983817,"score_gpt":0.2591615522076355,"score_spread":0.2151650717277973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116019577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058984947,0.00094006764,0.98905784,0.00013302322,0.000080481164,0.00005843136,0.00010466145,0.0014538581,0.002273064],"genre_scores_gemma":[0.12113319,0.0016793704,0.8702969,0.00017412285,0.00020398106,0.00030059373,0.0005565624,0.00011479616,0.0055404906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992367,0.00018201396,0.000043692227,0.00015202255,0.0003331859,0.000052326326],"domain_scores_gemma":[0.9995915,0.00012024669,0.000045382112,0.00008775841,0.00014182851,0.000013309719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007955332,0.00064675225,0.0009894501,0.0016935919,0.00054825685,0.00095737417,0.0006195496,0.0006283086,0.0026178728],"category_scores_gemma":[0.0016124332,0.0002988566,0.0008091461,0.0014682135,0.0003208634,0.00101683,0.00080931734,0.0007442452,0.0034090898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007434886,0.000069072135,0.0009523781,0.00014660705,0.000073689764,0.00004838928,0.0000616242,0.022251366,0.024995102,0.007965295,0.0070533343,0.9363089],"study_design_scores_gemma":[0.00003478119,0.000081168706,0.0030360166,0.000043053362,0.00005010679,0.0004611773,0.000066501234,0.9363773,0.023477407,0.015453317,0.020843253,0.00007588583],"about_ca_topic_score_codex":0.0014655063,"about_ca_topic_score_gemma":0.0016815629,"teacher_disagreement_score":0.0026178728,"about_ca_system_score_codex":0.00035893006,"about_ca_system_score_gemma":0.00035185931,"threshold_uncertainty_score":0.008757651},"labels":[],"label_agreement":null},{"id":"W2117319878","doi":"10.1109/tnn.2004.824248","title":"Branching Competitive Learning Network: A Novel Self-Creating Model","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"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":"Competitive learning; Vector quantization; Cluster analysis; Computer science; Learning vector quantization; Quantization (signal processing); Adaptability; Artificial intelligence; Artificial neural network; Branching (polymer chemistry); Algorithm; Theoretical computer science","score_opus":0.014163992524662574,"score_gpt":0.23384284334311212,"score_spread":0.21967885081844954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117319878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04139706,0.00034588785,0.94683665,0.00041357576,0.00006717778,0.000058458983,0.000036819092,0.00025312597,0.010591347],"genre_scores_gemma":[0.86423963,0.00047285043,0.12574977,0.00028497813,0.00009303823,0.00015407556,0.000062091945,0.00005899382,0.008884551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994892,0.00015775478,0.000016924238,0.00008273641,0.00019118754,0.00006212529],"domain_scores_gemma":[0.999186,0.00033923087,0.00010450665,0.00007350578,0.00018471052,0.00011205194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043212,0.00036466465,0.00063168607,0.0005669851,0.00062945887,0.0011934817,0.0022445833,0.0014182221,0.0024074547],"category_scores_gemma":[0.0023439887,0.00024023827,0.000480987,0.00065576914,0.0013981429,0.0015696648,0.0010049988,0.0010211742,0.0004078833],"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":[0.000089119836,0.00008380733,0.0010643716,0.00008153526,0.000045275472,0.00020376674,0.00022123312,0.6401084,0.009325528,0.28919718,0.0020179423,0.057561804],"study_design_scores_gemma":[0.000008665351,0.000022382714,0.00004278847,0.0000025124712,0.0000039669126,0.000034421922,0.000004980712,0.9830033,0.00038190634,0.015810892,0.000678071,0.0000061908104],"about_ca_topic_score_codex":0.0025599648,"about_ca_topic_score_gemma":0.0013325809,"teacher_disagreement_score":0.0025599648,"about_ca_system_score_codex":0.0010776073,"about_ca_system_score_gemma":0.00073642575,"threshold_uncertainty_score":0.00805378},"labels":[],"label_agreement":null},{"id":"W2117439573","doi":"10.1109/tnn.2006.875985","title":"Nonlinear Spatial–Temporal Prediction based on Optimal Fusion","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":29,"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":"Chinese University of Hong Kong; University of Hong Kong","keywords":"Computer science; Clutter; Artificial intelligence; Pattern recognition (psychology); Radar; Nonlinear system; Gaussian; Support vector machine; Sensor fusion; Statistic; Spatial analysis; Signal processing; Algorithm; Data mining; Mathematics; Statistics; Telecommunications","score_opus":0.010905054886476188,"score_gpt":0.2301694800242323,"score_spread":0.2192644251377561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117439573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0128935985,0.00016433457,0.9859066,0.000098119584,0.000030324021,0.000008412016,0.000014037266,0.00013911916,0.0007455225],"genre_scores_gemma":[0.72919214,0.00042277825,0.26745176,0.000102872466,0.0000965212,0.000060701597,0.00011561267,0.00004773674,0.0025098366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955744,0.00010885288,0.000028121303,0.00009386658,0.00016538924,0.000046294557],"domain_scores_gemma":[0.9995141,0.00019216405,0.00007696234,0.000076919336,0.000120415585,0.000019406458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007886032,0.00045158563,0.00065155624,0.00027512168,0.00031674144,0.000491938,0.0006147648,0.0005456302,0.0007599736],"category_scores_gemma":[0.0021239684,0.0002735075,0.00041006305,0.0005173521,0.0006140993,0.0011486186,0.00090534345,0.0006991439,0.00021724637],"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":[0.00022006953,0.000044600987,0.0010082828,0.00007358833,0.00006050348,0.000115660114,0.00010474982,0.76555246,0.014439962,0.03990185,0.0015107123,0.17696758],"study_design_scores_gemma":[0.0000028273612,0.000011232088,0.00006353054,0.0000014220147,0.0000030268257,0.00001385312,0.0000021105836,0.9954407,0.0012417088,0.002985947,0.00022922321,0.000004370592],"about_ca_topic_score_codex":0.002284871,"about_ca_topic_score_gemma":0.0015910859,"teacher_disagreement_score":0.002284871,"about_ca_system_score_codex":0.0004090415,"about_ca_system_score_gemma":0.0007632472,"threshold_uncertainty_score":0.0045431852},"labels":[],"label_agreement":null},{"id":"W2119698875","doi":"10.1109/tnn.2008.2000447","title":"A Neural Model for Compensation of Sensory Abnormalities in Autism Through Feedback From a Measure of Global Perception","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","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":"Nortel (Canada)","funders":"","keywords":"Autism; Sensory system; Measure (data warehouse); Perception; Compensation (psychology); Computer science; Neurophysiology; Artificial neural network; Artificial intelligence; Cognitive psychology; Speech recognition; Neuroscience; Psychology; Data mining; Developmental psychology","score_opus":0.07792584610969892,"score_gpt":0.3006184927202792,"score_spread":0.22269264661058025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119698875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20440301,0.000326213,0.78392667,0.000736336,0.000078981575,0.000043023683,0.00019063345,0.00056772074,0.009727425],"genre_scores_gemma":[0.95788425,0.00015482327,0.037239727,0.000043949414,0.00001779124,0.0000750007,0.0000692493,0.00002681549,0.0044884016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994695,0.000011569142,0.0000026378398,0.000014669886,0.000014474192,0.000009713695],"domain_scores_gemma":[0.9999137,0.000034170436,0.000013001387,0.000008495431,0.000019889438,0.000010771078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017366566,0.00029805562,0.0002276881,0.0002059159,0.00021475666,0.0003684275,0.00053245283,0.00047696484,0.0015962807],"category_scores_gemma":[0.00049854966,0.00015904925,0.00034989745,0.0001672641,0.00038008433,0.00054037815,0.00035300216,0.00045528964,0.00019542615],"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":[0.00007937138,0.000044290515,0.0012296377,0.000060131617,0.00004439861,0.00016262065,0.00012402855,0.9321454,0.021014493,0.021614257,0.0008989497,0.022582471],"study_design_scores_gemma":[0.0000059814774,0.000030453255,0.00045694178,0.0000033622684,0.0000067455176,0.000043908385,0.000011143732,0.9909746,0.0006862371,0.007419388,0.00035585492,0.000005418548],"about_ca_topic_score_codex":0.002485949,"about_ca_topic_score_gemma":0.003085202,"teacher_disagreement_score":0.002485949,"about_ca_system_score_codex":0.0003213808,"about_ca_system_score_gemma":0.00035516,"threshold_uncertainty_score":0.0053400397},"labels":[],"label_agreement":null},{"id":"W2120271349","doi":"10.1109/72.935098","title":"Cost functions and model combination for VaR-based asset allocation using neural networks","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":51,"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 de Montréal","funders":"","keywords":"Computer science; Asset allocation; Portfolio; Artificial neural network; Benchmark (surveying); Asset (computer security); Portfolio optimization; Hyperparameter; Artificial intelligence; Allocator; Machine learning; Mathematical optimization; Operations research; Economics; Finance","score_opus":0.1803604063505602,"score_gpt":0.3916355985386564,"score_spread":0.21127519218809623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120271349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015684959,0.0003903113,0.9819209,0.00026763428,0.000023660135,0.000046832865,0.000044205484,0.00020818805,0.0014132843],"genre_scores_gemma":[0.7211449,0.0005359131,0.27278677,0.00018004351,0.00015404755,0.00059047167,0.00035023678,0.00021076578,0.0040467605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816847,0.0011058461,0.0000917575,0.0001648073,0.0003489679,0.000120146746],"domain_scores_gemma":[0.997292,0.0019087854,0.00020330554,0.00018041182,0.00034822783,0.00006725373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050756596,0.001456511,0.0014990116,0.0011342495,0.00048711553,0.0015401747,0.0014967246,0.0016166292,0.001970058],"category_scores_gemma":[0.00967588,0.00065314263,0.0007926165,0.001207391,0.0007186448,0.0022304964,0.0015931748,0.0016380878,0.00043535265],"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":[0.00004686789,0.00003037494,0.0002204157,0.000021077803,0.000040224873,0.000015811873,0.0000097712045,0.967166,0.0002884,0.0060424814,0.00038780793,0.025730819],"study_design_scores_gemma":[0.0000031389795,0.000009582375,0.000043012325,0.0000025032377,0.000005179494,0.0000037497432,0.0000013558594,0.99674463,0.0001566561,0.0029177936,0.000109157474,0.0000033173333],"about_ca_topic_score_codex":0.0028465225,"about_ca_topic_score_gemma":0.0026861771,"teacher_disagreement_score":0.0050756596,"about_ca_system_score_codex":0.0011845975,"about_ca_system_score_gemma":0.0008961786,"threshold_uncertainty_score":0.026843011},"labels":[],"label_agreement":null},{"id":"W2120609681","doi":"10.1109/tnn.2009.2016959","title":"Adaptive Neural Control for a Class of Nonlinear Systems With Uncertain Hysteresis Inputs and Time-Varying State Delays","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Piezoelectric Actuators and Control","field":"Engineering","cited_by":89,"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":"Control theory (sociology); Nonlinear system; Hysteresis; Bounded function; Adaptive control; Representation (politics); Artificial neural network; Lyapunov function; Backstepping; Class (philosophy); Computer science; Mathematics; State variable; Control (management); Artificial intelligence; Mathematical analysis","score_opus":0.0074544644251497896,"score_gpt":0.1944398523086453,"score_spread":0.1869853878834955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120609681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1212469,0.0016854315,0.8688863,0.0002913549,0.00012512867,0.00003994359,0.00003378511,0.0001534542,0.0075376905],"genre_scores_gemma":[0.98348874,0.0006078071,0.013242226,0.000048373397,0.000058727786,0.000049542883,0.000027265207,0.0000067215074,0.0024706526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999881,0.000021535403,0.0000068043328,0.00003315413,0.000039857718,0.00001763234],"domain_scores_gemma":[0.99983513,0.00006835105,0.000043249544,0.000011546875,0.000035793975,0.0000058807736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029229434,0.00038937308,0.0002951214,0.00017312303,0.00026275462,0.00042994923,0.00056821835,0.00053704396,0.0005778199],"category_scores_gemma":[0.0005949584,0.00011689169,0.00022521698,0.00022049743,0.00046595777,0.00047122606,0.0003837126,0.0004647851,0.00006216391],"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":[0.00013101235,0.00007308658,0.001015242,0.00028477045,0.0000637289,0.0003920547,0.00019482989,0.8463297,0.02683057,0.035275105,0.0007294846,0.08868052],"study_design_scores_gemma":[0.0000069698103,0.0000454253,0.00022894496,0.0000044961525,0.0000069676894,0.000027143702,0.000012165155,0.995802,0.0010534571,0.002210988,0.0005974876,0.000003906451],"about_ca_topic_score_codex":0.0024445748,"about_ca_topic_score_gemma":0.0024206329,"teacher_disagreement_score":0.0024445748,"about_ca_system_score_codex":0.00032047025,"about_ca_system_score_gemma":0.0002980988,"threshold_uncertainty_score":0.004860699},"labels":[],"label_agreement":null},{"id":"W2121235350","doi":"10.1109/tnn.2006.873285","title":"OR/AND neurons and the development of interpretable logic models","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"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 Hospital; Alberta Hospital Edmonton; University of Alberta","funders":"","keywords":"Fuzzy logic; Computer science; Theoretical computer science; Modular design; Mathematics; Artificial intelligence","score_opus":0.021930472644109546,"score_gpt":0.23477964709518126,"score_spread":0.2128491744510717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121235350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014336155,0.0007485506,0.9687765,0.0008667123,0.000054917142,0.00005930161,0.00013459648,0.00024622015,0.0147770075],"genre_scores_gemma":[0.41566908,0.0018434874,0.56889224,0.00040370453,0.000094920615,0.00029978366,0.00035498652,0.00012563156,0.01231621],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992563,0.00027630737,0.000049062855,0.00015821592,0.00020442937,0.000055658682],"domain_scores_gemma":[0.99900913,0.0005851741,0.000103407634,0.00012197231,0.00014449678,0.000035811638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014891805,0.00049788743,0.00041060228,0.0006680625,0.0003002649,0.0021460853,0.0012177646,0.0008259809,0.0025516208],"category_scores_gemma":[0.0035776075,0.00037991657,0.0008035673,0.000372591,0.0020644276,0.002695443,0.0012871915,0.0014252708,0.0004896281],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027134278,0.000023141478,0.0003033571,0.00011572715,0.00003273543,0.00014500204,0.00026407474,0.14840518,0.0022009232,0.8255565,0.00054259395,0.022383716],"study_design_scores_gemma":[0.000013469106,0.000025156885,0.000100294616,0.000059211365,0.00001940182,0.000056117533,0.000050131264,0.4022115,0.0016685452,0.58554256,0.010240096,0.00001353439],"about_ca_topic_score_codex":0.0017383503,"about_ca_topic_score_gemma":0.0017825925,"teacher_disagreement_score":0.0025516208,"about_ca_system_score_codex":0.0013157869,"about_ca_system_score_gemma":0.0009817834,"threshold_uncertainty_score":0.009546757},"labels":[],"label_agreement":null},{"id":"W2121404151","doi":"10.1109/tnn.2007.899128","title":"Neural Network Control for Position Tracking of a Two-Axis Inverted Pendulum System: Experimental Studies","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Inverted pendulum; Artificial neural network; Double inverted pendulum; Control theory (sociology); Position (finance); Computer science; Tracking (education); Control system; Artificial intelligence; Control (management); Nonlinear system; Physics; Engineering","score_opus":0.02831380704934414,"score_gpt":0.27775143763716376,"score_spread":0.24943763058781962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121404151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9682728,0.00024352309,0.026894366,0.0001214303,0.00009019519,0.00017193012,0.00009223844,0.00025989636,0.0038536938],"genre_scores_gemma":[0.99494153,0.0000719708,0.0038627672,0.0000130578865,0.000006732472,0.00006815657,0.00004631684,0.0000060016946,0.0009834624],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976605,0.000040456838,0.000019346424,0.000045117333,0.00008465827,0.000044435434],"domain_scores_gemma":[0.9995721,0.00013291139,0.0000635723,0.000059153583,0.00014009594,0.00003202517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005743248,0.00040404292,0.00041669226,0.00024117276,0.0003479936,0.00029877998,0.0004466884,0.0005582295,0.0016665523],"category_scores_gemma":[0.0010277212,0.00018428416,0.00016403585,0.00019773535,0.00038646883,0.00039736816,0.0003270797,0.00044576413,0.00015935513],"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":[0.0027575048,0.002968027,0.0028994386,0.0013051586,0.00012268177,0.0007852992,0.00081040733,0.13963833,0.7448851,0.0029721453,0.0011195305,0.099736296],"study_design_scores_gemma":[0.00069215044,0.0139101315,0.011998192,0.000087905464,0.000070282265,0.000239624,0.00023311665,0.65757596,0.31067958,0.00095061254,0.003488338,0.0000740131],"about_ca_topic_score_codex":0.0025260216,"about_ca_topic_score_gemma":0.0017831535,"teacher_disagreement_score":0.0025260216,"about_ca_system_score_codex":0.00025135616,"about_ca_system_score_gemma":0.00030378037,"threshold_uncertainty_score":0.00557518},"labels":[],"label_agreement":null},{"id":"W2121472132","doi":"10.1109/tnn.2010.2046497","title":"Realization of the Conscience Mechanism in CMOS Implementation of Winner-Takes-All Self-Organizing Neural Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":70,"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":"Artificial neural network; Computer science; CMOS; Realization (probability); MATLAB; Winner-take-all; Mechanism (biology); Quantization (signal processing); Electronic engineering; Artificial intelligence; Control engineering; Engineering; Algorithm","score_opus":0.012075696766829336,"score_gpt":0.2522204979835713,"score_spread":0.24014480121674198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121472132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35450634,0.0006486562,0.6174144,0.00049145333,0.00030745554,0.000089378795,0.000051924602,0.0010999343,0.02539043],"genre_scores_gemma":[0.9648528,0.00006155307,0.033777922,0.00005525554,0.000011490884,0.000017460254,0.000008028481,0.000007150874,0.0012082733],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991655,0.000014453076,0.000005963862,0.000013743299,0.000034557896,0.000014703251],"domain_scores_gemma":[0.9999039,0.000027215825,0.000012849006,0.000015540629,0.00002975595,0.000010617027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015259262,0.00013985728,0.00011455655,0.0001071392,0.00020709274,0.0003138405,0.00063271343,0.00025390403,0.0010411895],"category_scores_gemma":[0.0003203111,0.00006518904,0.00013270389,0.00007553564,0.00020617286,0.0002679747,0.0001932749,0.00025020394,0.00013234324],"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":[0.00032208624,0.00029284362,0.0023747282,0.00034427806,0.00011408743,0.00086601987,0.00049054105,0.122228585,0.4398522,0.17014307,0.0021924037,0.2607792],"study_design_scores_gemma":[0.00008411069,0.0005863851,0.0014935118,0.000026887694,0.00004160145,0.00035946534,0.00005770647,0.82894707,0.13824864,0.019346362,0.010772613,0.000035589852],"about_ca_topic_score_codex":0.0005137248,"about_ca_topic_score_gemma":0.00087024225,"teacher_disagreement_score":0.0010411895,"about_ca_system_score_codex":0.00021584424,"about_ca_system_score_gemma":0.00020175416,"threshold_uncertainty_score":0.0034831762},"labels":[],"label_agreement":null},{"id":"W2123727571","doi":"10.1109/tnn.2004.824412","title":"Combining Expert Neural Networks Using Reinforcement Feedback for Learning Primitive Grasping Behavior","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":34,"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":"Computer science; GRASP; Reinforcement learning; Task (project management); Artificial intelligence; Object (grammar); Artificial neural network; Robot; Architecture; Machine learning","score_opus":0.03753288780465127,"score_gpt":0.26801828527163574,"score_spread":0.23048539746698446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123727571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057128716,0.000221786,0.9391344,0.000102809616,0.000044221873,0.00008272589,0.000011246071,0.00087864505,0.0023954671],"genre_scores_gemma":[0.7337132,0.00017541357,0.2618757,0.00022028938,0.00005818702,0.00019540246,0.000060587725,0.00007536928,0.0036257615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993249,0.00016195615,0.00004785274,0.00015040548,0.00022681178,0.00008806688],"domain_scores_gemma":[0.9987387,0.0006414337,0.00012617293,0.0001564454,0.00024760162,0.00008961637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017649117,0.0010993283,0.0008547869,0.00070276024,0.00034823854,0.00053988217,0.0015697815,0.001213329,0.0018594135],"category_scores_gemma":[0.0042435997,0.00063605205,0.0005558385,0.0004247791,0.000739747,0.0018139059,0.001564414,0.0011210744,0.00043203225],"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":[0.00017224217,0.00016703505,0.0009865585,0.00006222376,0.00010134975,0.00016191167,0.00012605605,0.7762165,0.011220375,0.0048252293,0.0006620192,0.20529862],"study_design_scores_gemma":[0.000009337422,0.000057549318,0.00009225799,0.0000037238096,0.000009974711,0.000028165556,0.000005947431,0.99516296,0.001920226,0.0023460737,0.0003562637,0.000007511578],"about_ca_topic_score_codex":0.0020644688,"about_ca_topic_score_gemma":0.0028717115,"teacher_disagreement_score":0.0020644688,"about_ca_system_score_codex":0.000729161,"about_ca_system_score_gemma":0.0004960168,"threshold_uncertainty_score":0.009333849},"labels":[],"label_agreement":null},{"id":"W2123847705","doi":"10.1109/tnn.2011.2163169","title":"A New Formulation for Feedforward Neural Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":133,"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":"Artificial neural network; Computer science; Feedforward neural network; Regularization (linguistics); Stochastic neural network; Artificial intelligence; Backpropagation; Time delay neural network; Types of artificial neural networks; Generalization; Measure (data warehouse); Probabilistic neural network; Feed forward; Machine learning; Algorithm; Mathematics; Data mining","score_opus":0.033822963046535905,"score_gpt":0.24762515996303097,"score_spread":0.21380219691649507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123847705","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093502115,0.0005899343,0.99475515,0.00026429503,0.00013641719,0.00002223891,0.00006684158,0.000046877805,0.0031832417],"genre_scores_gemma":[0.18334612,0.0037172711,0.7900551,0.0009209925,0.000992992,0.00053023093,0.0004542398,0.0001725902,0.01981057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990288,0.00030962855,0.00007709881,0.0002149618,0.00032052316,0.000049059265],"domain_scores_gemma":[0.99945444,0.00022527819,0.00005893643,0.000041583426,0.0001989854,0.000020713882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014210992,0.0012153243,0.0006869579,0.00073754665,0.0003587426,0.0012932817,0.0015972278,0.0016632876,0.003755886],"category_scores_gemma":[0.002623364,0.0003751529,0.00074499997,0.00080113753,0.001020354,0.0028274339,0.0009861554,0.0022546882,0.0008038902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003581277,0.00003335304,0.0002953162,0.00036010292,0.000056985904,0.00018864256,0.00014280871,0.30603147,0.0048895734,0.59401387,0.005018281,0.08893383],"study_design_scores_gemma":[0.000009738882,0.00005632013,0.00012363464,0.000051713767,0.000019409314,0.00012744249,0.000017970173,0.8294362,0.0011673315,0.15004186,0.018931648,0.000016709135],"about_ca_topic_score_codex":0.0014649036,"about_ca_topic_score_gemma":0.0018909574,"teacher_disagreement_score":0.003755886,"about_ca_system_score_codex":0.0008903337,"about_ca_system_score_gemma":0.0008398876,"threshold_uncertainty_score":0.012564719},"labels":[],"label_agreement":null},{"id":"W2124588103","doi":"10.1109/72.846745","title":"Logic operations based on single neuron rational model","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":18,"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":"Computer science; Logic gate; Partition (number theory); Mathematics; Algorithm","score_opus":0.03745958330615788,"score_gpt":0.24424282436584172,"score_spread":0.20678324105968382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124588103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10693949,0.00040987213,0.85718775,0.00029289417,0.000066180546,0.00006637377,0.00014014618,0.00051009463,0.034387227],"genre_scores_gemma":[0.9296118,0.0004121727,0.06277918,0.000085792126,0.000035115558,0.0000888286,0.00008655815,0.000079917416,0.0068206866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997832,0.000038258535,0.000010953144,0.000047717385,0.00008118603,0.000038732924],"domain_scores_gemma":[0.9998299,0.00006636968,0.000017782962,0.000028845969,0.00004316435,0.000013939889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002491097,0.00030315746,0.00050039357,0.0004629064,0.00030100465,0.000983618,0.00093346904,0.00043075354,0.0047482043],"category_scores_gemma":[0.0006594999,0.00014679266,0.000732451,0.0003358657,0.00086172344,0.0018733257,0.0004009026,0.0007171145,0.00063022395],"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":[0.00008941811,0.00003354468,0.00022890467,0.00010523134,0.000019727819,0.00020610291,0.0001533733,0.19277526,0.020541178,0.76141757,0.00073420204,0.02369545],"study_design_scores_gemma":[0.000013754509,0.00003355794,0.00008049256,0.000007341602,0.000009835718,0.00009019511,0.000024275198,0.81296283,0.003795495,0.18148641,0.0014813731,0.000014450057],"about_ca_topic_score_codex":0.001299311,"about_ca_topic_score_gemma":0.00074433634,"teacher_disagreement_score":0.0047482043,"about_ca_system_score_codex":0.0006542656,"about_ca_system_score_gemma":0.0004939677,"threshold_uncertainty_score":0.01588428},"labels":[],"label_agreement":null},{"id":"W2124700032","doi":"10.1109/tnn.2004.824409","title":"Fast Converging Minimum Probability of Error Neural Network Receivers for DS-CDMA Communications","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","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":"McGill University","funders":"","keywords":"Computer science; Code division multiple access; Additive white Gaussian noise; Bit error rate; Artificial neural network; Algorithm; Perceptron; Multilayer perceptron; Electronic engineering; White noise; Artificial intelligence; Decoding methods; Computer network; Telecommunications; Engineering","score_opus":0.040451058940625825,"score_gpt":0.27218938545904026,"score_spread":0.23173832651841442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124700032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036973014,0.00063900213,0.9600115,0.00020466026,0.00003246702,0.00001777164,0.0000112221005,0.00012169336,0.001988629],"genre_scores_gemma":[0.76448953,0.000736706,0.22932026,0.00011063207,0.00005889953,0.00008413418,0.000026673668,0.000024467428,0.0051488527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997707,0.000083597784,0.000010794092,0.000028896548,0.00008243966,0.000023512652],"domain_scores_gemma":[0.9995764,0.00024924346,0.000049226983,0.000023539871,0.0000922896,0.00000927335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008834078,0.00043355298,0.00043223822,0.00020549851,0.0002460071,0.00041240948,0.00057454297,0.00094491703,0.00058900635],"category_scores_gemma":[0.002384608,0.00023340563,0.00022520173,0.00030045875,0.00049169915,0.00077508664,0.00042135816,0.0008634532,0.00017799191],"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":[0.000055761604,0.000030335596,0.00045370695,0.000052434312,0.00001503766,0.00005352619,0.000049925162,0.9482449,0.003668998,0.014113655,0.00027240737,0.03298929],"study_design_scores_gemma":[0.0000021933902,0.000011654992,0.000042123134,0.0000021075048,0.000002088812,0.0000073390343,0.0000018956812,0.99781054,0.0005971005,0.001422935,0.00009803202,0.0000020215632],"about_ca_topic_score_codex":0.0019285345,"about_ca_topic_score_gemma":0.0023774754,"teacher_disagreement_score":0.0019285345,"about_ca_system_score_codex":0.000665689,"about_ca_system_score_gemma":0.0004241291,"threshold_uncertainty_score":0.004830003},"labels":[],"label_agreement":null},{"id":"W2124830618","doi":"10.1109/72.896802","title":"New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":55,"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":"Algorithm; Artificial neural network; Gradient descent; Feedforward neural network; Computer science; Convergence (economics); Controller (irrigation); Feed forward; Nonlinear system; Heuristic; Least-squares function approximation; Stochastic gradient descent; Control theory (sociology); Mathematics; Machine learning; Artificial intelligence; Control (management); Control engineering","score_opus":0.024803246493477215,"score_gpt":0.2658776239057754,"score_spread":0.2410743774122982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124830618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013499227,0.0003707304,0.9976635,0.00004316701,0.000027964887,0.0000155681,0.0000072290823,0.00022415978,0.00029776886],"genre_scores_gemma":[0.051086754,0.0007644239,0.9447767,0.000056844583,0.00007414391,0.00017763671,0.000083322346,0.000121892124,0.002858376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994604,0.00015494415,0.000048307975,0.000092926566,0.00021004469,0.000033481552],"domain_scores_gemma":[0.9989936,0.00056517456,0.00009096432,0.000062830746,0.00026848682,0.00001905111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014207906,0.001228229,0.0009122286,0.0007196674,0.00036617563,0.0008090213,0.0013437766,0.0011959751,0.0015508691],"category_scores_gemma":[0.0044777486,0.0006351002,0.0005886259,0.0008263068,0.0006393654,0.0013626439,0.0006431108,0.0016664348,0.0008327315],"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":[0.00007541247,0.00006364147,0.000328916,0.00025138352,0.000077831326,0.000048733014,0.00016580912,0.52455103,0.0062115304,0.024111303,0.0018123753,0.44230205],"study_design_scores_gemma":[0.000009835146,0.000017713748,0.00006947069,0.000010157736,0.000007864755,0.000015832942,0.0000056268495,0.9934,0.0017354602,0.0031868652,0.0015313425,0.000009900474],"about_ca_topic_score_codex":0.004274708,"about_ca_topic_score_gemma":0.0070629725,"teacher_disagreement_score":0.004274708,"about_ca_system_score_codex":0.00065737666,"about_ca_system_score_gemma":0.0008506755,"threshold_uncertainty_score":0.008499622},"labels":[],"label_agreement":null},{"id":"W2125593342","doi":"10.1109/tnn.2008.2004625","title":"Uncorrelated Multilinear Discriminant Analysis With Regularization and Aggregation for Tensor Object Recognition","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":122,"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; University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Multilinear map; Artificial intelligence; Linear discriminant analysis; Computer science; Subspace topology; Linear subspace; Discriminative model; Facial recognition system; Regularization (linguistics); Mathematics","score_opus":0.017454524189396282,"score_gpt":0.205382570848019,"score_spread":0.18792804665862273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125593342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028907913,0.00018625909,0.99634403,0.000058676862,0.000020451242,0.000014969214,0.000023535735,0.00018558583,0.00027565926],"genre_scores_gemma":[0.11004424,0.00039325468,0.88769144,0.00006428528,0.00009398681,0.000113820504,0.00024190362,0.0001111915,0.0012458956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814844,0.00084466627,0.000105818915,0.0002775425,0.0005378337,0.00008571724],"domain_scores_gemma":[0.9981305,0.0006444276,0.0002564786,0.00040792066,0.00047765495,0.00008306092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002174098,0.0011809112,0.0011218827,0.0013226956,0.0006243114,0.0010182157,0.0009902036,0.0005648927,0.0011269785],"category_scores_gemma":[0.004734403,0.0004032324,0.0011790039,0.0018179829,0.0007951425,0.0013566144,0.0011436826,0.0015226201,0.0008411899],"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":[0.00015415474,0.00013219613,0.0018271934,0.00020665753,0.00018792549,0.000120020915,0.00019574135,0.30778238,0.02502777,0.07271178,0.0051665,0.58648765],"study_design_scores_gemma":[0.0000053886997,0.00003018064,0.0002773693,0.000006457479,0.000011789808,0.000032980428,0.000009187474,0.982988,0.002863155,0.011870284,0.0018851447,0.00002011258],"about_ca_topic_score_codex":0.0028468233,"about_ca_topic_score_gemma":0.0027292962,"teacher_disagreement_score":0.0028468233,"about_ca_system_score_codex":0.00073967484,"about_ca_system_score_gemma":0.0010811831,"threshold_uncertainty_score":0.011497855},"labels":[],"label_agreement":null},{"id":"W2128734552","doi":"10.1109/tnn.2010.2101614","title":"MDS-Based Multiresolution Nonlinear Dimensionality Reduction Model for Color Image Segmentation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":34,"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 de Montréal","funders":"","keywords":"Artificial intelligence; Image segmentation; Pattern recognition (psychology); Computer science; Scale-space segmentation; Cluster analysis; Image texture; Dimensionality reduction; Segmentation-based object categorization; Segmentation; Embedding; Feature extraction; Multiresolution analysis; Computer vision; Wavelet transform; Wavelet","score_opus":0.049319042423821645,"score_gpt":0.257821001767294,"score_spread":0.20850195934347235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128734552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044357697,0.00013205985,0.99473107,0.000075687836,0.000012088717,0.000012022632,0.000051609208,0.00017195113,0.00037781714],"genre_scores_gemma":[0.32559198,0.0006840515,0.6684337,0.00012414386,0.000088752924,0.00017945672,0.0004919697,0.00019387751,0.0042120228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963844,0.0001047447,0.00001846343,0.000086573375,0.0001234956,0.00002829924],"domain_scores_gemma":[0.9997056,0.00008418187,0.000053660006,0.0000700456,0.00007030506,0.000016251033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054371037,0.0005639217,0.0008902429,0.00074529636,0.00031624688,0.0006911867,0.00088917767,0.0005887516,0.0015154615],"category_scores_gemma":[0.0013575818,0.00031223218,0.0010010517,0.0009540329,0.00061478326,0.0011288524,0.0008164264,0.0010022116,0.0006888486],"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":[0.0001420733,0.000043445314,0.00058406586,0.00011822574,0.000080144746,0.000108379594,0.00013715772,0.7699872,0.020478213,0.051955674,0.00279724,0.15356816],"study_design_scores_gemma":[0.000001905527,0.000009569159,0.00009220127,0.0000024377734,0.0000038231015,0.000018441573,0.0000049129403,0.9937774,0.001049012,0.0042732777,0.00076129264,0.000005718852],"about_ca_topic_score_codex":0.0034393603,"about_ca_topic_score_gemma":0.0030819348,"teacher_disagreement_score":0.0034393603,"about_ca_system_score_codex":0.00074529985,"about_ca_system_score_gemma":0.0005653886,"threshold_uncertainty_score":0.0068386793},"labels":[],"label_agreement":null},{"id":"W2131040183","doi":"10.1109/tnn.2006.890811","title":"Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks","year":2007,"lang":"en","type":"letter","venue":"IEEE Transactions on Neural Networks","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Kingston Health Sciences Centre","funders":"University of Calgary","keywords":"Global Positioning System; Computer science; Sensor fusion; Artificial neural network; Inertial navigation system; Real-time computing; Satellite; Inertial measurement unit; GPS/INS; GPS signals; Assisted GPS; Precision Lightweight GPS Receiver; Artificial intelligence; Remote sensing; Inertial frame of reference; Telecommunications; Gps receiver; Geography; Engineering; Aerospace engineering","score_opus":0.021802289981344654,"score_gpt":0.24559153898731628,"score_spread":0.22378924900597164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131040183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0382277,0.002928213,0.9435362,0.0021938854,0.00081529055,0.00006129941,0.00004035348,0.0010735154,0.011123406],"genre_scores_gemma":[0.7085692,0.0023253514,0.27466643,0.0010242587,0.00052028976,0.00008973885,0.00011950622,0.000056125922,0.012629039],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996804,0.000086479304,0.000017534736,0.00002842759,0.00016948064,0.000017675853],"domain_scores_gemma":[0.9997104,0.00008278409,0.00001883512,0.000025214995,0.00015548243,0.0000072580415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036765297,0.0002440943,0.0002210009,0.00022174916,0.0002366145,0.00041888515,0.0004196315,0.00094427844,0.0010288537],"category_scores_gemma":[0.0011486253,0.00012029467,0.00014813084,0.0002987629,0.00019969563,0.00050558714,0.00023619368,0.0005471342,0.00058945763],"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":[0.0006605569,0.000086311455,0.002407195,0.00030946202,0.00007889265,0.0016868201,0.00019373775,0.056201443,0.096555896,0.020103227,0.021955574,0.7997609],"study_design_scores_gemma":[0.000035718826,0.00018708064,0.00057127373,0.000051523377,0.000032431984,0.001213927,0.00005146931,0.93645346,0.02860684,0.004544608,0.028232217,0.000019327865],"about_ca_topic_score_codex":0.0009034421,"about_ca_topic_score_gemma":0.002982761,"teacher_disagreement_score":0.0010288537,"about_ca_system_score_codex":0.00045651713,"about_ca_system_score_gemma":0.0002002707,"threshold_uncertainty_score":0.0034418702},"labels":[],"label_agreement":null},{"id":"W2133702739","doi":"10.1109/tnn.2002.1031938","title":"A recurrent neural network for solving Sylvester equation with time-varying coefficients","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":660,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"iNano Medical (Canada)","funders":"","keywords":"Recurrent neural network; Artificial neural network; Computer science; Sylvester equation; Control theory (sociology); Nonlinear system; Convergence (economics); Inverted pendulum; Applied mathematics; Time delay neural network; Mathematics; Artificial intelligence","score_opus":0.031826097085271364,"score_gpt":0.24180964027847188,"score_spread":0.2099835431932005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133702739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003519452,0.00040753226,0.993346,0.00008388448,0.000074793556,0.000023576666,0.000026768603,0.00026509538,0.0022529021],"genre_scores_gemma":[0.3310238,0.0014809782,0.6560083,0.00017822799,0.00015413496,0.00029427104,0.0002651677,0.00011998114,0.010475076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998109,0.000042296473,0.000017192811,0.000049209,0.00006000223,0.00002049553],"domain_scores_gemma":[0.99986815,0.000045310684,0.00002018359,0.000015715248,0.000042805335,0.000007832347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048234005,0.00072301866,0.00054249226,0.0002731794,0.00027542797,0.0005251449,0.00090619177,0.0010815773,0.0024188382],"category_scores_gemma":[0.0010096879,0.0003311378,0.0006102104,0.00044358303,0.00040451888,0.001072765,0.00048150378,0.0012896863,0.0008981528],"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":[0.000102248814,0.000076986275,0.00060797896,0.000362902,0.00012261799,0.0003650611,0.00017466964,0.6352366,0.041929305,0.09179268,0.0036436198,0.22558536],"study_design_scores_gemma":[0.000008091125,0.000047983227,0.00006097245,0.000009715288,0.000014961433,0.000054278036,0.0000041977805,0.9892212,0.0032366419,0.004675928,0.00265314,0.000012766499],"about_ca_topic_score_codex":0.0025867969,"about_ca_topic_score_gemma":0.0038064956,"teacher_disagreement_score":0.0025867969,"about_ca_system_score_codex":0.00039863127,"about_ca_system_score_gemma":0.000570484,"threshold_uncertainty_score":0.008091867},"labels":[],"label_agreement":null},{"id":"W2133884199","doi":"10.1109/72.883433","title":"Asynchronous self-organizing maps","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Asynchronous communication; MIMD; Computer science; Convergence (economics); Computation; Artificial neural network; Function (biology); Set (abstract data type); Algorithm; Theoretical computer science; Topology (electrical circuits); Artificial intelligence; Mathematics; Parallel computing","score_opus":0.008797966435843,"score_gpt":0.21109895450788146,"score_spread":0.20230098807203847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133884199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032026082,0.00024669807,0.95784926,0.0002562039,0.00014138337,0.000052131163,0.00007933793,0.00026342823,0.009085514],"genre_scores_gemma":[0.82053906,0.00036488834,0.16779241,0.00018170524,0.0001782001,0.00020516162,0.00014864531,0.00009692961,0.010493021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997167,0.000085495034,0.000013807229,0.000059190923,0.00009765013,0.000027168826],"domain_scores_gemma":[0.99944085,0.00025221484,0.00005728013,0.000085525586,0.00011568995,0.000048410515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074677,0.00027479426,0.0003373226,0.0003977125,0.0004560089,0.00075790763,0.0007984518,0.0005855028,0.002036486],"category_scores_gemma":[0.0021985937,0.00015298072,0.00029778894,0.00036404133,0.0005706145,0.00095066795,0.0008102699,0.0005259266,0.0004032393],"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":[0.0000766347,0.00005438932,0.0005862026,0.0000923184,0.00003789592,0.00018768343,0.00012382417,0.39467123,0.011034654,0.5238618,0.004130359,0.06514304],"study_design_scores_gemma":[0.00000763534,0.000013715866,0.00011921684,0.0000034625937,0.000003903717,0.00003652883,0.000009174552,0.9244674,0.0010667682,0.07234269,0.0019237036,0.000005799151],"about_ca_topic_score_codex":0.00047384933,"about_ca_topic_score_gemma":0.00054619974,"teacher_disagreement_score":0.002036486,"about_ca_system_score_codex":0.0003933807,"about_ca_system_score_gemma":0.0003359072,"threshold_uncertainty_score":0.0068126917},"labels":[],"label_agreement":null},{"id":"W2138393638","doi":"10.1109/tnn.2010.2048759","title":"Common Asymptotic Behavior of Solutions and Almost Periodicity for Discontinuous, Delayed, and Impulsive Neural Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":74,"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":"Exponential stability; Artificial neural network; Sequence (biology); Applied mathematics; Computer science; Attractor; Mathematics; Cellular neural network; Discrete mathematics; Mathematical analysis; Artificial intelligence","score_opus":0.011920951295278148,"score_gpt":0.23716505332903706,"score_spread":0.22524410203375891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138393638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44287285,0.0006042184,0.55041647,0.00015770963,0.000042667194,0.000027215692,0.00004458547,0.00009870704,0.0057356176],"genre_scores_gemma":[0.9834884,0.00024018432,0.014926816,0.000020772477,0.00001744006,0.000027811971,0.00003377409,0.000012203756,0.0012324963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99978703,0.000044982942,0.000020564978,0.000050771207,0.00006747859,0.000029091127],"domain_scores_gemma":[0.99927443,0.0002958632,0.00024119548,0.00005940922,0.00008753743,0.000041547955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005803284,0.0003846771,0.00041927403,0.0008139469,0.0002219814,0.00057307753,0.00048341136,0.0006093279,0.0006823016],"category_scores_gemma":[0.0022171787,0.00017553689,0.00055123045,0.00035529092,0.00090156787,0.00063108857,0.0006907136,0.00049984653,0.000067522924],"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":[0.00015074448,0.00006140762,0.0059209685,0.00033424134,0.0001618783,0.001682865,0.0005941257,0.62680334,0.048687212,0.2748823,0.00055611,0.04016481],"study_design_scores_gemma":[0.000006205109,0.000049062048,0.00069628994,0.000011694161,0.000010432563,0.00017222308,0.000057555568,0.95086974,0.0025218974,0.045237314,0.00035750272,0.0000101525175],"about_ca_topic_score_codex":0.0005349939,"about_ca_topic_score_gemma":0.00033678097,"teacher_disagreement_score":0.0008139469,"about_ca_system_score_codex":0.00047443737,"about_ca_system_score_gemma":0.0002727648,"threshold_uncertainty_score":0.0034422874},"labels":[],"label_agreement":null},{"id":"W2138727659","doi":"10.1109/tnn.2009.2023120","title":"BAM Learning of Nonlinearly Separable Tasks by Using an Asymmetrical Output Function and Reinforcement Learning","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"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; University of Ottawa","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Artificial neural network; Content-addressable memory; Function (biology); Property (philosophy)","score_opus":0.020679957306906246,"score_gpt":0.2564761758607362,"score_spread":0.23579621855382993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138727659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13626722,0.00016912715,0.85987025,0.000161371,0.00003292585,0.00003904929,0.000020844369,0.0003994462,0.0030397573],"genre_scores_gemma":[0.93327355,0.00010318303,0.06446471,0.00004378672,0.000013289604,0.000064470056,0.000027983919,0.000019177478,0.001989959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982125,0.00007004301,0.000013512588,0.000034150013,0.000040090235,0.000021106956],"domain_scores_gemma":[0.9992747,0.0003315016,0.000104357474,0.00013200402,0.00011177754,0.00004575169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097387534,0.0005012177,0.0005219128,0.00022647194,0.0001758983,0.00049927895,0.00082196295,0.0005373051,0.0012588486],"category_scores_gemma":[0.0029865354,0.00024384189,0.00029907667,0.00020762242,0.0006072651,0.001176264,0.0006653563,0.0006193308,0.00026036412],"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":[0.00037803673,0.00018473247,0.0023134213,0.0001534249,0.00009613821,0.00019660039,0.00020284523,0.73591346,0.030300325,0.050149288,0.00078467483,0.17932706],"study_design_scores_gemma":[0.00001341897,0.000046128174,0.00012165679,0.0000039776864,0.000008701598,0.000025335718,0.0000060098223,0.98710674,0.0028814364,0.0095087765,0.00027225845,0.000005381784],"about_ca_topic_score_codex":0.0011054128,"about_ca_topic_score_gemma":0.00094786554,"teacher_disagreement_score":0.0012588486,"about_ca_system_score_codex":0.00039940132,"about_ca_system_score_gemma":0.00036014014,"threshold_uncertainty_score":0.0051504374},"labels":[],"label_agreement":null},{"id":"W2140389641","doi":"10.1109/tnn.2004.841784","title":"Optimizing the Kernel in the Empirical Feature Space","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":318,"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":"Kernel (algebra); Measure (data warehouse); Kernel embedding of distributions; Feature (linguistics); Kernel method; Computer science; Artificial intelligence; Feature vector; Pattern recognition (psychology); Euclidean space; Variable kernel density estimation; Graph kernel; Mathematics; Tree kernel; Algorithm; Data mining; Support vector machine","score_opus":0.043998601912425904,"score_gpt":0.27858648263350055,"score_spread":0.23458788072107464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140389641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075516575,0.000050249022,0.9921036,0.00003451089,0.0000043449613,0.000006237382,0.000007704769,0.00009251134,0.00014919222],"genre_scores_gemma":[0.37805235,0.00023167266,0.61960375,0.000058119676,0.0000425704,0.00010709099,0.00019840631,0.00022218715,0.0014838123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984621,0.0005115204,0.000106263615,0.00034882897,0.0004733462,0.00009789715],"domain_scores_gemma":[0.99794203,0.000967649,0.00019423287,0.00041950657,0.00042728317,0.00004922722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022081009,0.0005967037,0.0010731688,0.0005352602,0.0003160085,0.000961961,0.0010145367,0.0007949364,0.0005942536],"category_scores_gemma":[0.00806758,0.00036219155,0.00056944415,0.0007285956,0.001124608,0.0025350028,0.0012477522,0.0010868199,0.0004178924],"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":[0.00022905454,0.00012902184,0.0014776714,0.00018402329,0.000118531985,0.00008569728,0.00014640238,0.6702343,0.028633274,0.07052343,0.0011829411,0.22705558],"study_design_scores_gemma":[0.0000059797817,0.000025455538,0.00024308651,0.0000029277735,0.0000062768295,0.000028757064,0.000007564885,0.9843715,0.004596109,0.010144764,0.0005589854,0.0000086245855],"about_ca_topic_score_codex":0.0009213217,"about_ca_topic_score_gemma":0.000539956,"teacher_disagreement_score":0.0022081009,"about_ca_system_score_codex":0.000693208,"about_ca_system_score_gemma":0.00081432983,"threshold_uncertainty_score":0.011677682},"labels":[],"label_agreement":null},{"id":"W2141070932","doi":"10.1109/tnn.2004.824415","title":"Scalable Closed-Boundary Analog Neural Networks","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"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 Toronto","funders":"University of Toronto; Sharif University of Technology","keywords":"Computer science; Scalability; Quadratic equation; Artificial neural network; Boundary (topology); Classifier (UML); Activation function; Topology (electrical circuits); Artificial intelligence; Mathematics","score_opus":0.013883666511080792,"score_gpt":0.23672248705283175,"score_spread":0.22283882054175097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141070932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11710623,0.00041905508,0.87204134,0.00029361082,0.0000953623,0.00005750081,0.000057197656,0.001221063,0.0087086195],"genre_scores_gemma":[0.8995622,0.00013644039,0.09602217,0.00012235908,0.00003099815,0.000087533626,0.00009075689,0.00003637264,0.0039112177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999808,0.000034032066,0.0000095269725,0.00005342947,0.00007025282,0.000024809482],"domain_scores_gemma":[0.99949634,0.0002246727,0.00005211573,0.000092907045,0.00010432042,0.000029680095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042992405,0.00030281456,0.00042925696,0.00019226417,0.00029307985,0.0007255964,0.001379605,0.0007437872,0.0027508747],"category_scores_gemma":[0.0018117549,0.00021289913,0.0002000951,0.00027533897,0.000738608,0.0016736024,0.0010177774,0.000626187,0.00038169543],"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":[0.00020505924,0.000119216194,0.0006091927,0.0001282484,0.000030586725,0.00020925088,0.00014129972,0.6934443,0.035718575,0.059137557,0.0024935058,0.20776314],"study_design_scores_gemma":[0.000008847849,0.000029293918,0.00007495897,0.000003009663,0.0000028886732,0.00002689097,0.0000099115405,0.98331213,0.0022166686,0.013654168,0.0006576889,0.0000035619294],"about_ca_topic_score_codex":0.0010018713,"about_ca_topic_score_gemma":0.0015004892,"teacher_disagreement_score":0.0027508747,"about_ca_system_score_codex":0.00057002006,"about_ca_system_score_gemma":0.00028412897,"threshold_uncertainty_score":0.0092025995},"labels":[],"label_agreement":null},{"id":"W2141200867","doi":"10.1109/tnn.2007.901277","title":"MPCA: Multilinear Principal Component Analysis of Tensor Objects","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":867,"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; University of Toronto","funders":"University of Florida; University of South Florida; Minnesota Pollution Control Agency","keywords":"Multilinear map; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Tensor (intrinsic definition); Feature extraction; Computer science; Dimensionality reduction; Projection (relational algebra); Subspace topology; Discriminative model; Feature (linguistics); Curse of dimensionality; Mathematics; Algorithm","score_opus":0.021069219309914494,"score_gpt":0.22297582927317305,"score_spread":0.20190660996325857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141200867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019059731,0.00018339975,0.9959233,0.00006237744,0.000039433515,0.000039853432,0.00012633546,0.0012114097,0.000507974],"genre_scores_gemma":[0.077111386,0.00061061664,0.91795695,0.000088887544,0.00014460864,0.00023522158,0.00091506535,0.0005449225,0.0023923805],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989279,0.00026218884,0.00005471793,0.00021249331,0.00045859287,0.00008411124],"domain_scores_gemma":[0.9987691,0.00030495797,0.000182586,0.0003141368,0.0003507405,0.0000783699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010481062,0.0017636352,0.0011334389,0.0017511502,0.00071715406,0.0017108253,0.0013594481,0.0006916417,0.0041552237],"category_scores_gemma":[0.003142443,0.0005806938,0.0014134585,0.0019464515,0.00084837695,0.0020001326,0.0014899295,0.0016223723,0.002867798],"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":[0.0002384134,0.00013220956,0.0016633283,0.00042744333,0.00021861856,0.00024194969,0.00025058215,0.11334374,0.045259893,0.04590289,0.011940485,0.7803804],"study_design_scores_gemma":[0.000015787205,0.00009957307,0.0014428982,0.000026055175,0.00003668402,0.00021176455,0.000046432913,0.94749993,0.011949481,0.021610772,0.01699875,0.00006193255],"about_ca_topic_score_codex":0.0028502578,"about_ca_topic_score_gemma":0.0024441832,"teacher_disagreement_score":0.0041552237,"about_ca_system_score_codex":0.00046343182,"about_ca_system_score_gemma":0.0012961203,"threshold_uncertainty_score":0.013900578},"labels":[],"label_agreement":null},{"id":"W2141251259","doi":"10.1109/tnn.2010.2050600","title":"Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":38,"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":"National Institute of Neurological Disorders and Stroke; San Diego State University","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Spiking neural network; Process (computing); Artificial neural network; Support vector machine; Spike-timing-dependent plasticity; Synaptic plasticity; Computer vision","score_opus":0.011994412990190832,"score_gpt":0.2130355582378689,"score_spread":0.20104114524767808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141251259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20613694,0.0003857805,0.78985524,0.00013028932,0.00012151423,0.000057980902,0.000116886295,0.0017781674,0.0014171968],"genre_scores_gemma":[0.7060376,0.00023318142,0.29145458,0.000109596374,0.00003771639,0.00004994756,0.0001719718,0.000053160184,0.0018522649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998035,0.000021437349,0.000013455305,0.000064888474,0.00007061609,0.000026193073],"domain_scores_gemma":[0.99970907,0.00007922441,0.000051601794,0.00006304375,0.00006840229,0.000028674607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002999673,0.0002923459,0.00047273454,0.00025163745,0.0001473421,0.00045599655,0.00070065405,0.0005096527,0.0008056636],"category_scores_gemma":[0.0011497347,0.00028203864,0.00036957697,0.00028549752,0.00028753697,0.0007781509,0.00047690165,0.00042342837,0.00030919057],"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":[0.0004778315,0.00013633081,0.0055310405,0.00019218815,0.00013741343,0.00051540753,0.00016800399,0.076166496,0.44207558,0.003099725,0.0011814341,0.47031853],"study_design_scores_gemma":[0.000016257236,0.0002096825,0.0039785304,0.00001844475,0.000060342787,0.0004123776,0.000035912562,0.8910956,0.10007079,0.0020685943,0.0020003351,0.000033160995],"about_ca_topic_score_codex":0.0008953256,"about_ca_topic_score_gemma":0.001258504,"teacher_disagreement_score":0.0008953256,"about_ca_system_score_codex":0.00024758503,"about_ca_system_score_gemma":0.00026780128,"threshold_uncertainty_score":0.0026952624},"labels":[],"label_agreement":null},{"id":"W2142100971","doi":"10.1109/72.822508","title":"A new supervised learning algorithm for multilayered and interconnected neural networks","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Backpropagation; Computer science; Algorithm; Time delay neural network; Types of artificial neural networks; Supervised learning; Wake-sleep algorithm; Artificial intelligence; Rprop; Generalization error","score_opus":0.014730562225411959,"score_gpt":0.23788358253301103,"score_spread":0.22315302030759906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142100971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011149443,0.0001174456,0.99791986,0.000037081296,0.000035641184,0.000029892428,0.000019434925,0.0002639413,0.00046175846],"genre_scores_gemma":[0.04377825,0.00023414589,0.95271784,0.00009391259,0.00010887352,0.00033754812,0.00014663057,0.00010185234,0.0024810168],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991025,0.0002012599,0.00006845382,0.00017557899,0.00040489854,0.00004725924],"domain_scores_gemma":[0.99903333,0.00036851296,0.00010761651,0.00010133198,0.00035550067,0.000033684537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012878261,0.00080618233,0.0010355588,0.00091120007,0.0005139403,0.0008248487,0.0016685734,0.0011307013,0.0024158303],"category_scores_gemma":[0.002962933,0.00052283145,0.0008445957,0.00090658694,0.0006907208,0.001832218,0.0011225005,0.0016590529,0.0010491232],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091102294,0.00007671787,0.0007015184,0.0002691623,0.00015336223,0.00011105026,0.00013688313,0.319354,0.0112090185,0.039566807,0.00545949,0.6228709],"study_design_scores_gemma":[0.0000131636525,0.000037666312,0.00011125283,0.000015579879,0.000010657648,0.000047352667,0.000005915142,0.9827267,0.0021971238,0.010564888,0.0042581065,0.000011618521],"about_ca_topic_score_codex":0.0013979198,"about_ca_topic_score_gemma":0.0019669815,"teacher_disagreement_score":0.0024158303,"about_ca_system_score_codex":0.0006386595,"about_ca_system_score_gemma":0.0010592095,"threshold_uncertainty_score":0.008081794},"labels":[],"label_agreement":null},{"id":"W2143155105","doi":"10.1109/72.950143","title":"Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":27,"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 Manitoba; University of Ottawa","funders":"","keywords":"Vector quantization; Cluster analysis; Linde–Buzo–Gray algorithm; Adaptive resonance theory; Computer science; Image compression; Algorithm; Data compression; Learning vector quantization; Quantization (signal processing); Artificial intelligence; Computation; Pattern recognition (psychology); Image processing; Artificial neural network; Image (mathematics)","score_opus":0.04287775990973145,"score_gpt":0.298381628216452,"score_spread":0.2555038683067205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143155105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032918444,0.00012842119,0.9957561,0.000051586765,0.000022259463,0.000023362045,0.000013595314,0.00015834595,0.0005544894],"genre_scores_gemma":[0.105266705,0.00022235287,0.89255524,0.000063139225,0.000035894423,0.00011544794,0.000101529775,0.000059547434,0.0015801329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946386,0.00017445092,0.000033816174,0.00008258785,0.00021735212,0.00002797573],"domain_scores_gemma":[0.9994832,0.00020567662,0.000049196206,0.00008771644,0.00016149502,0.000012652452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079565257,0.00042610022,0.0005183222,0.0006140879,0.00024508915,0.00058664824,0.0008938011,0.0007899966,0.0017280163],"category_scores_gemma":[0.0025768247,0.00017573334,0.00045580073,0.0009467427,0.00045682638,0.0009666525,0.00061100244,0.00075867074,0.0006286327],"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":[0.0001636838,0.000057878373,0.00024508403,0.0001892557,0.000047947906,0.00007382333,0.00019891243,0.31486177,0.04747029,0.047135632,0.0035594758,0.5859963],"study_design_scores_gemma":[0.000009382984,0.000047138758,0.00013687131,0.0000067643196,0.000004825204,0.000040998002,0.000012173117,0.98653954,0.00493585,0.006633806,0.001617592,0.000015130571],"about_ca_topic_score_codex":0.001393957,"about_ca_topic_score_gemma":0.0014397728,"teacher_disagreement_score":0.0017280163,"about_ca_system_score_codex":0.0004233814,"about_ca_system_score_gemma":0.00038714937,"threshold_uncertainty_score":0.005780816},"labels":[],"label_agreement":null},{"id":"W2143304877","doi":"10.1109/tnn.2002.806629","title":"Face recognition using kernel direct discriminant analysis algorithms","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":608,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel principal component analysis; Pattern recognition (psychology); Facial recognition system; Kernel (algebra); Artificial intelligence; Computer science; Principal component analysis; Face (sociological concept); Discriminant; Kernel method; Feature extraction; Word error rate; Algorithm; Mathematics; Support vector machine","score_opus":0.04051875161713008,"score_gpt":0.26509096159119266,"score_spread":0.22457220997406258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143304877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008391825,0.00041852234,0.9894302,0.00007201613,0.000031055923,0.000027483657,0.000038297487,0.0007600924,0.0008304678],"genre_scores_gemma":[0.2506072,0.00080778793,0.74291587,0.0000855559,0.0000707794,0.00014719063,0.0003058316,0.0001027272,0.004957079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994087,0.00013916893,0.000037444985,0.0001264352,0.00024400579,0.000044290577],"domain_scores_gemma":[0.9994049,0.00021544589,0.000066161025,0.00012246071,0.00017550388,0.00001553225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077979657,0.00053277315,0.00093684,0.00097109255,0.00027023378,0.0007171172,0.00063550216,0.0006219426,0.0020582362],"category_scores_gemma":[0.0023202754,0.0002516876,0.0006052064,0.0008685622,0.00030640283,0.0011410726,0.0007398154,0.000756678,0.0019079398],"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":[0.00012641255,0.00008806463,0.00093227177,0.0001198352,0.00007354218,0.00006174284,0.000054875432,0.06299437,0.016291285,0.0108252475,0.0034268736,0.90500546],"study_design_scores_gemma":[0.000020438994,0.000051190334,0.0012344957,0.000011480435,0.000018050583,0.00018310593,0.000022256592,0.9734298,0.010471735,0.009704977,0.0048278584,0.00002466874],"about_ca_topic_score_codex":0.0012693669,"about_ca_topic_score_gemma":0.0009248243,"teacher_disagreement_score":0.0020582362,"about_ca_system_score_codex":0.00034730192,"about_ca_system_score_gemma":0.00037293977,"threshold_uncertainty_score":0.006885469},"labels":[],"label_agreement":null},{"id":"W2143885292","doi":"10.1109/tnn.2009.2031144","title":"Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Information Technology Research Centre; Universidade Federal do Rio de Janeiro; National Technical University of Athens; Concordia University; Royal Bank of Canada","keywords":"Multilinear map; Principal component analysis; Pattern recognition (psychology); Subspace topology; Linear subspace; Artificial intelligence; Projection (relational algebra); Tensor (intrinsic definition); Mathematics; Rank (graph theory); Computer science; Algorithm; Combinatorics","score_opus":0.016316583583636097,"score_gpt":0.2362605338137632,"score_spread":0.21994395023012708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143885292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011268869,0.00027208307,0.9976215,0.00007207745,0.00003269457,0.000027028087,0.000059525086,0.0002167767,0.00057138386],"genre_scores_gemma":[0.06616212,0.00090030686,0.92978495,0.00013192743,0.00019929606,0.0003174278,0.0006705019,0.00022249225,0.0016110556],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99754083,0.0010358877,0.00012882533,0.00048034254,0.0007128072,0.00010138412],"domain_scores_gemma":[0.997329,0.0012197722,0.0002786338,0.00054263213,0.0005629333,0.0000669907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001892778,0.0016243096,0.0010695303,0.0012162739,0.0008446895,0.0012412635,0.0011093837,0.00084107486,0.0033816614],"category_scores_gemma":[0.0075030955,0.000498495,0.0013341517,0.002485534,0.001229995,0.0015722398,0.001448941,0.0024889933,0.0019048016],"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":[0.00015034062,0.00013389296,0.0014800748,0.0005828994,0.00034592324,0.00022433652,0.00030652113,0.24633734,0.019684961,0.12435265,0.009516633,0.5968845],"study_design_scores_gemma":[0.000018333823,0.0000909645,0.00089017156,0.000047635247,0.00004890852,0.00018244216,0.000037998852,0.9047087,0.007822491,0.067615055,0.01847107,0.000066256456],"about_ca_topic_score_codex":0.0020561628,"about_ca_topic_score_gemma":0.0025209673,"teacher_disagreement_score":0.0033816614,"about_ca_system_score_codex":0.0005973692,"about_ca_system_score_gemma":0.0015717661,"threshold_uncertainty_score":0.011312783},"labels":[],"label_agreement":null},{"id":"W2144245426","doi":"10.1109/tnn.2010.2091428","title":"Count Data Modeling and Classification Using Finite Mixtures of Distributions","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":99,"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":"Multinomial distribution; Dirichlet distribution; Mixture model; Cluster analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Data modeling; Expectation–maximization algorithm; Data mining; Mathematics; Statistics; Maximum likelihood","score_opus":0.06841040994015102,"score_gpt":0.30705565796956996,"score_spread":0.23864524802941894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144245426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006327228,0.00019070684,0.9926433,0.00015906565,0.000022184884,0.00003783704,0.00009194037,0.0002511441,0.00027658883],"genre_scores_gemma":[0.35575822,0.001116319,0.6356862,0.00033468654,0.00030691834,0.0007906984,0.0018667262,0.00023263322,0.0039075254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952878,0.0020065987,0.0002782951,0.0011047209,0.0010770224,0.00024553342],"domain_scores_gemma":[0.9872392,0.0094027445,0.0010435295,0.0010920172,0.0010037047,0.00021878825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067789303,0.0012275962,0.0027095696,0.0049649226,0.0012432727,0.0040878095,0.004879139,0.0028206687,0.0021177842],"category_scores_gemma":[0.02719173,0.0011211039,0.002860039,0.004672697,0.0021425467,0.006386254,0.00219842,0.0033125551,0.0015065644],"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":[0.00022799014,0.00015411968,0.0063452893,0.0002510641,0.00019120619,0.0002138414,0.000525798,0.6787812,0.001961188,0.15379198,0.0029681958,0.15458801],"study_design_scores_gemma":[0.000004769968,0.000009710322,0.00022951914,0.000012941955,0.000007661918,0.000033595166,0.000020417056,0.9683954,0.00022048425,0.030565051,0.0004856342,0.000014924678],"about_ca_topic_score_codex":0.005660429,"about_ca_topic_score_gemma":0.005075087,"teacher_disagreement_score":0.0067789303,"about_ca_system_score_codex":0.0023026601,"about_ca_system_score_gemma":0.0009970232,"threshold_uncertainty_score":0.035850823},"labels":[],"label_agreement":null},{"id":"W2144884673","doi":"10.1109/72.925566","title":"Eigenpaxels and a neural-network approach to image classification","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre For Cold Ocean Resources Engineering","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Artificial neural network; Principal component analysis; Encoding (memory); Image (mathematics); Contextual image classification; Set (abstract data type); Image processing; Similarity (geometry); Face (sociological concept); Computer vision","score_opus":0.025535360179846357,"score_gpt":0.23256526395032026,"score_spread":0.2070299037704739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144884673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052455133,0.00079985213,0.99173063,0.00020782954,0.00005704884,0.00003254562,0.000038540704,0.00039993715,0.001488083],"genre_scores_gemma":[0.18426628,0.0018208872,0.80258733,0.0002981083,0.00032977824,0.00025390257,0.00026762448,0.00009849929,0.010077586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955803,0.00013627786,0.000027282904,0.00009712321,0.00014513996,0.000036144527],"domain_scores_gemma":[0.99954176,0.00022429263,0.000048496462,0.000060390517,0.00010929012,0.00001588038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007603329,0.00056938804,0.00064385956,0.0012076354,0.00039092143,0.001080062,0.00087036693,0.0010114796,0.0024415767],"category_scores_gemma":[0.0017873257,0.00033649086,0.00053950225,0.0012965612,0.0010626399,0.0017136632,0.00079860626,0.0012942528,0.0007734298],"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":[0.000142058,0.00010038877,0.00082724035,0.00017543981,0.000108024935,0.00013182295,0.0001342784,0.21994944,0.016250147,0.0922006,0.004519007,0.66546154],"study_design_scores_gemma":[0.0000050733543,0.000030021289,0.0003343835,0.000013771842,0.000008464228,0.00007499831,0.000012675364,0.9666385,0.0028280257,0.027184257,0.0028556178,0.000014084094],"about_ca_topic_score_codex":0.002184571,"about_ca_topic_score_gemma":0.0023171129,"teacher_disagreement_score":0.0024415767,"about_ca_system_score_codex":0.00055996596,"about_ca_system_score_gemma":0.00046221746,"threshold_uncertainty_score":0.0081679225},"labels":[],"label_agreement":null},{"id":"W2145295358","doi":"10.1109/72.991425","title":"Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":56,"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; Pipeline (software); Artificial intelligence; Feature extraction; Fuzzy logic; Preprocessor; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Neuro-fuzzy; Fuzzy set; Membership function; Fuzzy control system; Data mining","score_opus":0.016173012081470765,"score_gpt":0.22946898364158666,"score_spread":0.2132959715601159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145295358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25090578,0.00020093462,0.7456765,0.0001507916,0.000034304463,0.00013461568,0.00015761028,0.00082716503,0.0019122441],"genre_scores_gemma":[0.7318443,0.00012778971,0.26567498,0.000033661923,0.000015423226,0.00013763402,0.000231437,0.000017383469,0.0019174153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983704,0.000018926607,0.00001595368,0.000031227864,0.0000752477,0.000021577876],"domain_scores_gemma":[0.99956375,0.0001508096,0.000044317014,0.000027516719,0.00020064165,0.000012975363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003315728,0.000330762,0.00036616655,0.00086864043,0.00017774294,0.00035912995,0.0004058064,0.0006809602,0.0007334624],"category_scores_gemma":[0.001310343,0.00017549875,0.0003243301,0.00046160386,0.00022352098,0.00041450319,0.0001562129,0.0002485586,0.0002227436],"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":[0.0004979081,0.00022383012,0.0054746135,0.00014065509,0.00005591656,0.00027147372,0.00014264286,0.18764968,0.09507084,0.0014695211,0.0015434091,0.7074594],"study_design_scores_gemma":[0.00001002147,0.000051951374,0.0034057715,0.000008522893,0.000012651301,0.00005649528,0.000023013085,0.98393667,0.011627116,0.00049599324,0.00036163273,0.0000102829445],"about_ca_topic_score_codex":0.00439384,"about_ca_topic_score_gemma":0.003505595,"teacher_disagreement_score":0.00439384,"about_ca_system_score_codex":0.00045916493,"about_ca_system_score_gemma":0.00042291667,"threshold_uncertainty_score":0.008736551},"labels":[],"label_agreement":null},{"id":"W2145722250","doi":"10.1109/tnn.2005.857949","title":"Exponential Stability of Impulsive High-Order Hopfield-Type Neural Networks With Time-Varying Delays","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":128,"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":"Exponential stability; Convergence (economics); Artificial neural network; Hopfield network; Stability (learning theory); Exponential function; Control theory (sociology); Applied mathematics; Lyapunov function; Computer science; Rate of convergence; Exponential growth; Mathematics; Mathematical optimization; Artificial intelligence; Mathematical analysis; Nonlinear system; Physics; Machine learning","score_opus":0.01115897018912642,"score_gpt":0.21530398134877907,"score_spread":0.20414501115965264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145722250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2575022,0.000887088,0.73638797,0.00018626089,0.00003388823,0.000019885912,0.000027342403,0.00010764586,0.004847731],"genre_scores_gemma":[0.9913697,0.00027478306,0.0066418634,0.000010886947,0.000007858193,0.000018135805,0.000013606556,0.000005941147,0.0016572986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997595,0.00006117389,0.000014535449,0.000046489135,0.00008823249,0.000030085741],"domain_scores_gemma":[0.9991744,0.00045966703,0.00013656155,0.000032551983,0.00017240095,0.0000243593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000973745,0.00047672668,0.0003490204,0.0003702982,0.00028171696,0.0006022982,0.0006017172,0.0005616069,0.00039199452],"category_scores_gemma":[0.0026749314,0.0001614203,0.00029253523,0.0002778651,0.0008089958,0.0007857958,0.00054898,0.00045516988,0.00005315278],"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":[0.000110498375,0.0000154533,0.0011720104,0.00010073079,0.000035213412,0.0003073078,0.00015657714,0.9380042,0.012940493,0.030877668,0.0001465753,0.016133253],"study_design_scores_gemma":[0.0000062851195,0.000018903442,0.00021914944,0.000003848603,0.0000069586654,0.00002846071,0.000017082542,0.9907635,0.0017418652,0.0070642363,0.00012321102,0.000006413448],"about_ca_topic_score_codex":0.0030045486,"about_ca_topic_score_gemma":0.0019097024,"teacher_disagreement_score":0.0030045486,"about_ca_system_score_codex":0.0006671895,"about_ca_system_score_gemma":0.00044810664,"threshold_uncertainty_score":0.005974114},"labels":[],"label_agreement":null},{"id":"W2147030611","doi":"10.1109/tnn.2004.828755","title":"Learning Mixture Models With the Regularized Latent Maximum Entropy Principle","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","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 Alberta","funders":"","keywords":"Principle of maximum entropy; Computer science; Entropy (arrow of time); Mixture model; Artificial intelligence; Statistical physics; Mathematics; Physics; Thermodynamics","score_opus":0.013973562342100943,"score_gpt":0.23080299245883956,"score_spread":0.2168294301167386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147030611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011315886,0.00012191187,0.9982408,0.000077600984,0.000008450409,0.000009860825,0.00002537555,0.000109655884,0.00027475544],"genre_scores_gemma":[0.14258626,0.00080870226,0.85272545,0.00024598933,0.00021704072,0.00026411953,0.000572428,0.00031129658,0.0022687851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995388,0.0026162686,0.00017894234,0.00064659683,0.0010049619,0.00016536981],"domain_scores_gemma":[0.9921302,0.005980386,0.00050770974,0.0008059059,0.00044711278,0.00012868021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006309675,0.0011948125,0.0018839651,0.0020483327,0.0007595059,0.0025172147,0.0027295984,0.0018630417,0.0022060322],"category_scores_gemma":[0.021936653,0.0015001884,0.0022464446,0.0017575779,0.002128835,0.005821163,0.004189031,0.0037691754,0.0010479086],"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":[0.00015105744,0.00008487594,0.0016155491,0.0002212111,0.00036409823,0.00017973482,0.00039434936,0.49235046,0.0025014277,0.3653631,0.0029422515,0.13383184],"study_design_scores_gemma":[0.000012786477,0.000016134914,0.00021169992,0.000020099958,0.000021460177,0.0000434179,0.000011458424,0.8133447,0.00053679233,0.1839896,0.0017630848,0.000028761264],"about_ca_topic_score_codex":0.0017590901,"about_ca_topic_score_gemma":0.0017959706,"teacher_disagreement_score":0.006309675,"about_ca_system_score_codex":0.0009380937,"about_ca_system_score_gemma":0.0011430585,"threshold_uncertainty_score":0.033369124},"labels":[],"label_agreement":null},{"id":"W2148332992","doi":"10.1109/tnn.2009.2029858","title":"Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":155,"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":"Computer science; Mobile robot; Robot; Artificial neural network; Path (computing); Motion planning; Shortest path problem; Event (particle physics); Topology (electrical circuits); Artificial intelligence; Graph; Engineering; Theoretical computer science; Physics; Computer network","score_opus":0.025007147238864467,"score_gpt":0.2536678519390162,"score_spread":0.22866070470015173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148332992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018481156,0.0002210996,0.9776689,0.00011585305,0.000047579066,0.0000273922,0.000027605276,0.00017154464,0.0032389574],"genre_scores_gemma":[0.8666682,0.00045171607,0.12598212,0.00010244122,0.000040849893,0.00021911015,0.00007961112,0.000044535136,0.006411421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998116,0.00004365104,0.000008241139,0.000045479766,0.00007249848,0.000018618388],"domain_scores_gemma":[0.99978656,0.00009585974,0.000025436326,0.000014962402,0.00006496583,0.0000122655065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002699006,0.00045263694,0.000457346,0.00022242674,0.00024666826,0.00047172376,0.0013132767,0.0008955465,0.0012026586],"category_scores_gemma":[0.0007889892,0.00033647387,0.00041640594,0.00038183937,0.0005539081,0.00074482773,0.00043774294,0.00077288947,0.00018806047],"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":[0.000026075937,0.000010562673,0.00013683234,0.000026483038,0.000013785825,0.00005499342,0.000027874146,0.98620784,0.0018289894,0.0038399945,0.00016555388,0.007661083],"study_design_scores_gemma":[0.000001982824,0.000006619254,0.000019967036,8.02235e-7,0.0000021068938,0.0000056111285,8.4732903e-7,0.99937904,0.000113532966,0.0003840637,0.000083933235,0.0000014325586],"about_ca_topic_score_codex":0.006121258,"about_ca_topic_score_gemma":0.0041435394,"teacher_disagreement_score":0.006121258,"about_ca_system_score_codex":0.0006566525,"about_ca_system_score_gemma":0.00058076746,"threshold_uncertainty_score":0.012171209},"labels":[],"label_agreement":null},{"id":"W2148666284","doi":"10.1109/tnn.2004.837785","title":"Heterogeneous Fuzzy Logic Networks: Fundamentals and Development Studies","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":51,"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":"Interpretability; Computer science; Fuzzy logic; Artificial intelligence; Neuro-fuzzy; Transparency (behavior); Artificial neural network; Machine learning; Fuzzy control system","score_opus":0.03637206130340315,"score_gpt":0.25499311335560293,"score_spread":0.21862105205219978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148666284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0618567,0.093109585,0.62363875,0.006762726,0.0004697585,0.00014330896,0.00019911799,0.00014810095,0.21367194],"genre_scores_gemma":[0.8069122,0.05077296,0.11186636,0.000532434,0.00076976005,0.00015969074,0.0001864908,0.000060371152,0.028739918],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99957436,0.00010316257,0.0000251725,0.00010770961,0.0001434634,0.00004615938],"domain_scores_gemma":[0.99913484,0.00042386854,0.000100387384,0.000049450846,0.00023762044,0.00005380195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001260867,0.0003902259,0.00030119557,0.0015661288,0.00061803946,0.002170648,0.00094073184,0.0009609721,0.0023864838],"category_scores_gemma":[0.0025544455,0.0003040443,0.00038111547,0.0012835958,0.0014580223,0.0026478851,0.0009879863,0.0014110517,0.00047465108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016474749,0.000031411157,0.0011031958,0.00016536683,0.000013920456,0.00027904494,0.00063778367,0.016796762,0.001355481,0.9210778,0.0012328795,0.057289943],"study_design_scores_gemma":[0.000006162077,0.000060870032,0.0015118899,0.00027013308,0.00002083449,0.0006263989,0.0005178707,0.12658013,0.0022864523,0.7887069,0.07937405,0.000038350547],"about_ca_topic_score_codex":0.0025577133,"about_ca_topic_score_gemma":0.0013169158,"teacher_disagreement_score":0.0025577133,"about_ca_system_score_codex":0.0020971028,"about_ca_system_score_gemma":0.00078919745,"threshold_uncertainty_score":0.015215576},"labels":[],"label_agreement":null},{"id":"W2148959261","doi":"10.1109/72.896810","title":"On the use of separable Volterra networks to model discrete-time Volterra systems","year":2001,"lang":"en","type":"letter","venue":"IEEE Transactions on Neural Networks","topic":"Control Systems and Identification","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":"Queen's University","funders":"","keywords":"Volterra series; Volterra integral equation; Volterra equations; Separable space; Cascade; Nonlinear system; Computer science; Applied mathematics; Polynomial; Mathematical optimization; Mathematics; Control theory (sociology); Integral equation; Mathematical analysis; Artificial intelligence; Physics; Control (management)","score_opus":0.030893220381689446,"score_gpt":0.21273055532292304,"score_spread":0.18183733494123358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148959261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009432755,0.0048025576,0.93152326,0.010125885,0.0017938215,0.00004703621,0.0001289935,0.0006304329,0.04151523],"genre_scores_gemma":[0.62102437,0.017758435,0.28386182,0.0067226426,0.0026777212,0.00018699159,0.00032185868,0.00030743354,0.067138806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994949,0.0001839971,0.000019535695,0.000055151875,0.0002158606,0.000030460953],"domain_scores_gemma":[0.99896896,0.0006376326,0.00004690671,0.00012291697,0.0002002381,0.000023412225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067754707,0.0005837999,0.00038007923,0.0002930262,0.00031057466,0.0007921551,0.0007598204,0.0016888871,0.0019554715],"category_scores_gemma":[0.0027348825,0.00021603695,0.00027477095,0.0003589174,0.0008783638,0.0015564524,0.0006978037,0.0019787194,0.001606183],"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":[0.0002432777,0.000051119107,0.00062338123,0.0003930422,0.00007751086,0.0010890672,0.00032285356,0.11740997,0.013736647,0.57298434,0.047284435,0.2457844],"study_design_scores_gemma":[0.000026839904,0.00005010554,0.0002249707,0.000075103926,0.000021774289,0.0006386644,0.000047831334,0.64086646,0.007154824,0.23390284,0.11694634,0.00004422258],"about_ca_topic_score_codex":0.0013616153,"about_ca_topic_score_gemma":0.0019530042,"teacher_disagreement_score":0.0019554715,"about_ca_system_score_codex":0.0006105536,"about_ca_system_score_gemma":0.00018667609,"threshold_uncertainty_score":0.006541729},"labels":[],"label_agreement":null},{"id":"W2149372910","doi":"10.1109/tnn.2010.2071398","title":"New Approach for the Identification and Validation of a Nonlinear F/A-18 Model by Use of Neural Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":50,"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; Bombardier (Canada); Université du Québec à Montréal","funders":"National Aeronautics and Space Administration","keywords":"Artificial neural network; Flutter; Fast Fourier transform; Computer science; Transonic; Perceptron; Supersonic speed; Flight test; Identification (biology); Aerodynamics; Mach number; Nonlinear system; Data reduction; Reduction (mathematics); Ranging; Artificial intelligence; Algorithm; Simulation; Engineering; Mathematics; Data mining; Aerospace engineering","score_opus":0.03737278943260773,"score_gpt":0.26724766363409025,"score_spread":0.2298748742014825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149372910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059729207,0.00013468797,0.9377458,0.000059035465,0.00002970897,0.00006353442,0.00007430357,0.00063441094,0.0015293525],"genre_scores_gemma":[0.6520721,0.00013047701,0.3447739,0.000044914188,0.000022039552,0.00022077924,0.00024517183,0.000069268535,0.002421286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994209,0.00012280112,0.00004813453,0.00013190818,0.000235716,0.00004063176],"domain_scores_gemma":[0.9993831,0.00025505244,0.000082898856,0.00009575227,0.00016537,0.000017825978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011517868,0.0009728701,0.00059990626,0.0010557375,0.00043593446,0.0007034611,0.00068895594,0.0010061495,0.0008266719],"category_scores_gemma":[0.002645929,0.00039768402,0.0007510784,0.0002464795,0.00047128552,0.0006967959,0.0008641898,0.0009854833,0.0002617093],"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":[0.00013461286,0.00012462721,0.003877675,0.0000848872,0.00016338407,0.00011753377,0.00009993489,0.84290665,0.02978328,0.0020006145,0.00030982372,0.12039704],"study_design_scores_gemma":[0.0000046238583,0.00003966015,0.00065012655,0.000006071261,0.000008933884,0.000026456846,0.0000074610803,0.9945292,0.0039850823,0.00036985663,0.0003638818,0.000008729295],"about_ca_topic_score_codex":0.007055987,"about_ca_topic_score_gemma":0.0056185927,"teacher_disagreement_score":0.007055987,"about_ca_system_score_codex":0.00055934297,"about_ca_system_score_gemma":0.00080966984,"threshold_uncertainty_score":0.0140298605},"labels":[],"label_agreement":null},{"id":"W2149375037","doi":"10.1109/tnn.2005.845142","title":"Decision Feedback Recurrent Neural Equalization With Fast Convergence Rate","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","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":"University of Ottawa","funders":"","keywords":"Computer science; Recurrent neural network; Extended Kalman filter; Convergence (economics); Kalman filter; Control theory (sociology); Artificial neural network; Rate of convergence; Nonlinear system; Equalization (audio); Algorithm; Artificial intelligence; Channel (broadcasting); Decoding methods; Control (management)","score_opus":0.016166194613349737,"score_gpt":0.24887757157500331,"score_spread":0.2327113769616536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149375037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011554099,0.00045751658,0.9844518,0.000097927346,0.00008182431,0.000038153772,0.00002392021,0.0006659374,0.0026288242],"genre_scores_gemma":[0.6499111,0.00064510794,0.3395585,0.00019913692,0.000098987555,0.00013519588,0.00015404598,0.00008988148,0.009208042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993476,0.00015747931,0.000060136626,0.00013025523,0.00023719667,0.00006732388],"domain_scores_gemma":[0.9990169,0.00041566152,0.000095203104,0.00016028038,0.00029425946,0.000017724771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010893403,0.0006183028,0.00093954674,0.00025063992,0.00026579632,0.000632544,0.0007716108,0.0007885743,0.0017677904],"category_scores_gemma":[0.0031359477,0.00028396645,0.00038282163,0.0002950142,0.0003730283,0.0011387336,0.00059477065,0.0008548664,0.0008034877],"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":[0.00052703207,0.00020460767,0.0009603913,0.0002806541,0.00017707387,0.00027936962,0.00012358588,0.35395834,0.046472073,0.026579184,0.003852311,0.56658536],"study_design_scores_gemma":[0.000034168886,0.00008791392,0.00018910125,0.0000104421515,0.000019846511,0.00010898242,0.000005858946,0.98385066,0.0119177,0.002368266,0.0013893748,0.000017739607],"about_ca_topic_score_codex":0.0017966594,"about_ca_topic_score_gemma":0.0029979255,"teacher_disagreement_score":0.0017966594,"about_ca_system_score_codex":0.0003263872,"about_ca_system_score_gemma":0.00057646696,"threshold_uncertainty_score":0.0059138536},"labels":[],"label_agreement":null},{"id":"W2149807144","doi":"10.1109/tnn.2006.885436","title":"Face Recognition Using an Enhanced Independent Component Analysis Approach","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":91,"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":"Independent component analysis; Linear discriminant analysis; Pattern recognition (psychology); Eigenface; Facial recognition system; Computer science; Artificial intelligence; Subspace topology; Principal component analysis; Support vector machine; Face (sociological concept); Component analysis; Dimension (graph theory); Unsupervised learning; Speech recognition; Mathematics","score_opus":0.047209158689677976,"score_gpt":0.2904797751869109,"score_spread":0.24327061649723294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149807144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058422834,0.0006749146,0.9905138,0.00008701304,0.00007280223,0.000032940992,0.00005012073,0.00056405296,0.0021621122],"genre_scores_gemma":[0.14862648,0.0013069,0.8444596,0.00016942562,0.00020337164,0.000106359854,0.00033246764,0.000077664256,0.004717769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994124,0.00010251382,0.000030039493,0.00013588337,0.00027607052,0.000043071173],"domain_scores_gemma":[0.9997044,0.00007002088,0.000019678322,0.000070302565,0.00012654052,0.00000900843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005698921,0.00058091164,0.0007189449,0.0011363854,0.00023484523,0.00061174016,0.00058263366,0.0006486449,0.0018347192],"category_scores_gemma":[0.0011511501,0.00018494831,0.0008722092,0.0008836893,0.00032122392,0.0008713329,0.00053468853,0.0007038584,0.0012673163],"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":[0.000105893574,0.000077493045,0.00062544935,0.00015693906,0.00010974622,0.00012786561,0.00005563546,0.03609609,0.0854382,0.014386996,0.0033333222,0.85948634],"study_design_scores_gemma":[0.000015181099,0.00016938365,0.003425521,0.00003238168,0.000095981086,0.0007114166,0.00003322723,0.90492827,0.052541804,0.012140943,0.025842683,0.00006329206],"about_ca_topic_score_codex":0.0010719713,"about_ca_topic_score_gemma":0.0010731064,"teacher_disagreement_score":0.0018347192,"about_ca_system_score_codex":0.00024543804,"about_ca_system_score_gemma":0.00033185075,"threshold_uncertainty_score":0.0061377287},"labels":[],"label_agreement":null},{"id":"W2150035213","doi":"10.1109/72.991413","title":"Optimization-based learning with bounded error for feedforward neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Backpropagation; Artificial neural network; Robustness (evolution); Feedforward neural network; Feed forward; Rprop; Convergence (economics); Bounded function; Artificial intelligence; Algorithm; Time delay neural network; Types of artificial neural networks; Mathematics; Engineering","score_opus":0.021503306138241456,"score_gpt":0.23170742515867057,"score_spread":0.21020411902042913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150035213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014273928,0.00032305735,0.99746454,0.000049829643,0.00002157357,0.000015670026,0.000008212883,0.000114351664,0.0005754799],"genre_scores_gemma":[0.3737386,0.0015234628,0.6167143,0.0001671212,0.00015804092,0.00065155484,0.0001826053,0.00020812971,0.0066562113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992974,0.00023333568,0.000048725597,0.00013061862,0.00023608505,0.00005384217],"domain_scores_gemma":[0.9984249,0.0010911649,0.000133839,0.000064970714,0.00025051608,0.00003468957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016672593,0.0010053646,0.001191277,0.00048278,0.00041882542,0.0010697626,0.0011356139,0.0014342873,0.0020765243],"category_scores_gemma":[0.006026797,0.0005901372,0.00050331664,0.000744013,0.0010774734,0.0012440196,0.0012692672,0.0015378322,0.00056126283],"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":[0.000039929662,0.000020413241,0.000107342785,0.00009412281,0.000025085907,0.000035606692,0.0000306713,0.933535,0.0012254704,0.01899227,0.00068019255,0.045213945],"study_design_scores_gemma":[0.0000035061828,0.000008919203,0.00001816783,0.00000497921,0.0000020571138,0.0000036489666,8.917889e-7,0.9953153,0.00023696682,0.0041611097,0.00024173855,0.0000027916299],"about_ca_topic_score_codex":0.004005696,"about_ca_topic_score_gemma":0.003075675,"teacher_disagreement_score":0.004005696,"about_ca_system_score_codex":0.0010239338,"about_ca_system_score_gemma":0.0011066899,"threshold_uncertainty_score":0.008817434},"labels":[],"label_agreement":null},{"id":"W2150388527","doi":"10.1109/tnn.2009.2016339","title":"A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":19,"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 de Montréal","funders":"","keywords":"Pareto principle; Computer science; Lomax distribution; Mathematics; Mathematical optimization","score_opus":0.015510991328141828,"score_gpt":0.2623983043234745,"score_spread":0.24688731299533268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150388527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064889826,0.00012232758,0.99258727,0.00008370211,0.000013043646,0.000025369414,0.00004764938,0.00015728909,0.00047437663],"genre_scores_gemma":[0.46817827,0.0007503619,0.5228818,0.0003776118,0.00013225684,0.00028594094,0.00073696626,0.00021699037,0.0064397794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977477,0.00078817864,0.00010244169,0.00047082166,0.00071522413,0.0001756152],"domain_scores_gemma":[0.99538994,0.00271407,0.00035657786,0.0007153955,0.0006815491,0.00014245414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00713658,0.0011022814,0.0013362106,0.0026782127,0.00086793554,0.0021060358,0.002996247,0.0022195682,0.0026277245],"category_scores_gemma":[0.015027701,0.00094132725,0.0021111157,0.0020851772,0.0023043882,0.00391482,0.0026477946,0.0021946598,0.00094946544],"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":[0.00028169525,0.00014222162,0.0055409186,0.00014974053,0.0002358239,0.00027558173,0.0003270581,0.54185295,0.0054907016,0.3349516,0.0023664169,0.1083852],"study_design_scores_gemma":[0.000011727134,0.00001668379,0.00045287283,0.000017541617,0.0000234475,0.00007308157,0.0000126196755,0.9539383,0.0007794393,0.04368848,0.0009582463,0.000027558175],"about_ca_topic_score_codex":0.0045637717,"about_ca_topic_score_gemma":0.004126172,"teacher_disagreement_score":0.00713658,"about_ca_system_score_codex":0.0015263095,"about_ca_system_score_gemma":0.0013005871,"threshold_uncertainty_score":0.037742317},"labels":[],"label_agreement":null},{"id":"W2150616088","doi":"10.1109/tnn.2011.2165556","title":"Delay-Independent Stability of Genetic Regulatory Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"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":"Gene regulatory network; Stability (learning theory); Computer science; Genetic network; Control theory (sociology); Gene; Mathematical optimization; Mathematics; Genetics; Biology; Artificial intelligence; Gene expression; Machine learning","score_opus":0.016732998477795955,"score_gpt":0.21638660459837544,"score_spread":0.19965360612057947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150616088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18774863,0.00047132006,0.80440664,0.0003100206,0.000036746947,0.00003573265,0.00007596635,0.00012586711,0.006789168],"genre_scores_gemma":[0.9802372,0.00036243894,0.015769655,0.00003759785,0.000015735583,0.00006607028,0.000041678628,0.00001747437,0.0034521476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997559,0.000053352956,0.000009588602,0.00007411585,0.00007517547,0.00003182568],"domain_scores_gemma":[0.99940586,0.00032720534,0.00012656294,0.000021296886,0.000094916824,0.00002413808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042676408,0.0004785668,0.00027873192,0.00042650438,0.0003083989,0.00057425746,0.00045141584,0.0004520585,0.0009268709],"category_scores_gemma":[0.0019562934,0.00016487653,0.00039252438,0.00021351264,0.00080258056,0.00073181366,0.00040251267,0.00047654222,0.00013782432],"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":[0.0000719146,0.000022181473,0.0015372471,0.000066525296,0.000037992297,0.0002980881,0.00016576322,0.7809921,0.049692273,0.15590414,0.00035101158,0.01086066],"study_design_scores_gemma":[0.00000966447,0.000015990738,0.00020642907,0.000003691451,0.000006714492,0.00003358037,0.000018739818,0.9704052,0.0030931658,0.025718603,0.00047953968,0.000008715283],"about_ca_topic_score_codex":0.0029067902,"about_ca_topic_score_gemma":0.0016657305,"teacher_disagreement_score":0.0029067902,"about_ca_system_score_codex":0.0010102825,"about_ca_system_score_gemma":0.0005479298,"threshold_uncertainty_score":0.007330179},"labels":[],"label_agreement":null},{"id":"W2151017751","doi":"10.1109/tnn.2003.810598","title":"On the number of multilinear partitions and the computing capacity of multiple-valued multiple-threshold perceptrons","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":10,"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; University of Windsor","funders":"","keywords":"Multilinear map; Computer science; Perceptron; Artificial intelligence; Algorithm; Mathematics; Artificial neural network","score_opus":0.029490921595146106,"score_gpt":0.26580440332802663,"score_spread":0.23631348173288053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151017751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11613439,0.0014062467,0.87216115,0.0011518868,0.000075017306,0.000060222595,0.0003925958,0.00055664696,0.008061868],"genre_scores_gemma":[0.86750036,0.0009031399,0.12762511,0.00018263469,0.00020775407,0.00025324294,0.00039784628,0.00019264493,0.002737235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99750596,0.0007270133,0.0001956918,0.00050564297,0.0006159816,0.0004497376],"domain_scores_gemma":[0.9654668,0.029245198,0.0014674916,0.0016143563,0.0014216506,0.00078451436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035009605,0.0008848697,0.0013213343,0.001530788,0.00094592315,0.0027634678,0.0020607666,0.0011613512,0.0030779114],"category_scores_gemma":[0.03138746,0.00073914806,0.00077693164,0.0019073808,0.0039104526,0.0099071,0.0031292886,0.0023954704,0.00048682466],"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":[0.00084155245,0.000089277164,0.0022014584,0.000355855,0.0001002974,0.00017699652,0.0004345989,0.5196429,0.009487806,0.37682998,0.0025037301,0.08733554],"study_design_scores_gemma":[0.000013930398,0.000033360895,0.00027972562,0.000029647665,0.000014110637,0.000052402786,0.000033837878,0.7874923,0.0028327622,0.20859969,0.0006010536,0.000017234224],"about_ca_topic_score_codex":0.0012554032,"about_ca_topic_score_gemma":0.00088669953,"teacher_disagreement_score":0.0035009605,"about_ca_system_score_codex":0.0016789105,"about_ca_system_score_gemma":0.0011439703,"threshold_uncertainty_score":0.01851505},"labels":[],"label_agreement":null},{"id":"W2151602183","doi":"10.1109/tnn.2006.872350","title":"A softmin-based neural model for causal reasoning","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Modeling and Causal Inference","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":"Université de Sherbrooke","funders":"University of Waterloo","keywords":"Monotonic function; Computer science; Artificial intelligence; Artificial neural network; Causal model; Class (philosophy); Process (computing); Mechanism (biology); Causal reasoning; Fuzzy logic; Additive function; Machine learning; Mathematics; Cognition; Programming language","score_opus":0.024965401478727355,"score_gpt":0.2511315588216548,"score_spread":0.22616615734292742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151602183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019306388,0.00034204827,0.97032624,0.000761687,0.00008346549,0.000037204256,0.00018877939,0.00032051845,0.008633683],"genre_scores_gemma":[0.791884,0.00060506165,0.1936521,0.0003830953,0.00012836553,0.0002033911,0.00022752302,0.0000747774,0.012841666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950707,0.00014685313,0.00002801325,0.00013019676,0.00013560183,0.000052296724],"domain_scores_gemma":[0.9992269,0.00040241217,0.000090368485,0.00010038515,0.00011637443,0.00006351829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012283161,0.00048330144,0.00080289535,0.00065732875,0.00045932145,0.0015395838,0.0022712941,0.0013936907,0.0057909954],"category_scores_gemma":[0.0027205842,0.00048081123,0.00085289497,0.0006558604,0.0013535577,0.0032873342,0.001171347,0.0020755779,0.00061010016],"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":[0.00015885895,0.00006140724,0.00055954466,0.000133677,0.00009123095,0.00014415674,0.00017426141,0.4440484,0.00360284,0.497385,0.0017414048,0.05189922],"study_design_scores_gemma":[0.000009226629,0.0000148888585,0.00008735492,0.0000087218195,0.00001068744,0.000026949136,0.0000068849095,0.8833309,0.00045528592,0.115190245,0.00085010106,0.000008754737],"about_ca_topic_score_codex":0.0022161568,"about_ca_topic_score_gemma":0.003218252,"teacher_disagreement_score":0.0057909954,"about_ca_system_score_codex":0.0013071719,"about_ca_system_score_gemma":0.001150763,"threshold_uncertainty_score":0.01937288},"labels":[],"label_agreement":null},{"id":"W2152388511","doi":"10.1109/tnn.2005.844089","title":"Refractory Pulse Counting Processes in Stochastic Neural Computers","year":2005,"lang":"en","type":"letter","venue":"IEEE Transactions on Neural Networks","topic":"Neural dynamics and brain function","field":"Neuroscience","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 Manitoba","funders":"","keywords":"Dead time; Bernoulli's principle; Bernoulli distribution; Pulse (music); Artificial neural network; Stochastic process; Computer science; Transient (computer programming); Detector; Refractory period; Control theory (sociology); Mathematics; Statistics; Physics; Artificial intelligence; Telecommunications; Random variable","score_opus":0.023204648703050924,"score_gpt":0.23983144196753792,"score_spread":0.216626793264487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152388511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21446054,0.0099736,0.59320784,0.06155505,0.0055350177,0.00011020333,0.00021373518,0.0015790967,0.11336492],"genre_scores_gemma":[0.933678,0.0039338274,0.037045185,0.0052450728,0.0031075897,0.00008697723,0.000047420344,0.00011151675,0.01674444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996686,0.00008912093,0.000012058457,0.000038646205,0.000170049,0.000021606731],"domain_scores_gemma":[0.99808633,0.0014574061,0.00007155927,0.0001931159,0.00014027684,0.000051373445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067867443,0.00020012754,0.00027892305,0.00023917368,0.0003017014,0.0007218462,0.0007114873,0.0015152732,0.0016035036],"category_scores_gemma":[0.0041964315,0.00015730661,0.00013938731,0.00024653354,0.0013098152,0.0017018693,0.00037496933,0.0013578339,0.0005408358],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026940185,0.000041238018,0.000389791,0.00014787013,0.000016524296,0.0007534428,0.00025023113,0.024198782,0.0222193,0.8559627,0.016000835,0.07974989],"study_design_scores_gemma":[0.00008239993,0.000121957826,0.0005629472,0.000051764844,0.000011006581,0.0006970482,0.000045583743,0.32189643,0.01383869,0.6132321,0.049418475,0.00004169064],"about_ca_topic_score_codex":0.00027030078,"about_ca_topic_score_gemma":0.00026280223,"teacher_disagreement_score":0.0016035036,"about_ca_system_score_codex":0.0006110799,"about_ca_system_score_gemma":0.00019577116,"threshold_uncertainty_score":0.005364299},"labels":[],"label_agreement":null},{"id":"W2152500150","doi":"10.1109/tnn.2002.804285","title":"Gaussian activation functions using Markov chains","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","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":"University of Manitoba","funders":"","keywords":"Computer science; Markov chain; Gaussian; Stochastic computing; Artificial neural network; Signal processing; Nonlinear system; Offset (computer science); Stochastic resonance; Sigmoid function; Stochastic process; Algorithm; Markov process; Digital signal processing; Noise (video); Artificial intelligence; Mathematics; Machine learning; Computer hardware","score_opus":0.031213089371973087,"score_gpt":0.23627035196988516,"score_spread":0.20505726259791207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152500150","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006563693,0.0003015478,0.98777944,0.00036652898,0.000057490193,0.000055725868,0.00012899742,0.00036112586,0.004385479],"genre_scores_gemma":[0.634584,0.0021881508,0.3184781,0.0005102228,0.00024159785,0.0011867206,0.0008668275,0.00041534178,0.041529026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897975,0.0003940125,0.00005543438,0.00019093981,0.00023245184,0.00014747694],"domain_scores_gemma":[0.9956691,0.0031302727,0.00025983102,0.00031284447,0.00047900065,0.00014889709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026052326,0.00097587286,0.0012132493,0.0011159042,0.0007606297,0.0018605101,0.0018310603,0.0017407036,0.01178777],"category_scores_gemma":[0.00948695,0.0008144947,0.0013246029,0.0010000741,0.001597129,0.0028726754,0.0016383358,0.0022224288,0.002623127],"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":[0.00006619372,0.000032874694,0.0004718921,0.000053997104,0.00003492418,0.00009951231,0.000082949075,0.64627194,0.00058783,0.33178747,0.0017299576,0.018780474],"study_design_scores_gemma":[0.000010932415,0.0000073285896,0.00004146417,0.000010771415,0.0000056454564,0.0000129057935,0.000004004332,0.9361507,0.0002016266,0.06281644,0.0007297269,0.000008443501],"about_ca_topic_score_codex":0.008930781,"about_ca_topic_score_gemma":0.010138161,"teacher_disagreement_score":0.01178777,"about_ca_system_score_codex":0.0019287934,"about_ca_system_score_gemma":0.0018486205,"threshold_uncertainty_score":0.039434016},"labels":[],"label_agreement":null},{"id":"W2152511329","doi":"10.1109/72.846725","title":"Taking on the curse of dimensionality in joint distributions using neural networks","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":120,"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 de Montréal; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Curse of dimensionality; Joint probability distribution; Artificial neural network; Computer science; Artificial intelligence; Conditional probability distribution; Random variable; Graphical model; Bayesian network; Bayesian probability; Pattern recognition (psychology); Machine learning; Mathematics; Algorithm; Statistics","score_opus":0.05487977902440835,"score_gpt":0.27586548560015645,"score_spread":0.2209857065757481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152511329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070674694,0.00033089437,0.99128693,0.00052318256,0.000022130645,0.000013082969,0.00004050892,0.00031702485,0.0003987136],"genre_scores_gemma":[0.3371083,0.0019399473,0.6573416,0.00057120976,0.00030419376,0.00022336398,0.00034171558,0.0003325702,0.0018371791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968663,0.0014876503,0.0002090688,0.00059288123,0.00070388126,0.0001403184],"domain_scores_gemma":[0.978745,0.016300283,0.0010985915,0.0026940445,0.000898999,0.00026311938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058200234,0.0015508004,0.0022850446,0.0014563687,0.001357022,0.0031307586,0.0024522732,0.0022791624,0.0015728695],"category_scores_gemma":[0.030145153,0.0015672909,0.0017076497,0.0017548092,0.003162159,0.01251235,0.003945774,0.006150476,0.0005403831],"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":[0.00019747992,0.00006753993,0.0035088563,0.0002268177,0.00030587544,0.0003095353,0.00039637258,0.726782,0.0018576494,0.0968168,0.001958403,0.1675726],"study_design_scores_gemma":[0.0000110740475,0.000013182877,0.0002324091,0.000018531115,0.000021222637,0.000043176376,0.0000211764,0.9015224,0.00055624766,0.09691423,0.0006297401,0.000016600081],"about_ca_topic_score_codex":0.009060602,"about_ca_topic_score_gemma":0.010985736,"teacher_disagreement_score":0.009060602,"about_ca_system_score_codex":0.0013844168,"about_ca_system_score_gemma":0.0017951782,"threshold_uncertainty_score":0.0307796},"labels":[],"label_agreement":null},{"id":"W2152808281","doi":"10.1109/tnn.2007.912312","title":"Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":233,"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 de Montréal","funders":"","keywords":"Computer science; Language model; Artificial neural network; Vocabulary; Probabilistic neural network; Speedup; Artificial intelligence; Probabilistic logic; Computation; Machine learning; Feedforward neural network; Sampling (signal processing); Statistical model; Training (meteorology); Backpropagation; Importance sampling; Time delay neural network; Algorithm; Statistics; Mathematics; Monte Carlo method","score_opus":0.0677840225644307,"score_gpt":0.29191262992274414,"score_spread":0.22412860735831344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152808281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01263745,0.000106726606,0.9860241,0.00009055774,0.00003229607,0.000022184575,0.000015996347,0.00069803634,0.0003725924],"genre_scores_gemma":[0.35840714,0.0001908351,0.6390409,0.00012955524,0.00010771505,0.0001352105,0.00020670905,0.00019037991,0.0015915997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938524,0.0002446672,0.000030644253,0.00009257408,0.00018697378,0.000059931626],"domain_scores_gemma":[0.9979785,0.0014216742,0.00007275648,0.00021424243,0.00025057848,0.00006222086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015853784,0.0005938052,0.00072304794,0.0004931991,0.00028706284,0.00041603102,0.0014401644,0.00072441524,0.0019078827],"category_scores_gemma":[0.006871984,0.0005024433,0.00045091478,0.00061859016,0.00047970153,0.0015714638,0.0010470898,0.0018119178,0.00052977377],"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":[0.0002780461,0.00017043413,0.0011646148,0.000099629644,0.000053805757,0.00014577892,0.00012918474,0.6906656,0.0137467645,0.022577131,0.0028464831,0.26812258],"study_design_scores_gemma":[0.0000039422334,0.0000070909055,0.00003231261,8.3688565e-7,0.0000015576994,0.00000713231,0.0000014172009,0.997331,0.00070447277,0.0017710984,0.00013797531,0.0000012239425],"about_ca_topic_score_codex":0.004947551,"about_ca_topic_score_gemma":0.0072868317,"teacher_disagreement_score":0.004947551,"about_ca_system_score_codex":0.00054400606,"about_ca_system_score_gemma":0.00091350335,"threshold_uncertainty_score":0.009837508},"labels":[],"label_agreement":null},{"id":"W2152975148","doi":"10.1109/tnn.2006.882814","title":"Basic Difference Between Brain and Computer: Integration of Asynchronous Processes Implemented as Hardware Model of the Retina","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":15,"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","funders":"","keywords":"Computer science; Process (computing); Asynchronous communication; Synchronization (alternating current); Preprocessor; Retina; Receptive field; Artificial intelligence; Computer hardware; Neuroscience","score_opus":0.02941815770757968,"score_gpt":0.2619552074771494,"score_spread":0.2325370497695697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152975148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25932947,0.0017248816,0.69154197,0.0014653398,0.00029335517,0.00008225806,0.00020016037,0.0011690555,0.044193577],"genre_scores_gemma":[0.93987393,0.00060807215,0.052722573,0.0001065566,0.000063110965,0.00010372287,0.00004905703,0.00005826722,0.006414696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998242,0.000043451524,0.000009742662,0.00004637776,0.000052552183,0.000023617222],"domain_scores_gemma":[0.99981743,0.0000734534,0.000018322608,0.00003848354,0.000029960123,0.000022386004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000166817,0.0003094604,0.00037553237,0.000213786,0.00027828052,0.0010339785,0.00085041096,0.000954784,0.002568833],"category_scores_gemma":[0.00070410594,0.00025657652,0.00054780056,0.00019806047,0.0007967345,0.0013012375,0.00051766436,0.0006942734,0.00040300377],"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":[0.00032150807,0.00010706337,0.0016167018,0.00018813131,0.000084140054,0.0004921677,0.00048810674,0.34961626,0.06952253,0.5523274,0.0011751632,0.02406082],"study_design_scores_gemma":[0.00006731993,0.00014256782,0.00057775114,0.000017130124,0.000034120025,0.00011173791,0.00003703054,0.9305733,0.0039821286,0.06105892,0.0033744986,0.000023547158],"about_ca_topic_score_codex":0.001969041,"about_ca_topic_score_gemma":0.001083901,"teacher_disagreement_score":0.002568833,"about_ca_system_score_codex":0.00044941163,"about_ca_system_score_gemma":0.0005113052,"threshold_uncertainty_score":0.008593619},"labels":[],"label_agreement":null},{"id":"W2153846939","doi":"10.1109/tnn.2006.883002","title":"The Impact of Arithmetic Representation on Implementing MLP-BP on FPGAs: A Study","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":145,"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":"Computer science; Arithmetic; Field-programmable gate array; Representation (politics); Parallel computing; Theoretical computer science; Computer architecture; Mathematics; Computer hardware","score_opus":0.035797712668031376,"score_gpt":0.36823864259842787,"score_spread":0.3324409299303965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153846939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8887139,0.002403941,0.09577942,0.0004072961,0.00017112476,0.00014584948,0.0001313683,0.0009542675,0.0112928515],"genre_scores_gemma":[0.9555369,0.0007672006,0.04187674,0.00006603778,0.00003209416,0.000036948786,0.00009159017,0.00008812199,0.0015042828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989611,0.0003537448,0.0000935738,0.000099346384,0.00036586323,0.00012632746],"domain_scores_gemma":[0.9939522,0.004163241,0.00054315804,0.00049421674,0.0007885625,0.000058587328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008282962,0.0005764018,0.00028841555,0.00050853594,0.00028223742,0.0007696149,0.0010333182,0.0004555286,0.003657811],"category_scores_gemma":[0.007961618,0.00025962468,0.00024370363,0.0006704406,0.00023223311,0.0017801989,0.00024542937,0.00048230923,0.0004211192],"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":[0.0026053118,0.0004700448,0.012826964,0.00218399,0.00025652564,0.001640727,0.0003560008,0.3368564,0.14051615,0.009365857,0.0026507115,0.4902714],"study_design_scores_gemma":[0.00021746219,0.0052245553,0.009726258,0.00029239917,0.00041185474,0.0018813029,0.00051889947,0.6188613,0.3465391,0.0021585592,0.014099355,0.00006893369],"about_ca_topic_score_codex":0.0016954853,"about_ca_topic_score_gemma":0.0018784532,"teacher_disagreement_score":0.003657811,"about_ca_system_score_codex":0.0005184228,"about_ca_system_score_gemma":0.0003386895,"threshold_uncertainty_score":0.012236595},"labels":[],"label_agreement":null},{"id":"W2154338144","doi":"10.1109/72.914519","title":"STRIP - a strip-based neural-network growth algorithm for learning multiple-valued functions","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"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; University of Windsor","funders":"Purdue University","keywords":"Hyperplane; Artificial neural network; STRIPS; Algorithm; Computer science; Genetic algorithm; Artificial intelligence; Mathematics; Combinatorics; Machine learning","score_opus":0.019815359783414175,"score_gpt":0.24156098461776043,"score_spread":0.22174562483434626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154338144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02108208,0.00023778023,0.97593766,0.00015215583,0.00002983341,0.00003876547,0.00004789184,0.00065941905,0.0018143384],"genre_scores_gemma":[0.3232582,0.0002511078,0.67116207,0.00018167886,0.000033092678,0.000206979,0.0003333518,0.00018832646,0.004385224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997892,0.000067868874,0.000011743886,0.000041198407,0.00006780269,0.000022142514],"domain_scores_gemma":[0.99946266,0.00027609154,0.00004743353,0.0000603322,0.00012819236,0.000025299016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009327102,0.00056444283,0.00068605965,0.0004776942,0.00028253492,0.00050820375,0.001088062,0.0008359163,0.0023783182],"category_scores_gemma":[0.0013745079,0.00034904704,0.00038579412,0.0006290798,0.00060044165,0.0009976935,0.0007616259,0.0009111605,0.00068557466],"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":[0.00016622797,0.00006179303,0.0007133715,0.00007885575,0.000041276875,0.000069633905,0.000082593164,0.7983766,0.01071206,0.013752924,0.0020879179,0.1738567],"study_design_scores_gemma":[0.000007997731,0.000029777837,0.00002700676,0.0000032361304,0.0000025356146,0.0000075887183,0.000004323414,0.99614716,0.0009822856,0.0023949838,0.00039092894,0.0000021854592],"about_ca_topic_score_codex":0.0015144445,"about_ca_topic_score_gemma":0.0016698899,"teacher_disagreement_score":0.0023783182,"about_ca_system_score_codex":0.0005145638,"about_ca_system_score_gemma":0.000526832,"threshold_uncertainty_score":0.007956266},"labels":[],"label_agreement":null},{"id":"W2155092378","doi":"10.1109/72.977279","title":"Interpretation of artificial neural networks by means of fuzzy rules","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Neuro-fuzzy; Computer science; Artificial intelligence; Interpretation (philosophy); Fuzzy logic; Artificial neural network; Fuzzy classification; Fuzzy set operations; Operator (biology); Defuzzification; Fuzzy rule; Fuzzy control system; Antecedent (behavioral psychology); Extension (predicate logic); Fuzzy associative matrix; Fuzzy set; Data mining; Fuzzy number","score_opus":0.015683647162570504,"score_gpt":0.21480528278678826,"score_spread":0.19912163562421775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155092378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061919414,0.00247063,0.967373,0.00077168836,0.00048394894,0.0000866144,0.00013908531,0.00029248174,0.022190623],"genre_scores_gemma":[0.20604573,0.0045560636,0.77962655,0.0005742202,0.00050130446,0.00030679564,0.00040326835,0.00018088623,0.007805173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981723,0.000742522,0.00023294232,0.000251657,0.00053229573,0.00006835331],"domain_scores_gemma":[0.99891937,0.00056198344,0.000109270826,0.00017111238,0.00021613909,0.00002210524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021610218,0.0013174565,0.0008701203,0.0014982482,0.00055354735,0.0030665756,0.0014304782,0.001461549,0.003458784],"category_scores_gemma":[0.0056642466,0.00039942077,0.0011676785,0.00092990394,0.0020172459,0.0028625424,0.001253157,0.001575692,0.00087992643],"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":[0.000108563145,0.000047521356,0.00042499448,0.0006681748,0.00013446836,0.001298935,0.0013085322,0.07686255,0.0066403234,0.79456514,0.0035511348,0.1143896],"study_design_scores_gemma":[0.00003524014,0.000048594728,0.0002175612,0.00026425734,0.00006432428,0.00040473844,0.00023064844,0.20356137,0.0037567338,0.74855685,0.042815484,0.000044136425],"about_ca_topic_score_codex":0.00079827185,"about_ca_topic_score_gemma":0.0006379839,"teacher_disagreement_score":0.003458784,"about_ca_system_score_codex":0.0006823182,"about_ca_system_score_gemma":0.00055036,"threshold_uncertainty_score":0.011570811},"labels":[],"label_agreement":null},{"id":"W2155178156","doi":"10.1109/tnn.2005.860853","title":"Ensemble-based discriminant learning with boosting for face recognition","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Institute of Standards and Technology","keywords":"Boosting (machine learning); Linear discriminant analysis; Computer science; Artificial intelligence; Machine learning; Ensemble learning; Facial recognition system; Discriminant; Pairwise comparison; Pattern recognition (psychology)","score_opus":0.02248973911586177,"score_gpt":0.22837046837794747,"score_spread":0.2058807292620857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155178156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036276667,0.0006289926,0.9944882,0.00006723923,0.000058276233,0.000025399124,0.000017321803,0.00030161958,0.00078538805],"genre_scores_gemma":[0.24279873,0.0013763495,0.75212216,0.00021201643,0.00031932263,0.00020106879,0.00021229037,0.00011690818,0.00264114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987256,0.0004982406,0.000047497873,0.00019129562,0.00045347778,0.00008384356],"domain_scores_gemma":[0.9986319,0.00059426646,0.000089366804,0.00024254822,0.00038809015,0.000053810727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028938253,0.00090876024,0.0018621687,0.0013732179,0.0005198491,0.00079023716,0.0015348288,0.00088010635,0.0017380377],"category_scores_gemma":[0.0040144892,0.00040989928,0.0011722675,0.0013714926,0.00052245636,0.0014672434,0.0013768052,0.0014699188,0.0013952778],"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":[0.00017799223,0.00014129361,0.0020844676,0.00021184949,0.00021669616,0.0001114444,0.00009967981,0.2231408,0.010777577,0.025793627,0.0049102833,0.7323342],"study_design_scores_gemma":[0.000012243992,0.00006718114,0.00041646408,0.000014016282,0.000033520824,0.000086417596,0.000010844597,0.97851914,0.0036923673,0.012415116,0.0047123046,0.00002029383],"about_ca_topic_score_codex":0.0006531214,"about_ca_topic_score_gemma":0.00073672755,"teacher_disagreement_score":0.0028938253,"about_ca_system_score_codex":0.00039657086,"about_ca_system_score_gemma":0.0004305113,"threshold_uncertainty_score":0.015304148},"labels":[],"label_agreement":null},{"id":"W2155876893","doi":"10.1109/tnn.2008.2004373","title":"A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":234,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; Concordia University","funders":"","keywords":"Control theory (sociology); Fault detection and isolation; Artificial neural network; Actuator; Attitude control; Nonlinear system; Computer science; Backpropagation; Observer (physics); Fault (geology); Control engineering; Engineering; Artificial intelligence; Control (management)","score_opus":0.008522673110264485,"score_gpt":0.20553219006025203,"score_spread":0.19700951694998756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155876893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02932142,0.00065864064,0.96693456,0.00013823985,0.00007309977,0.000055848624,0.000027366388,0.0009440333,0.0018467584],"genre_scores_gemma":[0.7627182,0.00037811496,0.23369615,0.00008457153,0.000056710993,0.000102700935,0.00008500486,0.000029106059,0.0028495432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980706,0.000030735293,0.000015455384,0.000048463644,0.000078313205,0.00001996043],"domain_scores_gemma":[0.9997769,0.00006532131,0.000044940763,0.00002870802,0.00007177892,0.000012458267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037163583,0.00048583932,0.00043197355,0.00020413462,0.00019932864,0.00029876802,0.0006934379,0.0006411383,0.00080607826],"category_scores_gemma":[0.0007838267,0.00019115442,0.0003488019,0.00014501896,0.00027687315,0.0004386772,0.00039168372,0.00044611184,0.00022506993],"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":[0.000374113,0.00019102392,0.0014518123,0.0004745426,0.00016949675,0.0005675955,0.00020610477,0.40052542,0.15156525,0.009245227,0.0019200427,0.43330944],"study_design_scores_gemma":[0.000019262072,0.00015269985,0.00028940968,0.000006945809,0.00002481464,0.00007808381,0.000004039221,0.987914,0.010439038,0.00032821007,0.0007339144,0.000009594289],"about_ca_topic_score_codex":0.0026573455,"about_ca_topic_score_gemma":0.0030713272,"teacher_disagreement_score":0.0026573455,"about_ca_system_score_codex":0.00029942614,"about_ca_system_score_gemma":0.00047475065,"threshold_uncertainty_score":0.005283773},"labels":[],"label_agreement":null},{"id":"W2157540743","doi":"10.1109/72.991430","title":"Determination of neural-network topology for partial discharge pulse pattern recognition","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; University of Waterloo","funders":"","keywords":"Network topology; Artificial neural network; Computer science; Pattern recognition (psychology); Topology (electrical circuits); Feature (linguistics); Time delay neural network; Artificial intelligence; Pulse (music); Mathematics; Telecommunications; Computer network","score_opus":0.032644759415327115,"score_gpt":0.2556090050958931,"score_spread":0.222964245680566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157540743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13325839,0.00016598724,0.8638598,0.00010606124,0.000023851188,0.00008968099,0.00010749367,0.00045234547,0.0019364149],"genre_scores_gemma":[0.70954484,0.00019104852,0.28911862,0.000018452403,0.000011389086,0.00015901939,0.00019952959,0.00004203492,0.00071507326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979633,0.000078170815,0.000015478525,0.000036920104,0.000059186157,0.000013847984],"domain_scores_gemma":[0.99885345,0.0005686675,0.00008947858,0.0001034799,0.00034275872,0.000042128093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089246756,0.00036081372,0.0003118154,0.00060668803,0.00022205063,0.00042587076,0.00047369962,0.00044566402,0.00088902353],"category_scores_gemma":[0.004214215,0.000265022,0.00021646623,0.0003347742,0.0003287143,0.0009774468,0.0002849591,0.00048051702,0.00022713302],"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":[0.00012979817,0.000076946446,0.0023758283,0.00017903795,0.000030795414,0.00018128284,0.00008728825,0.70845985,0.047777776,0.01475504,0.0006140304,0.22533229],"study_design_scores_gemma":[0.0000033971826,0.00003333247,0.00043212232,0.0000052552555,0.000004245891,0.000042311924,0.000008621952,0.99119425,0.005477863,0.0025628796,0.00023012787,0.0000055589358],"about_ca_topic_score_codex":0.00071819604,"about_ca_topic_score_gemma":0.0015630868,"teacher_disagreement_score":0.00089246756,"about_ca_system_score_codex":0.0004213708,"about_ca_system_score_gemma":0.00034871843,"threshold_uncertainty_score":0.0047198534},"labels":[],"label_agreement":null},{"id":"W2158063174","doi":"10.1109/tnn.2011.2161999","title":"Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":197,"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 Windsor","funders":"","keywords":"Phishing; Computer science; Naive Bayes classifier; Artificial intelligence; Classifier (UML); Web page; Machine learning; Bayes classifier; Pattern recognition (psychology); Data mining; The Internet; Support vector machine; World Wide Web","score_opus":0.046669252832073044,"score_gpt":0.22979738627587545,"score_spread":0.1831281334438024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158063174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00791001,0.00047187784,0.9887326,0.0005021408,0.000031826017,0.000083642095,0.00010378589,0.0003503232,0.0018137647],"genre_scores_gemma":[0.48660544,0.0013234239,0.5043105,0.00048772228,0.0005401519,0.00045663805,0.00051599956,0.00017297894,0.005587179],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970386,0.0008698393,0.00018934038,0.0005350437,0.0011517742,0.00021549464],"domain_scores_gemma":[0.9937032,0.0032654763,0.0007501512,0.00049168675,0.0015396387,0.00024977163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003855479,0.001169644,0.0017914114,0.005584873,0.0009887354,0.0030901164,0.0029456194,0.0025583827,0.0023529397],"category_scores_gemma":[0.015801268,0.0012265852,0.0014278174,0.002460018,0.0019165039,0.0047964575,0.0018729736,0.0021774492,0.0013347135],"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":[0.00046467764,0.00077902543,0.015918033,0.00061223813,0.00045842427,0.00050755136,0.0012220277,0.29983556,0.017186418,0.11271407,0.006139331,0.5441627],"study_design_scores_gemma":[0.000018083862,0.00008083443,0.0024106745,0.000076971824,0.00006813858,0.000205456,0.00007970851,0.9457555,0.0019486205,0.04708703,0.0022060676,0.00006295259],"about_ca_topic_score_codex":0.010224813,"about_ca_topic_score_gemma":0.0103865545,"teacher_disagreement_score":0.010224813,"about_ca_system_score_codex":0.0020052905,"about_ca_system_score_gemma":0.0019242851,"threshold_uncertainty_score":0.020389974},"labels":[],"label_agreement":null},{"id":"W2158247472","doi":"10.1109/tnn.2002.1000134","title":"Face recognition with radial basis function (RBF) neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":661,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Radial basis function; Overfitting; Artificial intelligence; Computer science; Pattern recognition (psychology); Linear discriminant analysis; Artificial neural network; Facial recognition system; Principal component analysis; Radial basis function network; Hierarchical RBF; Classifier (UML); Machine learning","score_opus":0.025577086661139226,"score_gpt":0.20844895168478164,"score_spread":0.18287186502364242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158247472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009692232,0.00060865,0.9877082,0.00008244381,0.000036754947,0.000023019596,0.000024806468,0.00078835245,0.0010354858],"genre_scores_gemma":[0.26635873,0.0009397913,0.7282901,0.00015184483,0.00008093079,0.00011612396,0.00014069947,0.000064826556,0.0038569034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994875,0.00014275983,0.000023718541,0.00008657616,0.00021748853,0.00004190656],"domain_scores_gemma":[0.9996551,0.00010334915,0.00004361001,0.00005324379,0.0001347386,0.0000098261635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083069113,0.0005012134,0.0008263616,0.0005019392,0.00026400443,0.0005410794,0.0007954354,0.0010675961,0.0011163454],"category_scores_gemma":[0.0016384154,0.000281031,0.00049195276,0.0006019613,0.00029485085,0.0010503898,0.00050199183,0.0006917403,0.0011462856],"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":[0.00026446747,0.00010858133,0.00081661146,0.00020375801,0.0000823044,0.0001576031,0.00007942608,0.10277275,0.07638782,0.0076911957,0.0036712992,0.80776423],"study_design_scores_gemma":[0.000013036225,0.000084709136,0.0005573887,0.000016191509,0.000019116487,0.00024535818,0.0000116560905,0.9717808,0.021059597,0.00289949,0.003286164,0.000026594735],"about_ca_topic_score_codex":0.0017810465,"about_ca_topic_score_gemma":0.0013682839,"teacher_disagreement_score":0.0017810465,"about_ca_system_score_codex":0.0003058314,"about_ca_system_score_gemma":0.00023258457,"threshold_uncertainty_score":0.0043931603},"labels":[],"label_agreement":null},{"id":"W2160680757","doi":"10.1109/tnn.2004.824261","title":"Dynamics of Projective Adaptive Resonance Theory Model: The Foundation of PART Algorithm","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":58,"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":"Adaptive resonance theory; Computer science; Algorithm; Cluster analysis; Differential equation; Artificial neural network; Partial differential equation; Mathematics; Applied mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.01739360236093799,"score_gpt":0.24168147907590098,"score_spread":0.224287876714963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160680757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0156714,0.00009061631,0.9806646,0.00023623928,0.000028836537,0.0000211883,0.000020682437,0.00011331145,0.0031531693],"genre_scores_gemma":[0.8531462,0.00031448933,0.1414407,0.00018383386,0.00007836333,0.0001756894,0.00007224085,0.00008299122,0.004505404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997117,0.000102804195,0.000010960491,0.000059796235,0.000088594614,0.000026168022],"domain_scores_gemma":[0.99939847,0.00028318996,0.00007890841,0.00008554994,0.00011141387,0.000042567568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081385387,0.00038252815,0.0005920832,0.0003115789,0.00037434587,0.0007021249,0.0010081677,0.0007342317,0.0023652508],"category_scores_gemma":[0.003002096,0.00028398217,0.0004081112,0.00026706312,0.0013498032,0.0015947538,0.0013528777,0.0010968184,0.00034979588],"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":[0.000059154383,0.000020904607,0.00063268235,0.00006430591,0.00003831468,0.0001281508,0.00016507966,0.5969464,0.0071704364,0.3683294,0.0010893533,0.02535582],"study_design_scores_gemma":[0.0000041755197,0.00002030377,0.00006362589,0.0000027707706,0.0000024378178,0.000018350136,0.0000053839826,0.9691943,0.00033701552,0.030013729,0.00033215387,0.0000057315915],"about_ca_topic_score_codex":0.0010889933,"about_ca_topic_score_gemma":0.0005253592,"teacher_disagreement_score":0.0023652508,"about_ca_system_score_codex":0.00042421743,"about_ca_system_score_gemma":0.00051814516,"threshold_uncertainty_score":0.007912576},"labels":[],"label_agreement":null},{"id":"W2161913203","doi":"10.1109/72.950144","title":"Prediction of noisy chaotic time series using an optimal radial basis function neural network","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Chaos control and synchronization","field":"Physics and Astronomy","cited_by":194,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Intrinsik (Canada); University of Calgary","funders":"","keywords":"Overfitting; Chaotic; Subspace topology; Artificial neural network; Radial basis function; Computer science; Radial basis function network; Algorithm; Series (stratigraphy); Nonlinear system; Time series; Mathematics; Pattern recognition (psychology); Artificial intelligence; Mathematical optimization; Machine learning","score_opus":0.01573624489250338,"score_gpt":0.20879464245053467,"score_spread":0.19305839755803128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161913203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0902552,0.00041761418,0.9083551,0.00015650663,0.000026559543,0.000010845111,0.000018707478,0.00021963322,0.0005397685],"genre_scores_gemma":[0.87401026,0.00039387227,0.124758124,0.000037327703,0.000047441674,0.000033975015,0.00005799331,0.0000332872,0.0006277258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995703,0.00019085054,0.000025069909,0.000080235644,0.00010246922,0.00003107641],"domain_scores_gemma":[0.9990798,0.0005609331,0.000107053405,0.000052712305,0.00017948395,0.000020015386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013039259,0.00051474315,0.0007692581,0.0003044324,0.00020444492,0.00041478657,0.0003945414,0.00073802227,0.00024294406],"category_scores_gemma":[0.0047255876,0.0002911043,0.0002965463,0.00026371158,0.0004503976,0.00084679807,0.00029332808,0.0004770074,0.00012820461],"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":[0.0000970781,0.000026020245,0.00076280395,0.00003337913,0.000026708394,0.00005427788,0.000030400972,0.9597787,0.0040167077,0.002502844,0.00018799349,0.032483153],"study_design_scores_gemma":[0.0000015930502,0.000006545167,0.00006170818,0.0000010100176,0.0000015687187,0.0000033091915,0.0000011291754,0.9991547,0.00040832677,0.00033037554,0.00002820148,0.0000016267616],"about_ca_topic_score_codex":0.0021259503,"about_ca_topic_score_gemma":0.0013343348,"teacher_disagreement_score":0.0021259503,"about_ca_system_score_codex":0.00030565765,"about_ca_system_score_gemma":0.00038866678,"threshold_uncertainty_score":0.0068959},"labels":[],"label_agreement":null},{"id":"W2162560361","doi":"10.1109/tnn.2009.2030582","title":"Adachi-Like Chaotic Neural Networks Requiring <i>Linear</i>-Time Computations by Enforcing a Tree-Shaped Topology","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","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":"Carleton University","funders":"","keywords":"Artificial neural network; Computation; Computer science; Chaotic; Topology (electrical circuits); Content-addressable memory; Tree (set theory); Network topology; Quadratic equation; Time complexity; Algorithm; Spanning tree; Tree structure; Mathematics; Artificial intelligence; Discrete mathematics; Binary tree; Combinatorics","score_opus":0.014394666311383895,"score_gpt":0.24755057400835606,"score_spread":0.23315590769697217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162560361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09421587,0.00020603182,0.89878535,0.00013494937,0.00006500668,0.000038119833,0.000051968706,0.00068216363,0.005820583],"genre_scores_gemma":[0.67953,0.00019517665,0.31614956,0.00009174707,0.00003538241,0.00010044659,0.00017028562,0.0000755076,0.0036518385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984646,0.000030242265,0.000011483683,0.000030119476,0.000062939056,0.00001874793],"domain_scores_gemma":[0.9995371,0.00016914294,0.00005642926,0.00010872233,0.00010139111,0.000027153794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029622557,0.0003437269,0.0003903171,0.00019324891,0.00031165255,0.0005412344,0.00062618044,0.0004807539,0.0012949919],"category_scores_gemma":[0.0016788194,0.00022372545,0.00024878525,0.00039192426,0.0004742513,0.0011959672,0.00042853534,0.0006852477,0.0003923091],"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":[0.00030066428,0.00008033006,0.0018125625,0.0002849386,0.000068203975,0.00029662703,0.00021275585,0.56430614,0.093384475,0.102809004,0.002866951,0.23357737],"study_design_scores_gemma":[0.000008350721,0.000049952225,0.0003619421,0.000004620189,0.000009705092,0.00008636734,0.000013016135,0.9743251,0.008770887,0.014879866,0.0014791578,0.000011093398],"about_ca_topic_score_codex":0.0013068849,"about_ca_topic_score_gemma":0.0025442059,"teacher_disagreement_score":0.0013068849,"about_ca_system_score_codex":0.00041327788,"about_ca_system_score_gemma":0.0004262678,"threshold_uncertainty_score":0.004332185},"labels":[],"label_agreement":null},{"id":"W2163664861","doi":"10.1109/tnn.2007.902725","title":"Impulsive Stabilization of High-Order Hopfield-Type Neural Networks With Time-Varying Delays","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","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":"Artificial neural network; Control theory (sociology); Exponential stability; Hopfield network; Stability (learning theory); Computer science; Lyapunov function; Type (biology); Control (management); Mathematics; Mathematical optimization; Artificial intelligence; Nonlinear system; Machine learning","score_opus":0.012434610833243575,"score_gpt":0.21137847920433225,"score_spread":0.19894386837108868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163664861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31425983,0.0010852757,0.6767325,0.00021783357,0.0000699343,0.000021868562,0.000023683877,0.0001358633,0.007453177],"genre_scores_gemma":[0.9941163,0.00022265498,0.004320872,0.000012800976,0.000009497604,0.000011159376,0.000007136649,0.000003470451,0.0012961621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998851,0.000021887001,0.0000078732955,0.000025393892,0.00004128122,0.000018544935],"domain_scores_gemma":[0.9996767,0.00016418248,0.00006851368,0.00001673676,0.000057420606,0.000016384876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043258964,0.00040603647,0.0002942861,0.00022170052,0.00030756093,0.00039906698,0.0005036835,0.0004450954,0.00043417417],"category_scores_gemma":[0.0009784689,0.000122771,0.00026162498,0.00020743246,0.00073410576,0.0004920072,0.00046380586,0.00035192055,0.000043780103],"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":[0.00011976885,0.000023254772,0.0012823462,0.00016214835,0.000039725935,0.0004350038,0.00023212486,0.9039974,0.034240257,0.034390204,0.00024283919,0.024834843],"study_design_scores_gemma":[0.0000103627835,0.00004236962,0.00031287363,0.0000054876527,0.0000111979825,0.00003583368,0.000028212331,0.98789597,0.0033213333,0.008019864,0.0003091884,0.0000072552334],"about_ca_topic_score_codex":0.0023556163,"about_ca_topic_score_gemma":0.0019978657,"teacher_disagreement_score":0.0023556163,"about_ca_system_score_codex":0.00047589853,"about_ca_system_score_gemma":0.0002870713,"threshold_uncertainty_score":0.004683852},"labels":[],"label_agreement":null},{"id":"W2164310911","doi":"10.1109/tnn.2010.2047512","title":"On Some Necessary and Sufficient Conditions for a Recurrent Neural Network Model With Time Delays to Generate Oscillations","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","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":"Polytechnique Montréal","funders":"Guangxi University; Guangxi Normal University","keywords":"Artificial neural network; Control theory (sociology); Recurrent neural network; Equilibrium point; Computer science; Oscillation (cell signaling); Class (philosophy); Simple (philosophy); Stability (learning theory); Instability; Mathematics; Artificial intelligence; Differential equation; Control (management); Physics; Mathematical analysis; Machine learning","score_opus":0.012734366612742076,"score_gpt":0.23477126220077915,"score_spread":0.22203689558803708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164310911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12828712,0.001311368,0.8505267,0.0014071281,0.00018928476,0.0001340705,0.0004793659,0.00063317164,0.017031755],"genre_scores_gemma":[0.9587704,0.0010439605,0.036120217,0.00018298179,0.00015600403,0.00031072303,0.0003984065,0.00012136345,0.0028959962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993679,0.00016616155,0.00005074489,0.00016963508,0.0001342966,0.000111302565],"domain_scores_gemma":[0.99550617,0.003117764,0.0005506372,0.00017429386,0.0004886218,0.00016248344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016687742,0.0009971476,0.0012259498,0.00085223775,0.0006059931,0.0010275161,0.0010125282,0.0019438103,0.0061899093],"category_scores_gemma":[0.0102537405,0.00070398755,0.0009917349,0.00039089422,0.0012628074,0.0022835028,0.0011179183,0.0014988682,0.0006920155],"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":[0.000496061,0.00022713604,0.003428625,0.0010197122,0.00024873865,0.0040397053,0.0008513192,0.5611085,0.07132066,0.31665412,0.006400199,0.034205314],"study_design_scores_gemma":[0.00010477166,0.00018267255,0.000733732,0.00009693454,0.000068199624,0.000436446,0.00013604699,0.9199154,0.005182032,0.07100753,0.0020721585,0.00006417769],"about_ca_topic_score_codex":0.0011322573,"about_ca_topic_score_gemma":0.0013316663,"teacher_disagreement_score":0.0061899093,"about_ca_system_score_codex":0.00049125525,"about_ca_system_score_gemma":0.00085982465,"threshold_uncertainty_score":0.02070731},"labels":[],"label_agreement":null},{"id":"W2164554122","doi":"10.1109/tnn.2009.2027319","title":"Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","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 Alberta","funders":"","keywords":"Computer science; Curse of dimensionality; Cluster analysis; Receptive field; Artificial neural network; Artificial intelligence; Context (archaeology); Pattern recognition (psychology)","score_opus":0.02072436511733904,"score_gpt":0.2366613044974035,"score_spread":0.21593693938006445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164554122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038094055,0.0005959489,0.9560187,0.0003363162,0.00005166095,0.000044883298,0.00004317908,0.0002545447,0.0045606485],"genre_scores_gemma":[0.70935124,0.00047525286,0.28757718,0.00012628497,0.000033505265,0.00010750639,0.00006658363,0.00006485837,0.0021976822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995566,0.00014336525,0.000027638105,0.00010031556,0.00012271208,0.00004930814],"domain_scores_gemma":[0.99915755,0.00041226685,0.00011484339,0.00012902563,0.00013183056,0.000054519787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015407188,0.00032598144,0.00049343164,0.0006745225,0.00039827934,0.0014508658,0.0008067007,0.0008887559,0.0010091845],"category_scores_gemma":[0.004179369,0.00042096007,0.000446081,0.0005249272,0.0011430368,0.0019791676,0.0012474105,0.0010649058,0.00018080056],"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":[0.00009880117,0.000034253117,0.0015912486,0.00010966789,0.00004823696,0.00019312292,0.0002933088,0.72310144,0.009243814,0.18924795,0.0011207043,0.07491747],"study_design_scores_gemma":[0.0000068284303,0.000016349948,0.00029430003,0.000019447842,0.0000068716895,0.000047449354,0.000026786856,0.95703566,0.0013300229,0.03997689,0.0012273311,0.000012080439],"about_ca_topic_score_codex":0.0020046493,"about_ca_topic_score_gemma":0.0015504424,"teacher_disagreement_score":0.0020046493,"about_ca_system_score_codex":0.001026499,"about_ca_system_score_gemma":0.0005280567,"threshold_uncertainty_score":0.008148193},"labels":[],"label_agreement":null},{"id":"W2165866200","doi":"10.1109/tnn.2002.1031943","title":"Application of adaptive constructive neural networks to image compression","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":30,"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":"Constructive; Computer science; Artificial neural network; Quantization (signal processing); Artificial intelligence; Feedforward neural network; Data compression; Image compression; JPEG; Feed forward; Generalization; Image (mathematics); Pattern recognition (psychology); Algorithm; Image processing; Mathematics; Engineering; Control engineering","score_opus":0.01646623996808614,"score_gpt":0.23656812362640053,"score_spread":0.2201018836583144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165866200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07182546,0.0011086409,0.9210451,0.00021841556,0.00010403178,0.0000516224,0.00001995196,0.0005018844,0.0051248698],"genre_scores_gemma":[0.8144294,0.0007912482,0.18225193,0.000141686,0.00007215966,0.000044474167,0.00003438973,0.000029388457,0.0022053444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997354,0.00009437172,0.000010152032,0.00002463616,0.00011762483,0.000017783896],"domain_scores_gemma":[0.9993717,0.00037037363,0.000044432858,0.000052286847,0.00014922919,0.000011900816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051786593,0.00036887632,0.0002667181,0.0004257342,0.00013827992,0.00026963462,0.00061313197,0.0004486204,0.00081984303],"category_scores_gemma":[0.0018668034,0.00011578191,0.00021324611,0.0004124848,0.00057538226,0.00033369556,0.00042592923,0.00044189612,0.0001448117],"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":[0.00018793868,0.00008543913,0.00073877303,0.00018451792,0.00006899305,0.00037158685,0.00010488168,0.49335152,0.0723093,0.011744315,0.0009167781,0.41993594],"study_design_scores_gemma":[0.0000072228354,0.00010121674,0.00023325396,0.000009874131,0.0000116613965,0.00013421565,0.000008123394,0.97358334,0.0222932,0.0027908434,0.0008189308,0.000008122185],"about_ca_topic_score_codex":0.00073028373,"about_ca_topic_score_gemma":0.0008706892,"teacher_disagreement_score":0.00081984303,"about_ca_system_score_codex":0.00024425818,"about_ca_system_score_gemma":0.00017301296,"threshold_uncertainty_score":0.0027425885},"labels":[],"label_agreement":null},{"id":"W2166329820","doi":"10.1109/tnn.2010.2054109","title":"An Extension of the Standard Mixture Model for Image Segmentation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":61,"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 Windsor","funders":"","keywords":"Artificial intelligence; Mixture model; Image segmentation; Pixel; Robustness (evolution); Computer science; Pattern recognition (psychology); Markov random field; Segmentation; Grayscale; Scale-space segmentation; Computer vision; Mathematics","score_opus":0.015014905269373057,"score_gpt":0.2822433391842295,"score_spread":0.26722843391485646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166329820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005008141,0.00023383247,0.99858487,0.000054895634,0.000030983956,0.00001541113,0.000026940876,0.00026851575,0.0002837363],"genre_scores_gemma":[0.06438806,0.0014441641,0.9291103,0.00024623325,0.00021345426,0.00021518071,0.00037363594,0.00037666783,0.0036322696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982993,0.00039566422,0.00009185454,0.0004236284,0.00068057474,0.00010896852],"domain_scores_gemma":[0.99891806,0.00046270218,0.00008690294,0.0001763828,0.00031833918,0.000037657424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00197482,0.0011472398,0.001551214,0.0020155562,0.0005374833,0.0013615167,0.0023467434,0.00248736,0.0026026256],"category_scores_gemma":[0.0045674904,0.00090306165,0.002474135,0.0022207724,0.0009378747,0.0025546884,0.0014854677,0.0019010213,0.0019022315],"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":[0.00019176252,0.0000984915,0.0012568688,0.00046609092,0.00028455953,0.00028779893,0.00031382267,0.35867563,0.027465295,0.08139329,0.009649955,0.5199165],"study_design_scores_gemma":[0.0000073159254,0.000033485823,0.0003404544,0.000018910794,0.000035054207,0.00022239405,0.000012525441,0.96888363,0.0029915222,0.017868884,0.009547273,0.00003864773],"about_ca_topic_score_codex":0.005817731,"about_ca_topic_score_gemma":0.005772256,"teacher_disagreement_score":0.005817731,"about_ca_system_score_codex":0.00087407365,"about_ca_system_score_gemma":0.0013405113,"threshold_uncertainty_score":0.011567771},"labels":[],"label_agreement":null},{"id":"W2167011086","doi":"10.1109/tnn.2003.820618","title":"Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":140,"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; University of Guelph","funders":"","keywords":"Artificial neural network; Computer science; Mobile robot; Motion planning; Robot; Collision; Artificial intelligence; Stability (learning theory); Control theory (sociology); Machine learning","score_opus":0.02357518814957928,"score_gpt":0.25696351778645976,"score_spread":0.23338832963688047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167011086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016317822,0.00018345447,0.9809925,0.00014824767,0.000025645146,0.00002407122,0.000013132542,0.00016896734,0.0021262509],"genre_scores_gemma":[0.8286816,0.0003284412,0.1673724,0.0000755534,0.000026148333,0.00020610768,0.00004021322,0.000025471405,0.0032439502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992394,0.000018419662,0.00000439273,0.00001792643,0.000026181342,0.000009118168],"domain_scores_gemma":[0.9998857,0.000050078997,0.00002193522,0.000007760874,0.000025560243,0.000008971034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017144202,0.00041657212,0.00033925456,0.00027038582,0.00028163908,0.00033581597,0.00058089563,0.00064622203,0.0009797848],"category_scores_gemma":[0.00042936477,0.00023905511,0.00037136915,0.00021569426,0.00041226466,0.00054332486,0.0004200051,0.00042422005,0.0001227126],"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":[0.00002347732,0.000017849226,0.00017192066,0.000040960793,0.000016251024,0.000083213294,0.000034716228,0.9591486,0.0062869415,0.0067521054,0.00021066007,0.027213288],"study_design_scores_gemma":[0.0000034401671,0.000011993286,0.000032473043,0.0000017825357,0.0000029447515,0.00001040036,0.0000023275627,0.9984276,0.00044681274,0.00090960367,0.00014819545,0.000002560546],"about_ca_topic_score_codex":0.0043498804,"about_ca_topic_score_gemma":0.0037903301,"teacher_disagreement_score":0.0043498804,"about_ca_system_score_codex":0.00049028924,"about_ca_system_score_gemma":0.0006908195,"threshold_uncertainty_score":0.008649111},"labels":[],"label_agreement":null},{"id":"W2168599920","doi":"10.1109/tnn.2002.806949","title":"A neural-network appearance-based 3-D object recognition using independent component analysis","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"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; McGill University","funders":"","keywords":"Independent component analysis; Computer science; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Artificial neural network; Facial recognition system; Object (grammar); Feature extraction; Computer vision","score_opus":0.0341544514731379,"score_gpt":0.26286028180864285,"score_spread":0.22870583033550496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168599920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027383225,0.00079362135,0.9668482,0.00015989334,0.000106875894,0.00007507062,0.00009830332,0.001378094,0.0031567505],"genre_scores_gemma":[0.23598716,0.00086063833,0.7563417,0.00011862235,0.000050784813,0.00013078684,0.00034711044,0.0000686345,0.00609448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997352,0.000045907243,0.000010508608,0.000074000964,0.00010837944,0.000026090838],"domain_scores_gemma":[0.9997962,0.000066680346,0.000014260978,0.00003354897,0.00007615925,0.00001316502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049652345,0.00048628022,0.00046231833,0.00060877437,0.0002683861,0.00064257707,0.0006733901,0.00074965646,0.0018906469],"category_scores_gemma":[0.0008556638,0.0002471836,0.0005169385,0.00074939185,0.0003844278,0.0008366291,0.0005006419,0.0004666901,0.0008762137],"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":[0.00023157807,0.00014038179,0.0010910982,0.00015230932,0.00013300679,0.00012041929,0.00006127337,0.07047709,0.09371165,0.004186147,0.0032080847,0.8264869],"study_design_scores_gemma":[0.000015276886,0.00010473224,0.0018318266,0.000017390901,0.000038414146,0.0001357201,0.000013552008,0.9551235,0.03716158,0.001478979,0.0040529706,0.000026082398],"about_ca_topic_score_codex":0.003757173,"about_ca_topic_score_gemma":0.0036505836,"teacher_disagreement_score":0.003757173,"about_ca_system_score_codex":0.00039967254,"about_ca_system_score_gemma":0.00037679228,"threshold_uncertainty_score":0.0074706078},"labels":[],"label_agreement":null},{"id":"W2169077345","doi":"10.1109/tnn.2010.2050333","title":"Quaternion-Based Adaptive Output Feedback Attitude Control of Spacecraft Using Chebyshev Neural Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":142,"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":"Control theory (sociology); Quaternion; Adaptive control; Spacecraft; Controller (irrigation); Computer science; Attitude control; Artificial neural network; Backstepping; Mathematics; Control engineering; Artificial intelligence; Engineering; Control (management)","score_opus":0.018974768730073102,"score_gpt":0.23049629620319972,"score_spread":0.21152152747312664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169077345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056619495,0.00044040103,0.9388313,0.00012034154,0.000073880394,0.000023717455,0.000021991698,0.00023340812,0.003635534],"genre_scores_gemma":[0.97446376,0.00027941904,0.023154978,0.00005504606,0.0000272828,0.000038563627,0.000036185054,0.00001249405,0.0019321493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987423,0.000019989968,0.00000851241,0.00003600017,0.000044433084,0.000016811517],"domain_scores_gemma":[0.99981576,0.000048723352,0.000041151405,0.0000148923855,0.00007248641,0.0000070213805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030538038,0.00043374806,0.00028282896,0.00014609503,0.0002364093,0.0004123126,0.00052373746,0.00039974911,0.0005414849],"category_scores_gemma":[0.0005173839,0.00015707324,0.00023104913,0.00021288614,0.0004250926,0.00039026164,0.0003754663,0.00040083574,0.00010082816],"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":[0.00007737499,0.00002568963,0.0008564795,0.00008732329,0.00003941799,0.000073988005,0.00008757439,0.9013968,0.019353133,0.0067253546,0.0006123067,0.07066455],"study_design_scores_gemma":[0.0000032563858,0.000026327758,0.000121292156,0.0000022642512,0.0000048238653,0.0000060710136,0.0000029128014,0.99797636,0.0011400077,0.0004723912,0.00024194375,0.0000023228015],"about_ca_topic_score_codex":0.008048359,"about_ca_topic_score_gemma":0.004532064,"teacher_disagreement_score":0.008048359,"about_ca_system_score_codex":0.00041596577,"about_ca_system_score_gemma":0.00041959426,"threshold_uncertainty_score":0.016003013},"labels":[],"label_agreement":null},{"id":"W2169116170","doi":"10.1109/tnn.2004.839357","title":"Incremental Communication for Adaptive Resonance Theory Networks","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"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 New Brunswick","funders":"","keywords":"Adaptive resonance theory; Computer science; Convergence (economics); Artificial neural network; Implementation; Process (computing); Communications system; Bounded function; Telecommunications network; Artificial intelligence; Algorithm; Mathematics; Telecommunications","score_opus":0.02048169323534046,"score_gpt":0.25192284606960763,"score_spread":0.23144115283426717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169116170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034321465,0.0003054746,0.9591308,0.00019764609,0.000072671246,0.00003656182,0.000016207696,0.0003734634,0.005545739],"genre_scores_gemma":[0.83596474,0.00023841888,0.16018079,0.00015808409,0.00009355897,0.00011977198,0.00004080955,0.000047151843,0.003156527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968135,0.000104913655,0.000014422499,0.000039451206,0.00013647704,0.000023415298],"domain_scores_gemma":[0.99920964,0.0004744149,0.00005715514,0.000106270585,0.00013420852,0.000018202336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047846665,0.00032832185,0.00023668828,0.00027348273,0.0003375441,0.00045362161,0.00074029464,0.00050213566,0.0021939196],"category_scores_gemma":[0.0031851665,0.00014929746,0.00021083943,0.00026474395,0.00044865438,0.0010364564,0.0005455209,0.0006939919,0.00030558917],"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":[0.000363085,0.00007690824,0.0007706136,0.00022045764,0.000042532392,0.00041130406,0.00033196743,0.4871646,0.047677744,0.15991633,0.0026563702,0.30036804],"study_design_scores_gemma":[0.000023760907,0.00011102267,0.0001480488,0.000009593017,0.000010962415,0.00010004737,0.000017144988,0.965867,0.008311361,0.02254652,0.0028417332,0.000012745764],"about_ca_topic_score_codex":0.00068947126,"about_ca_topic_score_gemma":0.0009695667,"teacher_disagreement_score":0.0021939196,"about_ca_system_score_codex":0.00039903732,"about_ca_system_score_gemma":0.00035174348,"threshold_uncertainty_score":0.007339418},"labels":[],"label_agreement":null},{"id":"W2169421521","doi":"10.1109/tnn.2009.2016842","title":"Lag Synchronization of Unknown Chaotic Delayed Yang–Yang-Type Fuzzy Neural Networks With Noise Perturbation Based on Adaptive Control and Parameter Identification","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks Stability and Synchronization","field":"Computer Science","cited_by":97,"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":"Chaotic; Control theory (sociology); Fuzzy logic; Artificial neural network; Computer science; Synchronization (alternating current); Adaptive control; Perturbation (astronomy); Secure communication; Fuzzy control system; Noise (video); Artificial intelligence; Control (management); Encryption; Telecommunications","score_opus":0.010099435683366529,"score_gpt":0.21201180661893748,"score_spread":0.20191237093557096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169421521","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20527782,0.00050345267,0.7908509,0.00023578736,0.00008953039,0.00004216024,0.000028278797,0.00008674017,0.0028854331],"genre_scores_gemma":[0.9851394,0.00018630619,0.01306491,0.000023412545,0.000015522044,0.000036262198,0.000016953047,0.0000066908246,0.0015105259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978,0.000052113424,0.000015721087,0.00006766165,0.000061279425,0.000023201977],"domain_scores_gemma":[0.99969494,0.00013932752,0.000063036605,0.000020737883,0.00006320371,0.000018812767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005214188,0.00056128955,0.00050858146,0.00023003513,0.0003340957,0.0005450984,0.0005548666,0.00053192576,0.00054669136],"category_scores_gemma":[0.0014039028,0.00023896262,0.00038543163,0.00028576166,0.0006413577,0.00076141796,0.000616504,0.00046812813,0.000057863374],"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":[0.00027052054,0.000038573868,0.0023952797,0.00017412881,0.00010107033,0.0004051629,0.00025853235,0.90165234,0.022892077,0.038952954,0.00034926474,0.032510154],"study_design_scores_gemma":[0.000011038865,0.000038357146,0.00019837437,0.0000043451596,0.000010798935,0.000026101508,0.000011032244,0.9951414,0.0013173725,0.0030604121,0.00017303742,0.000007779555],"about_ca_topic_score_codex":0.0024068307,"about_ca_topic_score_gemma":0.001510602,"teacher_disagreement_score":0.0024068307,"about_ca_system_score_codex":0.00051117287,"about_ca_system_score_gemma":0.00050939264,"threshold_uncertainty_score":0.004785657},"labels":[],"label_agreement":null},{"id":"W2170857480","doi":"10.1109/tnn.2006.883005","title":"Model Risk for European-Style Stock Index Options","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Valuation of options; Nonparametric statistics; Black–Scholes model; Econometrics; Stochastic volatility; Artificial neural network; Computer science; Parametric statistics; Volatility (finance); Implied volatility; Mathematical optimization; Economics; Mathematics; Artificial intelligence; Statistics","score_opus":0.03438829206074718,"score_gpt":0.23936557050658439,"score_spread":0.2049772784458372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170857480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1293017,0.0030229967,0.8434576,0.0038791143,0.00017985728,0.00008311057,0.00068929116,0.0003123706,0.01907405],"genre_scores_gemma":[0.93602425,0.0015584877,0.03331077,0.00032366902,0.00018779785,0.0002559061,0.00059587567,0.00011276121,0.027630435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990895,0.0004483004,0.00004181842,0.00016730017,0.00017513972,0.000077876335],"domain_scores_gemma":[0.9982198,0.0011634348,0.00025295294,0.00009139968,0.00017206171,0.000100275174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026451766,0.0014483022,0.0012545269,0.0008681274,0.00040134462,0.0021575892,0.0018073079,0.0025067232,0.004263529],"category_scores_gemma":[0.008506419,0.00048347126,0.00095327786,0.0007397735,0.0012965822,0.0031525928,0.0014061374,0.0021075746,0.0005248006],"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":[0.0000520382,0.000045179633,0.0009914305,0.00006137759,0.00005235509,0.00011146346,0.000117388176,0.70795995,0.00050378795,0.28342655,0.0011051239,0.0055733756],"study_design_scores_gemma":[0.00000725838,0.000011916468,0.00015637954,0.000007552366,0.0000052096043,0.000021073742,0.0000099480485,0.9144124,0.0000461734,0.08491484,0.00039885548,0.00000833247],"about_ca_topic_score_codex":0.0058284663,"about_ca_topic_score_gemma":0.0044502574,"teacher_disagreement_score":0.0058284663,"about_ca_system_score_codex":0.0014810496,"about_ca_system_score_gemma":0.0008547369,"threshold_uncertainty_score":0.014262915},"labels":[],"label_agreement":null},{"id":"W2170902875","doi":"10.1109/tnn.2009.2034851","title":"A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":97,"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 de Sherbrooke; Concordia University","funders":"","keywords":"Hierarchical Dirichlet process; Dirichlet process; Dirichlet distribution; Cluster analysis; Gibbs sampling; Concentration parameter; Latent Dirichlet allocation; Mixture model; Generalized Dirichlet distribution; Mathematics; Pattern recognition (psychology); Computer science; Algorithm; Artificial intelligence; Applied mathematics; Bayesian probability; Topic model; Dirichlet series; Mathematical analysis","score_opus":0.04578269611782361,"score_gpt":0.3189366009787135,"score_spread":0.2731539048608899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170902875","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091187964,0.00014090439,0.99794024,0.00016987191,0.000056000237,0.000044813867,0.00004108157,0.0001314129,0.0005637198],"genre_scores_gemma":[0.06906915,0.00054524763,0.92483073,0.00041107912,0.0002556724,0.00071077293,0.00044251935,0.00025093893,0.003483927],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98919535,0.005887487,0.00048824548,0.0022132169,0.0018423286,0.0003734572],"domain_scores_gemma":[0.9904857,0.0065924395,0.0004486783,0.00128363,0.0009245191,0.00026498022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0109146545,0.001369238,0.0023790095,0.0028901885,0.002080045,0.004147116,0.005095477,0.0033209675,0.0062916665],"category_scores_gemma":[0.038953643,0.0013550945,0.0031242084,0.0033728243,0.0033643653,0.007373353,0.0049406854,0.006063505,0.0021366694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016472959,0.00009982859,0.0012038326,0.00025513818,0.00017169498,0.00018859522,0.0006969614,0.13452022,0.0018115634,0.7214123,0.005791477,0.13368368],"study_design_scores_gemma":[0.00003352099,0.00002577414,0.0002462621,0.00005911023,0.000037793907,0.00019992347,0.00007599536,0.5925168,0.0009341606,0.3962381,0.009569641,0.00006279512],"about_ca_topic_score_codex":0.00366824,"about_ca_topic_score_gemma":0.00323301,"teacher_disagreement_score":0.0109146545,"about_ca_system_score_codex":0.0024464524,"about_ca_system_score_gemma":0.0025695758,"threshold_uncertainty_score":0.057722867},"labels":[],"label_agreement":null}]}