{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":12,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":12,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"7dd011ccc510","filters":{"venue":"IEEE International Conference on Neural Networks"}},"results":[{"id":"W2161294075","doi":"10.1109/icnn.1993.298622","title":"Acceleration of back propagation through initial weight pre-training with delta rule","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Backpropagation; Convergence (economics); Acceleration; Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.1074472640247655,"gpt":0.3119474789161292,"spread":0.2045002148913637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007821844,0.0002074979,0.0001886144,0.00006752411,0.0001251906,0.0002281994,0.0008544168,0.00008672682,0.0002824034],"category_scores_gemma":[0.000007807869,0.0001695567,0.00006124371,0.0003127797,0.00009059426,0.0009585141,0.00006943095,0.0002866941,0.00002739548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003291997,"about_ca_system_score_gemma":0.00002399679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001849293,"about_ca_topic_score_gemma":0.00002273969,"domain_scores_codex":[0.9983925,0.0000600894,0.0003871876,0.0004466866,0.0004499387,0.0002635469],"domain_scores_gemma":[0.998891,0.00009111118,0.0002885367,0.0003453485,0.0003147529,0.00006924359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002034906,0.0005366555,0.0006614454,0.00002220997,0.000136637,0.00003408688,0.001565807,0.2957224,0.00384967,0.4627347,0.007365329,0.2271676],"study_design_scores_gemma":[0.0004007729,0.0002125668,0.0003962173,0.00007502594,0.000006090742,0.00002564849,0.00001103222,0.9942142,0.001982314,0.00195547,0.0005189509,0.0002016908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1197719,0.00003717788,0.8499989,0.005581388,0.001345021,0.0005818341,0.00001577436,0.0001721013,0.02249596],"genre_scores_gemma":[0.9938455,0.0000742116,0.004488138,0.0006327748,0.0005266329,0.00005984399,0.000032534,0.00001437488,0.0003260333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8740736,"threshold_uncertainty_score":0.6914322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2133796939","doi":"10.1109/icnn.1993.298720","title":"A new acceleration technique for the backpropagation algorithm","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Orthogonalization; Backpropagation; Momentum (technical analysis); Acceleration; Algorithm; Convergence (economics); Orthogonality; Computer science; Mathematics; Gradient method; Artificial neural network; Artificial intelligence; Physics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.07931899361394502,"gpt":0.2954020287187492,"spread":0.2160830351048041,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006748755,0.0001689753,0.00009992382,0.00006147719,0.00007607752,0.0001052852,0.0003528942,0.0000852618,0.0002567817],"category_scores_gemma":[0.00001576283,0.0001375057,0.00006062868,0.00008486267,0.00002457418,0.0002416106,0.00001808898,0.0002534499,0.00001167388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000874383,"about_ca_system_score_gemma":0.000004297205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005032152,"about_ca_topic_score_gemma":0.000004712514,"domain_scores_codex":[0.9992509,0.00001322809,0.0002006324,0.0001850765,0.0001693468,0.000180839],"domain_scores_gemma":[0.9994983,0.0001090007,0.00005251983,0.0001750043,0.0001207746,0.00004439385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002706878,0.00001990608,0.00000637415,0.000006151075,0.00005229891,0.000003517781,0.00004731633,0.3613981,0.01677139,0.01120411,0.02165581,0.5888079],"study_design_scores_gemma":[0.0001435605,0.00007155345,0.0000350468,0.00003372776,0.000005028375,0.000008502886,0.000004117498,0.9802777,0.01378828,0.002345215,0.003136072,0.0001512396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001226046,0.00004628037,0.9948474,0.0007055486,0.001226149,0.0005996859,0.00001434319,0.0004277866,0.002010241],"genre_scores_gemma":[0.9514306,0.0002269361,0.04586281,0.0002007768,0.001028325,0.0004467905,0.00002453432,0.00004164792,0.0007375707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.951308,"threshold_uncertainty_score":0.5607318,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2120241573","doi":"10.1109/icnn.1993.298670","title":"A cascaded recurrent neural network for real-time nonlinear adaptive filtering","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial neural network; Nonlinear system; Chaotic; Recurrent neural network; Artificial intelligence; Feature (linguistics); Relation (database); Time domain; Domain (mathematical analysis); Mathematics; Data mining; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.08572803928964218,"gpt":0.3038610795383356,"spread":0.2181330402486934,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001591376,0.000378876,0.0003253406,0.000092926,0.0002989725,0.0003969574,0.001589714,0.000139048,0.0001933863],"category_scores_gemma":[0.00001775821,0.0003527869,0.0002277644,0.0003512304,0.00007663776,0.0004717248,0.0001882567,0.0004567106,0.00007708519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000799608,"about_ca_system_score_gemma":0.00001547392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001897012,"about_ca_topic_score_gemma":0.0000160777,"domain_scores_codex":[0.9974706,0.00008487561,0.0005188865,0.0008232257,0.0004166501,0.0006857605],"domain_scores_gemma":[0.9983101,0.0003465559,0.0002809805,0.0005467438,0.0002961927,0.0002194616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002140682,0.0003380047,0.00008088297,0.000008139586,0.0001353073,0.00005922389,0.0001666231,0.5932277,0.001300789,0.08667433,0.08018912,0.2376058],"study_design_scores_gemma":[0.0004530258,0.0003205046,0.0001107417,0.00006458582,0.00001003459,0.00003179427,0.000004149274,0.9951946,0.0001040779,0.00104114,0.002292564,0.0003728017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07191803,0.0002892909,0.860781,0.02640001,0.01551976,0.003436322,0.0002339649,0.001734974,0.01968661],"genre_scores_gemma":[0.9807126,0.0002672705,0.01324256,0.001258417,0.002839804,0.0002911078,0.00005222548,0.00003878249,0.001297256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9087945,"threshold_uncertainty_score":0.9998924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147906280","doi":"10.1109/icnn.1993.298717","title":"Dynamics and stability of multilayered recurrent neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Stability (learning theory); Artificial neural network; Dynamics (music); Nonlinear system; Computer science; Equilibrium point; Artificial intelligence; Applied mathematics; Mathematics; Machine learning; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.06517075631685977,"gpt":0.2934302748070479,"spread":0.2282595184901881,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000155473,0.0002884376,0.0002946966,0.00008892275,0.0001338367,0.0002098996,0.00113048,0.0001278987,0.0001274201],"category_scores_gemma":[0.00002180512,0.0002627817,0.0001179677,0.0003003452,0.0001711621,0.0003912016,0.000226396,0.0005203307,0.000004447311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006279582,"about_ca_system_score_gemma":0.000007953993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002219776,"about_ca_topic_score_gemma":0.00006315996,"domain_scores_codex":[0.9979223,0.0001068742,0.0005359091,0.0006562822,0.0003895418,0.0003890675],"domain_scores_gemma":[0.9984465,0.0002458649,0.00029108,0.0005626933,0.000272569,0.0001813007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009322838,0.0005452178,0.004054106,0.0000170004,0.00007718131,0.0000246178,0.0001798202,0.2543574,0.0002468746,0.1472477,0.002011109,0.5911458],"study_design_scores_gemma":[0.0003357507,0.0001503289,0.001468947,0.00003141403,0.000006629843,0.00001803169,0.00001148713,0.997198,0.00006597766,0.000421167,0.00005312927,0.0002390828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5234485,0.0004073097,0.4509696,0.01403473,0.005402358,0.0009703849,0.00006392091,0.0003781589,0.004325053],"genre_scores_gemma":[0.9981405,0.00034381,0.0006324937,0.0004446095,0.0002883065,0.00004049885,0.00001220247,0.0000151363,0.00008246661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7428407,"threshold_uncertainty_score":0.9999824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2116221794","doi":"10.1109/icnn.1993.298618","title":"Dynamic neural controller with somatic adaptation","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sigmoid function; Artificial neural network; Computer science; Nonlinear system; Artificial intelligence; Controller (irrigation); Adaptation (eye); Control theory (sociology); Neuroscience; Biology; Control (management)","retraction":null,"screen_n_in":null,"score":{"opus":0.04864879469509891,"gpt":0.2688618344390509,"spread":0.220213039743952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000750871,0.0002765728,0.000223488,0.0001116207,0.0001829593,0.0004274297,0.001129881,0.00008032953,0.0001672051],"category_scores_gemma":[0.000008796502,0.0002181121,0.00009095638,0.0003079594,0.00007747497,0.0005928401,0.00006472433,0.0003809559,0.00008869897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005538996,"about_ca_system_score_gemma":0.00001062962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001122148,"about_ca_topic_score_gemma":0.00003754993,"domain_scores_codex":[0.9981635,0.00007052046,0.0003474685,0.0005486504,0.0004923013,0.0003775803],"domain_scores_gemma":[0.9988747,0.0001542839,0.0002154411,0.0003984016,0.0002161392,0.0001410675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007007219,0.0002139269,0.0001296884,0.000004437648,0.00007544713,0.00005349498,0.0001788051,0.7834671,0.0003168694,0.08467189,0.00294537,0.1278729],"study_design_scores_gemma":[0.0006689116,0.0001895204,0.0004285528,0.00003528368,0.000008129075,0.0000520373,0.00001570599,0.9966493,0.00001369837,0.001360289,0.0003201633,0.0002584227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06366273,0.0001250828,0.9027462,0.02019794,0.002752285,0.0007785442,0.00001127807,0.0005687273,0.009157217],"genre_scores_gemma":[0.9949416,0.0000714631,0.001865622,0.001748936,0.0002526971,0.0001098243,0.00001166416,0.00001975735,0.000978471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9312788,"threshold_uncertainty_score":0.8894354,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159972742","doi":"10.1109/icnn.1993.298648","title":"Dynamic neural unit and function approximation","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Activation function; Artificial neural network; Trigonometric functions; Function approximation; Feed forward; Function (biology); Feedforward neural network; Nonlinear system; Robotics; Artificial intelligence; Control theory (sociology); Trigonometry; Task (project management); Algorithm; Control engineering; Mathematics; Control (management); Robot; Engineering; Physics; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.05637254273485846,"gpt":0.278444129817011,"spread":0.2220715870821525,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008275305,0.0002087414,0.0001403142,0.000111638,0.0001874303,0.0004031853,0.0006540549,0.00008870683,0.0001126827],"category_scores_gemma":[0.000008136696,0.0001918934,0.00005763198,0.0002663585,0.00006818173,0.0006288998,0.00009973248,0.0003470452,0.00004335092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002836525,"about_ca_system_score_gemma":0.000004852571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000714164,"about_ca_topic_score_gemma":0.00001671542,"domain_scores_codex":[0.9985957,0.00005763911,0.0002722102,0.0004941158,0.0003087518,0.0002716392],"domain_scores_gemma":[0.9991747,0.00008869866,0.0001426676,0.0003347407,0.0001411792,0.0001180439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004829187,0.0001941221,0.000547217,0.000007974601,0.00005144844,0.0000203958,0.0001010957,0.1134692,0.001175659,0.3519581,0.004112266,0.5283143],"study_design_scores_gemma":[0.0002572812,0.0001128315,0.001864034,0.00001625738,0.000005680864,0.00003237344,0.000007033648,0.9931029,0.00002246809,0.003498284,0.0008868146,0.0001940383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1940268,0.0002292907,0.7687006,0.01970628,0.00433881,0.0006053628,0.00001145971,0.0006066718,0.01177472],"genre_scores_gemma":[0.9967468,0.0001747603,0.0006835795,0.001393908,0.0002158891,0.00005125316,0.00001797167,0.00001274624,0.0007031085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8796337,"threshold_uncertainty_score":0.7825184,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159498172","doi":"10.1109/icnn.1993.298666","title":"Derivation of momentum LMS algorithms by minimizing objective functions","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Least mean squares filter; Algorithm; Minification; Function (biology); Mean squared error; Square (algebra); Mathematics; Signal processing; Computer science; Momentum (technical analysis); Adaptive filter; Mathematical optimization; Statistics; Digital signal processing","retraction":null,"screen_n_in":null,"score":{"opus":0.05128955189035714,"gpt":0.2679466573095718,"spread":0.2166571054192147,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003058541,0.0001740136,0.0001481138,0.0001069498,0.0000401014,0.00003131038,0.0002233222,0.00007713721,0.0002576893],"category_scores_gemma":[0.00001494549,0.0001854109,0.00005545897,0.000123125,0.00005214962,0.0002400931,0.00002346573,0.0002520781,0.00001038998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054382,"about_ca_system_score_gemma":0.000002240904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006360047,"about_ca_topic_score_gemma":0.00000235023,"domain_scores_codex":[0.9991262,0.00001819946,0.0002588257,0.0001993108,0.0002133003,0.0001841592],"domain_scores_gemma":[0.9995298,0.00006079775,0.00008230044,0.0001423188,0.0001386886,0.00004607387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005500096,0.0001817272,0.0002837176,0.00002275833,0.0002351768,0.0000144677,0.0002774051,0.7653047,0.1018647,0.004559792,0.02437199,0.1028286],"study_design_scores_gemma":[0.0001659614,0.0000945293,0.0001149805,0.00007972102,0.000005295032,0.000004110368,0.00003344392,0.9865111,0.01190461,0.0003550997,0.0005600624,0.0001711451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05726899,0.0001440941,0.9227636,0.0002813327,0.002750041,0.0002925141,0.0001060965,0.0007139808,0.01567936],"genre_scores_gemma":[0.9975057,0.0001733414,0.00151493,0.00004431696,0.0001505095,0.00005339094,0.00003715375,0.00002851835,0.0004921647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9402367,"threshold_uncertainty_score":0.7560835,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149547948","doi":"10.1109/icnn.1993.298516","title":"A distributed adaptive control system for a quadruped mobile robot","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Reinforcement learning; Robot; Computer science; Actuator; Mobile robot; Gait; Artificial intelligence; Control (management); Controller (irrigation); Adaptive behavior; Control engineering; Control theory (sociology); Engineering; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.04070177490057268,"gpt":0.2489247269386018,"spread":0.2082229520380291,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006840654,0.0002228831,0.0002552956,0.00006663749,0.00006512587,0.0001050131,0.0002982893,0.0001077211,0.0002290111],"category_scores_gemma":[0.00001130664,0.0002085993,0.0001413408,0.00008100956,0.0000300579,0.0001178675,0.000007904125,0.0002232761,0.00005370592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001415813,"about_ca_system_score_gemma":0.000005050855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005694026,"about_ca_topic_score_gemma":0.000008369197,"domain_scores_codex":[0.9988919,0.000038338,0.0003184215,0.0002373708,0.0002141626,0.0002998191],"domain_scores_gemma":[0.9993241,0.0001413594,0.00006836461,0.0001649016,0.0001908784,0.0001103424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009217254,0.00004026846,0.0000148158,0.000008893343,0.0001137958,0.00001200531,0.00002887627,0.9846274,0.0002715087,0.006040467,0.002032732,0.006717038],"study_design_scores_gemma":[0.001792284,0.0001624122,0.00005107177,0.00005879468,0.00002124094,0.00001458316,0.00007396326,0.9971157,0.00006569218,0.00004726826,0.0003898801,0.0002071175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004572459,0.00009786716,0.9859723,0.0004626429,0.003547575,0.0008742919,0.0001817587,0.0005303078,0.003760744],"genre_scores_gemma":[0.9983248,0.00002063241,0.000118858,0.0001781192,0.0005248585,0.0005249636,0.00004283243,0.00002964714,0.0002352667],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9937524,"threshold_uncertainty_score":0.8506433,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2120439140","doi":"10.1109/icnn.1993.298746","title":"Minimum description length pruning and maximum mutual information training of adaptive probabilistic neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Minimum description length; Probabilistic logic; Pruning; Artificial neural network; Computer science; Artificial intelligence; Gaussian; Benchmark (surveying); Mutual information; Probabilistic neural network; Machine learning; Pattern recognition (psychology); Algorithm; Time delay neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.0888738075565767,"gpt":0.2649945237197897,"spread":0.176120716163213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001907816,0.0002975502,0.0002844277,0.0001697897,0.0001887393,0.0003687321,0.0008036863,0.0001424353,0.00004735715],"category_scores_gemma":[0.00003671342,0.0002808238,0.00009252736,0.0003479815,0.0001667841,0.001634939,0.0001484712,0.0005121076,0.000007358455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005435382,"about_ca_system_score_gemma":0.00001592415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001639104,"about_ca_topic_score_gemma":0.000014472,"domain_scores_codex":[0.9979869,0.0001009449,0.0006248013,0.0004413748,0.0004307661,0.0004152505],"domain_scores_gemma":[0.9985626,0.0002183999,0.0004159538,0.0003332371,0.0003155887,0.0001542357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008931642,0.0001037579,0.0002005194,0.00001089329,0.00005141153,0.00000991151,0.001044127,0.5906687,0.0001738114,0.1081151,0.00139234,0.2981401],"study_design_scores_gemma":[0.0004294956,0.000276229,0.0006083096,0.00007049058,0.00001142944,0.00004329638,0.00009578294,0.9959562,0.00001725213,0.002035405,0.0001925114,0.0002635427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1903632,0.000141275,0.7970912,0.003016603,0.002705316,0.0008514058,0.00002045537,0.0002876745,0.005522773],"genre_scores_gemma":[0.9973084,0.000108029,0.001427258,0.0005997568,0.0003498183,0.00006656776,0.00002351193,0.00001379122,0.0001028579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8069451,"threshold_uncertainty_score":0.9999644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103121832","doi":"10.1109/icnn.1993.298744","title":"Design of fault tolerant neurocontrollers using immunization technique","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fault tolerance; Nonlinear system; Computer science; Artificial neural network; Fault (geology); Noise (video); Control theory (sociology); Control (management); Artificial intelligence; Architecture; Control engineering; Engineering; Distributed computing","retraction":null,"screen_n_in":null,"score":{"opus":0.09625146585140336,"gpt":0.2964927239642496,"spread":0.2002412581128462,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001286634,0.000196749,0.0002088619,0.000133395,0.0001188251,0.0001442345,0.001164631,0.00009146512,0.00008886123],"category_scores_gemma":[0.0000126193,0.000181063,0.00008208128,0.0003644731,0.00006290691,0.000369809,0.00008771117,0.0002830815,0.000008004999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003733506,"about_ca_system_score_gemma":0.00001425159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000139968,"about_ca_topic_score_gemma":0.000001054914,"domain_scores_codex":[0.9985171,0.0001196835,0.0004116814,0.0003728057,0.0003306261,0.0002481325],"domain_scores_gemma":[0.9988263,0.0001413549,0.0002695359,0.0004173016,0.0002733685,0.00007211586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002078931,0.00008259284,0.00001947315,0.000001773597,0.00001749805,0.000008224598,0.0000343658,0.9503474,0.01789032,0.01874377,0.0004689556,0.01236483],"study_design_scores_gemma":[0.0002855859,0.00009885958,0.00003058374,0.00004195962,0.000005129253,0.00002692721,0.00000334897,0.9965548,0.001870813,0.0008196655,0.00009668306,0.0001656228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00452937,0.00004116479,0.9917545,0.001341211,0.0007496643,0.0004875618,0.000002943973,0.0001113842,0.0009822177],"genre_scores_gemma":[0.9904507,0.0001262798,0.008520999,0.0005377416,0.0001639792,0.00006585503,0.000003781701,0.00001460576,0.0001160704],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9859213,"threshold_uncertainty_score":0.7383534,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2115350005","doi":"10.1109/icnn.1993.298815","title":"An approximation network for measurement systems","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Artificial neural network; Computer science; Calibration; Selection (genetic algorithm); Value (mathematics); Approximation algorithm; Artificial intelligence; Mathematical optimization; Algorithm; Machine learning; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1201225881912841,"gpt":0.2930460027268754,"spread":0.1729234145355913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002529592,0.0001784667,0.0001587558,0.00006884131,0.00006627355,0.00007439424,0.0002620992,0.0001083989,0.0000514502],"category_scores_gemma":[0.00001361238,0.000173729,0.00005487396,0.00006716524,0.00002292174,0.0002204585,0.000005014389,0.0001856315,0.000006332717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210481,"about_ca_system_score_gemma":0.000002920489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001696889,"about_ca_topic_score_gemma":0.00000785206,"domain_scores_codex":[0.9989046,0.00003154651,0.0002529621,0.0002094709,0.0003233595,0.0002779957],"domain_scores_gemma":[0.9993972,0.00002742051,0.00006298353,0.0001735555,0.0002673958,0.00007142872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003455835,0.00003756232,0.0001292795,0.00001114841,0.00004681317,0.000001376636,0.00001979715,0.9659801,0.002463144,0.009742202,0.008961022,0.01257296],"study_design_scores_gemma":[0.0002740113,0.00015371,0.00006468001,0.00004624014,0.00001031669,0.000002950798,0.000006676722,0.9960873,0.0004153757,0.0007907105,0.001974164,0.0001739105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009062372,0.0003435922,0.9753871,0.0001618748,0.005394198,0.0006724689,0.00001103869,0.0007854682,0.008181914],"genre_scores_gemma":[0.996776,0.0001220068,0.001400891,0.0001337251,0.001153362,0.0002790139,0.00002805422,0.00002945631,0.00007752432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9877136,"threshold_uncertainty_score":0.7084463,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111658521","doi":"10.1109/icnn.1993.298695","title":"Robotic modeling and control using a fuzzy neural network","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Artificial neural network; Robustness (evolution); Computer science; Robot; Backpropagation; Artificial intelligence; Fuzzy logic; Fuzzy control system; Software; Neuro-fuzzy; Control engineering; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.08167200233173044,"gpt":0.2698360813100519,"spread":0.1881640789783214,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001936162,0.0002828775,0.0003283963,0.00008466437,0.0002028767,0.0005162741,0.0009032062,0.0001232629,0.00002369297],"category_scores_gemma":[0.00002385363,0.0002494757,0.0001044015,0.0001750356,0.00005880299,0.0005170836,0.0001041998,0.0003878748,0.0000152271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005485209,"about_ca_system_score_gemma":0.00001261451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005154581,"about_ca_topic_score_gemma":0.00001213153,"domain_scores_codex":[0.9979252,0.0001494259,0.0004147893,0.0005719447,0.000427688,0.0005109149],"domain_scores_gemma":[0.9990051,0.0001424727,0.0001634035,0.0003365881,0.0001817961,0.0001705852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002438825,0.00002448245,0.0003142022,0.000002255729,0.00003534579,0.0000405372,0.00003511446,0.9545071,0.00006762812,0.03676282,0.0001971911,0.007988991],"study_design_scores_gemma":[0.0007356395,0.0001029392,0.00007611793,0.00005252352,0.00001157758,0.00009787304,0.000007320307,0.9932802,8.11573e-7,0.00536437,0.00001457852,0.0002560102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04999221,0.0007389481,0.9314309,0.003137496,0.005851978,0.0003885925,0.000003007912,0.0002578094,0.008199084],"genre_scores_gemma":[0.995432,0.00005680074,0.001204013,0.001912142,0.001207367,0.00002009867,0.000001621589,0.00001560983,0.0001503089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9454398,"threshold_uncertainty_score":0.9999958,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}