{"meta":{"query_hash":"943b69e1532a","filters":{"topic":"Advanced Memory and Neural Computing"},"cohort_total":991,"direct_labels_cover":2,"predictions_cover":991,"exported":991,"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/943b69e1532a","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Memory+and+Neural+Computing"},"results":[{"id":"W1482483597","doi":"10.1109/iscas.2015.7169242","title":"Efficient event-driven approach using synchrony processing for hardware spiking neural networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"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":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Serialization; Field-programmable gate array; Spiking neural network; Event (particle physics); Artificial neural network; Virtex; Parallel computing; Process (computing); Computer architecture; Computation; Computer hardware; Artificial intelligence; Algorithm","score_opus":0.052411765878669817,"score_gpt":0.27917001157218824,"score_spread":0.22675824569351843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482483597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26353195,0.00034245267,0.7346924,0.000005051313,0.00033240163,0.00025349716,8.7442623e-7,0.00038036678,0.00046097842],"genre_scores_gemma":[0.9610729,5.912905e-7,0.038371384,0.000033940192,0.00041740757,0.000013852693,0.0000073600286,0.000051388204,0.000031163792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998949,0.00001837182,0.00023081094,0.00024200379,0.00012934135,0.00043050875],"domain_scores_gemma":[0.99958354,0.000036803332,0.000044852943,0.00013271814,0.00006711263,0.00013496236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017115595,0.00019606303,0.00019741477,0.000051492254,0.00015955535,0.00005668725,0.0001309357,0.00006594693,0.0000021277056],"category_scores_gemma":[0.000029160543,0.00018215115,0.00006951203,0.00018194222,0.000020045381,0.00011622264,0.000053630763,0.00017426626,9.795158e-7],"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":[0.000009396156,0.000011134504,0.000030501444,0.00009370442,0.0000062484414,0.000002644967,0.00014667811,0.98355204,0.0010930758,0.000027055956,0.0000419369,0.014985563],"study_design_scores_gemma":[0.00035670638,0.000030294266,0.0000073322344,0.000054166045,0.000016826138,0.000029790599,0.00025077278,0.9977126,0.001183751,0.000016806242,0.000101395315,0.00023954832],"about_ca_topic_score_codex":0.000001023404,"about_ca_topic_score_gemma":5.503234e-7,"teacher_disagreement_score":0.69754094,"about_ca_system_score_codex":0.00011579865,"about_ca_system_score_gemma":0.000017314307,"threshold_uncertainty_score":0.7427909},"labels":[],"label_agreement":null},{"id":"W1482580497","doi":"10.1002/9783527650446.ch10","title":"Diazonium Compounds in Molecular Electronics","year":2012,"lang":"en","type":"other","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"University of Alberta","keywords":"Molecular electronics; Fabrication; Nanotechnology; Electronics; Materials science; Reagent; Molecule; Chemistry; Combinatorial chemistry; Organic chemistry; Physical chemistry","score_opus":0.006841400244939533,"score_gpt":0.21552490288117956,"score_spread":0.20868350263624003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482580497","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030645225,0.008966236,0.012774012,0.000005186313,0.0003605301,0.00010154631,0.0000013320861,0.00072156143,0.9767631],"genre_scores_gemma":[0.19076432,0.0012493073,0.0058742757,0.0002334356,0.0008690117,0.000024145727,0.000050969255,0.0019830342,0.7989515],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994178,0.000008394717,0.00009646369,0.00010446596,0.000056797275,0.00031606737],"domain_scores_gemma":[0.999782,0.000011549546,0.000015770693,0.00014927443,0.0000019574118,0.000039420785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000028155251,0.00016940582,0.00017566449,0.00010864692,0.000006130446,0.000004906772,0.000095214236,0.00013609568,0.00038294157],"category_scores_gemma":[0.0000017910432,0.00017218005,0.000033042634,0.000094225936,0.000007776514,0.000021441825,0.000020870839,0.00029015733,0.00009303755],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016657425,0.00017569892,0.0004330582,0.0016985721,0.00033777306,0.00029879526,0.00017962561,0.062262166,0.15585017,0.025191529,0.70245403,0.051101923],"study_design_scores_gemma":[0.00014790098,0.000008933569,0.0000075617795,0.00006998226,0.000007043183,0.0000066632856,0.0000028052723,0.00070667936,0.031240536,0.00013463994,0.96731675,0.00035050302],"about_ca_topic_score_codex":0.0000028804275,"about_ca_topic_score_gemma":0.000027271839,"teacher_disagreement_score":0.26486272,"about_ca_system_score_codex":0.000045485205,"about_ca_system_score_gemma":0.0000048156962,"threshold_uncertainty_score":0.7021299},"labels":[],"label_agreement":null},{"id":"W1514207267","doi":"10.1109/iscas.2015.7168755","title":"Comparison of low-power biopotential processors for on-the-fly spike detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Microcontroller; Computer science; Detector; Spike (software development); Field-programmable gate array; Application-specific integrated circuit; Embedded system; Computer hardware; Overhead (engineering); Power (physics); Real-time computing; SIGNAL (programming language); Signal processing; Digital signal processing; Telecommunications","score_opus":0.05659964008497343,"score_gpt":0.3087766896728244,"score_spread":0.252177049587851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514207267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8294252,0.00002616754,0.16847782,0.000025661948,0.00038461472,0.00016232455,0.000001005784,0.00013388356,0.0013633242],"genre_scores_gemma":[0.99929464,3.9797152e-7,0.000514952,0.000020678166,0.000058714366,0.000008270943,7.1274457e-7,0.000012384568,0.000089263194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995887,0.000005318241,0.00013858025,0.00007691108,0.00007747309,0.00011296333],"domain_scores_gemma":[0.9997567,0.00005228413,0.000027895016,0.00008212213,0.000047136607,0.00003383948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007834825,0.00007183848,0.00010037403,0.000027378606,0.00003850771,0.000006531656,0.00006684992,0.00003072683,0.000008237673],"category_scores_gemma":[0.000046204714,0.00005193394,0.000034344732,0.00008339144,0.000014859684,0.000053003238,0.000009855744,0.00007291274,0.000008976393],"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":[0.00023634323,0.00010213637,0.00014233487,0.00023984633,0.000038748207,9.966524e-7,0.0008965976,0.55722874,0.4172728,0.0010095189,0.0016943222,0.021137606],"study_design_scores_gemma":[0.00020295497,0.00014502856,0.00002853425,0.000016047763,0.0000039508727,9.3449495e-7,0.0001784319,0.07624004,0.92192715,0.0002738736,0.0009106303,0.000072422015],"about_ca_topic_score_codex":5.9469784e-7,"about_ca_topic_score_gemma":0.0000033602375,"teacher_disagreement_score":0.50465435,"about_ca_system_score_codex":0.000014891093,"about_ca_system_score_gemma":0.0000043562723,"threshold_uncertainty_score":0.21178046},"labels":[],"label_agreement":null},{"id":"W1571743437","doi":"10.1109/iscas.1994.409599","title":"A CMOS current-mode PWM technique for analog neural network implementations","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Technical University of Nova Scotia","funders":"","keywords":"Pulse-width modulation; Integrator; CMOS; Artificial neural network; Computer science; Electronic engineering; Modular design; Operational transconductance amplifier; Transconductance; Amplifier; Modulation (music); Current (fluid); Operational amplifier; Topology (electrical circuits); Electrical engineering; Voltage; Engineering; Artificial intelligence; Bandwidth (computing); Transistor; Physics; Telecommunications","score_opus":0.04631079664824732,"score_gpt":0.3191814215907528,"score_spread":0.27287062494250547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1571743437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017606083,0.00041346447,0.9781368,0.00006807373,0.00062349957,0.0006556409,0.000016043952,0.00065276976,0.0018276674],"genre_scores_gemma":[0.980374,0.00003560006,0.018836739,0.000089055335,0.0003292954,0.00016019616,0.000016829023,0.000025073525,0.00013325167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993965,0.0000075217204,0.00015905319,0.00011853114,0.000042580534,0.00027582512],"domain_scores_gemma":[0.99974364,0.00007099708,0.000016687063,0.00010626268,0.000015825206,0.000046572968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037857786,0.00010178127,0.00009301966,0.000030201703,0.00011547189,0.000013051141,0.00007736668,0.000021802263,0.00012135465],"category_scores_gemma":[0.000007658337,0.00009936829,0.00005438974,0.00014623407,0.000008108728,0.00011081095,0.000017504633,0.00010488564,0.000008511071],"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":[0.0000064744054,0.000029543866,0.0003659825,0.00011348975,0.000025934498,0.0000031574793,0.00013708106,0.80283207,0.034200363,0.0033750592,0.044704244,0.1142066],"study_design_scores_gemma":[0.00047930624,0.00007559218,0.00013400672,0.000030857278,0.000022519287,0.000016913977,0.000030345855,0.9294021,0.034590248,0.0034806696,0.03135006,0.0003873681],"about_ca_topic_score_codex":9.3439354e-7,"about_ca_topic_score_gemma":0.000010681421,"teacher_disagreement_score":0.9627679,"about_ca_system_score_codex":0.000019522764,"about_ca_system_score_gemma":0.0000012634339,"threshold_uncertainty_score":0.40521213},"labels":[],"label_agreement":null},{"id":"W1589682973","doi":"10.1109/iscas.2015.7169030","title":"Live demonstration: Efficient event-driven approach using synchrony processing for hardware spiking neural networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Spiking neural network; Computer science; Neuromorphic engineering; Field-programmable gate array; Event (particle physics); Serialization; Artificial neural network; Feature extraction; Image processing; Virtex; Computer hardware; Artificial intelligence; Hardware acceleration; Process (computing); Computer architecture; Image (mathematics)","score_opus":0.05144981140138083,"score_gpt":0.27614739501348984,"score_spread":0.224697583612109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589682973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22124097,0.00036183977,0.7769927,0.0000072456996,0.00030628784,0.0003107785,0.0000010209111,0.0003363137,0.00044285713],"genre_scores_gemma":[0.9458416,0.0000010269731,0.05358014,0.00002885659,0.00046006983,0.000016680766,0.000011697065,0.000039041144,0.000020860543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989712,0.000022586411,0.00025509638,0.0002461015,0.00013345653,0.0003715199],"domain_scores_gemma":[0.99956095,0.00004542127,0.000055001274,0.00011986893,0.00008950328,0.00012924847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001647546,0.00019565878,0.00018662323,0.000045551933,0.00019824479,0.00007387411,0.00011650733,0.0000742356,0.0000030986935],"category_scores_gemma":[0.000029658262,0.00018638333,0.00006651595,0.00016519062,0.00002558908,0.00021511174,0.000039605125,0.00017770419,0.0000013632638],"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":[0.000009682433,0.00001217513,0.000066984125,0.000077635996,0.0000074276213,0.0000029853861,0.0002419434,0.9870012,0.00053774874,0.000053448024,0.000032832708,0.011955948],"study_design_scores_gemma":[0.00032128583,0.000039854567,0.000013629115,0.00006106348,0.000020947204,0.000044538592,0.00056597014,0.9975585,0.0010720219,0.000027711016,0.00003954706,0.0002349692],"about_ca_topic_score_codex":0.0000010822437,"about_ca_topic_score_gemma":0.0000012664347,"teacher_disagreement_score":0.7246007,"about_ca_system_score_codex":0.00011241145,"about_ca_system_score_gemma":0.000027207965,"threshold_uncertainty_score":0.76004916},"labels":[],"label_agreement":null},{"id":"W1759783608","doi":"10.1016/j.neuron.2015.08.016","title":"Formula for Unsilencing Plasticity: Spike with GABA","year":2015,"lang":"en","type":"letter","venue":"Neuron","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Neuroscience; Postsynaptic potential; Plasticity; Spike-timing-dependent plasticity; Neuron; Synaptic plasticity; Neuroplasticity; Bursting; Metaplasticity; Action (physics); Inhibitory postsynaptic potential; Psychology; Physics; Biology","score_opus":0.028874364579315335,"score_gpt":0.2299743954385482,"score_spread":0.20110003085923286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1759783608","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39952704,0.0013172558,0.4529436,0.09024303,0.016628293,0.0066857473,0.00072952424,0.00953648,0.02238902],"genre_scores_gemma":[0.17023,0.00008363012,0.013877006,0.7593454,0.041155096,0.0003737213,0.00075511506,0.0021350752,0.012044911],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998944,0.000010788896,0.00016939682,0.00027225228,0.0001875274,0.00041606216],"domain_scores_gemma":[0.9994799,0.00020271551,0.000051162104,0.00016689052,0.000046252582,0.000053115342],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000331893,0.00030296182,0.0002811238,0.00007562813,0.0000618734,0.000028765335,0.0001492416,0.00021699657,0.000005709589],"category_scores_gemma":[0.000035707988,0.00026837786,0.000059200767,0.00007543812,0.000016648351,0.000105706335,0.000027098085,0.0009913807,0.000022500626],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030912157,0.0000030801834,0.0000025903018,0.00071222393,0.000020810177,0.00030565442,0.000067959656,0.073570386,0.002982941,0.0000059344407,0.9200528,0.0022446655],"study_design_scores_gemma":[0.00043171062,0.00021935639,0.000005616998,0.00018804082,0.000056130782,0.00008049096,0.0000063599487,0.017098423,0.007368436,0.000103862876,0.9739941,0.00044748976],"about_ca_topic_score_codex":6.751865e-7,"about_ca_topic_score_gemma":0.0000026797306,"teacher_disagreement_score":0.66910243,"about_ca_system_score_codex":0.000060615625,"about_ca_system_score_gemma":0.000022028085,"threshold_uncertainty_score":0.9999768},"labels":[],"label_agreement":null},{"id":"W1814548317","doi":"10.1007/s00521-015-2047-0","title":"Modular neuron comprises of memristor-based synapse","year":2015,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Memristor; Computer science; Synapse; Modular design; Artificial neural network; Synaptic weight; Neuron; Modularity (biology); Physical neural network; Spiking neural network; Artificial intelligence; Time delay neural network; Neuroscience; Types of artificial neural networks; Electronic engineering; Engineering","score_opus":0.026448737614557544,"score_gpt":0.25631440847047227,"score_spread":0.22986567085591472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1814548317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89269876,0.00042360483,0.10582623,0.000051044422,0.00007779433,0.00016611953,0.0000040870627,0.00033606676,0.0004162669],"genre_scores_gemma":[0.9980472,0.0000045221304,0.001753085,0.000058432015,0.0000964561,0.000007476558,0.000007118339,0.000017580196,0.000008154206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993833,0.000021624222,0.00019624569,0.00016121192,0.00009030862,0.00014736346],"domain_scores_gemma":[0.99951583,0.00010630544,0.000048288675,0.00017943753,0.00004811077,0.000102031154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006900977,0.00011518326,0.00015405758,0.000047494053,0.00009137984,0.000011972155,0.00010965223,0.000031475505,8.7506135e-7],"category_scores_gemma":[0.000014425536,0.000117910175,0.000031477204,0.00016595756,0.000053733344,0.00004334671,0.00003654957,0.0001379604,0.0000027854733],"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":[0.0000056554795,0.000023843764,0.00036504868,0.000077440054,0.000005760429,0.0000016882417,0.00006508621,0.9483617,0.033050008,0.0005565551,0.00010812361,0.017379114],"study_design_scores_gemma":[0.00041270236,0.000070961156,0.0014372128,0.000026012221,0.000017310178,0.000015384212,0.000055536104,0.96780765,0.025200635,0.00037328474,0.004370105,0.00021322233],"about_ca_topic_score_codex":0.0000028421605,"about_ca_topic_score_gemma":4.361095e-7,"teacher_disagreement_score":0.105348386,"about_ca_system_score_codex":0.000013933993,"about_ca_system_score_gemma":0.000008014969,"threshold_uncertainty_score":0.48082376},"labels":[],"label_agreement":null},{"id":"W1822654415","doi":"10.1109/pacrim.1993.407167","title":"Standard cells for auto scaling pulsed analog neural circuits","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Capacitance; Excitatory postsynaptic potential; Pulse (music); Computer science; Scaling; Range (aeronautics); Electronic circuit; Biological neural network; Electronic engineering; Neuroscience; Artificial intelligence; Biological system; Electrical engineering; Physics; Materials science; Engineering; Mathematics; Telecommunications; Machine learning; Electrode; Biology","score_opus":0.02929740146504693,"score_gpt":0.23096378064442902,"score_spread":0.2016663791793821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822654415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5870607,0.0004765638,0.39705324,0.000094791605,0.0012392085,0.00044580363,0.00002444995,0.0015421933,0.012063072],"genre_scores_gemma":[0.99771136,0.000010489106,0.0014584088,0.00011464969,0.0001457468,0.000006114178,0.0000022281513,0.00002789074,0.0005231321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931717,0.000006745964,0.00016088951,0.00014657875,0.00007860229,0.00029003015],"domain_scores_gemma":[0.99969083,0.00008576905,0.000014676644,0.00011537189,0.000023043134,0.00007029018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055168013,0.00011976447,0.00014134603,0.000042113796,0.00009045671,0.000025227131,0.000086266315,0.000039544837,0.00014982911],"category_scores_gemma":[0.000014912883,0.00011521199,0.0000639777,0.00010936385,0.0000105315785,0.00012092095,0.0000125957085,0.00010241302,0.000020078727],"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":[0.0000059942236,0.000008678738,0.000020904916,0.00009299264,0.000016585393,0.000011626575,0.00013956169,0.63119227,0.23109764,0.00015512985,0.0021875734,0.13507107],"study_design_scores_gemma":[0.00033643062,0.0000399555,0.000020610725,0.000011413975,0.0000069992466,0.00000483063,0.0000157563,0.77988887,0.21514231,0.00015551083,0.0041999114,0.00017737405],"about_ca_topic_score_codex":3.5605368e-7,"about_ca_topic_score_gemma":0.0000010711382,"teacher_disagreement_score":0.41065067,"about_ca_system_score_codex":0.000030145096,"about_ca_system_score_gemma":0.000001406968,"threshold_uncertainty_score":0.46982086},"labels":[],"label_agreement":null},{"id":"W1858742897","doi":"10.1007/978-3-642-33269-2_12","title":"Modeling of Spiking Analog Neural Circuits with Hebbian Learning, Using Amorphous Semiconductor Thin Film Transistors with Silicon Oxide Nitride Semiconductor Split Gates","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Materials science; Hebbian theory; Transistor; Electronic circuit; Optoelectronics; Semiconductor; Computer science; Electronic engineering; Artificial neural network; Nanotechnology; Electrical engineering; Artificial intelligence; Voltage; Engineering","score_opus":0.025667835271532484,"score_gpt":0.22433316833213615,"score_spread":0.19866533306060366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1858742897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5971141,0.0010579186,0.40099147,0.0000057778443,0.00028563762,0.00026011342,0.0000056099466,0.00019517084,0.0000842275],"genre_scores_gemma":[0.9787377,0.000027421675,0.020653939,0.0000801858,0.00031875644,0.0000027154588,0.000009971082,0.00014537481,0.000023928234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968812,0.000035994242,0.000639319,0.0009363387,0.0006137994,0.0008933513],"domain_scores_gemma":[0.99854934,0.0002516927,0.00027064566,0.0005356893,0.00017343818,0.00021920433],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036313126,0.00079837156,0.00088192924,0.00063041685,0.0002525487,0.00009630743,0.00070530345,0.0002832288,0.000016666529],"category_scores_gemma":[0.000038690345,0.0006841978,0.00012661362,0.0004954283,0.0004584559,0.00065499695,0.00012384262,0.0016691734,0.0000015925561],"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":[0.00001809369,0.0000071948216,0.0002736576,0.00018972135,0.00002720896,0.000050965085,0.00080988253,0.88763887,0.10277056,0.000019947827,1.7441639e-7,0.008193752],"study_design_scores_gemma":[0.00032078067,0.00013886412,0.000023910115,0.0012208512,0.00006139598,0.00028403872,0.000004968364,0.9261079,0.07070604,0.00029175682,0.000016018694,0.0008235033],"about_ca_topic_score_codex":0.000059942377,"about_ca_topic_score_gemma":0.000100156765,"teacher_disagreement_score":0.38162363,"about_ca_system_score_codex":0.0003160034,"about_ca_system_score_gemma":0.0001581605,"threshold_uncertainty_score":0.9995609},"labels":[],"label_agreement":null},{"id":"W186273348","doi":"10.1007/978-3-642-28305-5_18","title":"A Case Study on Hardware/Software Codesign in Embedded Artificial Neural Networks","year":2012,"lang":"en","type":"book-chapter","venue":"Topics in intelligent engineering and informatics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Software; Design space exploration; Artificial neural network; Scope (computer science); Computer architecture; Domain (mathematical analysis); Embedded system; Coprocessor; Software engineering; Operating system; Artificial intelligence; Programming language","score_opus":0.046240037926351854,"score_gpt":0.2525285264969291,"score_spread":0.20628848857057724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W186273348","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6144621,0.0034522621,0.35621104,0.000012292564,0.005799399,0.0033472052,0.00003892119,0.0016333438,0.01504347],"genre_scores_gemma":[0.9952638,0.00039292226,0.0018998063,0.000055514534,0.0006269145,0.000034224944,0.000022836695,0.00013803484,0.0015659389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985013,0.000007961111,0.0008329209,0.00013054378,0.00014645387,0.00038082927],"domain_scores_gemma":[0.99938524,0.00016185029,0.00007391673,0.00025880727,0.000019993358,0.00010017548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020190739,0.00042297563,0.00044476322,0.00032387552,0.00003804428,0.00004513158,0.00012278525,0.000248347,0.0000138594505],"category_scores_gemma":[0.000038495444,0.00044736319,0.00005568267,0.00007075568,0.00001670546,0.00015794588,0.00006844425,0.0011216138,0.000007714996],"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":[0.0000073480273,0.000019236664,0.00003863638,0.00020384214,0.000021164395,0.0004404873,0.0031228985,0.95627975,0.0000010028948,0.0011277412,0.000014010513,0.03872391],"study_design_scores_gemma":[0.00019598422,0.00015660016,0.000013345855,0.0003912129,0.000025102296,0.00035868067,0.0006993935,0.9949101,0.00011097532,0.00016142393,0.0023456165,0.00063152914],"about_ca_topic_score_codex":0.000002284667,"about_ca_topic_score_gemma":0.000009683843,"teacher_disagreement_score":0.38080174,"about_ca_system_score_codex":0.00012797758,"about_ca_system_score_gemma":0.0000058825353,"threshold_uncertainty_score":0.9997978},"labels":[],"label_agreement":null},{"id":"W1866891130","doi":"10.1002/adma.201503125","title":"In Situ Tuning of Switching Window in a Gate‐Controlled Bilayer Graphene‐Electrode Resistive Memory Device","year":2015,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Resistive random-access memory; Materials science; Electrode; Optoelectronics; Graphene; Bilayer; Bilayer graphene; Gating; Voltage; Window (computing); Resistive touchscreen; Nanotechnology; Electrical engineering; Membrane","score_opus":0.018211366232866646,"score_gpt":0.25948999236309056,"score_spread":0.2412786261302239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866891130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959238,0.0005775455,0.0015560564,0.000025720376,0.00047920315,0.00044807384,0.0000033603078,0.00012491387,0.00086132286],"genre_scores_gemma":[0.99710834,0.000045955836,0.0025736408,0.00006414705,0.00008048572,0.000052620788,0.0000035053163,0.000046799836,0.000024525423],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99826837,0.00013001796,0.0006886976,0.00027836108,0.00018838934,0.00044618038],"domain_scores_gemma":[0.99927086,0.00020552492,0.00014870771,0.00022203237,0.000073424155,0.00007948418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000720564,0.00025450287,0.0007482758,0.00027483323,0.0000287715,0.000016467815,0.0001671692,0.00007966045,0.00001046575],"category_scores_gemma":[0.00035388893,0.00024978246,0.000048333055,0.00038786276,0.000019047724,0.00049330556,0.00005311976,0.00017969884,0.000006228133],"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":[0.00062831194,0.000016498507,0.000030280773,0.00007568298,0.000012710685,0.000039664257,0.0004969954,0.14026703,0.8578762,0.00008840901,0.0000034824868,0.0004647519],"study_design_scores_gemma":[0.004805334,0.00005749294,0.00033803403,0.0002478067,0.000011140466,0.000007775777,0.00025721383,0.00041806858,0.9907328,0.0028209775,0.000040182364,0.00026320148],"about_ca_topic_score_codex":0.000024932617,"about_ca_topic_score_gemma":0.0001326435,"teacher_disagreement_score":0.13984896,"about_ca_system_score_codex":0.00013851412,"about_ca_system_score_gemma":0.000034698285,"threshold_uncertainty_score":0.99999547},"labels":[],"label_agreement":null},{"id":"W1900647423","doi":"10.1109/iscas.1993.394264","title":"A novel current mode winner-take-all circuit for artificial neural networks","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Computer science; Winner-take-all; Artificial intelligence","score_opus":0.08470991916270026,"score_gpt":0.2833517676096183,"score_spread":0.198641848446918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1900647423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43618315,0.0020988411,0.52292013,0.00038033104,0.03184357,0.0013474029,0.00027416254,0.00068772875,0.0042646597],"genre_scores_gemma":[0.9971222,0.00008729236,0.00000843282,0.000095034105,0.0023509262,0.0000765098,0.000028798362,0.000046990317,0.0001837724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985628,0.000018256766,0.00044710832,0.00035020345,0.00026394482,0.0003576494],"domain_scores_gemma":[0.9993815,0.00016823885,0.00009472101,0.00015594627,0.00007624188,0.00012336358],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012288672,0.0002540599,0.00025740248,0.00008185935,0.00012443459,0.000144421,0.0002129496,0.000088469045,0.000008866812],"category_scores_gemma":[0.000013923707,0.00024870722,0.00010194699,0.0000771233,0.000024790947,0.00018348635,0.000014308262,0.00024183103,0.000009799893],"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":[0.000009698792,0.000073312476,0.000039694456,0.00008083221,0.00006479115,0.000005108282,0.00021234273,0.92837965,0.043316744,0.0034562815,0.0007602668,0.023601294],"study_design_scores_gemma":[0.00043015112,0.00006879501,0.00002907982,0.000107221604,0.000015879094,0.000062320076,0.000017716538,0.9884258,0.0007915007,0.00006789822,0.009695849,0.00028776785],"about_ca_topic_score_codex":0.000009129903,"about_ca_topic_score_gemma":0.000004945163,"teacher_disagreement_score":0.5609391,"about_ca_system_score_codex":0.00009853103,"about_ca_system_score_gemma":0.0000024651013,"threshold_uncertainty_score":0.99999654},"labels":[],"label_agreement":null},{"id":"W1905603351","doi":"10.1155/2015/643869","title":"Action Selection and Operant Conditioning: A Neurorobotic Implementation","year":2015,"lang":"en","type":"article","venue":"Journal of Robotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Action selection; Artificial intelligence; Novelty; Process (computing); Operant conditioning; Hebbian theory; Sensory system; Artificial neural network; Machine learning; Neuroscience","score_opus":0.06173083832695074,"score_gpt":0.32293634993488224,"score_spread":0.2612055116079315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1905603351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8118547,0.00011961815,0.18722378,0.00009272827,0.0006041689,0.000040456987,2.6104289e-7,0.000031365787,0.00003291067],"genre_scores_gemma":[0.99265647,0.000047441077,0.0070719733,0.000027049442,0.00017870287,2.0948643e-7,8.228277e-7,0.000009400869,0.000007931728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958855,0.00001843023,0.00018744305,0.00003575346,0.00009188614,0.00007793228],"domain_scores_gemma":[0.99972576,0.000022979193,0.00007398672,0.000022229677,0.00008572392,0.0000692917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108371954,0.000054783548,0.00008708494,0.00006002361,0.00003859898,0.000023178405,0.000024900519,0.000019299396,0.0000031526465],"category_scores_gemma":[0.000020940368,0.00005160142,0.000018042749,0.0000758643,0.0000061413266,0.00028335705,0.0000060160537,0.00013947062,0.000001150634],"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":[0.000008098822,0.000004313814,0.00042344775,0.000011972328,0.000010186855,0.000006782016,0.00011894575,0.97831964,0.01706389,0.00006386713,0.00019358001,0.0037752518],"study_design_scores_gemma":[0.005152803,0.001964005,0.014493262,0.00018556476,0.00027925958,0.006308645,0.0033175256,0.79924595,0.1628804,0.0033858386,0.002138804,0.0006479077],"about_ca_topic_score_codex":5.027594e-7,"about_ca_topic_score_gemma":0.0000028547006,"teacher_disagreement_score":0.18080176,"about_ca_system_score_codex":0.00005104324,"about_ca_system_score_gemma":0.000014997285,"threshold_uncertainty_score":0.21042447},"labels":[],"label_agreement":null},{"id":"W1913735411","doi":"10.1063/1.4930867","title":"Pentacene organic ferroelectric transistors with [P(VDF-TrFE)] gate by Langmuir-Blodgett process","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Pentacene; Ferroelectricity; Materials science; Langmuir–Blodgett film; Optoelectronics; Transistor; Threshold voltage; Field-effect transistor; Crystallization; Non-volatile memory; Polarization (electrochemistry); Fabrication; Thin-film transistor; Nanotechnology; Voltage; Dielectric; Chemical engineering; Layer (electronics); Monolayer; Electrical engineering; Chemistry; Physical chemistry","score_opus":0.01206924079835826,"score_gpt":0.21287256569087104,"score_spread":0.2008033248925128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1913735411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9720027,0.00034517568,0.02566661,0.000014933844,0.00013532345,0.00009452027,0.0000018278735,0.000078122874,0.0016607898],"genre_scores_gemma":[0.9990606,0.000024741788,0.0004918172,0.00005220351,0.00029093417,0.0000018721304,0.0000029530634,0.00004947066,0.00002542053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916893,0.0000075449657,0.00023363403,0.000097730706,0.0002650778,0.0002270598],"domain_scores_gemma":[0.9995088,0.000024636429,0.00013441194,0.00009245272,0.00008733253,0.00015237981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009058459,0.00017215166,0.00026636524,0.000037825474,0.000043490363,0.000018404131,0.00016105869,0.00003768553,0.000004564267],"category_scores_gemma":[0.0000031993509,0.00013968592,0.00004439634,0.00034887565,0.000018079234,0.00016823587,0.0000066235025,0.00036381593,0.0000076575425],"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":[0.00040181892,0.00011723285,0.00006465364,0.00015377301,0.00014711877,0.00004225685,0.0016806417,0.7651704,0.22297561,0.00010504459,0.003023392,0.0061180615],"study_design_scores_gemma":[0.0028477635,0.0004644968,0.000051255345,0.000081643644,0.00014819688,0.00013880129,0.00047949518,0.011039767,0.9786468,0.002414221,0.0031244948,0.00056305405],"about_ca_topic_score_codex":1.8453555e-7,"about_ca_topic_score_gemma":5.601831e-7,"teacher_disagreement_score":0.7556712,"about_ca_system_score_codex":0.00009038613,"about_ca_system_score_gemma":0.000050781906,"threshold_uncertainty_score":0.56962264},"labels":[],"label_agreement":null},{"id":"W1926430595","doi":"","title":"Sensorial substitution system with encoding of visual objects into sounds","year":2011,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Encoding (memory); Computer science; Sensory substitution; Sound (geography); Computer vision; ENCODE; Substitution (logic); Auditory system; Artificial intelligence; Speech recognition; Communication; Acoustics; Perception; Psychology; Neuroscience","score_opus":0.014915347767889267,"score_gpt":0.20050699377398418,"score_spread":0.1855916460060949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1926430595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9243938,0.000035561297,0.06986739,6.1106147e-7,0.00084301835,0.000081877195,0.000005802149,0.00012597574,0.0046459897],"genre_scores_gemma":[0.9980337,0.0000026318237,0.0016987488,0.000007740084,0.0002195501,0.0000014769214,0.0000031281115,0.000019431978,0.000013586005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994756,0.000008169136,0.00013009427,0.000092548624,0.00007039179,0.00022321637],"domain_scores_gemma":[0.9996338,0.000022604783,0.000027005215,0.00009452252,0.00004636053,0.00017570399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004643932,0.000100314945,0.00012059167,0.00010052409,0.00008139725,0.0000071527734,0.000064536245,0.000058812566,0.000005804976],"category_scores_gemma":[0.000020328882,0.000100384816,0.000017319817,0.00013938514,0.00003853708,0.00006876902,0.000005621545,0.00010927021,0.000005990919],"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":[0.00006256696,0.000014212978,0.0008616561,0.0011247657,0.00008650361,0.00056140183,0.004836765,0.50649697,0.47643557,0.0072915247,0.00010953456,0.0021185218],"study_design_scores_gemma":[0.0020977026,0.0007799684,0.0061483383,0.0013847769,0.0003171592,0.0004889086,0.008055305,0.6614589,0.3165323,0.00035937084,0.00040732897,0.0019699135],"about_ca_topic_score_codex":0.0015334566,"about_ca_topic_score_gemma":0.004756284,"teacher_disagreement_score":0.15990326,"about_ca_system_score_codex":0.00022685068,"about_ca_system_score_gemma":0.00009468831,"threshold_uncertainty_score":0.4093574},"labels":[],"label_agreement":null},{"id":"W1928324897","doi":"10.3389/fncom.2015.00077","title":"Editorial: State-dependent brain computation","year":2015,"lang":"en","type":"editorial","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"James S. McDonnell Foundation","keywords":"Computation; State (computer science); Volume (thermodynamics); Computer science; Front (military); Brain size; Neuroscience; Geology; Psychology; Physics; Algorithm; Medicine; Oceanography; Magnetic resonance imaging","score_opus":0.01221894680928565,"score_gpt":0.2672095081521213,"score_spread":0.25499056134283565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1928324897","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025962366,0.00013985655,0.25096768,0.000031915413,0.74794453,0.00021058785,0.00011285861,0.00022705788,0.00010585447],"genre_scores_gemma":[0.002913446,0.000046558183,0.008095627,0.00009913386,0.98819864,0.000022287522,0.00023697829,0.00010416659,0.000283146],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962296,0.00012402594,0.00059014,0.000712792,0.0018205916,0.0005228672],"domain_scores_gemma":[0.9984449,0.00071662595,0.00016299757,0.00018486712,0.000304343,0.00018626922],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006248216,0.00042549052,0.00047238052,0.00041933072,0.00012662908,0.00015211763,0.00058777287,0.0003203266,0.0000014752234],"category_scores_gemma":[0.0009469981,0.0004935234,0.000068745816,0.0006528469,0.00014664997,0.00044020836,0.00013060214,0.001280346,0.000019329973],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001084957,0.000010878402,0.00001181411,0.00003251864,0.0000017427282,0.000014281227,0.000030597774,0.49313626,0.00003815586,0.0000016020359,0.50588876,0.0008225687],"study_design_scores_gemma":[0.0005710715,0.00007355445,0.000042680327,0.000072540504,0.0000049869436,0.0000032272103,0.000010674908,0.3782621,0.00002944417,0.0070769424,0.6133702,0.00048260475],"about_ca_topic_score_codex":0.000005535935,"about_ca_topic_score_gemma":0.0000065584554,"teacher_disagreement_score":0.24287207,"about_ca_system_score_codex":0.0004564257,"about_ca_system_score_gemma":0.00036481977,"threshold_uncertainty_score":0.9997516},"labels":[],"label_agreement":null},{"id":"W1953553490","doi":"10.1109/ecctd.2015.7300100","title":"Memristor-based linear feedback shift register based on material implication logic","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; Memistor; Computer science; Logic gate; CMOS; Electronic engineering; Logic family; Pass transistor logic; Resistive random-access memory; Logic synthesis; Computer architecture; Electronic circuit; Electrical engineering; Digital electronics; Engineering; Algorithm; Voltage","score_opus":0.04636886800786656,"score_gpt":0.2676674347459529,"score_spread":0.22129856673808634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953553490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6181122,0.000026743992,0.34214774,0.0010941857,0.0017679454,0.00044950403,0.000015043536,0.0019875064,0.034399148],"genre_scores_gemma":[0.99313074,3.0428842e-7,0.0057991985,0.00060354033,0.00025535043,0.0000088595825,0.000028081782,0.000026287504,0.0001476284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993687,0.000020338714,0.0001556817,0.00016745746,0.00011070449,0.00017707115],"domain_scores_gemma":[0.9995489,0.000041441202,0.000027282347,0.00026309682,0.000025010057,0.0000942341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010126823,0.00013201524,0.0001155993,0.00004422439,0.000035635923,0.000018683517,0.00010278846,0.00006001329,0.000076648255],"category_scores_gemma":[0.000024586121,0.00011506934,0.000035641493,0.00008052487,0.000015292779,0.000068239344,0.000011128808,0.000094068375,0.0001525765],"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":[0.00020160702,0.000039949395,0.000069190384,0.00006799899,0.0000049103096,0.00001081321,0.000042791045,0.9591562,0.03417378,0.00058923784,0.0040103323,0.0016331528],"study_design_scores_gemma":[0.0014457682,0.0002884241,0.0005247202,0.000045021578,0.000012042351,0.0000032019195,0.000017132774,0.48899013,0.48674825,0.00074811757,0.020692172,0.0004850338],"about_ca_topic_score_codex":0.0000027745814,"about_ca_topic_score_gemma":0.0000016811928,"teacher_disagreement_score":0.47016612,"about_ca_system_score_codex":0.00007784834,"about_ca_system_score_gemma":0.000014046593,"threshold_uncertainty_score":0.46923918},"labels":[],"label_agreement":null},{"id":"W1966087949","doi":"10.5555/1639809.1655378","title":"Modeling of neural decoder based on binary spiking neurons in DEVS","year":2009,"lang":"en","type":"article","venue":"Spring Simulation Multiconference","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Decoding methods; Computer science; Binary number; Spike (software development); Spiking neural network; Spike sorting; Algorithm; SIGNAL (programming language); DEVS; Neural decoding; Artificial neural network; Artificial intelligence; Modeling and simulation; Simulation; Mathematics","score_opus":0.04017904174037024,"score_gpt":0.28969107683490475,"score_spread":0.2495120350945345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966087949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.829312,0.000015306725,0.17014125,0.00001772415,0.000085822016,0.00009765254,6.225345e-7,0.00013423961,0.00019534497],"genre_scores_gemma":[0.99686867,0.0000018628277,0.0030047284,0.000082456565,0.000023230083,0.0000012748475,0.0000013605091,0.000014849301,0.0000015461852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918926,0.000024242394,0.00028924918,0.00017337935,0.00011681132,0.00020706479],"domain_scores_gemma":[0.99954605,0.00016805499,0.000032646036,0.00018017732,0.00003328726,0.000039800034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007626252,0.00013960533,0.00015489708,0.00016697664,0.000043252956,0.00001361434,0.00010517171,0.00004589999,0.000005180179],"category_scores_gemma":[0.00008739439,0.00015428902,0.000037341593,0.00015841455,0.000009780464,0.00015876754,0.000012711871,0.00021488684,0.0000023905077],"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":[0.000020169131,0.000018438768,0.0011748108,0.000019773348,8.113527e-7,0.0000047744084,0.00006609204,0.97581947,0.01573855,0.00008527089,4.0464442e-8,0.0070518265],"study_design_scores_gemma":[0.0003775023,0.00004201405,0.015284798,0.000109975845,0.0000027896658,2.0905968e-7,0.000007945418,0.97948265,0.0044664657,0.00008185096,0.000002779163,0.00014099998],"about_ca_topic_score_codex":0.0000064970163,"about_ca_topic_score_gemma":0.000004998477,"teacher_disagreement_score":0.16755664,"about_ca_system_score_codex":0.000033297503,"about_ca_system_score_gemma":0.0000097866905,"threshold_uncertainty_score":0.6291724},"labels":[],"label_agreement":null},{"id":"W1968944283","doi":"10.1016/j.mseb.2010.01.020","title":"Printed flexible memory devices using copper phthalocyanine","year":2010,"lang":"en","type":"article","venue":"Materials Science and Engineering B","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Materials science; Electrode; Electrical impedance; Electrical conductor; Layer (electronics); Optoelectronics; Copper; Printed electronics; Copper phthalocyanine; Electronics; Flexible electronics; Polymer; Flexible display; Active layer; Voltage; Non-volatile memory; Nanotechnology; Composite material; Electrical engineering; Thin-film transistor; Chemistry; Inkwell; Metallurgy","score_opus":0.016531201099334442,"score_gpt":0.24254110618222433,"score_spread":0.22600990508288987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968944283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965283,0.000042599066,0.0010896632,0.000009202587,0.0015436673,0.00007547833,0.0000024621534,0.00042118164,0.0002874071],"genre_scores_gemma":[0.9956089,0.0000052689534,0.0041445363,0.000022796614,0.00017625673,0.0000032107807,7.521444e-7,0.0000216766,0.000016570884],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915314,0.0000029303058,0.00015478814,0.00019670527,0.0001553439,0.0003370846],"domain_scores_gemma":[0.9996568,0.000019970554,0.000017818102,0.00015524373,0.00004520684,0.00010493723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034962813,0.00014453473,0.00014663201,0.00011913308,0.00014656824,0.00013067237,0.00017253899,0.000042066094,0.000041122465],"category_scores_gemma":[0.00006104102,0.00013516944,0.000011057859,0.00027587658,0.00009550248,0.00040996162,0.00007640137,0.00012606717,0.0000091538905],"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":[0.0000015069385,0.0000020455125,0.000025764099,0.000056949724,0.0000019846632,0.0000037649895,0.000049302518,0.031929318,0.96749836,0.00009356963,0.000005653155,0.00033178122],"study_design_scores_gemma":[0.000086371,0.000009321213,0.0011795044,0.000033541754,0.000004105352,0.000077229335,0.000015121797,0.035027005,0.96289593,0.000012727972,0.0004784369,0.00018069787],"about_ca_topic_score_codex":0.0000053345298,"about_ca_topic_score_gemma":8.134954e-7,"teacher_disagreement_score":0.004602423,"about_ca_system_score_codex":0.000022526441,"about_ca_system_score_gemma":0.000018510325,"threshold_uncertainty_score":0.551205},"labels":[],"label_agreement":null},{"id":"W1970563530","doi":"10.4028/www.scientific.net/amr.31.4","title":"Charge Effect in Organic Field-Effect Transistors - Analyzing Hall Measurements in the Accumulation Layer","year":2007,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Rubrene; Materials science; Field-effect transistor; Organic semiconductor; Hall effect; Layer (electronics); Transistor; Optoelectronics; Semiconductor; Field effect; Charge (physics); Electron mobility; Active layer; Charge carrier; Field (mathematics); Thin-film transistor; Nanotechnology; Electrical engineering; Electrical resistivity and conductivity; Physics; Voltage; Engineering","score_opus":0.11291346844865298,"score_gpt":0.4058821642713922,"score_spread":0.2929686958227392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970563530","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99645907,0.00020860459,0.0017900431,0.000028952178,0.0002912064,0.00069023395,0.0000010003183,0.0000713038,0.00045960088],"genre_scores_gemma":[0.9996832,0.00003458215,0.00007041209,0.00002555149,0.00009355454,0.000040895564,0.0000058517016,0.000031166815,0.000014803745],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99789184,0.00049984717,0.00034762485,0.00024345884,0.0003905988,0.0006266532],"domain_scores_gemma":[0.99847555,0.0011780867,0.00002541236,0.00024214944,0.000034846555,0.000043949894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056332983,0.0001668171,0.00025714468,0.00030246406,0.00010501569,0.000041689873,0.00025513646,0.00008969436,0.00008722813],"category_scores_gemma":[0.00041035726,0.00012989888,0.000031734697,0.0006922304,0.000018069764,0.00026107006,0.00003103126,0.00040235056,0.000030310344],"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":[0.00020555049,0.000012464517,0.0011322726,0.00014737174,0.0000054026186,0.000026914226,0.0002922265,0.0040274872,0.9795936,0.000008199233,0.0000037226043,0.014544789],"study_design_scores_gemma":[0.0007429881,0.00016686122,0.0052358396,0.0001052021,0.0000029154112,0.0000022753688,0.000022259135,0.00021004361,0.99317396,0.00009926074,0.00010956421,0.00012884387],"about_ca_topic_score_codex":0.000025545785,"about_ca_topic_score_gemma":0.0002615226,"teacher_disagreement_score":0.014415945,"about_ca_system_score_codex":0.00017469573,"about_ca_system_score_gemma":0.000006873008,"threshold_uncertainty_score":0.52971226},"labels":[],"label_agreement":null},{"id":"W1971505027","doi":"10.1002/syn.20378","title":"Spiking neurons, dopamine, and plasticity: Timing is everything, but concentration also matters","year":2007,"lang":"en","type":"article","venue":"Synapse","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Neuroscience; Long-term potentiation; Synaptic plasticity; Postsynaptic potential; Plasticity; Spike-timing-dependent plasticity; Multiplicative function; Neuroplasticity; Synapse; Computer science; Biological system; Metaplasticity; Chemistry; Physics; Psychology; Biology; Mathematics","score_opus":0.013530188759158509,"score_gpt":0.23603054752389158,"score_spread":0.22250035876473306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971505027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95572686,0.00015964184,0.04310096,0.00009934173,0.00027883332,0.000081134225,0.0000024862502,0.00021611893,0.00033463436],"genre_scores_gemma":[0.9980361,0.000021170594,0.0010805128,0.00066713814,0.0001197004,9.365935e-7,0.0000024093936,0.000021871441,0.000050160823],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992876,0.000009443603,0.00017798922,0.00016318724,0.00008717196,0.0002746264],"domain_scores_gemma":[0.9996656,0.00014430056,0.00003296066,0.00007391735,0.000011576672,0.00007165005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010147888,0.00012732034,0.00011299866,0.00003875135,0.000101296115,0.000030043737,0.00005284486,0.00004350565,0.000016212014],"category_scores_gemma":[0.00003324873,0.00013615395,0.000020482768,0.0000724475,0.000027707472,0.00018929454,0.000028327662,0.00016791208,0.000010741205],"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":[0.00005006073,0.000018345741,0.0038734928,0.00029723742,0.000047280624,0.00030887703,0.0014136479,0.03190206,0.9274469,0.0003629445,0.0010948773,0.03318429],"study_design_scores_gemma":[0.00083719706,0.00009841395,0.011019183,0.00025024224,0.000050329363,0.00027895358,0.0003988001,0.092285156,0.88977104,0.00024376542,0.0040242244,0.00074268813],"about_ca_topic_score_codex":0.0000043156415,"about_ca_topic_score_gemma":0.000003570932,"teacher_disagreement_score":0.0603831,"about_ca_system_score_codex":0.00002855941,"about_ca_system_score_gemma":0.0000037689008,"threshold_uncertainty_score":0.5552197},"labels":[],"label_agreement":null},{"id":"W1973661972","doi":"10.1109/ijcnn.2007.4371154","title":"The Trouble with Weight-Dependent STDP","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Dalhousie University","funders":"","keywords":"Forgetting; Computer science; Slowness; Learning rule; Artificial intelligence; Range (aeronautics); Machine learning; Artificial neural network; Cognitive psychology; Psychology; Engineering; Physics","score_opus":0.04943824084566123,"score_gpt":0.29656109613571185,"score_spread":0.24712285529005062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973661972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6223977,0.0003988457,0.20905,0.007682407,0.06041147,0.0022500271,0.00013761017,0.0023901272,0.09528182],"genre_scores_gemma":[0.9912107,0.0008089625,0.0001381003,0.0014417515,0.004109272,0.000090176574,0.000105167885,0.00015486385,0.0019410045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99415433,0.00016527103,0.001280854,0.0011832819,0.0016859882,0.0015302687],"domain_scores_gemma":[0.99645466,0.0009440656,0.0005006259,0.00076629786,0.00084820605,0.00048614302],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006738975,0.0011102333,0.00063332904,0.00034156977,0.0006726643,0.00085736945,0.00250218,0.00036575293,0.000401859],"category_scores_gemma":[0.00005217443,0.00084503996,0.00031208902,0.00043967413,0.00036133896,0.0008264233,0.0001607791,0.0025414703,0.000072852396],"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":[0.0018349899,0.00012013584,0.0003153677,0.00000907497,0.00029077966,0.0003129431,0.000059418395,0.9212606,0.0018105232,0.031061161,0.0019786404,0.040946353],"study_design_scores_gemma":[0.0014997144,0.0005904734,0.00045970018,0.0002435049,0.000034623816,0.00018833549,0.00014171266,0.99061817,0.002628968,0.000988686,0.0016177031,0.000988381],"about_ca_topic_score_codex":0.000021637768,"about_ca_topic_score_gemma":0.00032167623,"teacher_disagreement_score":0.368813,"about_ca_system_score_codex":0.00042626934,"about_ca_system_score_gemma":0.000058798814,"threshold_uncertainty_score":0.99975973},"labels":[],"label_agreement":null},{"id":"W1974324992","doi":"10.1007/s10867-009-9153-0","title":"Neural cytoskeleton capabilities for learning and memory","year":2009,"lang":"en","type":"article","venue":"Journal of Biological Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":75,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Allard Foundation; Alberta Cancer Foundation","keywords":"Cytoskeleton; Neuroscience; Dendritic spine; Biology; Computer science; Cell","score_opus":0.024904361926972124,"score_gpt":0.26265846487350986,"score_spread":0.23775410294653773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974324992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9863251,0.0005116718,0.012752849,0.000055769164,0.00012109102,0.00004320559,6.0738773e-7,0.0000394749,0.0001502398],"genre_scores_gemma":[0.99794936,0.000090901296,0.0014064147,0.00006865889,0.00046782734,3.39905e-7,5.400055e-7,0.0000043600107,0.000011621281],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959004,0.00001827708,0.00016962644,0.00005509539,0.000042142266,0.00012483055],"domain_scores_gemma":[0.9996565,0.00017900635,0.000059776394,0.00002576854,0.00003451643,0.00004442634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009509023,0.000078852856,0.00017196384,0.000012775819,0.000052509513,0.000010136609,0.00004857761,0.000036796006,0.0000014973806],"category_scores_gemma":[0.00007157009,0.00005511425,0.00007283498,0.000035874567,0.000028682633,0.00009357745,0.000007679836,0.0002197142,2.568039e-7],"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":[0.0001049335,0.000033546276,0.00032034022,0.000050432755,0.000021699147,0.0000121955145,0.00023376434,0.12507306,0.4089633,0.0007079436,0.00012486722,0.46435392],"study_design_scores_gemma":[0.004518576,0.014695088,0.014823122,0.0002957366,0.00011698546,0.00059354294,0.0021184315,0.1393633,0.53255343,0.2770143,0.012203579,0.0017039359],"about_ca_topic_score_codex":3.9692182e-8,"about_ca_topic_score_gemma":1.6760668e-8,"teacher_disagreement_score":0.46265,"about_ca_system_score_codex":0.000012328095,"about_ca_system_score_gemma":0.0000025147467,"threshold_uncertainty_score":0.22474939},"labels":[],"label_agreement":null},{"id":"W1975062838","doi":"10.1557/proc-0997-i01-02","title":"Memory Effect in Organic Diodes Containing Self-assembled Gold Nanoparticles","year":2007,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; OLA Display (Canada); Université de Montréal; Regroupement Québécois sur les Matériaux de Pointe","funders":"","keywords":"Materials science; Diode; Optoelectronics; Fabrication; Nanoparticle; Nanotechnology; Nanopillar; Nanostructure","score_opus":0.0071306851316952635,"score_gpt":0.22394588282311953,"score_spread":0.21681519769142427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975062838","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944995,0.0002456619,0.00017571416,0.000013632617,0.00017486738,0.0002044841,1.0480419e-7,0.00090512406,0.0037809154],"genre_scores_gemma":[0.9991474,0.000012723877,0.00057050184,0.00006174179,0.000120134006,0.000009774379,3.2423918e-7,0.000047472284,0.00002993257],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988454,0.0000054683596,0.00027990728,0.00022402566,0.00013145345,0.00051373045],"domain_scores_gemma":[0.9996248,0.00014661615,0.00003881566,0.00006383322,0.000031151507,0.000094777235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060304604,0.00020188696,0.00024436272,0.00012485529,0.00006202493,0.000038781545,0.00014117504,0.00007822137,0.000009580579],"category_scores_gemma":[0.0000757828,0.00019815464,0.00003915619,0.00042929914,0.000008895249,0.00030824772,0.000045517296,0.00027051556,0.000024221721],"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":[0.00004221336,0.000012774651,0.006005708,0.00014928855,0.000011698124,0.000028084714,0.0010926469,0.00036574554,0.9855451,0.000079955695,0.00004104703,0.006625727],"study_design_scores_gemma":[0.00065285387,0.0001317967,0.0041607916,0.0001069581,0.000012564345,0.000019303185,0.00034757672,0.0028117977,0.9912011,0.00013891008,0.00016155551,0.00025479693],"about_ca_topic_score_codex":0.0000019100448,"about_ca_topic_score_gemma":0.000007250075,"teacher_disagreement_score":0.00637093,"about_ca_system_score_codex":0.0001292974,"about_ca_system_score_gemma":0.000006079128,"threshold_uncertainty_score":0.80805117},"labels":[],"label_agreement":null},{"id":"W1976927164","doi":"10.5555/2616606.2616808","title":"A hybrid non-volatile SRAM cell with concurrent SEU detection and correction","year":2014,"lang":"en","type":"article","venue":"Design, Automation, and Test in Europe","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Static random-access memory; Computer science; Transistor; Field-programmable gate array; Embedded system; Context (archaeology); Error detection and correction; Memory cell; Electronic circuit; Computer hardware; Electronic engineering; Electrical engineering; Engineering; Voltage; Algorithm","score_opus":0.0063726359078406765,"score_gpt":0.18471613753523777,"score_spread":0.1783435016273971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976927164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6472281,0.000054652726,0.35178125,0.0000032238897,0.00020455974,0.0001302304,5.683015e-7,0.00020855917,0.00038886088],"genre_scores_gemma":[0.999014,0.00003574375,0.0007956138,0.000021266194,0.000051169576,0.0000073769897,0.000002874703,0.000019911176,0.00005201522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995072,0.000040592262,0.00013623343,0.00014578739,0.00005492549,0.00011526349],"domain_scores_gemma":[0.9995091,0.00031126064,0.00003726576,0.00006586097,0.00003883343,0.00003770545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012049048,0.00011187549,0.00010025182,0.000067552406,0.00008811786,0.00003543671,0.000025800575,0.000019781593,0.0000019340223],"category_scores_gemma":[0.00006988983,0.00010283586,0.0000056344966,0.00015500897,0.000020297695,0.00017129305,0.000008618756,0.000105797146,0.000004711715],"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":[0.000043224463,0.00011504797,0.011009214,0.00045678794,0.00001576316,0.000022219136,0.001488699,0.428784,0.17125082,0.000032562544,0.00040169447,0.38637993],"study_design_scores_gemma":[0.00037844587,0.00019059618,0.020751169,0.000051871917,0.0000077059885,0.00004850666,0.0000083373125,0.91526645,0.06219955,0.00002368144,0.00093636697,0.00013732532],"about_ca_topic_score_codex":0.0000029752869,"about_ca_topic_score_gemma":0.0000049612104,"teacher_disagreement_score":0.4864824,"about_ca_system_score_codex":0.000013989565,"about_ca_system_score_gemma":0.000005741717,"threshold_uncertainty_score":0.41935247},"labels":[],"label_agreement":null},{"id":"W1980670173","doi":"10.4028/www.scientific.net/kem.609-610.728","title":"Research on Fabrication and Electronic Characteristics of Dual-Extended Nano Structure Memristor","year":2014,"lang":"en","type":"article","venue":"Key engineering materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Memristor; Fabrication; Dual (grammatical number); Nanostructure; Materials science; Nanotechnology; Nano-; Nanoscopic scale; Electrode; Optoelectronics; Electronic engineering; Physics; Engineering; Composite material","score_opus":0.012062428909046845,"score_gpt":0.2511107285934739,"score_spread":0.23904829968442703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980670173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963066,0.00006017075,0.002817561,0.00001021657,0.00044498642,0.00011299368,0.000017562848,0.00016895463,0.000060935905],"genre_scores_gemma":[0.9992306,0.000037678747,0.00037720968,0.000005674529,0.0002607448,0.0000070249444,0.000015785245,0.000039691346,0.000025587125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991171,0.000040216637,0.00023868562,0.0001582347,0.00014004554,0.00030572104],"domain_scores_gemma":[0.9995396,0.00012617146,0.00003780824,0.00019685115,0.00005184272,0.00004770684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032475378,0.00013941045,0.00023120911,0.0001305474,0.000043710996,0.000024150107,0.00007790681,0.000072696304,0.000023813758],"category_scores_gemma":[0.0001307735,0.0001389722,0.000015064207,0.00012225822,0.00001934857,0.00006290985,0.000029722918,0.00016066215,0.0000052580476],"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":[0.000012507732,0.0000039609267,0.0000033324234,0.00020304092,0.000011446416,6.374315e-7,0.00006139139,0.0093416795,0.9859062,0.003176949,0.000024971661,0.0012539156],"study_design_scores_gemma":[0.00014507466,0.00009925958,0.004133849,0.000076343335,0.0000071397526,0.0000069637904,0.00000463657,0.003156736,0.9903076,0.00025372664,0.0016558702,0.00015277283],"about_ca_topic_score_codex":0.0000014257618,"about_ca_topic_score_gemma":1.6894208e-7,"teacher_disagreement_score":0.0061849435,"about_ca_system_score_codex":0.000054786193,"about_ca_system_score_gemma":0.0000059842732,"threshold_uncertainty_score":0.5667122},"labels":[],"label_agreement":null},{"id":"W1981746438","doi":"10.2478/nsmmt-2012-0004","title":"Signals generated in memristive circuits","year":2012,"lang":"en","type":"article","venue":"Nanoscale Systems Mathematical Modeling Theory and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Electronic circuit; Memristor; Computer science; Electronic engineering; Electrical engineering; Engineering","score_opus":0.026802853636267034,"score_gpt":0.26084053456314193,"score_spread":0.2340376809268749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981746438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19344427,0.0014882324,0.8013443,0.0000042931165,0.00004305759,0.00041010225,0.0000052398086,0.00019459274,0.0030659128],"genre_scores_gemma":[0.99892884,0.000020457026,0.0004261421,0.000012073137,0.00016742098,0.00028863695,0.0000037762184,0.00002510077,0.00012755909],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990766,0.00008885796,0.0003329917,0.00014488409,0.00008313311,0.00027353602],"domain_scores_gemma":[0.999316,0.00035238458,0.000027698348,0.00016276969,0.000024139681,0.000117027885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006297709,0.00014005593,0.00022053764,0.000058046706,0.00009845016,0.000022218188,0.000081974016,0.000083291074,0.000017041608],"category_scores_gemma":[0.000042030184,0.00012706425,0.000026794993,0.00017337524,0.00003003083,0.0001331042,0.000023593862,0.00015747499,0.000069201786],"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":[0.000007826976,0.00008842372,0.000015427342,0.00039748035,0.000019097486,7.3966845e-7,0.00053931586,0.400689,0.03096208,0.56310993,0.000009113913,0.0041615185],"study_design_scores_gemma":[0.00020820163,0.000009927092,0.0000046551345,0.00018707727,0.00002109895,0.000030169174,0.00037206538,0.9182567,0.004589463,0.07575268,0.00029041566,0.00027754874],"about_ca_topic_score_codex":4.726605e-7,"about_ca_topic_score_gemma":9.3033044e-8,"teacher_disagreement_score":0.80548453,"about_ca_system_score_codex":0.000029308323,"about_ca_system_score_gemma":0.0000038932885,"threshold_uncertainty_score":0.51815295},"labels":[],"label_agreement":null},{"id":"W1984849013","doi":"10.1177/1059712312442231","title":"Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks","year":2012,"lang":"en","type":"article","venue":"Adaptive Behavior","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Spiking neural network; Computer science; Spike-timing-dependent plasticity; Classical conditioning; Artificial intelligence; Artificial neural network; Spike (software development); Robot; Learning rule; Machine learning; Conditioning; Synaptic plasticity","score_opus":0.028390114497312435,"score_gpt":0.277296608919017,"score_spread":0.24890649442170454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984849013","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8732204,0.00023176541,0.1251845,0.000009357935,0.00041904347,0.0006359851,0.000012249477,0.00022815417,0.000058560552],"genre_scores_gemma":[0.99828917,0.0000024164176,0.00090086856,0.00003126848,0.00033389544,0.00024314498,0.0000991318,0.0000545916,0.000045520617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985409,0.00009728031,0.00037111502,0.00023406718,0.00011650397,0.00064014655],"domain_scores_gemma":[0.9993241,0.00027448218,0.000087767694,0.00010106549,0.0000317094,0.00018090928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018298498,0.00028031773,0.00045088967,0.000067688525,0.0001773291,0.000037512178,0.00010080378,0.00011855011,0.000032895365],"category_scores_gemma":[0.00003797388,0.00026283535,0.000110103065,0.00008441616,0.00008445391,0.0004683377,0.000029699782,0.00054607616,0.0000017505203],"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":[0.0006349403,0.00058993575,0.10013426,0.00003451077,0.00007317599,0.00004615448,0.0006923778,0.7575523,0.075319976,0.0010470195,0.0001354769,0.063739814],"study_design_scores_gemma":[0.008264897,0.0005133209,0.043099526,0.00011164115,0.00014221533,0.000034586425,0.0009738158,0.93936443,0.006463086,0.000042835632,0.00012485881,0.0008647628],"about_ca_topic_score_codex":0.000002924872,"about_ca_topic_score_gemma":0.000006328915,"teacher_disagreement_score":0.1818121,"about_ca_system_score_codex":0.000108808366,"about_ca_system_score_gemma":0.000005328526,"threshold_uncertainty_score":0.99998236},"labels":[],"label_agreement":null},{"id":"W1990955851","doi":"10.1021/ja802673w","title":"Conducting Polymer Memory Devices Based on Dynamic Doping","year":2008,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Government of Canada; University of Alberta; Government of Alberta; Advanced Micro Devices","keywords":"Polypyrrole; Doping; Conductance; Chemistry; Polaron; Polymer; Layer (electronics); Electrode; Optoelectronics; Conductive polymer; Chemical physics; Nanotechnology; Analytical Chemistry (journal); Materials science; Electrochemistry; Condensed matter physics; Electron; Organic chemistry","score_opus":0.025032658146855517,"score_gpt":0.2574464301093061,"score_spread":0.2324137719624506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990955851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99792486,0.00020230023,0.0011171282,0.0002961499,0.00023179974,0.00002954037,7.426331e-7,0.000045365443,0.00015210305],"genre_scores_gemma":[0.99620146,0.000027192911,0.0022156476,0.0013022601,0.00021385979,4.495847e-7,2.0130325e-7,0.000020781006,0.000018132076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992093,0.000025105044,0.00025985794,0.000085721455,0.00021719566,0.00020283347],"domain_scores_gemma":[0.9992988,0.00020836074,0.00026005344,0.00013312856,0.000032567692,0.000067089655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010576936,0.00012561999,0.00024298833,0.000014230217,0.00013198238,0.0000075631506,0.0002571063,0.000027296946,0.000006638872],"category_scores_gemma":[0.000043709682,0.00008651537,0.0003417436,0.00023154769,0.0001739482,0.00008195182,0.000033716326,0.0005392162,0.0000014863717],"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":[0.000014478032,0.000014956874,0.00033503596,0.000019164478,0.000036165606,0.0000066682264,0.00017990131,0.038087685,0.958599,5.833144e-7,0.0003903797,0.0023159962],"study_design_scores_gemma":[0.00030264322,0.00004159036,0.0005277659,0.000110756606,0.000022121008,0.00016722332,0.0003376189,0.041422237,0.9567454,0.00001166472,0.0001417772,0.00016922948],"about_ca_topic_score_codex":0.0000015018167,"about_ca_topic_score_gemma":7.905611e-8,"teacher_disagreement_score":0.003334549,"about_ca_system_score_codex":0.00012331057,"about_ca_system_score_gemma":0.000024646502,"threshold_uncertainty_score":0.35279945},"labels":[],"label_agreement":null},{"id":"W1993339808","doi":"10.1063/1.3703063","title":"Resistive switching behavior in diamond-like carbon films grown by pulsed laser deposition for resistance switching random access memory application","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Materials science; Ohmic contact; Pulsed laser deposition; Resistive random-access memory; Diamond-like carbon; Optoelectronics; Thin film; Diamond; Thermal conduction; Carbon fibers; Deposition (geology); Diffusion; Doping; Analytical Chemistry (journal); Voltage; Nanotechnology; Composite material; Electrical engineering; Chemistry","score_opus":0.011778527731659749,"score_gpt":0.25257728457990086,"score_spread":0.24079875684824112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993339808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83951604,0.0002696386,0.15889187,0.0000054155003,0.00026740888,0.0004556148,0.000005627227,0.00004348602,0.0005449041],"genre_scores_gemma":[0.9971407,0.000024562194,0.002133036,0.000049937837,0.0004854203,0.00009351563,0.000013749452,0.000049928687,0.000009119863],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988125,0.000021600848,0.0004958911,0.00014426152,0.00020494309,0.00032080704],"domain_scores_gemma":[0.9991414,0.00023970247,0.00032429319,0.00014209955,0.00005584131,0.000096688455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032677606,0.00020159066,0.00033775085,0.00007293766,0.00010656453,0.00004049868,0.00019961908,0.00007760146,7.478392e-7],"category_scores_gemma":[0.000013232976,0.00020178985,0.00008861275,0.00023179945,0.000010218812,0.0005488535,0.000027880638,0.00042907085,4.9600345e-7],"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":[0.0007877413,0.00010424072,0.00023663469,0.00013486497,0.000030412717,0.000002977314,0.00047477602,0.12268812,0.8641782,0.000061410494,0.00014770398,0.011152956],"study_design_scores_gemma":[0.0033134834,0.00003379392,0.0011510112,0.00014614519,0.00013639401,0.0000064861647,0.00019848844,0.02646168,0.9664901,0.0014792674,0.00015467388,0.00042851563],"about_ca_topic_score_codex":0.0000033571932,"about_ca_topic_score_gemma":0.0000066135162,"teacher_disagreement_score":0.15762469,"about_ca_system_score_codex":0.00012454008,"about_ca_system_score_gemma":0.00001409533,"threshold_uncertainty_score":0.82287514},"labels":[],"label_agreement":null},{"id":"W1994897823","doi":"10.1139/cjp-2013-0521","title":"Photolithography-free Ge–Se based memristive arrays; materials characterization and device testing","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chalcogenide; Stack (abstract data type); Memristor; Electrical conductor; Photolithography; Optoelectronics; Lithography; Nanotechnology; Materials science; Chalcogenide glass; Surface roughness; Electronic engineering; Composite material; Computer science","score_opus":0.02215946925657801,"score_gpt":0.20595113922805852,"score_spread":0.1837916699714805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994897823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97726154,0.000047012047,0.02216395,0.0000360621,0.00027417054,0.00008341476,0.000025819596,0.000019735053,0.00008827042],"genre_scores_gemma":[0.9958733,0.0000027895346,0.0036519251,0.00011843177,0.00031931236,0.000001992735,0.000005693863,0.000021691434,0.0000049125106],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942344,0.00002134163,0.00020956609,0.00006774905,0.000059603844,0.000218271],"domain_scores_gemma":[0.9993058,0.000062559935,0.00011261386,0.000092650684,0.0001435582,0.00028281924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007572013,0.000111245405,0.00016293574,0.00007758256,0.00009162849,0.00007065262,0.000109393244,0.000030417292,0.00002424263],"category_scores_gemma":[0.000075307464,0.00011150548,0.000026663269,0.00017342734,0.00003054372,0.00033456404,0.000005483398,0.00012891802,0.0000029907833],"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":[0.0000029790222,0.0000034520522,0.0013946578,0.000085211344,0.00002255821,0.000033296954,0.00027039007,0.004556556,0.9752893,0.00007021234,0.00007585785,0.01819553],"study_design_scores_gemma":[0.0008332808,0.00013369984,0.05587589,0.0005186626,0.00006365381,0.00011028027,0.000156107,0.015185461,0.9229774,0.002546114,0.0010493946,0.00055004033],"about_ca_topic_score_codex":0.0001827392,"about_ca_topic_score_gemma":0.00008718677,"teacher_disagreement_score":0.05448123,"about_ca_system_score_codex":0.000038857106,"about_ca_system_score_gemma":0.00008280516,"threshold_uncertainty_score":0.45470613},"labels":[],"label_agreement":null},{"id":"W1996478510","doi":"10.1021/ja9070909","title":"Compensation Doping in Conjugated Polymers: Engineering Dopable Heterojunctions for Modulating Conductivity in the Solid State","year":2009,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Doping; Conjugated system; Chemistry; Rectification; Interfacing; Polymer; Heterojunction; Optoelectronics; Conductivity; Compensation (psychology); Ion; Nanotechnology; Semiconductor; Materials science; Electrical engineering; Organic chemistry; Voltage; Physical chemistry; Computer science","score_opus":0.017461855304489005,"score_gpt":0.2629297448171211,"score_spread":0.2454678895126321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996478510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869454,0.000054612825,0.012315191,0.0004301241,0.0001106949,0.000111269605,0.0000012942697,0.000020534695,0.000010865627],"genre_scores_gemma":[0.99708754,0.000013378241,0.0024783749,0.00030525652,0.00010204888,0.0000020347786,5.510151e-7,0.00000918895,0.0000016570174],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922913,0.000027727947,0.00034211716,0.000074522985,0.00011460937,0.00021189635],"domain_scores_gemma":[0.99944544,0.00022949034,0.00018328968,0.000088979694,0.00002695109,0.00002582268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029985118,0.0001007827,0.00022941704,0.000022578148,0.00005594566,0.000020428748,0.00016832787,0.000020686139,6.9213064e-7],"category_scores_gemma":[0.000082563776,0.00006927098,0.00019261807,0.0003373627,0.000040787327,0.00015838695,0.00001484693,0.00043437514,8.897681e-8],"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":[0.000010644119,0.000013269938,0.00015762595,0.000008707034,0.000011273157,6.319961e-7,0.00043600262,0.3290099,0.6689967,0.0000034375219,0.000022529284,0.001329301],"study_design_scores_gemma":[0.0009450895,0.00010173541,0.007172099,0.00019128133,0.000022766946,0.000069101116,0.00093603553,0.41162878,0.57800853,0.0005856504,0.00010292657,0.00023600699],"about_ca_topic_score_codex":0.000005886559,"about_ca_topic_score_gemma":9.704635e-7,"teacher_disagreement_score":0.09098818,"about_ca_system_score_codex":0.00014141659,"about_ca_system_score_gemma":0.0000106541,"threshold_uncertainty_score":0.28247884},"labels":[],"label_agreement":null},{"id":"W1998038097","doi":"10.1088/0957-4484/21/12/125201","title":"Highly stable resistive switching on monocrystalline ZnO","year":2010,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Materials science; Monocrystalline silicon; Planar; Electrode; Resistive touchscreen; Annihilation; Thermal; Optoelectronics; Metal; Thermal stability; Condensed matter physics; Silicon; Thermodynamics; Metallurgy; Chemical engineering; Electrical engineering; Physical chemistry","score_opus":0.0075726993719944045,"score_gpt":0.2176626977359796,"score_spread":0.2100899983639852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998038097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801404,0.000056235578,0.016012654,0.00028160086,0.0006420981,0.00009329156,0.000002908632,0.001428774,0.0013420462],"genre_scores_gemma":[0.99582356,0.000016262717,0.00378868,0.00007305064,0.00010485452,0.000008154618,0.0000021703877,0.00003174006,0.00015151074],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925476,0.0000069862904,0.00015527726,0.00020735367,0.000069134825,0.0003064847],"domain_scores_gemma":[0.9995273,0.00008696339,0.000028881866,0.00029952335,0.00001980564,0.000037525828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007353316,0.00014493146,0.00016203646,0.00013410076,0.00010318893,0.000009164114,0.00019509517,0.00024596552,0.00002302257],"category_scores_gemma":[0.000092701164,0.0001419215,0.000030786472,0.00018871044,0.000032766664,0.00006954427,0.000048576057,0.00089096586,0.000058190617],"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":[0.000014384153,0.000010407169,0.000015618094,0.000010905651,0.000007875754,0.000025365387,0.000019839139,0.008441454,0.96082664,0.006584119,0.00010477686,0.023938643],"study_design_scores_gemma":[0.00025522482,0.00008293377,0.00005375851,0.00001865455,0.000004992362,0.000019854695,0.000018927536,0.007649741,0.95716465,0.0055879015,0.028936768,0.00020661062],"about_ca_topic_score_codex":0.000002527491,"about_ca_topic_score_gemma":0.000020669164,"teacher_disagreement_score":0.02883199,"about_ca_system_score_codex":0.000026855534,"about_ca_system_score_gemma":0.000006662055,"threshold_uncertainty_score":0.5787391},"labels":[],"label_agreement":null},{"id":"W1998380171","doi":"10.1007/s00422-007-0152-6","title":"Computational consequences of experimentally derived spike-time and weight dependent plasticity rules","year":2007,"lang":"en","type":"article","venue":"Biological Cybernetics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Spike-timing-dependent plasticity; Synaptic plasticity; Uncorrelated; Spike (software development); Spike train; Mathematics; Associative learning; Statistical physics; Learning rule; Neuroscience; Biological system; Statistics; Computer science; Physics; Biology; Artificial intelligence; Artificial neural network","score_opus":0.023309755072315683,"score_gpt":0.25028559183173915,"score_spread":0.22697583675942345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998380171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98454773,0.00040492296,0.014206738,0.0000059629447,0.00005879398,0.00006859381,0.000011278096,0.000082187944,0.0006137977],"genre_scores_gemma":[0.9867914,0.00004016633,0.013080407,0.000027298662,0.000038424725,8.2945405e-7,0.000008773028,0.0000053359217,0.000007372886],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994105,0.000016304517,0.00020854971,0.00012467364,0.00008095243,0.0001590261],"domain_scores_gemma":[0.9995354,0.00030962893,0.00003547592,0.000034910398,0.000021153764,0.00006342261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074204196,0.00010577258,0.00014535969,0.000021887025,0.000037402027,0.0000063115076,0.000069114765,0.000069897804,0.000040692692],"category_scores_gemma":[0.000034822315,0.000083346866,0.000022493487,0.00003422371,0.00021585201,0.000022903074,0.000045155604,0.00007990038,0.000008241338],"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":[0.000034395605,0.000026745513,0.0016880292,0.000011675899,0.00001543831,0.000019542336,0.0000735209,0.022272743,0.97225535,0.0012071347,0.0000069013367,0.002388497],"study_design_scores_gemma":[0.00034907975,0.00021247171,0.039165974,0.000025794096,0.0000074858463,0.000042437023,0.00005017058,0.008585442,0.948152,0.003085433,0.000098029224,0.00022568874],"about_ca_topic_score_codex":0.0000014676812,"about_ca_topic_score_gemma":8.6175373e-7,"teacher_disagreement_score":0.037477944,"about_ca_system_score_codex":0.000015064621,"about_ca_system_score_gemma":0.000004173381,"threshold_uncertainty_score":0.33987865},"labels":[],"label_agreement":null},{"id":"W1999861638","doi":"10.1145/2464576.2464590","title":"Dynamic memory for robot control via delay neural networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Robot; Task (project management); Coincidence; Artificial neural network; Transmission (telecommunications); Limit (mathematics); Coincidence detection in neurobiology; Transmission delay; Control (management); Artificial intelligence; Real-time computing; Control theory (sociology); Engineering","score_opus":0.00615277322375553,"score_gpt":0.2090423750509073,"score_spread":0.20288960182715177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999861638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085158736,0.00020782392,0.9126689,0.00006245244,0.0005280655,0.00040873274,7.9282256e-7,0.0004708196,0.0004937135],"genre_scores_gemma":[0.9936012,0.0000037013253,0.005492845,0.00034543738,0.000114271745,0.000058242123,0.0000046371374,0.00003427644,0.00034539474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932885,0.000009198015,0.00016432382,0.00013338354,0.00004335516,0.0003209088],"domain_scores_gemma":[0.9996046,0.00014550409,0.000017699958,0.0001308541,0.000027840515,0.00007352598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004281977,0.00013714036,0.00015039973,0.000025063142,0.00007079988,0.000021788237,0.00009993396,0.00005368585,0.00008379737],"category_scores_gemma":[0.000008206124,0.00012067427,0.00006772488,0.0000557475,0.000013058287,0.00018570329,0.000013659824,0.00013282182,0.000025694124],"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":[0.000004743172,0.000002891959,0.00000777443,0.000012127188,0.000011291724,0.0000013726475,0.000007092032,0.93358606,0.010278323,0.000016887756,0.00024693925,0.055824503],"study_design_scores_gemma":[0.0004609673,0.000031934876,0.00014706279,0.0000041328567,0.000008514592,0.000013102174,0.0000094159095,0.9977264,0.0011293383,0.00020613361,0.000104614744,0.00015838577],"about_ca_topic_score_codex":0.0000032014746,"about_ca_topic_score_gemma":0.000006256847,"teacher_disagreement_score":0.90844244,"about_ca_system_score_codex":0.000022695815,"about_ca_system_score_gemma":0.0000014018653,"threshold_uncertainty_score":0.49209538},"labels":[],"label_agreement":null},{"id":"W2001099397","doi":"10.1103/physrevlett.92.038102","title":"Neuron-Semiconductor Chip with Chemical Synapse between Identified Neurons","year":2004,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":64,"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":"Soma; Synapse; Excitatory postsynaptic potential; Postsynaptic potential; Neuroscience; Materials science; Transistor; Chip; Neuron; Capacitor; Inhibitory postsynaptic potential; Optoelectronics; Computer science; Physics; Chemistry; Biology; Voltage; Telecommunications","score_opus":0.021037842149921382,"score_gpt":0.2612815290620396,"score_spread":0.24024368691211823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001099397","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958391,0.00084147375,0.0013816984,0.0012548435,0.00008559831,0.00023691577,0.00000386426,0.00029954003,0.00005696869],"genre_scores_gemma":[0.99530315,0.00026332543,0.00015306384,0.0038174046,0.0003859035,0.000016781387,0.000011025379,0.00004752977,0.0000017920177],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990118,0.000024070187,0.00019713068,0.00027708663,0.0001651575,0.0003247367],"domain_scores_gemma":[0.99945194,0.00009694879,0.0000400186,0.00027351576,0.00001037982,0.00012717307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038955066,0.00023175625,0.0003778091,0.000020521955,0.00004428358,0.000017363856,0.0002082799,0.00000983401,0.0000051132442],"category_scores_gemma":[0.000030801384,0.00019050967,0.00011337339,0.00021147971,0.000059344977,0.00016876205,0.00004084248,0.0003962776,0.00007808062],"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":[0.0000035913824,0.000021313976,0.00004481693,0.00078834465,0.000028449787,0.000042786112,0.000035529007,0.009236822,0.98767376,0.00006308199,0.0002987014,0.0017627828],"study_design_scores_gemma":[0.0018850237,0.0001927198,0.0038982064,0.005773003,0.0005518064,0.00012911782,0.0000070708816,0.0009600735,0.9786715,0.00072069914,0.005206527,0.002004277],"about_ca_topic_score_codex":9.685972e-7,"about_ca_topic_score_gemma":1.3346707e-7,"teacher_disagreement_score":0.009002306,"about_ca_system_score_codex":0.000040001218,"about_ca_system_score_gemma":0.0000063456187,"threshold_uncertainty_score":0.7768759},"labels":[],"label_agreement":null},{"id":"W2004963987","doi":"10.1007/s11571-009-9083-3","title":"Are binary synapses superior to graded weight representations in stochastic attractor networks?","year":2009,"lang":"en","type":"article","venue":"Cognitive Neurodynamics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Attractor; Binary number; Representation (politics); Mathematics; Synaptic weight; Noise (video); Applied mathematics; Statistical physics; Computer science; Artificial neural network; Mathematical analysis; Physics; Artificial intelligence; Arithmetic","score_opus":0.019959418920308874,"score_gpt":0.26812816496367065,"score_spread":0.24816874604336178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004963987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9551524,0.00011007622,0.04345103,0.0001012391,0.00041137627,0.00034709836,0.000017972628,0.00028104667,0.00012779537],"genre_scores_gemma":[0.999112,0.000024717272,0.00010069314,0.0005306087,0.00012795418,0.00001778739,0.000023201861,0.000035783225,0.000027235883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989382,0.000042673215,0.00024399292,0.00031937147,0.00010046221,0.00035531595],"domain_scores_gemma":[0.9993158,0.0003202507,0.00004448113,0.00014680093,0.000053755943,0.00011888622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039030154,0.0002115843,0.00022184168,0.00016984309,0.000088599416,0.000029722049,0.000120206234,0.00005888289,0.000007788417],"category_scores_gemma":[0.00028961574,0.00023699348,0.000053436037,0.0005199757,0.000028133722,0.00020782863,0.0000349516,0.0003652573,0.00001752095],"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":[0.000058293444,0.00006192549,0.001244027,0.000013231753,0.000008622567,0.00031638608,0.00016133492,0.98202276,0.011598355,0.00004609198,0.00003163665,0.004437312],"study_design_scores_gemma":[0.000760869,0.00021278212,0.48522332,0.00032664646,0.000034324534,0.000056734512,0.0003126028,0.5108038,0.0013673406,0.0003041438,0.000018880002,0.0005785211],"about_ca_topic_score_codex":6.3219153e-7,"about_ca_topic_score_gemma":0.000015004289,"teacher_disagreement_score":0.48397928,"about_ca_system_score_codex":0.000046444344,"about_ca_system_score_gemma":0.00000657493,"threshold_uncertainty_score":0.9664314},"labels":[],"label_agreement":null},{"id":"W2005959124","doi":"10.1002/pssc.200776806","title":"Understanding negative capacitance effect using an equivalent resistor‐capacitor circuit","year":2007,"lang":"en","type":"article","venue":"Physica status solidi. C, Conferences and critical reviews/Physica status solidi. C, Current topics in solid state physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Capacitance; Negative impedance converter; Resistor; Conductance; Capacitor; Equivalent circuit; Universality (dynamical systems); Condensed matter physics; Physics; Chemistry; Materials science; Voltage; Quantum mechanics; Electrode","score_opus":0.2109350696935498,"score_gpt":0.3872262695497925,"score_spread":0.1762911998562427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005959124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.532484,0.012882734,0.44244558,0.0001027874,0.0040163496,0.0028056542,0.0005421957,0.00071716047,0.004003568],"genre_scores_gemma":[0.9854915,0.011770004,0.00083530654,0.00006730181,0.0015238195,0.00008574077,0.000066033674,0.00014800593,0.000012272522],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99240434,0.00055032753,0.0016043955,0.0014343876,0.0008875595,0.0031189756],"domain_scores_gemma":[0.99582505,0.0015406221,0.0003687153,0.0008546269,0.00028308388,0.0011278795],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.000794637,0.0013616191,0.0021355941,0.0002126343,0.00070422643,0.0003535755,0.00053527934,0.00017995192,0.000017555945],"category_scores_gemma":[0.00040938493,0.0013133925,0.0004322708,0.0009673981,0.0008891175,0.0014505045,0.00019564613,0.0017851333,0.000020087142],"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":[0.0006847815,0.0027375668,0.0030400415,0.012220078,0.00051250577,0.00020482209,0.025437107,0.015507041,0.055549823,0.41771597,0.00051352096,0.46587673],"study_design_scores_gemma":[0.0059779645,0.0026984434,0.0024104118,0.008734934,0.0010348931,0.00003256526,0.0054934802,0.19768591,0.0763735,0.67211664,0.018574698,0.008866536],"about_ca_topic_score_codex":0.000053442982,"about_ca_topic_score_gemma":0.000083106475,"teacher_disagreement_score":0.4570102,"about_ca_system_score_codex":0.0009827043,"about_ca_system_score_gemma":0.00023261993,"threshold_uncertainty_score":0.99991345},"labels":[],"label_agreement":null},{"id":"W2008970031","doi":"10.1117/12.541032","title":"Passive illumination info retrieval used for status identification","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Inkwell; Brightness; Quantum dot; Optoelectronics; Optics; Materials science; Physics","score_opus":0.012346294524979056,"score_gpt":0.23954727255440794,"score_spread":0.22720097802942887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008970031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955208,0.0000683898,0.0019071214,0.0006691029,0.00046459245,0.0007842431,0.00006211312,0.0002132658,0.00031038813],"genre_scores_gemma":[0.9447167,0.00007368765,0.054424457,0.000038488535,0.00043543513,0.00012285713,0.000026066728,0.00007737314,0.00008495612],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980526,8.380211e-9,0.0007098983,0.00031211597,0.0004951249,0.00043023474],"domain_scores_gemma":[0.998065,0.00016156549,0.00030466102,0.000055389377,0.001301431,0.00011194125],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036540953,0.00028912502,0.00032618488,0.00011101589,0.000111451045,0.00009380549,0.0005097243,0.00017377907,0.000003279944],"category_scores_gemma":[0.00067257567,0.00027187177,0.0004490769,0.0003462702,0.00012923845,0.00077083695,0.00006765087,0.00026356947,0.000001363016],"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":[0.00006546057,0.000041353895,0.00002910624,0.00051469216,0.00016870527,4.736748e-8,0.00036530066,0.009045887,0.78812605,0.20090324,0.00029195906,0.00044820496],"study_design_scores_gemma":[0.0015400684,0.00021700717,0.0005583001,0.0002296921,0.00010561595,0.000007718791,0.00088533515,0.038921904,0.95099914,0.004541013,0.0016337765,0.00036044806],"about_ca_topic_score_codex":0.0000017855506,"about_ca_topic_score_gemma":1.4133454e-7,"teacher_disagreement_score":0.19636223,"about_ca_system_score_codex":0.00036766214,"about_ca_system_score_gemma":0.000027242755,"threshold_uncertainty_score":0.99997336},"labels":[],"label_agreement":null},{"id":"W2011851524","doi":"10.1016/j.neunet.2011.09.002","title":"A programmable triangular neighborhood function for a Kohonen self-organizing map implemented on chip","year":2011,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Application-specific integrated circuit; Block (permutation group theory); Self-organizing map; Function (biology); Chip; Topology (electrical circuits); Field-programmable gate array; Artificial neural network; Algorithm; Artificial intelligence; Embedded system; Mathematics; Electrical engineering; Telecommunications; Engineering","score_opus":0.02656221677926991,"score_gpt":0.2222518671901695,"score_spread":0.1956896504108996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011851524","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51015216,0.0009825742,0.47459266,0.00008937668,0.004765809,0.0030094702,0.0000107460955,0.0045982064,0.0017989844],"genre_scores_gemma":[0.9967928,0.000009082878,0.0020443741,0.00018530666,0.00073441304,0.00007988179,0.000028848173,0.00007896568,0.000046308807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987897,0.000027489146,0.00026307523,0.00027162262,0.000094435374,0.00055367424],"domain_scores_gemma":[0.99952704,0.00006979952,0.0000583367,0.00021013602,0.000032840893,0.00010182508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119348675,0.00023713375,0.00020781203,0.000056247627,0.00017725393,0.000031934,0.00013165774,0.000092851056,0.00004143351],"category_scores_gemma":[0.000011617145,0.00022676842,0.000103449805,0.00021784277,0.000009141848,0.00016568709,0.000031951473,0.00028347378,0.0000103844095],"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":[0.0020751178,0.0004128206,0.0016818896,0.0005425473,0.0006021505,0.00008893509,0.0015800582,0.6158538,0.012829707,0.003225471,0.0056914994,0.355416],"study_design_scores_gemma":[0.0019342059,0.000898247,0.00070048904,0.000052670202,0.00009090645,0.00001341201,0.000061513434,0.982672,0.007048291,0.00079262984,0.005263548,0.0004721244],"about_ca_topic_score_codex":0.000001858473,"about_ca_topic_score_gemma":0.0000035364294,"teacher_disagreement_score":0.48664063,"about_ca_system_score_codex":0.000041978114,"about_ca_system_score_gemma":0.0000041775243,"threshold_uncertainty_score":0.9247348},"labels":[],"label_agreement":null},{"id":"W2013672279","doi":"10.1109/newcas.2009.5290509","title":"Event-driven data and power management in high-density neural recording microsystems","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; CMC Microsystems","keywords":"Computer science; Power management; Microsystem; Duty cycle; Power (physics); Electronic circuit; Spike (software development); Biological neural network; Artificial neural network; Signal processing; Detector; Event (particle physics); Electronic engineering; Electrical engineering; Computer hardware; Artificial intelligence; Engineering; Digital signal processing; Telecommunications; Voltage; Machine learning","score_opus":0.018815819047801167,"score_gpt":0.25233943032333783,"score_spread":0.23352361127553667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013672279","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920622,0.00009889016,0.006270974,0.000083047955,0.00027679995,0.00012603049,0.0000020403602,0.00015796469,0.0009220157],"genre_scores_gemma":[0.9976357,0.000027675691,0.0021030365,0.00008241752,0.00003324678,5.6853304e-7,0.0000068433237,0.000007996441,0.000102488964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941427,0.000012928629,0.00014957193,0.00019920802,0.00004965513,0.00017437025],"domain_scores_gemma":[0.9996783,0.000017002067,0.000013832247,0.00025126952,0.0000041711723,0.000035438028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008099514,0.000095081356,0.00011701777,0.000047599246,0.000035644855,0.000022299893,0.00013711237,0.000023176126,0.0000066399152],"category_scores_gemma":[0.0000035497335,0.00009232802,0.000009673172,0.00008730347,0.0000050998474,0.00020697797,0.000104310624,0.00010177428,0.0000050663684],"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":[0.00010883506,0.000120372424,0.011182649,0.00041647893,0.00011657893,0.0011018115,0.0006131188,0.21172859,0.30731538,0.0036283536,0.005667061,0.45800078],"study_design_scores_gemma":[0.0023914424,0.00017488768,0.100495584,0.00034117867,0.000036517704,0.00016479488,0.00057397195,0.86134225,0.027488144,0.001380287,0.0043832418,0.0012276703],"about_ca_topic_score_codex":0.000006931018,"about_ca_topic_score_gemma":0.000018861698,"teacher_disagreement_score":0.6496137,"about_ca_system_score_codex":0.00002115191,"about_ca_system_score_gemma":6.6330557e-7,"threshold_uncertainty_score":0.37650272},"labels":[],"label_agreement":null},{"id":"W2013997877","doi":"10.1063/1.3272684","title":"Origin of inverse tunneling magnetoresistance in a symmetric junction revealed by delaminating the buried electronic interface","year":2009,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","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":"National Research Council Canada","funders":"","keywords":"Magnetoresistance; Quantum tunnelling; Materials science; Oxide; Ion milling machine; Electronic structure; Photoemission spectroscopy; X-ray photoelectron spectroscopy; Condensed matter physics; Chemical physics; Optoelectronics; Nanotechnology; Chemical engineering; Chemistry; Magnetic field; Metallurgy; Layer (electronics)","score_opus":0.007190402064212771,"score_gpt":0.2108415705547089,"score_spread":0.20365116849049614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013997877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94949913,0.00016197118,0.04952092,0.000095447765,0.00006626881,0.00018096286,7.554522e-7,0.00008776677,0.00038676927],"genre_scores_gemma":[0.9990639,0.000019068602,0.00040367956,0.0003777429,0.000093361115,0.000009743091,0.000004008714,0.000019062874,0.000009405292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991524,0.000019000392,0.00023320291,0.00017164467,0.00011786639,0.00030586452],"domain_scores_gemma":[0.9996181,0.00010995201,0.00007537802,0.00016602398,0.000010302344,0.000020239055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013335742,0.0001524745,0.0001695099,0.000054519263,0.000050840878,0.000010882249,0.00015065991,0.000029935003,0.0000011931552],"category_scores_gemma":[0.000008768576,0.00014239144,0.000036446098,0.000669494,0.00002493278,0.00009040675,0.000013532948,0.00034711184,0.0000032090222],"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":[0.000018518647,0.000010239686,0.000019695903,0.000025500984,0.000007074727,7.6043403e-7,0.00017141341,0.21488065,0.7746262,0.0011689626,0.00035138612,0.008719599],"study_design_scores_gemma":[0.0015701941,0.00009921022,0.00077433116,0.00015895821,0.000048546146,0.000003228588,0.00029479506,0.082586505,0.909451,0.00318737,0.001094254,0.00073161034],"about_ca_topic_score_codex":0.000003926153,"about_ca_topic_score_gemma":0.0000024613353,"teacher_disagreement_score":0.1348248,"about_ca_system_score_codex":0.00012508802,"about_ca_system_score_gemma":0.000005860623,"threshold_uncertainty_score":0.5806554},"labels":[],"label_agreement":null},{"id":"W2018000085","doi":"10.1109/biocas.2011.6107715","title":"Low-power energy-based CMOS digital detector for neural recording arrays","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Division of Materials Research; CMC Microsystems; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Thresholding; Computer science; Detector; CMOS; Bandwidth (computing); Channel (broadcasting); Artificial neural network; Energy consumption; Energy (signal processing); Electronic engineering; Computer hardware; Artificial intelligence; Electrical engineering; Telecommunications; Engineering; Mathematics","score_opus":0.02271226220443663,"score_gpt":0.20542371769064424,"score_spread":0.1827114554862076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018000085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41468057,0.000019080971,0.577523,0.0000070384767,0.0006226683,0.000098700824,0.000006094099,0.0006713674,0.006371521],"genre_scores_gemma":[0.9934814,6.296709e-7,0.0060887793,0.0001001389,0.00009676681,0.000017150074,0.000004051846,0.00004177932,0.00016929446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934274,0.0000038076337,0.00015528295,0.00016304462,0.0000525033,0.00028263967],"domain_scores_gemma":[0.9996552,0.00008637357,0.000020220548,0.00014038211,0.000021426935,0.00007641926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000027054708,0.00014864835,0.00012442633,0.000044133998,0.00006360051,0.000026043184,0.00010516533,0.00004283242,0.00008713733],"category_scores_gemma":[0.000025291461,0.00013703438,0.0000911479,0.000079260826,0.00001297151,0.00024791577,0.000015629827,0.000075327975,0.000009088165],"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":[0.00045838096,0.00016289776,0.0007220617,0.00037632821,0.000121184734,0.000077015255,0.0006072015,0.1991979,0.4572167,0.0026299793,0.0022245278,0.3362058],"study_design_scores_gemma":[0.00041632517,0.00013501517,0.000036095298,0.00002456875,0.000005781188,0.0000050928766,0.00003076942,0.16436641,0.8322195,0.0005629127,0.0018763408,0.0003211894],"about_ca_topic_score_codex":0.0000011185787,"about_ca_topic_score_gemma":0.0000021902858,"teacher_disagreement_score":0.57880086,"about_ca_system_score_codex":0.00002069345,"about_ca_system_score_gemma":0.0000046729047,"threshold_uncertainty_score":0.55881},"labels":[],"label_agreement":null},{"id":"W2018111008","doi":"10.1109/tnano.2014.2329915","title":"Design of a Nonvolatile 7T1R SRAM Cell for Instant-on Operation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":72,"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":"Static random-access memory; Non-volatile memory; Resistive random-access memory; Computer science; Electronic engineering; Energy consumption; Memory cell; CMOS; Dissipation; Electrical engineering; Power (physics); Context (archaeology); Energy (signal processing); Embedded system; Computer hardware; Voltage; Engineering; Transistor","score_opus":0.01744827832190212,"score_gpt":0.2232445749627179,"score_spread":0.20579629664081578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018111008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114167705,0.000011679427,0.88470715,0.000039135037,0.00030144985,0.00028962488,0.0000064564833,0.00036504123,0.00011173061],"genre_scores_gemma":[0.99047595,0.000015201324,0.009324977,0.000036598496,0.00001427536,0.00005499764,9.2830805e-7,0.00002613966,0.000050912866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941766,0.000019076513,0.00018651842,0.00015528878,0.000052284224,0.0001691913],"domain_scores_gemma":[0.99954087,0.00018716806,0.000030201038,0.00019877638,0.000023002765,0.000019962228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007305076,0.000120421304,0.00016206164,0.00017057582,0.00009302048,0.0000041074622,0.0001026705,0.0001796596,0.000010709967],"category_scores_gemma":[0.0000054704815,0.0001228203,0.000045395744,0.0001430801,0.00003153337,0.00005998774,4.853315e-7,0.00021012554,0.000012809711],"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":[0.000034839377,0.00002676915,5.6382305e-8,0.00002258338,0.00000460393,2.2308122e-7,0.000024001425,0.60594046,0.36906546,0.00012158274,0.000007281714,0.024752116],"study_design_scores_gemma":[0.00035016655,0.00047953124,2.0060446e-7,0.000014557029,0.00000783969,0.0000020235727,0.000009484851,0.315643,0.68273526,0.00024391088,0.0004343133,0.00007969754],"about_ca_topic_score_codex":5.0429423e-7,"about_ca_topic_score_gemma":0.0000018914701,"teacher_disagreement_score":0.87630826,"about_ca_system_score_codex":0.000030525352,"about_ca_system_score_gemma":0.000007309749,"threshold_uncertainty_score":0.5008467},"labels":[],"label_agreement":null},{"id":"W2027562347","doi":"10.1109/nano.2014.6967951","title":"A memristor-based memory cell with no refresh","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Memristor; Biasing; Memory cell; Ambipolar diffusion; Memistor; Computer science; Resistive random-access memory; Non-volatile memory; Transistor; Memory refresh; Electronic engineering; Semiconductor memory; Electrical engineering; Computer hardware; Voltage; Computer memory; Engineering; Physics","score_opus":0.004047203034944227,"score_gpt":0.16342674916093924,"score_spread":0.15937954612599503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027562347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43162778,0.00006204897,0.19224218,0.0000338963,0.0003416077,0.00012452682,7.994528e-7,0.0012060473,0.3743611],"genre_scores_gemma":[0.98673666,9.373898e-7,0.010434755,0.00022124527,0.000120130775,0.00000368035,0.0000016542554,0.000026766384,0.0024541835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995366,0.00001180974,0.000084940315,0.00011890425,0.00007795452,0.000169824],"domain_scores_gemma":[0.9996638,0.000061652056,0.000013132144,0.00018120543,0.000020175747,0.00006005717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000585095,0.00010590554,0.00009933692,0.00002789747,0.000044942408,0.000008509638,0.000079379504,0.000027405362,0.00007647211],"category_scores_gemma":[0.000008571982,0.00008300211,0.000022394846,0.00006736104,0.000014734977,0.000056104665,0.000008711328,0.000112044305,0.00014023436],"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":[0.00006262914,0.0000331647,0.00009480532,0.00027288956,0.00001176946,0.0000215182,0.000080532955,0.7839108,0.19861649,0.00022198092,0.0091056805,0.0075677168],"study_design_scores_gemma":[0.00089988264,0.00019288585,0.000068005305,0.00003762375,0.000010840381,0.0000049742157,0.00002166133,0.2163734,0.7267454,0.00005140285,0.05520729,0.0003866107],"about_ca_topic_score_codex":0.0000017545372,"about_ca_topic_score_gemma":0.0000042308952,"teacher_disagreement_score":0.5675374,"about_ca_system_score_codex":0.000020297179,"about_ca_system_score_gemma":0.000005023184,"threshold_uncertainty_score":0.33847278},"labels":[],"label_agreement":null},{"id":"W2032151330","doi":"10.1063/1.3367752","title":"Investigation of the electroforming process in resistively switching TiO2 nanocrosspoint junctions","year":2010,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Electroforming; Materials science; Optoelectronics; Polarity (international relations); Reliability (semiconductor); Electrode; Resistive touchscreen; Voltage; Current (fluid); Electrical engineering; Nanotechnology; Engineering; Layer (electronics); Chemistry; Physics","score_opus":0.008917586656126507,"score_gpt":0.20692153181830872,"score_spread":0.1980039451621822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032151330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946471,0.0000018697525,0.004506955,0.00009650136,0.00015597521,0.00012400428,4.663158e-7,0.00006316968,0.00040399886],"genre_scores_gemma":[0.99932486,3.291005e-7,0.00029130216,0.00024829342,0.00009763512,0.000016767981,0.0000015895628,0.000016504458,0.0000027313938],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995056,0.000005214363,0.00014429001,0.00010475228,0.00008575495,0.00015440377],"domain_scores_gemma":[0.99975795,0.00003732264,0.00005179057,0.00012493691,0.000010561087,0.00001741786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006616192,0.00009018926,0.000087755354,0.000024510666,0.00009581988,0.000008876799,0.00010749058,0.000025101734,6.726129e-7],"category_scores_gemma":[0.000007067711,0.00007840854,0.00002753245,0.00028452734,0.000036760583,0.00009971256,0.000017534114,0.00038817248,0.000001543277],"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":[0.0000035069559,0.000002011798,0.00064275716,0.000032018,0.0000035724213,1.6411754e-7,0.0004769393,0.03170529,0.96469533,0.0014489635,0.000018497947,0.00097093894],"study_design_scores_gemma":[0.0001350825,0.0000017268753,0.007880995,0.000023898609,0.0000047645412,9.070147e-7,0.000033187585,0.0012212583,0.9880115,0.0025594565,0.00002724774,0.00010000029],"about_ca_topic_score_codex":0.0000020143088,"about_ca_topic_score_gemma":0.0000059998383,"teacher_disagreement_score":0.030484032,"about_ca_system_score_codex":0.000021037078,"about_ca_system_score_gemma":0.000010599012,"threshold_uncertainty_score":0.31974074},"labels":[],"label_agreement":null},{"id":"W2032735501","doi":"10.1143/jjap.51.04dd07","title":"Reset Current Reduction with Excellent Filament Controllability by Using Area Minimized and Field Enhanced Unipolar Resistive Random Access Memory Structure","year":2012,"lang":"en","type":"article","venue":"Japanese Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"Natural Sciences and Engineering Research Council of Canada; Council of Scientific and Industrial Research, India; Alfred P. Sloan Foundation; Department of Science and Technology, Ministry of Science and Technology, India; David and Lucile Packard Foundation; National Science Foundation","keywords":"Resistive random-access memory; Reset (finance); Controllability; Materials science; Resistive touchscreen; Fabrication; Voltage; Optoelectronics; Circuit breaker; Reduction (mathematics); Computer science; Electronic engineering; Electrical engineering; Engineering; Mathematics","score_opus":0.01771875885065771,"score_gpt":0.26148061739658296,"score_spread":0.24376185854592525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032735501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9837905,0.00048142072,0.015127151,0.000010457597,0.00023547496,0.0002457879,0.0000053156723,0.000022625576,0.000081254955],"genre_scores_gemma":[0.9989842,0.00004240372,0.0005986109,0.000017573107,0.0003320054,0.0000037009777,0.0000032146388,0.00001651673,0.0000017564485],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905246,0.000041153406,0.00032654812,0.00012493093,0.00022008405,0.00023482258],"domain_scores_gemma":[0.9992218,0.0002000318,0.00022549805,0.00013228474,0.00009007181,0.00013031893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019279151,0.00018623915,0.00036926265,0.00003268059,0.000113717026,0.000030399646,0.00010640253,0.000041433053,0.0000070917345],"category_scores_gemma":[0.000018254856,0.00013900324,0.00004782594,0.00013613931,0.000045639135,0.0003697178,0.000033042816,0.0003476443,1.5870387e-7],"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":[0.0030238985,0.000064121865,0.000028977696,0.00007559786,0.00006828772,8.368611e-7,0.001962603,0.124747,0.8625746,0.000010120853,0.00006738725,0.0073765777],"study_design_scores_gemma":[0.004564362,0.00009411373,0.00006891894,0.000081720566,0.000089554254,0.000025649875,0.0012586188,0.0036278772,0.9893328,0.0005862974,0.00004848346,0.00022159958],"about_ca_topic_score_codex":0.0000012237748,"about_ca_topic_score_gemma":2.0654043e-7,"teacher_disagreement_score":0.12675822,"about_ca_system_score_codex":0.000076930286,"about_ca_system_score_gemma":0.000014146463,"threshold_uncertainty_score":0.5668388},"labels":[],"label_agreement":null},{"id":"W2033812802","doi":"10.1039/c2cc34557a","title":"Controlling volatility in solid-state, redox-based memory devices using heterojunction barriers to ion transport","year":2012,"lang":"en","type":"article","venue":"Chemical Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Redox; Volatility (finance); Materials science; Heterojunction; Ion; Solid-state; Non-volatile memory; Optoelectronics; Nanotechnology; Computer science; Engineering physics; Chemistry; Business; Physics","score_opus":0.03690128040286048,"score_gpt":0.29776245751081587,"score_spread":0.26086117710795537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033812802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97241664,0.0003903286,0.026260821,0.00007399123,0.00015619438,0.00020786417,0.0000061581864,0.0001972825,0.00029074404],"genre_scores_gemma":[0.99340236,0.0000074838786,0.00631472,0.00015325446,0.000044426193,0.000023705501,0.000027202655,0.000022938339,0.0000039143956],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911636,0.000050105737,0.00032828018,0.0001239977,0.000084334824,0.0002969398],"domain_scores_gemma":[0.9989416,0.00017617214,0.000035226003,0.00058526016,0.000035363606,0.0002263944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023299147,0.0001369542,0.00018800514,0.00007638473,0.00013940608,0.000009326001,0.0002902463,0.00006696834,0.000009396137],"category_scores_gemma":[0.00009299449,0.00015328555,0.00005741571,0.00028047885,0.000049159065,0.0002496653,0.00004170825,0.00032877762,0.000004061361],"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":[0.000032152922,0.000038030354,0.0075224526,0.000048898884,0.000010095418,3.9131078e-7,0.0006124078,0.15437084,0.8362356,0.000008322205,0.000007001933,0.0011138355],"study_design_scores_gemma":[0.00045173545,0.000008536114,0.0016432824,0.00010930355,0.000019713165,0.0000024262174,0.0001455975,0.38966545,0.606937,0.000063363856,0.00068371184,0.0002698715],"about_ca_topic_score_codex":0.000011712642,"about_ca_topic_score_gemma":0.000030657066,"teacher_disagreement_score":0.23529461,"about_ca_system_score_codex":0.00015595809,"about_ca_system_score_gemma":0.000018810353,"threshold_uncertainty_score":0.62508035},"labels":[],"label_agreement":null},{"id":"W2034038434","doi":"10.1063/1.3167810","title":"The influence of copper top electrodes on the resistive switching effect in TiO2 thin films studied by conductive atomic force microscopy","year":2009,"lang":"en","type":"article","venue":"Applied Physics Letters","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Electroforming; Materials science; Electrical conductor; Electrode; Copper; Conductive atomic force microscopy; Layer (electronics); Thin film; Diffusion barrier; Platinum; Atomic layer deposition; Resistive touchscreen; Stack (abstract data type); Atomic force microscopy; Optoelectronics; Nanotechnology; Composite material; Chemistry; Metallurgy; Electrical engineering","score_opus":0.005251240081101947,"score_gpt":0.2280854398130629,"score_spread":0.22283419973196095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034038434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99815255,0.000055686043,0.00075819605,0.00026504055,0.000036184865,0.00037797078,0.000002436481,0.000052567768,0.00029933875],"genre_scores_gemma":[0.9983366,0.000011543654,0.00007276763,0.0014985199,0.000030477828,0.000026221906,0.0000021118826,0.000016977721,0.000004742596],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921536,0.000044816847,0.00016935235,0.00018367487,0.0001143779,0.0002724312],"domain_scores_gemma":[0.99888355,0.000812821,0.000067509696,0.00020877388,0.000010391493,0.000016930639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017628298,0.00018368526,0.00019417821,0.000015613276,0.00018011761,0.00001845696,0.0002061155,0.00002725974,2.6899752e-7],"category_scores_gemma":[0.000017441018,0.00012083436,0.000041358096,0.00018011944,0.00005775271,0.00006646955,0.00002108446,0.0004057997,0.000003847227],"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":[0.000073236224,0.000006521887,0.000019394545,0.000008116479,0.00002073453,5.7153187e-7,0.0005258016,0.14802061,0.8483606,0.0017613169,0.0006043986,0.00059866556],"study_design_scores_gemma":[0.0002828121,0.000051823645,0.0008183872,0.000043488937,0.000008946452,2.2704715e-7,0.000067121575,0.000611851,0.99632597,0.0016303806,0.000019191346,0.00013982026],"about_ca_topic_score_codex":0.000003262208,"about_ca_topic_score_gemma":6.711809e-7,"teacher_disagreement_score":0.14796533,"about_ca_system_score_codex":0.000056926412,"about_ca_system_score_gemma":0.0000048136826,"threshold_uncertainty_score":0.49274823},"labels":[],"label_agreement":null},{"id":"W2034276439","doi":"","title":"On the number of inference regions of deep feed forward networks with piece-wise linear activations","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":59,"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":"Piecewise linear function; Perceptron; Mathematics; Linear model; Deep learning; Inference; Rectifier (neural networks); Constant (computer programming); Infinity; Omega; Computer science; Combinatorics; Algorithm; Artificial neural network; Artificial intelligence; Mathematical analysis; Recurrent neural network; Statistics; Physics","score_opus":0.035466296856493953,"score_gpt":0.18249079439542745,"score_spread":0.14702449753893349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034276439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54310507,0.0000012089074,0.4551068,0.000008237156,0.000019972562,0.000041152285,5.079624e-7,0.000033736007,0.0016833058],"genre_scores_gemma":[0.99955714,0.000011755027,0.00030471678,0.000020666806,0.000019485753,2.1300484e-7,0.0000012679761,0.0000099518575,0.000074802265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996383,0.000026543952,0.00007885893,0.000111584646,0.000029833023,0.00011488193],"domain_scores_gemma":[0.99920046,0.00042286378,0.00005887018,0.0002326384,0.00005059264,0.000034561224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004752928,0.00008478332,0.000110616995,0.00003274271,0.00007036703,0.000002341986,0.00014519985,0.000036090092,0.000017241866],"category_scores_gemma":[0.00004360874,0.00006769725,0.000038590264,0.0003401386,0.000057042103,0.00009251597,0.000027353466,0.00014765764,0.000004310325],"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":[0.000026036483,0.000012236797,0.0013968666,0.000009498352,0.000016774164,0.0000015192561,0.00005236659,0.9293929,0.00030133608,0.068618596,0.000009777225,0.00016212072],"study_design_scores_gemma":[0.00030875157,0.000061514445,0.0014819243,0.00007662737,0.000030805477,0.0000013901916,0.00010962349,0.98783433,0.005356042,0.0045575905,0.000057385052,0.00012400132],"about_ca_topic_score_codex":0.000003226324,"about_ca_topic_score_gemma":0.0000084904395,"teacher_disagreement_score":0.45645207,"about_ca_system_score_codex":0.000016237485,"about_ca_system_score_gemma":0.0000052269684,"threshold_uncertainty_score":0.2760614},"labels":[],"label_agreement":null},{"id":"W2036865045","doi":"10.5555/1400549.1400707","title":"Modeling spiking neural terminals in DEVS","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Spike (software development); Spiking neural network; Computer science; Timer; Biological neuron model; Neuron; Spike train; Normalization property; Domain (mathematical analysis); Terminal (telecommunication); Artificial neural network; Artificial intelligence; Algorithm; Neuroscience; Computer hardware; Mathematics; Telecommunications","score_opus":0.04196254798219799,"score_gpt":0.24238706212639488,"score_spread":0.20042451414419687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036865045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96349883,0.000101553174,0.032984048,0.000008585596,0.000107018,0.00003882239,9.9451405e-8,0.0002421921,0.0030188598],"genre_scores_gemma":[0.9978821,0.000016721959,0.0018976417,0.00006151999,0.000058571466,0.0000017365188,4.3910956e-7,0.000013200058,0.00006801737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952793,0.0000052227583,0.00014006841,0.00008939972,0.0000499736,0.00018739187],"domain_scores_gemma":[0.9998678,0.000018645045,0.0000048699526,0.00007338656,0.000005720443,0.000029602777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003371188,0.00007850416,0.000090836875,0.00005471173,0.00004398988,0.000004554818,0.000060544102,0.00002345089,0.0000128096],"category_scores_gemma":[0.00000883435,0.00007657701,0.000021983627,0.000100476,0.000006390322,0.00015016635,0.000018613187,0.00012739368,0.00000966725],"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":[0.0000014843661,0.0000020567973,0.00039703737,0.0000081520575,7.24746e-7,0.00006875975,0.000104188184,0.98754543,0.007633093,0.000020235582,0.0000053358085,0.0042134975],"study_design_scores_gemma":[0.00009968839,0.0000054025822,0.00024389259,0.000014335391,6.127526e-7,0.00007220718,0.000024920846,0.9870307,0.012307721,0.00006813855,0.000031585343,0.0001008007],"about_ca_topic_score_codex":0.0000047967496,"about_ca_topic_score_gemma":0.0000077562945,"teacher_disagreement_score":0.03438333,"about_ca_system_score_codex":0.000017648872,"about_ca_system_score_gemma":0.0000025136162,"threshold_uncertainty_score":0.31227198},"labels":[],"label_agreement":null},{"id":"W2041333130","doi":"10.1109/tcsii.2015.2407711","title":"A Variation-Tolerant MRAM-Backed-SRAM Cell for a Nonvolatile Dynamically Reconfigurable FPGA","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Magnetoresistive random-access memory; Field-programmable gate array; Static random-access memory; Non-volatile memory; Computer science; Spin-transfer torque; Racetrack memory; Universal memory; Random access; Embedded system; Computer hardware; Random access memory; Semiconductor memory; Memory management; Physics; Interleaved memory; Magnetic field; Magnetization","score_opus":0.027924682880609043,"score_gpt":0.22913375631169922,"score_spread":0.20120907343109018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041333130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050519858,0.0002295035,0.94053686,0.000025347328,0.00356056,0.0011886619,0.0001943945,0.00082680857,0.0029179924],"genre_scores_gemma":[0.99655277,0.000010161647,0.00055473513,0.000056802266,0.00026139856,0.0004290584,0.000015967951,0.000117723546,0.0020014169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978489,0.00008184503,0.0006666257,0.00050028274,0.00032064746,0.00058167207],"domain_scores_gemma":[0.9985504,0.00029909037,0.00012527173,0.00050624483,0.00021135632,0.00030759702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002968805,0.00038754978,0.0004953654,0.00015974777,0.00040233557,0.00011497281,0.00029160836,0.00021832563,0.000026363494],"category_scores_gemma":[0.000013256852,0.00042110783,0.00018231873,0.00027010296,0.000031748958,0.00043026273,0.0000016232526,0.00040635667,0.000053676216],"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":[0.000034788904,0.00011626083,5.3149955e-7,0.0002894658,0.00005575632,0.000008335983,0.0014948918,0.9286713,0.065331474,0.00005233131,0.00046927208,0.0034755855],"study_design_scores_gemma":[0.0034484249,0.00051748747,0.000012141387,0.0005495341,0.00012612947,0.00012289977,0.0004697813,0.850743,0.1206643,0.00021369832,0.021962645,0.0011699377],"about_ca_topic_score_codex":0.00006093746,"about_ca_topic_score_gemma":0.000014825521,"teacher_disagreement_score":0.9460329,"about_ca_system_score_codex":0.00023429138,"about_ca_system_score_gemma":0.0000910329,"threshold_uncertainty_score":0.99982405},"labels":[],"label_agreement":null},{"id":"W2041347804","doi":"10.1109/iscas.2012.6271415","title":"Compact chopper-stabilized neural amplifier with low-distortion high-pass filter in 0.13&amp;#x00B5;m CMOS","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Total harmonic distortion; CMOS; Amplifier; Differential amplifier; Flicker noise; Operational amplifier; Analogue filter; Electrical engineering; Capacitor; Low-pass filter; Instrumentation amplifier; Electronic engineering; Noise (video); Computer science; Physics; Filter (signal processing); Engineering; Noise figure; Voltage; Artificial intelligence; Digital filter","score_opus":0.0193356928950463,"score_gpt":0.2392777110350263,"score_spread":0.21994201813998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041347804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801994,0.000099508565,0.017056346,0.000063853535,0.00043876778,0.00022105809,0.0000035214393,0.00038267547,0.0015348701],"genre_scores_gemma":[0.99821454,0.000004498932,0.0010692715,0.00012371781,0.00018492891,0.000009841253,0.000020110156,0.00004485658,0.00032823358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988342,0.000037537633,0.0002624371,0.00018967704,0.00014917173,0.000526969],"domain_scores_gemma":[0.9994609,0.00008620882,0.00003356164,0.00025397597,0.000019934747,0.00014544123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011143358,0.00023486948,0.0002585275,0.00008036486,0.000060980256,0.000022180424,0.00010405032,0.000066548695,0.00032829947],"category_scores_gemma":[0.000015789057,0.00018685304,0.000049061462,0.0002078666,0.00003147697,0.0005026948,0.000020477188,0.00029148904,0.000072718874],"study_design_candidate":"observational","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":[0.0005261639,0.00031763146,0.08122257,0.00035704635,0.00007275999,0.00002284609,0.0018334885,0.6540901,0.248736,0.00038402548,0.0019154903,0.010521861],"study_design_scores_gemma":[0.011117023,0.00039606172,0.65813065,0.0004892981,0.00010826421,0.00022388107,0.0005835049,0.066794164,0.23286785,0.0003960467,0.024873117,0.004020155],"about_ca_topic_score_codex":0.000034363882,"about_ca_topic_score_gemma":0.00012589773,"teacher_disagreement_score":0.58729595,"about_ca_system_score_codex":0.00011200925,"about_ca_system_score_gemma":0.0000058114147,"threshold_uncertainty_score":0.76196456},"labels":[],"label_agreement":null},{"id":"W2044582166","doi":"10.1088/0957-4484/21/13/134003","title":"Field enhanced charge carrier reconfiguration in electronic and ionic coupled dynamic polymer resistive memory","year":2010,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Manitoba","funders":"Research Manitoba; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Materials science; Transient (computer programming); Optoelectronics; Ion; Resistive touchscreen; Conductance; Electric field; Control reconfiguration; Space charge; Nanotechnology; Electrical engineering; Electron; Condensed matter physics; Computer science; Physics","score_opus":0.002804103430655379,"score_gpt":0.21214644560971913,"score_spread":0.20934234217906375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044582166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950082,0.0007044872,0.0031389536,0.0002560415,0.00027908743,0.00013614004,0.0000012682194,0.00029659225,0.00017927377],"genre_scores_gemma":[0.9994289,0.00016688604,0.00009928865,0.000064255415,0.000024174922,0.000022261498,0.0000034847112,0.000017444072,0.00017325474],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928087,0.000008759507,0.00015801747,0.00020569848,0.000039256513,0.000307375],"domain_scores_gemma":[0.99967647,0.000085535656,0.00002926242,0.00017275487,0.000013343121,0.000022635706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007089563,0.00011922195,0.00015454179,0.00012114562,0.000053204996,0.0000056761196,0.00008962315,0.00030281855,0.00005442589],"category_scores_gemma":[0.000069800786,0.00012792824,0.00002192093,0.00017367779,0.000041853058,0.00007645899,0.000021058258,0.00074102724,0.000007558616],"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":[0.000019390223,0.000003625951,0.000008877007,0.0000108765125,0.00000596897,0.0000031868808,0.00005891224,0.00008901336,0.97524434,0.00075538066,0.000005290252,0.023795117],"study_design_scores_gemma":[0.00032722144,0.00007690747,0.000056330977,0.000013088613,0.0000041833846,0.000017581422,0.0000304815,0.022369865,0.9759637,0.0008348537,0.00016155359,0.00014424897],"about_ca_topic_score_codex":0.0000071780178,"about_ca_topic_score_gemma":0.0004492126,"teacher_disagreement_score":0.023650868,"about_ca_system_score_codex":0.000041302123,"about_ca_system_score_gemma":0.000017693468,"threshold_uncertainty_score":0.52167624},"labels":[],"label_agreement":null},{"id":"W2044785660","doi":"10.1007/s00339-011-6268-5","title":"Redox driven conductance changes for resistive memory","year":2011,"lang":"en","type":"article","venue":"Applied Physics A","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; Xerox (Canada); University of Alberta","funders":"National Research Council Canada","keywords":"Polaron; Polythiophene; Polymer; Raman spectroscopy; Redox; Materials science; Conductive polymer; Oxide; Conductivity; Chemical physics; Chemistry; Inorganic chemistry; Physical chemistry; Organic chemistry; Electron","score_opus":0.04654923318773738,"score_gpt":0.23034327229415935,"score_spread":0.18379403910642197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044785660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.748256,0.0002609411,0.13053526,0.000029900513,0.0008420433,0.0013489401,0.00005537954,0.0012086816,0.11746282],"genre_scores_gemma":[0.99266154,0.000008939323,0.0067420383,0.00007570531,0.00028816107,0.00009796636,0.000007784048,0.000035101104,0.00008277052],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952316,0.000002984274,0.000076767275,0.00015621279,0.000047613237,0.00019326959],"domain_scores_gemma":[0.9997088,0.000051782037,0.000028286533,0.00015542573,0.000019493986,0.0000362432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000028559487,0.00011727625,0.00013276172,0.000012274594,0.00006658613,0.0000046535188,0.000100547695,0.000030834435,0.0000069159837],"category_scores_gemma":[0.0000032669293,0.00012370657,0.00002918329,0.00007852231,0.000028109462,0.000041874915,0.000021487427,0.00009059985,0.000015799025],"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":[0.000086306725,0.000033989178,0.000005236487,0.00017265324,0.00007417014,0.0000045618185,0.0029613536,0.009118769,0.8740312,0.069230214,0.0012909835,0.04299056],"study_design_scores_gemma":[0.00032599937,0.000030697858,0.00005620462,0.000019418649,0.000016839365,7.3669275e-7,0.00015914268,0.0027542491,0.9679123,0.027026378,0.0014463546,0.00025164048],"about_ca_topic_score_codex":5.436393e-7,"about_ca_topic_score_gemma":0.0000017518813,"teacher_disagreement_score":0.2444055,"about_ca_system_score_codex":0.000016104503,"about_ca_system_score_gemma":0.000003979996,"threshold_uncertainty_score":0.50446075},"labels":[],"label_agreement":null},{"id":"W2048419549","doi":"10.1063/1.4765655","title":"Suppression of spin relaxation in rubrene nanowire spin valves","year":2012,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"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":"Rubrene; Nanowire; Spin (aerodynamics); Condensed matter physics; Relaxation (psychology); Materials science; Amorphous solid; Thin film; Spin polarization; Optoelectronics; Nanotechnology; Chemistry; Physics; Crystallography; Electron","score_opus":0.012807033646110631,"score_gpt":0.23480295358784603,"score_spread":0.2219959199417354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048419549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98992425,0.000052990104,0.008946547,0.000031094423,0.00020439296,0.000104115556,0.0000011398661,0.00007584878,0.0006596352],"genre_scores_gemma":[0.9985653,0.0000060623297,0.0010654097,0.00012149737,0.00019673802,0.000009139791,0.0000111204,0.000022074664,0.0000026633431],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994359,0.000009435846,0.00016169372,0.00009186709,0.000088772,0.00021230361],"domain_scores_gemma":[0.9997477,0.00003545295,0.00004908837,0.00013281642,0.000004007778,0.00003092558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057924637,0.00010818343,0.00013233075,0.0000318416,0.000023873064,0.0000035318137,0.00007052361,0.000032839333,0.0000032545408],"category_scores_gemma":[0.0000027631725,0.00011190175,0.000033167667,0.00015187256,0.000018175548,0.00015328669,0.000024473482,0.0001371241,0.000010616544],"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":[0.000005734748,0.000014676195,0.0003600352,0.00004715339,0.0000045496913,3.3262583e-7,0.0003197003,0.09844845,0.89097,0.0011746669,0.000118729586,0.0085359635],"study_design_scores_gemma":[0.00023761894,0.000004564455,0.0045857714,0.00004933612,0.000005306574,4.380935e-7,0.000026158372,0.001132011,0.9934172,0.0002584011,0.0001403837,0.00014280832],"about_ca_topic_score_codex":0.0000016204551,"about_ca_topic_score_gemma":2.1875006e-7,"teacher_disagreement_score":0.1024472,"about_ca_system_score_codex":0.000026961756,"about_ca_system_score_gemma":0.000002164173,"threshold_uncertainty_score":0.45632207},"labels":[],"label_agreement":null},{"id":"W2048787677","doi":"10.1063/1.3080617","title":"Dynamic resistive crossbar memory based on conjugated polymer composite","year":2009,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Materials science; Ohmic contact; Electric field; Optoelectronics; Composite number; Space charge; Conductivity; Resistive touchscreen; Nanotechnology; Condensed matter physics; Electrical engineering; Composite material; Electron; Chemistry; Physics; Engineering","score_opus":0.006389005993232115,"score_gpt":0.21255389671252176,"score_spread":0.20616489071928965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048787677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95804304,0.000025209813,0.03439691,0.00031291036,0.0001555933,0.00020577913,0.000010498682,0.0005727103,0.006277372],"genre_scores_gemma":[0.9915479,8.9253626e-7,0.0006021784,0.0076514115,0.00009490067,0.0000073051438,0.000039306313,0.000036959525,0.000019148669],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912673,0.000013088072,0.00015442209,0.00024739088,0.0001469385,0.00031140292],"domain_scores_gemma":[0.99953943,0.00009366638,0.000037166144,0.0002552769,0.000009325238,0.00006510925],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000034585293,0.000234975,0.00020535529,0.000038414582,0.0001333756,0.000027330962,0.00013636347,0.000040576826,0.0000058696173],"category_scores_gemma":[0.0000013152439,0.00025349244,0.00006491666,0.00018117897,0.000049147482,0.000059517206,0.000010378706,0.00028052204,0.00006154891],"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":[0.000041938412,0.000014616256,0.0000013145079,0.0000081415765,0.000008995073,0.0000084048115,0.00004025884,0.35651502,0.63950455,0.00030255722,0.00017007864,0.0033841047],"study_design_scores_gemma":[0.000761732,0.000035721365,0.0007212623,0.00003603351,0.000018682696,9.181173e-7,0.000009564933,0.05008531,0.9476832,0.00015129727,0.00008726887,0.00040899485],"about_ca_topic_score_codex":6.8555073e-7,"about_ca_topic_score_gemma":1.3034781e-7,"teacher_disagreement_score":0.30817863,"about_ca_system_score_codex":0.0000633261,"about_ca_system_score_gemma":0.0000046784508,"threshold_uncertainty_score":0.9999917},"labels":[],"label_agreement":null},{"id":"W2049430977","doi":"10.1007/s00521-009-0254-2","title":"AI-SIMCOG: a simulator for spiking neurons and multiple animats’ behaviours","year":2009,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Spiking neural network; Hebbian theory; Artificial neural network; Robot; Artificial intelligence; Coding (social sciences); Simulation","score_opus":0.017204635792097543,"score_gpt":0.2825926493139543,"score_spread":0.26538801352185676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049430977","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.903327,0.000267009,0.09516537,0.00023752214,0.00005336116,0.00043174773,0.000010367521,0.00046307826,0.000044563218],"genre_scores_gemma":[0.9975693,0.000009280705,0.0017472658,0.00043284878,0.00019277127,0.000009682543,0.000008992421,0.000021484144,0.000008344673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992414,0.000008764397,0.00018350736,0.00026013915,0.000052507203,0.0002536993],"domain_scores_gemma":[0.9994976,0.00021130296,0.000032272234,0.00013239554,0.000026686153,0.000099752135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005138679,0.00015724561,0.00015231928,0.00004552812,0.000383181,0.00005937372,0.000077833705,0.000043259013,4.3814666e-7],"category_scores_gemma":[0.000016085825,0.00016601932,0.000036837682,0.00011801021,0.000029750203,0.00008745581,0.00003015899,0.00017968529,6.2557785e-7],"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":[0.000022188624,0.000067727466,0.0040459554,0.00012073392,0.000013959198,0.0000042099377,0.0003123998,0.39587018,0.06844352,0.0034559213,0.00022869528,0.5274145],"study_design_scores_gemma":[0.0004725624,0.00009560675,0.00921437,0.000025878775,0.00002479116,0.00003558845,0.000027885208,0.9819173,0.0044481424,0.00085968,0.002606686,0.00027149156],"about_ca_topic_score_codex":0.0000010332066,"about_ca_topic_score_gemma":9.545748e-7,"teacher_disagreement_score":0.5860471,"about_ca_system_score_codex":0.000009612989,"about_ca_system_score_gemma":0.0000029375146,"threshold_uncertainty_score":0.67700714},"labels":[],"label_agreement":null},{"id":"W2049604781","doi":"10.1088/1748-3182/8/1/016007","title":"Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation","year":2013,"lang":"en","type":"article","venue":"Bioinspiration & Biomimetics","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Université du Québec à Montréal","funders":"RIKEN","keywords":"Habituation; Associative learning; Computer science; Artificial intelligence; Associative property; Learning rule; Artificial neural network; Perception; Neuroscience; Psychology; Mathematics","score_opus":0.028239329343464275,"score_gpt":0.29409884960619065,"score_spread":0.26585952026272636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049604781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45672897,0.00005543295,0.5420325,0.000030434077,0.00013125197,0.0007971869,0.0000034083173,0.00019047211,0.000030382409],"genre_scores_gemma":[0.92959416,0.000016111373,0.06988569,0.00006367546,0.00013420824,0.0001903135,0.000056615987,0.000033474902,0.000025757856],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991938,0.000049156148,0.000263164,0.00020356943,0.00007292747,0.00021742063],"domain_scores_gemma":[0.99952835,0.00012346078,0.000100744735,0.0000869314,0.00009085862,0.00006963859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016947436,0.00015102605,0.00013456146,0.00008501315,0.00027608953,0.00011436828,0.000055509976,0.00006432151,0.000016126407],"category_scores_gemma":[0.000032915726,0.00016529665,0.00003109537,0.00013624881,0.000021065105,0.00047358873,0.000018924922,0.00009190981,0.000010898278],"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":[0.000007923697,0.000030027226,0.0007505841,0.000047584683,0.00002529197,5.1485694e-7,0.0014784914,0.24097332,0.49365097,0.0001356542,0.00010476764,0.26279485],"study_design_scores_gemma":[0.0006728387,0.0006294939,0.0038951756,0.00001310769,0.00002959444,0.000002054846,0.00046978198,0.8333481,0.15957503,0.00047198142,0.0006097137,0.00028314712],"about_ca_topic_score_codex":0.000007437366,"about_ca_topic_score_gemma":0.000004796516,"teacher_disagreement_score":0.59237474,"about_ca_system_score_codex":0.000071022885,"about_ca_system_score_gemma":0.000019169462,"threshold_uncertainty_score":0.67406017},"labels":[],"label_agreement":null},{"id":"W2050937086","doi":"10.1063/1.3599952","title":"Effects of metal contacts and dopants on the performance of ZnO-based memristive devices","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Engineering, McGill University","keywords":"Dopant; Materials science; Doping; Fabrication; Optoelectronics; Nanotechnology; Non-volatile memory; Metal; Reproducibility; Metallurgy; Chemistry","score_opus":0.016878025549689042,"score_gpt":0.20912384158703956,"score_spread":0.19224581603735053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050937086","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984212,0.00006992589,0.00061303604,9.002273e-7,0.0000666105,0.00007300931,9.697491e-7,0.0000057855686,0.00074856967],"genre_scores_gemma":[0.9994939,0.000018126888,0.0004006981,0.000024247352,0.000052566233,8.5456304e-7,1.2627484e-7,0.000008837046,6.487205e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999577,0.000009085895,0.00018407406,0.00004052263,0.00011056101,0.00007875589],"domain_scores_gemma":[0.99941635,0.00024673608,0.00021358617,0.000056496585,0.000040655465,0.000026177153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091287344,0.00008429501,0.00020761696,0.000020454041,0.000025250887,0.0000021300402,0.00008357429,0.000017490107,0.0000014773038],"category_scores_gemma":[0.0000059241456,0.000054770742,0.000036808175,0.0000633321,0.00003513537,0.00006225889,0.000010474438,0.00015888775,5.6690715e-7],"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":[0.00052535435,0.00010363921,0.00022402054,0.0006945438,0.00017445847,0.0000072592866,0.0011408717,0.105997205,0.88107383,0.0017481056,0.000011932376,0.008298793],"study_design_scores_gemma":[0.0003629672,0.00020385323,0.0034412318,0.000117698866,0.000046497702,0.0000015695979,0.000044187673,0.006580183,0.9888968,0.00024189297,0.000009038898,0.000054101663],"about_ca_topic_score_codex":2.9950675e-7,"about_ca_topic_score_gemma":9.3006626e-8,"teacher_disagreement_score":0.10782296,"about_ca_system_score_codex":0.0000072222397,"about_ca_system_score_gemma":0.000008751331,"threshold_uncertainty_score":0.2233486},"labels":[],"label_agreement":null},{"id":"W2056818248","doi":"10.1109/iscas.2010.5537951","title":"A novel scalable parallel architecture for biological neural simulations","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Saskatchewan","funders":"","keywords":"Field-programmable gate array; Scalability; Computer science; Computer architecture; Modularity (biology); Flexibility (engineering); MATLAB; Architecture; Process (computing); Embedded system; Distributed computing; Parallel computing","score_opus":0.03284494567815434,"score_gpt":0.26713109971662213,"score_spread":0.2342861540384678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056818248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5408648,0.0000069012517,0.45801362,0.00007051862,0.0002036853,0.00011927056,0.0000071519103,0.000277012,0.0004369885],"genre_scores_gemma":[0.9212954,3.8379875e-7,0.07831056,0.000103911516,0.00015091353,0.000007945627,0.000008670636,0.0000108479235,0.00011135566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995917,0.0000021392134,0.0000908287,0.000106888074,0.000027582566,0.00018086233],"domain_scores_gemma":[0.99967015,0.000167408,0.000008008357,0.000093829134,0.000013649756,0.000046943594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002445918,0.000084195206,0.00008024703,0.000022252005,0.00007839108,0.000011683183,0.000072717026,0.000056882884,0.000048176298],"category_scores_gemma":[0.00004630872,0.00006365459,0.000041735042,0.000060016137,0.000018472289,0.000044197972,0.00001630281,0.00019363333,0.0000042557617],"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":[0.000004507315,0.00000605706,0.00003644032,0.000004750496,0.0000023757475,2.1570142e-7,0.0000102326785,0.57610106,0.41925046,0.0007407692,0.000035704783,0.003807386],"study_design_scores_gemma":[0.00043125753,0.000039951105,0.00051012327,0.000002682611,0.00000349099,0.000018478408,0.000006011279,0.96067625,0.02760758,0.0027406076,0.0077800932,0.00018345428],"about_ca_topic_score_codex":5.7083224e-7,"about_ca_topic_score_gemma":0.000015868467,"teacher_disagreement_score":0.3916429,"about_ca_system_score_codex":0.0000029631124,"about_ca_system_score_gemma":0.0000020085272,"threshold_uncertainty_score":0.25957587},"labels":[],"label_agreement":null},{"id":"W2057001908","doi":"10.1145/2613908.2613916","title":"Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Field-programmable gate array; Computer architecture; Spiking neural network; Artificial neural network; Point (geometry); Reconfigurable computing; Parallel computing; Embedded system; Artificial intelligence","score_opus":0.06300619091133776,"score_gpt":0.2320508895789061,"score_spread":0.16904469866756833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057001908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93759644,0.00004888508,0.0118409395,0.00005977828,0.0012778897,0.000098348,3.3950323e-7,0.0010048037,0.048072588],"genre_scores_gemma":[0.99695396,0.000008175846,0.0016662886,0.0003334265,0.00075245876,0.00001157766,0.0000014977024,0.000039746854,0.00023286353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909043,0.000034343695,0.00017173226,0.00019299873,0.00009289901,0.0004175798],"domain_scores_gemma":[0.999491,0.00020273047,0.000020830457,0.00019801734,0.000008003832,0.00007945217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001515685,0.00017579216,0.00016617481,0.0000504186,0.00013678754,0.000029897625,0.00012350945,0.000028770763,0.000043352797],"category_scores_gemma":[0.00003935421,0.00015785905,0.00005654932,0.00013266683,0.000014143398,0.0000909209,0.000015410838,0.00029527425,0.00003763278],"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":[0.00000766814,0.000002113241,0.000026247515,0.000015709407,0.0000048582588,0.0000033191275,0.00006376862,0.86898977,0.004297641,0.0007854214,0.0002103007,0.12559322],"study_design_scores_gemma":[0.00044559335,0.00019824319,0.0007750841,0.00018360294,0.000009611383,0.000030111607,0.00006930779,0.83815277,0.13303022,0.0033629227,0.02307247,0.00067009084],"about_ca_topic_score_codex":0.0000030498193,"about_ca_topic_score_gemma":0.000008772972,"teacher_disagreement_score":0.12873258,"about_ca_system_score_codex":0.000018081624,"about_ca_system_score_gemma":0.0000014706528,"threshold_uncertainty_score":0.6437305},"labels":[],"label_agreement":null},{"id":"W2061876185","doi":"10.1142/s0129183101001535","title":"THE HAMMING ASSOCIATIVE MEMORY REVISITED","year":2001,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Content-addressable memory; Computer science; Associative property; Bidirectional associative memory; Hamming distance; Hamming code; Theoretical computer science; Parallel computing; Content-addressable storage; Arithmetic; Algorithm; Topology (electrical circuits); Mathematics; Artificial neural network; Artificial intelligence; Pure mathematics; Combinatorics","score_opus":0.01595730740798628,"score_gpt":0.26296367082251176,"score_spread":0.24700636341452548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061876185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6598595,0.0009891626,0.3317311,0.00047080996,0.0018329524,0.000054648655,0.0000039361876,0.00006329074,0.0049946075],"genre_scores_gemma":[0.99807894,0.00020776164,0.00038127127,0.000080558704,0.0011034126,4.5809807e-7,9.982326e-7,0.000013310532,0.00013327587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992843,0.000020897995,0.00023819496,0.000047200487,0.00029903036,0.000110325396],"domain_scores_gemma":[0.99928236,0.00020915158,0.00016163567,0.000058040376,0.0002572153,0.00003159615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016547652,0.000075301876,0.00009770072,0.000024638417,0.00007201395,0.000046710116,0.00029017983,0.000016168406,0.0000028475845],"category_scores_gemma":[0.00006296372,0.000056758414,0.00009114894,0.000057995177,0.000016358988,0.00024212999,0.000029341625,0.00024364718,0.0000054009342],"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":[0.00005873719,0.000024195891,0.00028503753,0.000004237232,0.00027678398,0.000113522576,0.0007000996,0.21517344,0.025686827,0.0008373342,0.0006795953,0.7561602],"study_design_scores_gemma":[0.00268811,0.00015063386,0.0036346347,0.0005108095,0.00010290241,0.00075565645,0.0005099864,0.6063705,0.13115254,0.22536802,0.027953345,0.0008028598],"about_ca_topic_score_codex":2.2763096e-7,"about_ca_topic_score_gemma":4.0760125e-7,"teacher_disagreement_score":0.7553573,"about_ca_system_score_codex":0.000094497,"about_ca_system_score_gemma":0.000010805171,"threshold_uncertainty_score":0.23145409},"labels":[],"label_agreement":null},{"id":"W2061938839","doi":"10.1016/j.neunet.2013.02.005","title":"Event management for large scale event-driven digital hardware spiking neural networks","year":2013,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Neuromorphic engineering; Spiking neural network; Field-programmable gate array; Queue; Computer hardware; Parallel computing; Event (particle physics); Segmentation; Artificial neural network; Embedded system; Computer architecture; Real-time computing; Artificial intelligence","score_opus":0.009794711919150293,"score_gpt":0.22814711720720035,"score_spread":0.21835240528805006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061938839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3244759,0.0007939251,0.6686808,0.00014133324,0.002797233,0.0015434482,0.000017096738,0.0009552386,0.0005950101],"genre_scores_gemma":[0.99689925,0.00004444912,0.0007398769,0.0003031214,0.0012574503,0.00017559892,0.00009981167,0.00011329099,0.00036713612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770474,0.000027028134,0.00048308092,0.00047351557,0.00019784564,0.0011137574],"domain_scores_gemma":[0.9991497,0.00011540492,0.00009144762,0.00036600218,0.00005491792,0.00022256059],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000083840285,0.000427696,0.00035817304,0.000069723545,0.00029930344,0.00022719357,0.00036285698,0.00014542202,0.00005938672],"category_scores_gemma":[0.000008209466,0.00041828136,0.00026974978,0.00025134755,0.000025643023,0.0007165487,0.0002166125,0.0004949179,0.000020295707],"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":[0.000017378217,0.000023534918,0.00042496814,0.00005825701,0.000035382644,0.00001803439,0.000034380068,0.9193948,0.000046797766,0.0000522112,0.0027999838,0.077094294],"study_design_scores_gemma":[0.00068922224,0.00008120018,0.0009248294,0.00007953416,0.000029912977,0.000019461557,0.000082821556,0.9941456,0.00007059403,0.00011133932,0.0033112192,0.00045432092],"about_ca_topic_score_codex":0.0000010102647,"about_ca_topic_score_gemma":0.000005609768,"teacher_disagreement_score":0.67242336,"about_ca_system_score_codex":0.000073624586,"about_ca_system_score_gemma":0.0000020026857,"threshold_uncertainty_score":0.9998269},"labels":[],"label_agreement":null},{"id":"W2065216265","doi":"10.1016/j.neunet.2014.07.015","title":"New approximation method for smooth error backpropagation in a quantron network","year":2014,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Backpropagation; Computer science; Perceptron; Artificial neural network; Artificial intelligence; Nonlinear system; Propagation of uncertainty; Algorithm","score_opus":0.018569675324150898,"score_gpt":0.2709817042676523,"score_spread":0.2524120289435014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065216265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025885953,0.00018361124,0.9721751,0.00008478635,0.0007766246,0.00040324495,4.105386e-7,0.0002682233,0.00022202017],"genre_scores_gemma":[0.8867287,0.0000077812965,0.111388035,0.00018355015,0.0015380371,0.000033112217,0.000025570007,0.000046801953,0.00004838107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989415,0.00007210219,0.00028044323,0.0002229457,0.000078436104,0.00040457363],"domain_scores_gemma":[0.99939656,0.00030318656,0.000057733963,0.00015236564,0.000016880163,0.00007328252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003130042,0.00017090834,0.00021581566,0.000042820015,0.00007111171,0.000027727367,0.00010857429,0.000101717196,0.0000070715096],"category_scores_gemma":[0.000038781593,0.00017003635,0.000060438222,0.00025163952,0.0000065353106,0.0001863772,0.00001895046,0.0002511952,0.000002442369],"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":[0.000030454665,0.0000024918572,0.000058641614,0.000022738508,0.000002364783,3.5647378e-7,0.000023296465,0.80295914,0.00057653605,0.0006234397,0.00062827376,0.19507228],"study_design_scores_gemma":[0.0005282077,0.00006631713,0.0005582986,0.000038048085,0.0000079779265,0.000002911388,0.0000051346337,0.99399847,0.0005229517,0.002419087,0.0016710114,0.00018161176],"about_ca_topic_score_codex":0.0000044531703,"about_ca_topic_score_gemma":0.000033514065,"teacher_disagreement_score":0.86084276,"about_ca_system_score_codex":0.000037893926,"about_ca_system_score_gemma":0.00000396189,"threshold_uncertainty_score":0.6933881},"labels":[],"label_agreement":null},{"id":"W2066555771","doi":"10.1109/tcsi.2015.2388833","title":"A Low Power and High Sensing Margin Non-Volatile Full Adder Using Racetrack Memory","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University","funders":"","keywords":"Adder; Racetrack memory; Margin (machine learning); Computer science; Computer hardware; Power (physics); Embedded system; Semiconductor memory; Memory refresh; Telecommunications; Physics; Computer memory","score_opus":0.01990137282649179,"score_gpt":0.21734570147895615,"score_spread":0.19744432865246436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066555771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7771969,0.00048681092,0.21993367,0.000013267084,0.0013078626,0.00027363532,0.00001165649,0.00016832964,0.0006078642],"genre_scores_gemma":[0.9992993,0.000030502815,0.0002195411,0.00003464653,0.000095607764,0.0000038524868,0.0000012024105,0.000057012643,0.0002583244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882555,0.00006028798,0.0002811521,0.00031412407,0.00020376401,0.00031512429],"domain_scores_gemma":[0.9993235,0.000073321666,0.000048386362,0.00022279202,0.000048249636,0.00028373956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020579564,0.0002615237,0.00032660176,0.00012931027,0.00021942846,0.000082954015,0.000052157808,0.00013887152,0.000009679711],"category_scores_gemma":[0.00000578398,0.000257616,0.000057757043,0.00016660978,0.00006138693,0.00021436952,0.0000015270632,0.0002692248,0.00000507275],"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":[0.000022670363,0.000020995554,0.0000033814242,0.00022882203,0.00009390935,0.000057619618,0.0011090706,0.60756236,0.3627005,0.000011385675,0.000059758186,0.028129542],"study_design_scores_gemma":[0.0031841777,0.0003645542,0.00013684813,0.0012366205,0.0001865648,0.0017222764,0.0046957782,0.8811012,0.10455157,0.00004273116,0.0012905018,0.0014871609],"about_ca_topic_score_codex":0.000019655465,"about_ca_topic_score_gemma":0.000007890151,"teacher_disagreement_score":0.27353886,"about_ca_system_score_codex":0.0000800512,"about_ca_system_score_gemma":0.00002857748,"threshold_uncertainty_score":0.9999876},"labels":[],"label_agreement":null},{"id":"W2073083699","doi":"10.1038/nphys2845","title":"Chaotic memory","year":2013,"lang":"en","type":"article","venue":"Nature Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Physics; Chaotic; Statistical physics; Chaotic systems; Quantum mechanics; Artificial intelligence; Nonlinear system; Computer science","score_opus":0.005799603623704373,"score_gpt":0.20907622730344994,"score_spread":0.20327662367974556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073083699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9724651,0.0008976589,0.008574824,0.00008551106,0.001174009,0.00018807933,0.0000013358355,0.0007887338,0.01582477],"genre_scores_gemma":[0.99843407,0.000006843321,0.0007040336,0.0002089139,0.00048478792,0.000004886139,0.0000023777213,0.000019584186,0.00013449922],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996476,0.000004162122,0.00005580215,0.00008040739,0.00006602246,0.00014601038],"domain_scores_gemma":[0.99978626,0.000027915386,0.000009067136,0.000120718585,0.000020216983,0.000035825455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000012348479,0.00008489792,0.00007566605,0.000009615859,0.00003319372,0.000011924829,0.000078027166,0.00007207247,0.000025678242],"category_scores_gemma":[0.0000065782638,0.00007762913,0.000030614803,0.00009682308,0.00000910855,0.00014836318,0.000015800277,0.00044094375,0.000200034],"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":[0.0000034446612,0.000037978974,0.00006370938,0.00021295472,0.000056865523,0.000014766305,0.0005990136,0.20264272,0.36860216,0.006229476,0.015288574,0.40624833],"study_design_scores_gemma":[0.00051519123,0.000039675488,0.0021684244,0.00007521183,0.000022129048,0.000016208074,0.000120100995,0.11016895,0.83387196,0.045546193,0.0066894335,0.0007665168],"about_ca_topic_score_codex":3.1873276e-7,"about_ca_topic_score_gemma":9.5050765e-8,"teacher_disagreement_score":0.4652698,"about_ca_system_score_codex":0.000014883163,"about_ca_system_score_gemma":0.0000020577104,"threshold_uncertainty_score":0.3165624},"labels":[],"label_agreement":null},{"id":"W2074663260","doi":"10.1016/j.orgel.2012.09.026","title":"Modeling of spiking analog neural circuits using organic semiconductor thin film transistors with silicon oxide nitride semiconductor gates","year":2012,"lang":"en","type":"article","venue":"Organic Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Electronic circuit; Transistor; Optoelectronics; Semiconductor; Organic semiconductor; Electronic engineering; Computer science; Electrical engineering; Voltage; Engineering","score_opus":0.019576719037236512,"score_gpt":0.21998988349226886,"score_spread":0.20041316445503235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074663260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98401046,0.0059707635,0.009138141,0.0000097810025,0.00022967679,0.00021816895,0.000005805242,0.00037438524,0.000042812884],"genre_scores_gemma":[0.99891603,0.0001358156,0.0004812274,0.000051065395,0.00020432135,0.0000025752531,0.000014047617,0.00017706271,0.00001782742],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977316,0.00005572224,0.00053214666,0.0003452995,0.0002534013,0.0010818086],"domain_scores_gemma":[0.999128,0.000100155754,0.00012627573,0.00036935034,0.0000881051,0.00018809967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023979993,0.00044292968,0.0005214436,0.0001595012,0.00017584782,0.000024257179,0.0002920512,0.00016735219,0.00007897989],"category_scores_gemma":[0.00006020254,0.00043302446,0.0001034339,0.00065744173,0.000047179605,0.00065933407,0.00003787915,0.00077324023,0.0000050674275],"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":[0.000008950894,0.000017813356,0.0003664405,0.000087832195,0.00005619419,0.000002914126,0.00043349076,0.2760734,0.722814,0.000031271906,0.0000030715462,0.000104611645],"study_design_scores_gemma":[0.00030740965,0.000057633708,0.000019635447,0.00007483571,0.00009660187,0.00013347405,0.00017205797,0.3366868,0.66200686,0.000045637673,0.000019537121,0.00037955388],"about_ca_topic_score_codex":0.000010931936,"about_ca_topic_score_gemma":0.000039548762,"teacher_disagreement_score":0.06080718,"about_ca_system_score_codex":0.00041702503,"about_ca_system_score_gemma":0.00012686964,"threshold_uncertainty_score":0.9998121},"labels":[],"label_agreement":null},{"id":"W2075832940","doi":"10.1364/oic.2010.pdtud10","title":"From Passive to Active: Future Optical Security Devices","year":2010,"lang":"en","type":"article","venue":"Optical Interference Coatings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Electrochromism; Computer science; Fabrication; Computer security","score_opus":0.009749593856401824,"score_gpt":0.26146315797936026,"score_spread":0.2517135641229584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075832940","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9768918,0.000008354637,0.015841857,0.0004694315,0.0011254001,0.00012367692,0.000014816178,0.00030766186,0.0052169906],"genre_scores_gemma":[0.9871882,0.000002109501,0.011443882,0.00026526712,0.0010205677,0.000015914446,0.000008107449,0.0000266324,0.000029307903],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988955,0.000012121044,0.00023776686,0.00033652977,0.00014398913,0.00037405948],"domain_scores_gemma":[0.99908054,0.00028190576,0.000028652797,0.00023796689,0.00007924388,0.0002916812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050911345,0.00022883469,0.00023676232,0.00003918795,0.0000802235,0.00008183494,0.00032453693,0.00015652637,0.0002403259],"category_scores_gemma":[0.00023482616,0.00020789492,0.000058040052,0.00014559207,0.000066960725,0.00022921206,0.00016189543,0.001003985,0.00018227047],"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":[0.000060552607,0.00003979371,0.00007530523,0.000032888667,0.00002196922,0.000032495056,0.0019465213,0.00044647168,0.90891474,0.0045776493,0.0003859988,0.0834656],"study_design_scores_gemma":[0.00022728012,0.00014937518,0.0016494062,0.00012427288,0.000022169872,0.000015706111,0.0008033393,0.008034119,0.9817177,0.0036679604,0.003051815,0.000536879],"about_ca_topic_score_codex":0.0000073039296,"about_ca_topic_score_gemma":0.000043922315,"teacher_disagreement_score":0.08292872,"about_ca_system_score_codex":0.000031944914,"about_ca_system_score_gemma":0.00001167198,"threshold_uncertainty_score":0.84777087},"labels":[],"label_agreement":null},{"id":"W2077066011","doi":"10.1109/tvlsi.2015.2389260","title":"High-Density and High-Reliability Nonvolatile Field-Programmable Gate Array With Stacked 1D2R RRAM Array","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":25,"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":"Resistive random-access memory; Static random-access memory; Standby power; Non-volatile memory; NMOS logic; Logic block; Transistor; Electronic engineering; Field-programmable gate array; Data retention; Electrical engineering; Gate array; Block (permutation group theory); Computer science; Engineering; Embedded system; Voltage","score_opus":0.012298666944403482,"score_gpt":0.21830669806615846,"score_spread":0.20600803112175498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077066011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45130038,0.000036163678,0.5462593,0.00003848658,0.0014335247,0.00038502994,0.000031709922,0.0003960134,0.00011942502],"genre_scores_gemma":[0.99629915,0.000017426584,0.002807579,0.000043733067,0.00016690564,0.00009322017,0.000022073205,0.00005232393,0.00049757696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99809605,0.00014075193,0.00048558257,0.0004794451,0.00035555047,0.00044259545],"domain_scores_gemma":[0.99877053,0.0001679472,0.00009843332,0.00046887767,0.00023474908,0.00025945547],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040928117,0.0003773356,0.000463778,0.0001278885,0.0003004025,0.00014417597,0.00013414088,0.00020070531,0.00002459311],"category_scores_gemma":[0.000016047103,0.00031811773,0.00008545173,0.00033278792,0.000058301666,0.0006157754,0.0000016920346,0.0006047482,0.000039677336],"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":[0.00054903724,0.00030207736,0.00015195206,0.0003195048,0.00013265302,0.000018264278,0.0019504073,0.9233186,0.06549767,0.000093115865,0.00038744288,0.007279261],"study_design_scores_gemma":[0.002357736,0.0009218696,0.00013097089,0.00046174834,0.00011139731,0.000071977396,0.0021820206,0.12884344,0.86213005,0.00022329188,0.0016834994,0.0008819808],"about_ca_topic_score_codex":0.00023476026,"about_ca_topic_score_gemma":0.00066540437,"teacher_disagreement_score":0.7966324,"about_ca_system_score_codex":0.00019289668,"about_ca_system_score_gemma":0.000038477738,"threshold_uncertainty_score":0.9999271},"labels":[],"label_agreement":null},{"id":"W2077661887","doi":"10.1109/ccece.2010.5575160","title":"FPGA based pipelined architecture for action potential simulation in biological neural systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Field-programmable gate array; Computer science; Adder; Parallel computing; Scalability; MATLAB; Floating point; Multiplier (economics); Artificial neural network; Computation; FLOPS; Computer architecture; Computer hardware; Embedded system; Algorithm; Latency (audio); Artificial intelligence","score_opus":0.034428494399044426,"score_gpt":0.28404566420267297,"score_spread":0.24961716980362852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077661887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56767833,0.0000048567285,0.43135193,0.000020723006,0.00059596234,0.00015203649,0.0000013972715,0.00016671997,0.00002802497],"genre_scores_gemma":[0.9975774,2.7640584e-7,0.002040448,0.000029354427,0.00029906226,0.0000118519565,0.00001526715,0.000010665243,0.0000156565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954534,0.00001386568,0.00013959626,0.00011461246,0.00004022545,0.00014634494],"domain_scores_gemma":[0.9997366,0.00013194769,0.000016233631,0.00007066414,0.000015679389,0.000028868297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006962426,0.000085328065,0.00009166424,0.000054567143,0.000036233825,0.000014187656,0.000045328397,0.00008595747,0.000010279677],"category_scores_gemma":[0.00004500426,0.000068330155,0.000036418296,0.00006793788,0.000007933044,0.000054940305,0.000006000678,0.00020807174,0.0000016763629],"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":[0.000027097438,0.0000041726685,0.00003069216,0.000017525652,7.6081096e-7,9.459268e-7,0.000004295077,0.7557671,0.23668487,0.000013563076,0.000004106788,0.00744486],"study_design_scores_gemma":[0.00036222482,0.000029481418,0.0004710967,0.0000045451798,0.0000016739509,0.000003043154,0.000008422446,0.97679824,0.021733448,0.00007716384,0.00042318326,0.00008746111],"about_ca_topic_score_codex":0.000002056137,"about_ca_topic_score_gemma":0.000015040435,"teacher_disagreement_score":0.42989907,"about_ca_system_score_codex":0.000010134181,"about_ca_system_score_gemma":0.0000025402906,"threshold_uncertainty_score":0.2786423},"labels":[],"label_agreement":null},{"id":"W2078185678","doi":"10.1063/1.4757584","title":"Oxygen vacancy filament formation in TiO2: A kinetic Monte Carlo study","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":43,"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":"National Natural Science Foundation of China","keywords":"Kinetic Monte Carlo; Protein filament; Vacancy defect; Condensed matter physics; Diffusion; Electric field; Monte Carlo method; Chemical physics; Materials science; Density functional theory; Chemistry; Computational chemistry; Physics; Thermodynamics","score_opus":0.018756489601711848,"score_gpt":0.24023979570364654,"score_spread":0.22148330610193467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078185678","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99491644,0.00011175264,0.003695654,0.0000022874117,0.00019087267,0.00013672638,4.6931044e-7,0.00001716554,0.0009286454],"genre_scores_gemma":[0.9990772,0.0000129054215,0.00047827346,0.000018120032,0.00039197243,0.000003960406,2.7646524e-7,0.000012914697,0.0000044051008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938375,0.000008055407,0.00027500067,0.00003580151,0.00013536471,0.00016205091],"domain_scores_gemma":[0.99973977,0.000022633274,0.00009998083,0.00006893886,0.000019088853,0.00004958271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104647756,0.00008786957,0.00015357029,0.00003940491,0.000021173768,0.000008408292,0.000071432565,0.000017678973,0.0000025485213],"category_scores_gemma":[0.0000020184712,0.000080214944,0.00003374877,0.0001251847,0.0000034210761,0.0002458356,0.000015608242,0.00021005805,0.0000047775034],"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":[0.00002932266,0.00012499049,0.00023316313,0.000034309483,0.000020141591,0.000004600832,0.0034967647,0.9549119,0.028887289,0.00007964067,0.00010176584,0.012076103],"study_design_scores_gemma":[0.013421147,0.0013976066,0.112295076,0.0006184083,0.00038268778,0.00019176485,0.014380767,0.36409116,0.47675204,0.007742325,0.006537468,0.0021895426],"about_ca_topic_score_codex":4.9666784e-7,"about_ca_topic_score_gemma":7.662349e-7,"teacher_disagreement_score":0.5908207,"about_ca_system_score_codex":0.00007661505,"about_ca_system_score_gemma":0.0000034309658,"threshold_uncertainty_score":0.32710704},"labels":[],"label_agreement":null},{"id":"W2080031665","doi":"10.1016/j.electacta.2012.11.111","title":"Direct spectroscopic monitoring of conductance switching in polythiophene memory devices","year":2012,"lang":"en","type":"article","venue":"Electrochimica Acta","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; Xerox (Canada); University of Alberta","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; University of Alberta; National Institute for Nanotechnology","keywords":"Polaron; Conductance; Conductivity; Materials science; Polythiophene; Raman spectroscopy; Optoelectronics; Electrode; Redox; Conductive polymer; Chemistry; Chemical physics; Nanotechnology; Analytical Chemistry (journal); Polymer; Inorganic chemistry; Optics; Composite material; Condensed matter physics; Electron","score_opus":0.014222168974244425,"score_gpt":0.24733470439866106,"score_spread":0.23311253542441665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080031665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99358815,0.002372793,0.00023612191,0.000019057967,0.00044858316,0.000100656456,4.998713e-7,0.00018210085,0.0030520181],"genre_scores_gemma":[0.9979444,0.000102068,0.0014886116,0.000017169978,0.00037631465,0.000006210951,9.753676e-7,0.000035865556,0.000028362203],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884343,0.000030098932,0.00027788265,0.00015552991,0.00012207954,0.0005710005],"domain_scores_gemma":[0.999548,0.00011621221,0.000063357766,0.00018618295,0.000011324092,0.00007487901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014303433,0.00018492062,0.00028024483,0.00011107951,0.00004997439,0.000009359329,0.00017506069,0.000056276032,0.000013319997],"category_scores_gemma":[0.00003531174,0.0001923558,0.00004591161,0.00028306563,0.000012628384,0.00037870964,0.000030071995,0.00034847928,0.000005443485],"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":[0.000011005645,0.000018242466,0.007973676,0.00006547429,0.000013148748,0.0000013686646,0.00034084995,0.0003484942,0.99065185,0.00003338223,0.000006030495,0.0005364866],"study_design_scores_gemma":[0.00017716974,0.000026334654,0.016733836,0.00010105575,0.000007982519,0.0000079129,0.00006184464,0.00021098746,0.9823695,0.0000561112,0.00006465261,0.00018264474],"about_ca_topic_score_codex":0.0000026372763,"about_ca_topic_score_gemma":0.0000043109617,"teacher_disagreement_score":0.008760161,"about_ca_system_score_codex":0.00008916493,"about_ca_system_score_gemma":0.0000115432895,"threshold_uncertainty_score":0.7844042},"labels":[],"label_agreement":null},{"id":"W2080248884","doi":"10.1039/c0nr00886a","title":"Electrochemical synthesis of Ag(0)/Ag2S heterojunctions templated on pre-formed Ag2S nanowires","year":2011,"lang":"en","type":"article","venue":"Nanoscale","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nanowire; Heterojunction; Materials science; Ionic bonding; Nanotechnology; Electrochemistry; Nanostructure; Resistive touchscreen; Optoelectronics; Electrode; Ion; Chemistry; Computer science","score_opus":0.02018493750791929,"score_gpt":0.2257550834604346,"score_spread":0.20557014595251533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080248884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99145925,0.0000677258,0.0018627995,0.0000072326893,0.00024951462,0.00012148173,0.000005091883,0.00032819703,0.005898735],"genre_scores_gemma":[0.99885213,0.000020314115,0.0008433013,0.000022262871,0.000052446598,0.000021519421,0.0000030531817,0.000030388037,0.00015457402],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991768,0.000017500697,0.00023846206,0.00017348722,0.000116431176,0.00027730304],"domain_scores_gemma":[0.9995167,0.00013179307,0.000041361378,0.00021431754,0.000026458283,0.000069321526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004410713,0.00015761114,0.00020679957,0.0000750816,0.00006786612,0.0000044180497,0.00013632524,0.00010604355,0.00008308123],"category_scores_gemma":[0.000072181196,0.00014609273,0.00008775714,0.00018008554,0.00003935766,0.00009017723,0.00002267841,0.00018316632,0.000027651751],"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":[0.000077699035,0.000059884544,0.00020035442,0.000053475134,0.00003177746,0.0000035175233,0.00015778841,0.00022820735,0.9944395,0.00006955357,0.00022124137,0.004457024],"study_design_scores_gemma":[0.00014435512,0.000107106585,0.0013631235,0.000075436335,0.000017830043,0.000009777064,0.000012818559,0.0009225386,0.99657667,0.00021830268,0.00039802518,0.000154024],"about_ca_topic_score_codex":0.000004167524,"about_ca_topic_score_gemma":0.0000044366725,"teacher_disagreement_score":0.0073929187,"about_ca_system_score_codex":0.000037316833,"about_ca_system_score_gemma":0.000007934757,"threshold_uncertainty_score":0.59574884},"labels":[],"label_agreement":null},{"id":"W2082770563","doi":"10.1149/2.100406jes","title":"Polymer-Based Memory Structures on Copper Substrates","year":2014,"lang":"en","type":"article","venue":"Journal of The Electrochemical Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Materials science; Transistor; Tungsten; Oxide; Nanotechnology; CMOS; Polymer; Optoelectronics; Capacitor; Copper; Voltage; Electrical engineering; Metallurgy; Composite material; Engineering","score_opus":0.007053474576653876,"score_gpt":0.21588023931938094,"score_spread":0.20882676474272707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082770563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927293,0.00039216253,0.0058227307,0.00041992287,0.00027290147,0.0000326703,3.3713422e-7,0.000043198477,0.0002867929],"genre_scores_gemma":[0.9976796,0.000008957203,0.0011221174,0.0007120444,0.00043421824,3.4278258e-7,2.9880835e-7,0.00001598259,0.000026468337],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932414,0.000024200077,0.00019339574,0.00006609684,0.00018476212,0.00020738915],"domain_scores_gemma":[0.99955016,0.00015912825,0.000085844236,0.00011178317,0.000031080814,0.00006198551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115525945,0.00011645447,0.00015296537,0.000009215293,0.00008070008,0.000013567179,0.00023964453,0.0000701531,0.0000107220585],"category_scores_gemma":[0.00004376099,0.00007280766,0.0002889518,0.000098843484,0.000037960486,0.000041235075,0.000012104322,0.000590926,0.0000011857875],"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":[0.000018408859,0.000008583577,0.000025900705,0.000014232543,0.000025506351,3.0587594e-7,0.00003309146,0.0039354474,0.99409014,0.000063063526,0.0011674882,0.0006178022],"study_design_scores_gemma":[0.00026642578,0.000062377454,0.00010701627,0.000026199627,0.000015229765,0.000019273213,0.000012183866,0.0036159044,0.9945087,0.00094994856,0.00033024754,0.000086486754],"about_ca_topic_score_codex":1.0707799e-7,"about_ca_topic_score_gemma":7.6635814e-8,"teacher_disagreement_score":0.0049502864,"about_ca_system_score_codex":0.00006440435,"about_ca_system_score_gemma":0.00001558161,"threshold_uncertainty_score":0.29690105},"labels":[],"label_agreement":null},{"id":"W2086373044","doi":"10.1109/tvlsi.2015.2411258","title":"Logic-in-Memory With a Nonvolatile Programmable Metallization Cell","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pass transistor logic; Logic family; Logic gate; Computer science; CMOS; Logic level; Transistor; Programmable logic array; Programmable logic device; Memory cell; Electronic engineering; Electrical engineering; Logic synthesis; Voltage; Engineering; Computer hardware; Algorithm","score_opus":0.02034403170740979,"score_gpt":0.22662307263740905,"score_spread":0.20627904092999927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086373044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11799081,0.00016521983,0.87685484,0.000013835827,0.0012243161,0.0006760125,0.000020928528,0.00051487994,0.0025391732],"genre_scores_gemma":[0.99651474,0.000016629581,0.001658422,0.00002711822,0.000087248976,0.00017828133,0.000022772472,0.00005507325,0.0014397339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843913,0.000100801924,0.00043827327,0.00032631817,0.00031703207,0.0003784258],"domain_scores_gemma":[0.9992861,0.00006091563,0.00007871712,0.00028071157,0.00014504477,0.00014846803],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030023823,0.0002899657,0.0003158022,0.00023815596,0.00013159848,0.00009509255,0.00012872345,0.00014179948,0.000021198392],"category_scores_gemma":[0.0000045825345,0.00024713794,0.00007820199,0.0005772757,0.000026896292,0.00060631667,0.0000010972615,0.00039961972,0.00007933771],"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":[0.000078835816,0.0001649064,0.000023441311,0.00009720287,0.000019342837,0.000014597686,0.0007753215,0.9896397,0.0072054504,0.000037418442,0.00009029308,0.0018534984],"study_design_scores_gemma":[0.0017802987,0.00040162855,0.00001619446,0.0003313136,0.000049353384,0.0000522499,0.003052018,0.82253546,0.1694733,0.000050321312,0.0017233219,0.0005345408],"about_ca_topic_score_codex":0.00003262864,"about_ca_topic_score_gemma":0.00032351882,"teacher_disagreement_score":0.8785239,"about_ca_system_score_codex":0.00023666378,"about_ca_system_score_gemma":0.00004293979,"threshold_uncertainty_score":0.9999981},"labels":[],"label_agreement":null},{"id":"W2091699212","doi":"10.1142/s0219581x1240025x","title":"EFFECT OF VERTICAL MECHANICAL COMPRESSION ON THE RESISTIVE SWITCHING CURRENTS OF TITANIUM DIOXIDE THIN FILMS","year":2012,"lang":"en","type":"article","venue":"International Journal of Nanoscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Materials science; Thin film; Scanning electron microscope; Atomic force microscopy; Biasing; Titanium dioxide; Resistive touchscreen; Electrical conductor; Horizontal scan rate; Voltage; Composite material; Conductive atomic force microscopy; Layer (electronics); Nanotechnology; Electrode; Electrical engineering; Electrochemistry","score_opus":0.016395920236426824,"score_gpt":0.2976468778883843,"score_spread":0.28125095765195746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091699212","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9836587,0.000081575265,0.014342081,0.0000534076,0.001687861,0.000037345137,0.000001972601,0.0000063732487,0.00013072958],"genre_scores_gemma":[0.99950314,0.000013599813,0.00036146154,0.000019105031,0.00009522639,3.634314e-7,1.2540468e-7,0.000004218628,0.0000027680794],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899924,0.000059143367,0.00028155884,0.00005081746,0.0004996223,0.000109640845],"domain_scores_gemma":[0.9988764,0.0008031959,0.000116515854,0.00006374627,0.0000909841,0.000049190796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063203584,0.00006816941,0.00013288797,0.000062338775,0.000031188338,0.0000066852913,0.00040092235,0.000022710596,0.00001072938],"category_scores_gemma":[0.00057483604,0.00004001048,0.00007251547,0.000081377526,0.000039086466,0.00021342801,0.000060938488,0.00021977046,0.0000017609227],"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":[0.00013769955,0.000034820663,0.00042654018,0.000011581564,0.00001590023,0.0000045136967,0.000088165514,0.008256944,0.98776335,0.0011989562,0.000056500034,0.0020050486],"study_design_scores_gemma":[0.00018873079,0.00018477373,0.0017047337,0.0003540173,0.000007721349,0.00002959457,0.000017837658,0.00983659,0.9874905,0.00011013452,0.000036253667,0.000039149596],"about_ca_topic_score_codex":6.266161e-7,"about_ca_topic_score_gemma":6.918165e-8,"teacher_disagreement_score":0.015844477,"about_ca_system_score_codex":0.000031826265,"about_ca_system_score_gemma":0.000009107604,"threshold_uncertainty_score":0.163158},"labels":[],"label_agreement":null},{"id":"W2091973789","doi":"10.1145/2786572.2786595","title":"Improving DVFS in NoCs with Coherence Prediction","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":40,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Coherence (philosophical gambling strategy); Statistics","score_opus":0.01958690243106449,"score_gpt":0.20529053293633057,"score_spread":0.1857036305052661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091973789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9273511,0.00004106529,0.06562592,0.000007929142,0.00012518214,0.00006870604,4.2027588e-7,0.00032033568,0.0064593204],"genre_scores_gemma":[0.9966748,9.2589625e-7,0.0031190736,0.000014036721,0.0000370329,0.0000032346784,7.9000415e-7,0.000007590651,0.00014251165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997145,0.0000038874796,0.00006486409,0.00006765517,0.000049704726,0.00009937647],"domain_scores_gemma":[0.9998749,0.000011536936,0.0000069598264,0.000057685796,0.000011385708,0.00003755047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004531324,0.000049521972,0.00004747219,0.000022800135,0.0000100366415,0.00000651504,0.000032721462,0.00001729065,0.0000040505847],"category_scores_gemma":[0.000009624174,0.00004019215,0.000004253178,0.000092211616,0.0000060044035,0.00014008301,0.000009355334,0.000083266845,0.0000072685616],"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":[0.000038661747,0.000015084291,0.009110551,0.000061036815,0.000005813266,0.00004107649,0.000519452,0.88662213,0.05620136,0.00017832044,0.00027145454,0.04693503],"study_design_scores_gemma":[0.0013916002,0.00021555324,0.0035204212,0.00009642566,0.0000059448316,0.000060485345,0.0005519579,0.7845588,0.2080217,0.00032588234,0.00090321654,0.00034802422],"about_ca_topic_score_codex":0.000008065417,"about_ca_topic_score_gemma":0.000035902005,"teacher_disagreement_score":0.15182035,"about_ca_system_score_codex":0.000030075322,"about_ca_system_score_gemma":0.00000663652,"threshold_uncertainty_score":0.16389883},"labels":[],"label_agreement":null},{"id":"W2093071448","doi":"10.3389/fnbot.2014.00021","title":"Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller","year":2014,"lang":"en","type":"article","venue":"Frontiers in Neurorobotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"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; Spiking neural network; Artificial intelligence; Robot; Context (archaeology); Artificial neural network; Component (thermodynamics); Set (abstract data type); Operant conditioning; Behavior-based robotics; Habituation; Process (computing); Machine learning; Robotics; Neuroscience","score_opus":0.03343783858844007,"score_gpt":0.24821892528741557,"score_spread":0.2147810866989755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093071448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3781632,0.000071914714,0.6186769,0.00014280276,0.001998265,0.0006930257,0.000007482192,0.00016480824,0.000081638995],"genre_scores_gemma":[0.9659432,0.000012042838,0.03340791,0.00032639544,0.00015948177,0.000046879173,0.000021350384,0.000067116634,0.000015599926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981463,0.00011090606,0.0006179788,0.00037720494,0.00016341209,0.0005842126],"domain_scores_gemma":[0.99940836,0.00019005302,0.00008078717,0.00019618233,0.00003035881,0.000094259834],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025968556,0.00028130497,0.00047799075,0.00025394512,0.00012550318,0.000050978742,0.0002225204,0.00009303776,0.0000022523227],"category_scores_gemma":[0.00016985598,0.00031992482,0.000089776,0.00024361051,0.000060359573,0.00018039471,0.000042492367,0.00027717737,0.0000026934827],"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":[0.00015628214,0.0000669701,0.001542326,0.00006229345,0.000012933243,0.000026184238,0.00012595132,0.88022274,0.11431502,0.00019898,0.0003547931,0.0029155405],"study_design_scores_gemma":[0.0025001368,0.00021684454,0.0038301768,0.00011209003,0.00002417724,0.000006182746,0.000060434082,0.9728492,0.018594816,0.0007537052,0.000659665,0.00039260098],"about_ca_topic_score_codex":0.0000020301127,"about_ca_topic_score_gemma":0.000008921561,"teacher_disagreement_score":0.58778006,"about_ca_system_score_codex":0.00011403513,"about_ca_system_score_gemma":0.0000121010835,"threshold_uncertainty_score":0.99992526},"labels":[],"label_agreement":null},{"id":"W2093923760","doi":"10.1109/iscas.2012.6271416","title":"Bidirectional current conveyer with chopper stabilization and dynamic element matching","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Chopper; Current mirror; Flicker noise; Interfacing; Dynamic range; Current (fluid); Electronic engineering; Bandwidth (computing); Current conveyor; Current sensor; Noise (video); Transistor; Flicker; Electrical engineering; CMOS; Computer science; Voltage; Engineering; Capacitor; Computer hardware; Noise figure; Telecommunications","score_opus":0.010378654005180241,"score_gpt":0.24300617538889924,"score_spread":0.232627521383719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093923760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78151494,0.0005113597,0.21703643,0.00000917678,0.0002212427,0.000061866915,8.6249685e-7,0.0001476941,0.00049642485],"genre_scores_gemma":[0.9983281,0.000048497157,0.0015051961,0.000013248801,0.000050208237,0.0000044298886,0.0000051090624,0.00001100052,0.00003420658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968976,0.0000060058155,0.000065588254,0.00006119554,0.000048874466,0.0001285488],"domain_scores_gemma":[0.999883,0.00001781823,0.000008635014,0.00003860692,0.00000927508,0.000042654527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047247897,0.000064746455,0.000047010686,0.00002237544,0.000045018143,0.000008321817,0.000013925626,0.000009906419,0.00005158289],"category_scores_gemma":[0.0000015883486,0.000050810857,0.000006483987,0.000044296532,0.000007450089,0.00017842541,0.000010591985,0.00006139903,0.000004381497],"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":[0.000055283617,0.00018360012,0.12518984,0.00068953173,0.00011796722,0.0000012918299,0.003006649,0.31084764,0.16149597,0.014112678,0.00023988802,0.38405964],"study_design_scores_gemma":[0.0038541206,0.00026585854,0.13095804,0.00047804273,0.00012623932,0.00015032706,0.0014676502,0.6216015,0.167174,0.0033255825,0.067864425,0.0027342127],"about_ca_topic_score_codex":6.1036053e-7,"about_ca_topic_score_gemma":0.000003206343,"teacher_disagreement_score":0.38132542,"about_ca_system_score_codex":0.000028481303,"about_ca_system_score_gemma":0.0000019187346,"threshold_uncertainty_score":0.20720066},"labels":[],"label_agreement":null},{"id":"W2095573813","doi":"10.1107/s1600577513026696","title":"Scanning transmission X-ray microscopy probe for<i>in situ</i>mechanism study of graphene-oxide-based resistive random access memory","year":2013,"lang":"en","type":"article","venue":"Journal of Synchrotron Radiation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"National Children's Research Centre; Canadian Light Source; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Electronics and Telecommunications Research Institute; National Research Foundation of Korea; Western Economic Diversification Canada; Center for Advanced Soft Electronics; Ministry of Science, ICT and Future Planning; University of Saskatchewan; National Research Council Canada; National Research Foundation","keywords":"Resistive random-access memory; XANES; Graphene; Transmission electron microscopy; Materials science; Synchrotron; Oxide; In situ; Microscopy; Optoelectronics; Analytical Chemistry (journal); Nanotechnology; Optics; Chemistry; Spectral line; Electrode; Physics","score_opus":0.01243087103103507,"score_gpt":0.26870095579422815,"score_spread":0.2562700847631931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095573813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7386237,0.00032060524,0.25999165,0.000026587499,0.00017644484,0.000826714,9.526701e-7,0.000021164453,0.000012221006],"genre_scores_gemma":[0.99398184,0.000028175375,0.0058029275,0.00002167575,0.00009364353,0.000036658606,0.000001930661,0.000028562126,0.0000045834577],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860835,0.0000913184,0.00072890706,0.0001367008,0.00022406827,0.00021067196],"domain_scores_gemma":[0.99906605,0.00020731999,0.0003973863,0.00010293053,0.000148561,0.00007773047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004372043,0.00016837829,0.0003934089,0.00034581922,0.000075751545,0.00003573298,0.0001877278,0.00006792347,0.000008147307],"category_scores_gemma":[0.00004806985,0.0001524319,0.00011914315,0.0002468984,0.00001426844,0.0006463319,0.000008973249,0.00023281129,4.95768e-7],"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":[0.00027742772,0.000092894246,0.0003303462,0.00012605514,0.00002651082,0.0000039194065,0.00050112925,0.32280725,0.6696738,0.000004865284,0.000024968334,0.006130843],"study_design_scores_gemma":[0.009567786,0.00071274774,0.013584766,0.0004893906,0.00007053839,0.0000053734625,0.00053280225,0.037075676,0.9369905,0.00072729867,0.000016348726,0.0002267437],"about_ca_topic_score_codex":0.000013756537,"about_ca_topic_score_gemma":0.000005815326,"teacher_disagreement_score":0.28573155,"about_ca_system_score_codex":0.000112854665,"about_ca_system_score_gemma":0.000041010062,"threshold_uncertainty_score":0.62159926},"labels":[],"label_agreement":null},{"id":"W2098074240","doi":"10.3389/fncir.2013.00119","title":"Inhibitory synaptic plasticity: spike timing-dependence and putative network function","year":2013,"lang":"en","type":"review","venue":"Frontiers in Neural Circuits","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":158,"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; Université Laval","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; U.S. Public Health Service; National Institute on Deafness and Other Communication Disorders; National Eye Institute; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust; National Institutes of Health; National Science Foundation","keywords":"Neuroscience; Spike-timing-dependent plasticity; Spike (software development); Synaptic plasticity; Inhibitory postsynaptic potential; Neuroplasticity; Nerve net; Plasticity; Psychology; Metaplasticity; Nonsynaptic plasticity; Synaptic scaling; Biology; Computer science; Physics","score_opus":0.03780092389500502,"score_gpt":0.257962548860904,"score_spread":0.22016162496589894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098074240","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003603776,0.96953136,0.02452535,0.0000014464714,0.0043171505,0.00075543,0.000008487922,0.00027351305,0.00022691066],"genre_scores_gemma":[0.009794719,0.9886419,0.00043197538,0.00003330615,0.00073888304,0.00011447644,0.00002580107,0.00013235101,0.00008657373],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99795204,0.00013071219,0.00058598234,0.00054417964,0.00018575674,0.0006013026],"domain_scores_gemma":[0.9992148,0.00027641415,0.00017005332,0.00017204396,0.000025049829,0.00014158917],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012146461,0.0005744892,0.0012823493,0.00023859227,0.00011694752,0.00006026605,0.00020766836,0.00032766705,0.0000070283027],"category_scores_gemma":[0.00007229003,0.0005559158,0.00014109451,0.00041853255,0.00008576281,0.00035508658,0.000089474466,0.0011423447,0.000024453984],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021832093,0.0000047817034,0.00002068851,0.0045162807,0.00007377956,0.000058310652,0.000040133487,0.024543675,0.000004365965,0.0000114540035,0.0011113255,0.969613],"study_design_scores_gemma":[0.0018406609,0.0007754139,0.00031539405,0.048428945,0.0025807854,0.0009834525,0.0003158465,0.31305075,0.00004406423,0.003657677,0.62111604,0.0068909773],"about_ca_topic_score_codex":0.0000011371866,"about_ca_topic_score_gemma":9.207364e-7,"teacher_disagreement_score":0.96272206,"about_ca_system_score_codex":0.0001860238,"about_ca_system_score_gemma":0.000028050641,"threshold_uncertainty_score":0.9996892},"labels":[],"label_agreement":null},{"id":"W2104017197","doi":"10.1109/iscas.2011.5937649","title":"FPGA implementation of a spiking neural network for pattern matching","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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","funders":"","keywords":"Field-programmable gate array; Computer science; Synchronization (alternating current); Block (permutation group theory); Process (computing); Computer hardware; Artificial neural network; Spiking neural network; Embedded system; Task (project management); Computer architecture; Matching (statistics); Artificial intelligence; Engineering; Computer network","score_opus":0.040460636796920794,"score_gpt":0.28180333926672113,"score_spread":0.24134270246980033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104017197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6390062,0.000018038332,0.3602714,0.0000027016808,0.00017160708,0.00009447136,0.000001248134,0.000091552276,0.00034276565],"genre_scores_gemma":[0.99017465,0.0000015768677,0.0096421335,0.000044867596,0.00010567096,0.000006585906,0.0000029822625,0.000014219669,0.000007309325],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959946,0.000004416988,0.00014812557,0.00006405141,0.000031627464,0.00015234383],"domain_scores_gemma":[0.9998586,0.00002974166,0.000027218775,0.000056489953,0.000010146462,0.000017776627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005439663,0.00006109984,0.00007689251,0.000015891057,0.000033815497,0.0000034681912,0.00004721586,0.0000133660715,0.00005109996],"category_scores_gemma":[0.000001134553,0.00005867847,0.000033400323,0.000040904222,0.000003588974,0.000094978546,0.000014317439,0.000038715134,9.036247e-7],"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":[0.000018062308,0.000009328945,0.0058077313,0.00026300445,0.000039630093,0.0000035764667,0.002631741,0.23273824,0.029311355,0.0006842139,0.00025158987,0.7282415],"study_design_scores_gemma":[0.0011227017,0.00021432448,0.022381794,0.00009059656,0.000040197556,0.000014047017,0.0016304691,0.104178816,0.8611138,0.008403223,0.00030843003,0.00050159293],"about_ca_topic_score_codex":0.000019065139,"about_ca_topic_score_gemma":0.000031573596,"teacher_disagreement_score":0.8318024,"about_ca_system_score_codex":0.0000066212183,"about_ca_system_score_gemma":0.0000012850992,"threshold_uncertainty_score":0.23928384},"labels":[],"label_agreement":null},{"id":"W2104917074","doi":"10.1109/isscc.2000.839817","title":"Two phase non-overlapping clock adiabatic differential cascode voltage switch logic (ADCVSL)","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Adiabatic circuit; Adiabatic process; Electronic circuit; Cascode; Electronic engineering; Dissipation; Pass transistor logic; CMOS; Logic gate; Computer science; Logic family; Digital electronics; Electrical engineering; Physics; Topology (electrical circuits); Engineering; Amplifier; Quantum mechanics","score_opus":0.025939718292716397,"score_gpt":0.26145496709757016,"score_spread":0.23551524880485375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104917074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7558905,0.00016976634,0.23344599,0.000015395553,0.0005273655,0.00015620058,0.0000027772062,0.0005017924,0.009290244],"genre_scores_gemma":[0.99719167,0.000036421043,0.001088549,0.00013997145,0.00032913988,0.000008380619,0.0000049446635,0.000040837087,0.0011600761],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988815,0.000011613622,0.00027241834,0.00025196924,0.00013520793,0.00044726083],"domain_scores_gemma":[0.9994776,0.00008357488,0.000043632055,0.0002458855,0.000016834876,0.0001324401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003802144,0.00024737837,0.00025421227,0.000074396754,0.00012533736,0.000038420836,0.00015997818,0.000056307694,0.00090255524],"category_scores_gemma":[0.00002062418,0.00022872588,0.000090069145,0.00017613947,0.000019544606,0.00018790334,0.0000513957,0.0002721909,0.00023725885],"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":[0.000017772369,0.00017309803,0.00015975349,0.00019965565,0.000079796264,0.0001964422,0.00049495033,0.07302554,0.8740165,0.0016430614,0.0014292086,0.048564166],"study_design_scores_gemma":[0.0022230728,0.00009822075,0.00010073851,0.00006149252,0.000031524778,0.000044844888,0.000067554996,0.9067956,0.08888482,0.0005804214,0.00059935113,0.0005123513],"about_ca_topic_score_codex":0.000006020304,"about_ca_topic_score_gemma":0.000009580774,"teacher_disagreement_score":0.83377004,"about_ca_system_score_codex":0.00004952688,"about_ca_system_score_gemma":0.0000024151364,"threshold_uncertainty_score":0.9882347},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41359222,0.00010560216,0.5847376,0.000058350906,0.00097116455,0.00018436532,0.000055048422,0.00025805872,0.000037623802],"genre_scores_gemma":[0.99879193,0.000053099997,0.00037537958,0.00009813767,0.00032054746,0.000058925627,0.000008738731,0.00003950179,0.00025376034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921095,0.000024523846,0.00018406111,0.00021510731,0.000083233885,0.00028209298],"domain_scores_gemma":[0.99955887,0.00024152728,0.00003124666,0.00008693999,0.000027657401,0.000053755146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047972855,0.0001874077,0.00016274827,0.000072648625,0.00024827445,0.00002504798,0.00005421969,0.00008909542,0.000010586872],"category_scores_gemma":[0.0000010588941,0.00019279371,0.00009926082,0.00012215858,0.000025724088,0.00016230662,9.493211e-7,0.00028873983,0.0000013242304],"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":[0.000036945166,0.000025570302,0.000031707248,0.000018606794,0.00002261153,0.000004566864,0.0000204779,0.9776401,0.0015855385,0.0000034380369,0.00023261092,0.020377863],"study_design_scores_gemma":[0.000620279,0.00013214613,0.0015596758,0.000034204437,0.000029557645,0.000026203275,0.000018241582,0.985032,0.012041494,0.000098716366,0.00013070632,0.000276752],"about_ca_topic_score_codex":0.000012194192,"about_ca_topic_score_gemma":0.00007418928,"teacher_disagreement_score":0.5851997,"about_ca_system_score_codex":0.000049093214,"about_ca_system_score_gemma":0.000002631928,"threshold_uncertainty_score":0.78619},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09973122,0.00012251902,0.8979649,0.000019637388,0.0014092661,0.00027674384,0.000008646503,0.0003635933,0.00010350882],"genre_scores_gemma":[0.99761206,0.000015695641,0.0016416545,0.000051733532,0.00047042852,0.000020755233,0.000004399747,0.00006239416,0.00012086295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878293,0.000022984865,0.00037608098,0.00025165404,0.00010616256,0.00046021433],"domain_scores_gemma":[0.9991757,0.0004643395,0.00006376848,0.0001677501,0.000028229839,0.000100165445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000044931425,0.00025610067,0.00028666836,0.00008713132,0.00017973711,0.00002231868,0.00015090464,0.00012395778,0.00002395668],"category_scores_gemma":[0.00000303678,0.00026879695,0.00019141933,0.0002833469,0.000034598317,0.00014067754,0.000001490258,0.00045615618,0.0000017086755],"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":[0.00006764069,0.000024949752,0.0000045626953,0.000026270805,0.000023697323,0.0000046886375,0.0000067969886,0.96779525,0.0037481682,0.00003284589,0.0002435094,0.028021635],"study_design_scores_gemma":[0.00047714636,0.00013670334,0.00010800065,0.00005154152,0.000059980874,0.000017984643,0.000003671084,0.987113,0.011589007,0.00011468268,0.000097870754,0.00023038914],"about_ca_topic_score_codex":0.000012053959,"about_ca_topic_score_gemma":0.000032417807,"teacher_disagreement_score":0.89788085,"about_ca_system_score_codex":0.000034405253,"about_ca_system_score_gemma":0.00000764993,"threshold_uncertainty_score":0.9999764},"labels":[],"label_agreement":null},{"id":"W2109845132","doi":"10.1109/iedm.2012.6479147","title":"Highly endurable floating body cell memory: Vertical biristor","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Pillar; Resistor; Bipolar junction transistor; Silicon; Electrical engineering; Physics; Topology (electrical circuits); Computer science; Optoelectronics; Transistor; Engineering; Voltage; Mechanical engineering","score_opus":0.013174140759424559,"score_gpt":0.21562045104117838,"score_spread":0.20244631028175383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109845132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90716916,0.00035407796,0.012327983,0.00003246869,0.0009473826,0.00007725322,6.2796784e-7,0.00080623524,0.078284815],"genre_scores_gemma":[0.9951887,0.0000052418054,0.0033329308,0.000093458286,0.0003769968,0.0000022733882,0.0000015837072,0.00003135186,0.00096744805],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991889,0.000014330764,0.00015524589,0.000103986655,0.000095901436,0.00044163113],"domain_scores_gemma":[0.9995924,0.00008890187,0.000008431829,0.00014597914,0.000011059583,0.00015322978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000096337564,0.0001279406,0.00012333183,0.00003288089,0.00008723095,0.000012542098,0.000084917265,0.00003982848,0.00015635927],"category_scores_gemma":[0.000025275383,0.00011781765,0.000040445848,0.000071440496,0.000013159259,0.00027715907,0.00003250298,0.00017956455,0.00028599752],"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":[0.000006983342,0.000041805222,0.0005251743,0.00009431491,0.000011160618,0.0000092642795,0.00019631651,0.012900451,0.9787705,0.0013596499,0.0041296836,0.0019547257],"study_design_scores_gemma":[0.00019394114,0.000016774367,0.00024999506,0.000009811324,0.000009326032,0.000010810613,0.00007194175,0.017166166,0.96969604,0.00003148264,0.012325265,0.00021841409],"about_ca_topic_score_codex":0.000002887329,"about_ca_topic_score_gemma":4.2664144e-7,"teacher_disagreement_score":0.08801956,"about_ca_system_score_codex":0.00004669847,"about_ca_system_score_gemma":0.0000038964195,"threshold_uncertainty_score":0.48044646},"labels":[],"label_agreement":null},{"id":"W2111156328","doi":"10.1109/tbcas.2011.2131140","title":"Wireless Neural/EMG Telemetry Systems for Small Freely Moving Animals","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":93,"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":"Telemetry; Chip; CMOS; Electrical engineering; Accelerometer; Electronic circuit; Wireless; Noise (video); Engineering; System on a chip; Acceleration; Computer science; Electronic engineering; Computer hardware; Embedded system; Telecommunications; Artificial intelligence; Physics","score_opus":0.05976938574688331,"score_gpt":0.23900334086178282,"score_spread":0.1792339551148995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111156328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35569414,0.0010867276,0.63905656,0.0000065271906,0.0029233205,0.000526633,0.000079577505,0.00037090556,0.00025559167],"genre_scores_gemma":[0.9992588,0.000086555134,0.00007384212,0.000018922117,0.0002695052,0.00011629032,0.0000031335303,0.000057963818,0.00011498019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983971,0.00005590658,0.00052414235,0.00035670702,0.00020984827,0.00045626584],"domain_scores_gemma":[0.9991355,0.00023608569,0.000067838206,0.00021813037,0.000053008156,0.00028944362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002976913,0.0002739037,0.00041631318,0.0002007661,0.0002566077,0.00006529718,0.00017583763,0.00020037203,0.000010955577],"category_scores_gemma":[0.000007273772,0.0002427292,0.0001045147,0.00025929065,0.00008216307,0.000112622154,0.0000015970058,0.00029671422,0.000007102041],"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":[0.00024017994,0.00077165477,0.00022513403,0.011799078,0.0010645861,0.00036905124,0.0043559694,0.099283695,0.42615566,0.0024320437,0.0008969779,0.45240596],"study_design_scores_gemma":[0.0024567037,0.0011486104,0.0003066883,0.0010438106,0.00018011415,0.00072795065,0.0016343126,0.9577121,0.028071085,0.0000827781,0.0052052387,0.0014305888],"about_ca_topic_score_codex":0.00005227397,"about_ca_topic_score_gemma":0.0000039648776,"teacher_disagreement_score":0.8584284,"about_ca_system_score_codex":0.000047549507,"about_ca_system_score_gemma":0.000013123396,"threshold_uncertainty_score":0.989821},"labels":[],"label_agreement":null},{"id":"W2113505992","doi":"10.1109/iembs.2006.260702","title":"Simulated Mossy Fiber Associated Feedforward Circuit Functioning as a Highpass Filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mossy fiber (hippocampus); Dentate gyrus; Computer science; Neuroscience; High-pass filter; Hippocampus; Feed forward; Filter (signal processing); Low-pass filter; Psychology; Engineering","score_opus":0.011216803206641382,"score_gpt":0.20318633696286814,"score_spread":0.19196953375622677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113505992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89042145,0.000062168445,0.009221245,0.000028537957,0.00038555556,0.00011795712,0.0000029596717,0.0016947882,0.09806535],"genre_scores_gemma":[0.98281246,0.000001254374,0.00014173107,0.000108017164,0.00016300433,0.0000027891642,0.00002912107,0.000041158517,0.01670045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908227,0.000016527163,0.00023229756,0.0001869469,0.00013372739,0.0003482305],"domain_scores_gemma":[0.999627,0.00010594511,0.000032634634,0.00013859449,0.000043123946,0.000052681316],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000068509245,0.00017536698,0.00016852799,0.000058079524,0.00013110373,0.000045829576,0.00007830716,0.000099673554,0.0009710046],"category_scores_gemma":[0.000031064916,0.00017247043,0.000072762574,0.00025713537,0.000012651254,0.00018980908,0.000024684572,0.00020798884,0.0004501111],"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":[0.0000060673483,0.00001699605,0.000254334,0.000012942424,0.000038528808,0.000021194512,0.000027958626,0.97696793,0.016795177,0.00023893591,0.0028045087,0.0028154484],"study_design_scores_gemma":[0.0041335765,0.00025195826,0.026843827,0.0002403246,0.00015170561,0.00008138694,0.00010360972,0.6335581,0.24840178,0.011246415,0.072413616,0.0025736855],"about_ca_topic_score_codex":0.000032311527,"about_ca_topic_score_gemma":0.0000077729455,"teacher_disagreement_score":0.34340978,"about_ca_system_score_codex":0.00009231528,"about_ca_system_score_gemma":0.0000060075618,"threshold_uncertainty_score":0.99994224},"labels":[],"label_agreement":null},{"id":"W2114682135","doi":"10.1149/1.3100263","title":"Printed Organic Memory Devices","year":2009,"lang":"en","type":"article","venue":"ECS Transactions","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stack (abstract data type); Bistability; Layer (electronics); Materials science; Electrode; Non-volatile memory; Electrical conductor; Electronics; Nanotechnology; Deposition (geology); Organic electronics; Active layer; Optoelectronics; Computer science; Electrical engineering; Voltage; Transistor; Composite material; Thin-film transistor; Engineering; Chemistry","score_opus":0.0106713194187744,"score_gpt":0.22195100451440847,"score_spread":0.21127968509563408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114682135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4601536,0.00022067511,0.5297727,0.00017060249,0.00040728837,0.00009480145,0.0000019527668,0.0010913188,0.008087039],"genre_scores_gemma":[0.9985419,0.000020881653,0.0010119901,0.000080188285,0.00005416647,0.0000016491741,9.816604e-7,0.00001167732,0.00027659233],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962175,0.000006125511,0.00009858025,0.00008717753,0.000047587462,0.00013877176],"domain_scores_gemma":[0.9998122,0.000021638725,0.00000790099,0.00010076376,0.000011103085,0.000046405003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000022814165,0.00008097439,0.00007285661,0.000038510043,0.00009562591,0.000010312047,0.000062859915,0.000031090458,0.00022313815],"category_scores_gemma":[0.000002199993,0.00008425382,0.00003761012,0.00016266432,0.0000073285173,0.00013077864,6.848508e-7,0.00016361124,0.000060292063],"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":[0.000007470656,0.000048354486,0.000008845217,0.000034502653,0.000032977012,0.0000148016725,0.00046478125,0.3776579,0.5250516,0.00006640885,0.00009086426,0.09652154],"study_design_scores_gemma":[0.0004525469,0.00007364001,0.0039461427,0.00004937168,0.000043451313,0.00007065319,0.00017157225,0.021966482,0.96125424,0.00052570493,0.01102463,0.00042157987],"about_ca_topic_score_codex":2.9257058e-7,"about_ca_topic_score_gemma":0.0000069651132,"teacher_disagreement_score":0.53838825,"about_ca_system_score_codex":0.000021107127,"about_ca_system_score_gemma":0.000004587148,"threshold_uncertainty_score":0.3435771},"labels":[],"label_agreement":null},{"id":"W2117017943","doi":"10.1145/2770287.2770295","title":"STT-MRAM based low power synchronous non-volatile logic with timing demultiplexing","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Logic gate; Pass transistor logic; Logic family; AND-OR-Invert; Electronic engineering; Logic level; Magnetoresistive random-access memory; Very-large-scale integration; Logic synthesis; Embedded system; Electrical engineering; Computer hardware; Digital electronics; Engineering; Electronic circuit","score_opus":0.008807005630045125,"score_gpt":0.2091282902334884,"score_spread":0.20032128460344326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117017943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3925457,0.000016039434,0.59717155,0.000013622405,0.00011779907,0.00010274325,4.8126105e-7,0.0004936601,0.009538404],"genre_scores_gemma":[0.96339697,5.867467e-7,0.036147248,0.00022298915,0.00007822739,0.000005874609,0.0000032283126,0.000043372132,0.000101495316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915504,0.0000151084,0.00016338225,0.00021226746,0.000109119894,0.0003450876],"domain_scores_gemma":[0.99946225,0.00017640417,0.000029183562,0.00021872122,0.000024588615,0.00008885866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009384881,0.00019613012,0.00018533425,0.000054102202,0.00011495518,0.00002974956,0.00011678665,0.000043826854,0.00013435361],"category_scores_gemma":[0.000026470809,0.00015803718,0.000039156475,0.00012420645,0.000023208044,0.0001521401,0.000023696388,0.00019258566,0.000054271255],"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":[0.000015206797,0.000014136359,0.00041401654,0.000065556225,0.000010872647,0.000011995823,0.000068520014,0.9665629,0.027298274,0.00008338216,0.000058474114,0.0053966204],"study_design_scores_gemma":[0.00045909657,0.00009923711,0.0005642344,0.00009377639,0.0000068374097,0.000008986034,0.000027049131,0.9405421,0.05747185,0.00005927468,0.00037884404,0.000288691],"about_ca_topic_score_codex":0.0000026560845,"about_ca_topic_score_gemma":0.000006833746,"teacher_disagreement_score":0.57085127,"about_ca_system_score_codex":0.000041465526,"about_ca_system_score_gemma":0.000009832969,"threshold_uncertainty_score":0.6444569},"labels":[],"label_agreement":null},{"id":"W2117263073","doi":"10.1109/ramech.2006.252641","title":"Use-dependent Synaptic Connection Modification in SNN Generating Autonomous Behavior in a Khepera Mobile Robot","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Spiking neural network; Mobile robot; Robot; Computer science; Task (project management); Genetic algorithm; Obstacle avoidance; Autonomous robot; Artificial neural network; Artificial intelligence; Behavior-based robotics; Engineering; Machine learning","score_opus":0.026846234266756207,"score_gpt":0.2515177443355762,"score_spread":0.22467151006882002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117263073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731129,0.00008214661,0.025884213,0.0000041672,0.00015522794,0.0003018364,7.5093396e-7,0.00031010882,0.00014865289],"genre_scores_gemma":[0.9968614,0.0000057491725,0.0027974127,0.000011721988,0.00006092877,0.00013585725,0.000009702043,0.000023783674,0.00009346723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992145,0.000023562041,0.00029827756,0.00019097276,0.000066000015,0.00020674043],"domain_scores_gemma":[0.99976987,0.00005245898,0.000025228612,0.0001180069,0.00001295454,0.000021484284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008826522,0.000116956486,0.00012356187,0.00012622606,0.00004014859,0.000034861412,0.000051197007,0.0000613204,0.000016758655],"category_scores_gemma":[0.000010549263,0.0001279422,0.00002270959,0.00015805171,0.00000688913,0.000263527,0.000014269564,0.00017281166,0.000011102329],"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":[0.0000018840963,0.00002664191,0.0016594974,0.000006964869,9.1048565e-7,0.000011888042,0.000041409712,0.72981745,0.26529557,0.00008053095,0.0000014895263,0.0030557665],"study_design_scores_gemma":[0.00026451633,0.00002979909,0.010683522,0.000016524433,0.0000044680564,0.000018972793,0.000083187224,0.89278716,0.09587657,0.000034038694,0.000012673748,0.00018860055],"about_ca_topic_score_codex":0.0002821353,"about_ca_topic_score_gemma":0.00062393286,"teacher_disagreement_score":0.169419,"about_ca_system_score_codex":0.00019582741,"about_ca_system_score_gemma":0.0000063009816,"threshold_uncertainty_score":0.5217331},"labels":[],"label_agreement":null},{"id":"W2117373069","doi":"10.1162/jocn_a_00466","title":"A Trade-off between Local and Distributed Information Processing Associated with Remote Episodic versus Semantic Memory","year":2013,"lang":"en","type":"article","venue":"Journal of Cognitive Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Baycrest Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canadian Institutes of Health Research","keywords":"Psychology; Semantic memory; Episodic memory; Cognitive psychology; Autobiographical memory; Information processing; Cognition; Neuroscience; Recall","score_opus":0.018098856779809602,"score_gpt":0.2431778868396818,"score_spread":0.2250790300598722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117373069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79842925,0.000056730103,0.2011326,0.000062992105,0.00012190564,0.000094587165,0.0000049415794,0.00003679818,0.000060191396],"genre_scores_gemma":[0.99976414,0.0000208916,0.00008295853,0.00008576009,0.000033884367,5.34082e-7,0.0000022512052,0.000007825461,0.000001726052],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99912685,0.0000363992,0.00028391503,0.000082067425,0.0002613307,0.0002094659],"domain_scores_gemma":[0.9991887,0.00030265911,0.00021550119,0.000033035678,0.00014389996,0.00011618257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015302256,0.00011839951,0.00017953625,0.000095455514,0.00013580706,0.000091360474,0.00009468887,0.000033737782,0.0000013692455],"category_scores_gemma":[0.0005535173,0.000094249466,0.000024680035,0.0003560453,0.00016516083,0.0017147592,0.000019687981,0.00032133123,0.0000013751173],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000173919,0.00003108617,0.0018328475,0.000121799276,0.000028381557,0.00008960403,0.0010492479,0.027719771,0.015764197,0.0000026510581,0.00004116833,0.9531453],"study_design_scores_gemma":[0.005642716,0.0020796326,0.53675926,0.002162244,0.0002066041,0.00049277995,0.0016351113,0.41148254,0.038573004,0.00018620868,0.000095803625,0.0006840808],"about_ca_topic_score_codex":5.339805e-7,"about_ca_topic_score_gemma":3.8640357e-7,"teacher_disagreement_score":0.95246124,"about_ca_system_score_codex":0.000035842182,"about_ca_system_score_gemma":0.000031942564,"threshold_uncertainty_score":0.38433817},"labels":[],"label_agreement":null},{"id":"W2119145853","doi":"10.1186/1471-2202-8-s2-p93","title":"An efficient Ca2+ based plasticity rule with combined Ca2+sources","year":2007,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Plasticity; Neuroscience; Artificial intelligence; Biology; Physics","score_opus":0.0185841988535622,"score_gpt":0.24176247612478544,"score_spread":0.22317827727122325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119145853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6814688,0.000005226285,0.317744,0.0000027045512,0.00023722794,0.00008356719,0.0000020010375,0.00030241953,0.00015408851],"genre_scores_gemma":[0.99669397,2.7997424e-7,0.0030704741,0.00015001392,0.000043067932,0.000002291189,6.7513383e-7,0.000019499368,0.0000197211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988427,0.000022841381,0.00014995917,0.00031303114,0.0002525366,0.00041893465],"domain_scores_gemma":[0.9993685,0.0002052948,0.000033675915,0.00019455682,0.000022918131,0.00017504829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018319425,0.00015379349,0.00011663462,0.00007608075,0.00022084852,0.000044597193,0.0002652728,0.000025347646,0.0000038633634],"category_scores_gemma":[0.00007347208,0.00013042995,0.000022534718,0.00041299098,0.00012530595,0.00012038296,0.000023758515,0.00015610078,0.0000052549276],"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":[0.000031078172,0.000030073477,0.0017871757,0.000016349873,1.712728e-7,0.000023603076,0.000031750278,0.76331896,0.23455833,0.00003536907,0.000002124193,0.0001649991],"study_design_scores_gemma":[0.00030585116,0.00020349868,0.03811356,0.000018330662,0.0000033343422,0.000014696615,0.000022797743,0.61279255,0.3482339,0.000005583963,0.00010082836,0.00018502871],"about_ca_topic_score_codex":0.00000267276,"about_ca_topic_score_gemma":0.000016905251,"teacher_disagreement_score":0.31522518,"about_ca_system_score_codex":0.000026333511,"about_ca_system_score_gemma":0.000023726776,"threshold_uncertainty_score":0.5318779},"labels":[],"label_agreement":null},{"id":"W2123536347","doi":"10.1017/cbo9781139034807.021","title":"Topological codes","year":2013,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Locality; Qubit; Code (set theory); Toric code; Constraint (computer-aided design); Computer science; Theoretical computer science; Lattice (music); Topology (electrical circuits); Quantum computer; Mathematics; Physics; Quantum; Set (abstract data type); Combinatorics; Quantum mechanics; Programming language","score_opus":0.026484621222958315,"score_gpt":0.19229165051037928,"score_spread":0.16580702928742097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123536347","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013559832,0.00014525854,0.0015913144,0.0000027324395,0.00028474123,0.00019066568,0.00004351246,0.0005931275,0.9957927],"genre_scores_gemma":[0.008196909,0.00010186607,0.00024455352,0.000029698518,0.00015461002,3.2059904e-7,0.000015034543,0.00004158642,0.9912154],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992853,0.000010098633,0.00011669942,0.00025398048,0.0001004369,0.00023349779],"domain_scores_gemma":[0.99947387,0.00006045867,0.000045694887,0.0002617495,0.000039764163,0.00011847414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000022973376,0.00028594988,0.00029531927,0.0000643935,0.00010467162,0.000018968743,0.00026933046,0.00027819182,0.00002081412],"category_scores_gemma":[0.000003378829,0.0003186992,0.00012399457,0.000003439432,0.00011793618,0.000064777116,0.00016201488,0.0004903339,0.00006246884],"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":[0.0000132424875,0.0000019516804,2.347862e-7,0.000089416935,0.00006797908,0.00028271915,0.000011962271,0.0005083499,0.00072649185,0.9751557,0.019898849,0.0032431167],"study_design_scores_gemma":[0.00018600833,0.000027975651,0.0000036294132,0.00008432591,0.00005332782,0.000021682612,0.0000091080565,0.0008563654,0.0028923042,0.000041478634,0.9953877,0.00043614148],"about_ca_topic_score_codex":0.0000048245442,"about_ca_topic_score_gemma":1.9107253e-7,"teacher_disagreement_score":0.9754888,"about_ca_system_score_codex":0.00009157435,"about_ca_system_score_gemma":0.000008843643,"threshold_uncertainty_score":0.9999265},"labels":[],"label_agreement":null},{"id":"W2123624060","doi":"10.1021/am4032828","title":"Redox-Gated Three-Terminal Organic Memory Devices: Effect of Composition and Environment on Performance","year":2013,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology; Xerox (Canada); University of Alberta","funders":"National Institutes of Natural Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Redox; Materials science; Electrolyte; Polymer; Non-volatile memory; Polaron; Electrode; Ion; Acetonitrile; Nanotechnology; Chemical physics; Optoelectronics; Electron; Chemistry; Organic chemistry","score_opus":0.005932802269420179,"score_gpt":0.19353679779298605,"score_spread":0.18760399552356588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123624060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99895674,0.000073997384,0.000063957734,0.000007460826,0.00015187587,0.000383932,0.0000047678295,0.0001116781,0.0002455793],"genre_scores_gemma":[0.9996863,0.000058951737,0.0001137187,0.000018541503,0.000046872457,0.00003051139,0.000009492315,0.000027612381,0.000008026647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999279,0.000020449415,0.00025504484,0.00018064378,0.00008984442,0.00017502504],"domain_scores_gemma":[0.99966526,0.00006959146,0.00007660821,0.00015148646,0.000006628273,0.000030427293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010291689,0.00019837543,0.0002742672,0.000037927643,0.000055254404,0.00003362458,0.00010881895,0.000059261678,0.00020012872],"category_scores_gemma":[0.0000016688662,0.00016373699,0.0000066115354,0.000030497651,0.000048962323,0.0001230765,0.000066996145,0.000081034064,0.00010975958],"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":[0.000078855235,0.000008063817,0.000016837294,0.000329803,0.000018686022,9.3772803e-7,0.00007457021,0.0028486326,0.9893362,0.000007761028,0.000009829668,0.007269856],"study_design_scores_gemma":[0.00030639983,0.00023292548,0.0006115074,0.00012631716,0.000019876808,0.000009754334,0.000015831623,0.00014761604,0.99833935,0.00002891934,0.0000063561633,0.00015516549],"about_ca_topic_score_codex":0.0000026559646,"about_ca_topic_score_gemma":3.7336412e-7,"teacher_disagreement_score":0.009003163,"about_ca_system_score_codex":0.000024532465,"about_ca_system_score_gemma":0.0000013043643,"threshold_uncertainty_score":0.66770005},"labels":[],"label_agreement":null},{"id":"W2125269450","doi":"10.1557/opl.2011.857","title":"Influence of Copper on the Switching Properties of Hafnium Oxide-Based Resistive Memory","year":2011,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Research Laboratory; University at Albany; Ryerson University","keywords":"Materials science; Hafnium; Copper; X-ray photoelectron spectroscopy; Electrode; Atomic layer deposition; Oxide; Layer (electronics); Optoelectronics; Resistive random-access memory; Resistive touchscreen; Analytical Chemistry (journal); Nanotechnology; Chemical engineering; Metallurgy; Electrical engineering; Physical chemistry","score_opus":0.03258787347686669,"score_gpt":0.20082929012777567,"score_spread":0.16824141665090897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125269450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961967,0.00006744076,0.0000635798,0.000028123655,0.000031746265,0.00016233779,6.689113e-7,0.00010287549,0.003346521],"genre_scores_gemma":[0.99953234,0.0000034097216,0.0002955471,0.00008677294,0.000017045939,0.00001157402,8.247981e-8,0.000021171467,0.00003207415],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993296,0.000004046523,0.00022824333,0.00013560549,0.00013909595,0.00016340455],"domain_scores_gemma":[0.99963015,0.000037280886,0.00009003775,0.00009991953,0.0001145788,0.000028044607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017935176,0.00012742025,0.00016302403,0.000058089816,0.00006550504,0.0000056851627,0.00022058039,0.000039331484,0.0000034891168],"category_scores_gemma":[0.00012948405,0.000086133114,0.000041975152,0.00015163125,0.00007109793,0.00014121008,0.00003616176,0.00018555371,0.0000036220129],"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":[0.000093686496,0.000015779646,0.00066088996,0.00028933995,0.000013256193,7.2991656e-7,0.0022603415,0.013048021,0.9826935,0.0007138587,0.000049353912,0.0001612399],"study_design_scores_gemma":[0.000113142996,0.00006531102,0.0028468205,0.00043990283,0.0000098118835,0.0000011185658,0.0003894653,0.0019730183,0.9938206,0.00022114896,0.000016447764,0.00010319682],"about_ca_topic_score_codex":0.000010009512,"about_ca_topic_score_gemma":4.924073e-7,"teacher_disagreement_score":0.011127112,"about_ca_system_score_codex":0.000019765623,"about_ca_system_score_gemma":0.000009510184,"threshold_uncertainty_score":0.35124063},"labels":[],"label_agreement":null},{"id":"W2127570561","doi":"10.1088/0957-4484/21/41/412001","title":"Nanoscale memory devices","year":2010,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Resistive random-access memory; Materials science; Memristor; Flash memory; Nanotechnology; Phase-change memory; Random access memory; Magnetoresistive random-access memory; Non-volatile memory; Nanoscopic scale; Random access; Computer memory; Nanomaterials; Semiconductor memory; Optoelectronics; Computer science; Electrical engineering; Embedded system; Computer hardware; Engineering","score_opus":0.006464967033070447,"score_gpt":0.2160221102540617,"score_spread":0.20955714322099125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127570561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99314284,0.000203188,0.0022135826,0.00015512937,0.0010475927,0.0000567915,8.6357454e-7,0.0015949648,0.0015850274],"genre_scores_gemma":[0.9970432,0.000009453041,0.0026074029,0.00006315869,0.00008568,0.000006399659,8.9531903e-7,0.00001941135,0.00016439753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994931,0.00000374081,0.000106802356,0.00013039346,0.000044329314,0.00022163738],"domain_scores_gemma":[0.9996811,0.000034926677,0.000015044096,0.00022798264,0.000012042543,0.00002889421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004145304,0.00009609022,0.00010824631,0.000079000776,0.000060290902,0.000005482226,0.00019254195,0.00023179816,0.000057157617],"category_scores_gemma":[0.00003319033,0.00009456462,0.000026237069,0.00015057878,0.00006350152,0.0000660502,0.000042193813,0.0004978989,0.00011081185],"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":[0.0000012094454,0.0000041830367,0.00006554746,0.000011604607,0.0000038416692,0.000011068637,0.000014629528,0.0005495988,0.9297209,0.0014442462,0.00011353067,0.06805967],"study_design_scores_gemma":[0.00012206418,0.000017680293,0.00008884287,0.0000046946266,0.0000032230068,0.00005982696,0.00001790236,0.0015032837,0.94963217,0.0013143499,0.047100358,0.00013560378],"about_ca_topic_score_codex":7.952924e-7,"about_ca_topic_score_gemma":0.000032421314,"teacher_disagreement_score":0.06792407,"about_ca_system_score_codex":0.0000073197402,"about_ca_system_score_gemma":0.0000041636745,"threshold_uncertainty_score":0.3856233},"labels":[],"label_agreement":null},{"id":"W2128165074","doi":"10.7554/elife.00491","title":"How to train a neuron","year":2013,"lang":"en","type":"article","venue":"eLife","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Neuron; Neuroscience; Biology; Computer science","score_opus":0.014152631810941665,"score_gpt":0.20637194408341367,"score_spread":0.192219312272472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128165074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9840756,0.000046540492,0.013234598,0.00061365607,0.00023842204,0.00010576595,4.87688e-7,0.00030758284,0.0013773234],"genre_scores_gemma":[0.99726707,0.000002603793,0.0014092054,0.00053054467,0.00016261137,0.000009052453,4.038929e-7,0.000013768829,0.0006047504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969655,0.0000047246426,0.00004415924,0.00006900256,0.00005219438,0.00013338194],"domain_scores_gemma":[0.9998201,0.00001772772,0.000004006997,0.000080261714,0.000008774646,0.00006910071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001686063,0.000055693803,0.00005219728,0.000019342877,0.000021691323,0.000026023892,0.000055059638,0.000013584457,0.000021664828],"category_scores_gemma":[0.000025798483,0.00005310159,0.000015765943,0.000063068976,0.0000029830812,0.00009565827,0.00001301902,0.00006639904,0.000192167],"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":[0.0000023879002,0.0000076076526,0.00006953155,0.00003373409,0.00000718341,0.000013714868,0.00045047593,0.056698848,0.77584255,0.00017654388,0.023497427,0.14320002],"study_design_scores_gemma":[0.0004591339,0.00015834134,0.013718695,0.000049877373,0.000006274994,0.000025640533,0.00021260124,0.052119907,0.65631515,0.00033612287,0.27590543,0.00069285114],"about_ca_topic_score_codex":6.3735627e-7,"about_ca_topic_score_gemma":6.259898e-7,"teacher_disagreement_score":0.252408,"about_ca_system_score_codex":0.0000069644434,"about_ca_system_score_gemma":0.0000010835921,"threshold_uncertainty_score":0.2469982},"labels":[],"label_agreement":null},{"id":"W2128587608","doi":"10.1109/isscc.2003.1234309","title":"A current-saving match-line sensing scheme for content-addressable memories","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":46,"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":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Scheme (mathematics); Line (geometry); Content-addressable storage; Power consumption; Computer science; Power (physics); CMOS; Content-addressable memory; Computer hardware; Electronic engineering; Electrical engineering; Engineering; Artificial intelligence; Artificial neural network","score_opus":0.08155183605874393,"score_gpt":0.285336116808571,"score_spread":0.20378428074982707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128587608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48985788,0.0013156855,0.50266135,0.000030226527,0.0013103542,0.00032540318,0.0000068513214,0.0007873007,0.0037049337],"genre_scores_gemma":[0.9432913,0.00003679394,0.055792734,0.000060180264,0.00017625617,0.0000059494682,0.00000680554,0.000048179456,0.000581802],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99918246,0.000011849825,0.00021313212,0.00016991161,0.00007630559,0.00034634795],"domain_scores_gemma":[0.99958307,0.00012445416,0.000026542388,0.00014197337,0.000057981408,0.00006599568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012384473,0.00015962815,0.00018567475,0.000039580034,0.00013465616,0.00003445137,0.000057732133,0.000035986825,0.00003388055],"category_scores_gemma":[0.00013174837,0.00015180517,0.00006376483,0.00011165536,0.000018827737,0.00017517104,0.0000166771,0.00013762616,0.000016773965],"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":[0.000048413494,0.000050669663,0.00043595707,0.0010057783,0.0000927664,0.000018290692,0.00033935378,0.06813859,0.84966964,0.01634023,0.0025127784,0.061347518],"study_design_scores_gemma":[0.0008427321,0.000032740303,0.000019042875,0.00015235203,0.000017917155,0.000031300566,0.00034577187,0.08615535,0.8686359,0.0018049906,0.041527856,0.00043402714],"about_ca_topic_score_codex":7.280235e-7,"about_ca_topic_score_gemma":0.000003889721,"teacher_disagreement_score":0.45343342,"about_ca_system_score_codex":0.000029073271,"about_ca_system_score_gemma":0.000007963457,"threshold_uncertainty_score":0.6190435},"labels":[],"label_agreement":null},{"id":"W2128826368","doi":"10.1109/glsv.1996.497624","title":"Design and VLSI implementation of a unified synapse-neuron architecture","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Very-large-scale integration; Computer science; Synapse; Computer architecture; Architecture; Embedded system; Neuroscience; Psychology","score_opus":0.025156261070368276,"score_gpt":0.2426593745033115,"score_spread":0.2175031134329432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128826368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7412289,0.00009773788,0.2578924,0.0000274104,0.000028624445,0.0000954324,4.3335362e-7,0.000103400045,0.0005256842],"genre_scores_gemma":[0.9939116,0.000025612957,0.0059768604,0.000025028528,0.000011071862,0.0000013905341,4.6962134e-7,0.000007418384,0.000040549312],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997322,0.000013860917,0.00008143902,0.000059615715,0.000033061016,0.00007980343],"domain_scores_gemma":[0.9998636,0.00005124293,0.000011267679,0.000049158516,0.0000049236533,0.00001980853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000273914,0.00005229439,0.000057026937,0.000027964177,0.000019234561,0.0000035121957,0.000024898161,0.0000132916575,0.000072865696],"category_scores_gemma":[0.0000030246306,0.000046987057,0.0000085382535,0.000053040178,0.000008023135,0.000037288348,0.000008674193,0.00005223968,0.0000015680771],"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":[0.0000060720113,0.0000062851345,0.000047625697,0.00009441531,0.000013371974,0.0000060824823,0.0007519615,0.29329875,0.57184786,0.00025532214,0.00019258802,0.13347968],"study_design_scores_gemma":[0.00044150068,0.00013291539,0.00079774624,0.000012343101,0.00001014074,0.00002596819,0.00015476227,0.081498705,0.9160395,0.0004704626,0.00026567065,0.00015028406],"about_ca_topic_score_codex":0.0000010980455,"about_ca_topic_score_gemma":0.0000012707245,"teacher_disagreement_score":0.34419167,"about_ca_system_score_codex":0.000003686513,"about_ca_system_score_gemma":5.5930184e-7,"threshold_uncertainty_score":0.19160767},"labels":[],"label_agreement":null},{"id":"W2130017144","doi":"10.1145/2770287.2770299","title":"HSPICE macromodel of a programmable metallization cell (PMC) and its application to memory design","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","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":"University of Alberta","funders":"","keywords":"Crossbar switch; Computer science; Resistive touchscreen; Electronic engineering; Resistive random-access memory; CMOS; Spice; Simple (philosophy); Electrical engineering; Voltage; Engineering","score_opus":0.014576889630644558,"score_gpt":0.21817352477389637,"score_spread":0.20359663514325183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130017144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19864058,0.00007898155,0.7995893,0.000009206337,0.000017446988,0.00026511025,2.0450733e-7,0.0001271141,0.0012720911],"genre_scores_gemma":[0.95729274,0.000009887089,0.042425543,0.000020542713,0.000017967848,0.000022840777,0.0000012008686,0.000013128245,0.00019615232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996058,0.00001418232,0.00011437125,0.00011008548,0.00005298945,0.00010257892],"domain_scores_gemma":[0.9997836,0.00003653014,0.00001993271,0.00008530571,0.000029367598,0.000045306464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011323467,0.000069559064,0.00008941232,0.000031879263,0.000025836547,0.000006444394,0.000049038717,0.000024717421,0.0000035747787],"category_scores_gemma":[0.0000109297625,0.000067155925,0.000011116448,0.000116277646,0.000003900334,0.00008766414,0.00001730989,0.000034638088,0.000008674171],"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":[0.0000042233787,0.000006189596,0.0000038005367,0.00007743495,0.0000022047739,6.676672e-8,0.00006214181,0.49093974,0.49039632,0.00028036357,0.0000073457263,0.01822017],"study_design_scores_gemma":[0.000073684234,0.000027995125,0.0000139160165,0.0000056404533,0.0000051250454,7.443957e-7,0.000007309849,0.42270902,0.5767544,0.00018015619,0.00016896798,0.000053068725],"about_ca_topic_score_codex":0.0000011685702,"about_ca_topic_score_gemma":7.836146e-7,"teacher_disagreement_score":0.75865215,"about_ca_system_score_codex":0.0000071969434,"about_ca_system_score_gemma":0.0000020022028,"threshold_uncertainty_score":0.27385393},"labels":[],"label_agreement":null},{"id":"W2130904159","doi":"10.1021/ja304458s","title":"Spatially Resolved Raman Spectroelectrochemistry of Solid-State Polythiophene/Viologen Memory Devices","year":2012,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology; Xerox (Canada); University of Alberta","funders":"National Institutes of Natural Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Polythiophene; Raman spectroscopy; Polaron; Chemistry; Viologen; Conductivity; Molecular switch; Redox; Conductive polymer; Electrode; Photochemistry; Analytical Chemistry (journal); Inorganic chemistry; Electron; Molecule; Physical chemistry; Organic chemistry; Optics","score_opus":0.00885402111746708,"score_gpt":0.24421432753104774,"score_spread":0.23536030641358066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130904159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99748933,0.0005919125,0.0014695028,0.00011579424,0.00014851286,0.000039386516,0.0000031544953,0.000030088415,0.00011229259],"genre_scores_gemma":[0.9958329,0.000102006445,0.0032187216,0.00025187412,0.00055350043,6.1507313e-7,5.0116273e-7,0.000022802487,0.000017088218],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887276,0.00003018536,0.0004244901,0.00008026696,0.00021844546,0.00037386175],"domain_scores_gemma":[0.9990139,0.00013211163,0.0004899578,0.0001807092,0.000059872884,0.0001234653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023022493,0.00015764116,0.0003868496,0.0000107291835,0.000050722083,0.000005548713,0.00039517463,0.000042851072,0.000008399749],"category_scores_gemma":[0.00006476409,0.00011187215,0.00042499678,0.00020855438,0.0002001958,0.00010078063,0.00008575131,0.0005417729,8.303466e-7],"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":[0.000035114783,0.000024183082,0.0008312236,0.00003676308,0.000109669796,8.476375e-7,0.00029217312,0.0019011438,0.9948481,0.0000010136272,0.00037987938,0.0015399427],"study_design_scores_gemma":[0.00018130113,0.00003834845,0.0010685137,0.00003830078,0.000042521475,0.00006459244,0.00015440158,0.00041815563,0.9976209,0.00009021033,0.00016058057,0.00012219614],"about_ca_topic_score_codex":0.000002373551,"about_ca_topic_score_gemma":2.2554983e-7,"teacher_disagreement_score":0.0027728323,"about_ca_system_score_codex":0.00011408803,"about_ca_system_score_gemma":0.000029234137,"threshold_uncertainty_score":0.45620137},"labels":[],"label_agreement":null},{"id":"W2131468059","doi":"10.1063/1.4931663","title":"Memory operation devices based on light-illumination ambipolar carbon-nanotube thin-film-transistors","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université du Québec en Abitibi-Témiscamingue; Raymor (Canada); Université TÉLUQ; Institut National de la Recherche Scientifique","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Qatar Foundation","keywords":"Ambipolar diffusion; Materials science; Optoelectronics; Transistor; Carbon nanotube; Thin-film transistor; Trapping; Nanotechnology; Thin film; Non-volatile memory; Nanotube; Field-effect transistor; Electron; Electrical engineering; Voltage; Physics","score_opus":0.015337043739444344,"score_gpt":0.2223207285699242,"score_spread":0.20698368483047985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131468059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97062206,0.000114636816,0.016953578,0.00005216925,0.00082145236,0.00013497856,0.0000014446858,0.000093252194,0.011206437],"genre_scores_gemma":[0.9980302,0.0000047560425,0.0011396238,0.00015774296,0.00062330125,0.0000018595798,0.0000031933012,0.0000304295,0.000008904689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911326,0.000018791408,0.00029806787,0.00009717267,0.0003259282,0.00014676602],"domain_scores_gemma":[0.99949265,0.00004919304,0.00013555263,0.00011934122,0.000099760975,0.000103518025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022295435,0.00015594627,0.00021396787,0.00006723277,0.000057384554,0.00002698538,0.00012587124,0.000057346748,0.0000024002704],"category_scores_gemma":[0.00000875654,0.0001413523,0.00006704866,0.00015023453,0.000013867056,0.00019615726,0.000007136264,0.00031221888,0.0000062355903],"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":[0.00005375343,0.000030063959,0.000006112864,0.000029585492,0.000014873061,0.000005542581,0.00063539995,0.9347212,0.060967773,0.00011140951,0.00011054961,0.0033137768],"study_design_scores_gemma":[0.0006432301,0.000117332536,0.000052819134,0.0000481527,0.000034939123,0.000003976522,0.00022510097,0.4387836,0.5583472,0.00042842422,0.0011382005,0.00017704624],"about_ca_topic_score_codex":7.97435e-7,"about_ca_topic_score_gemma":0.0000014252125,"teacher_disagreement_score":0.49737942,"about_ca_system_score_codex":0.00012774361,"about_ca_system_score_gemma":0.00004575134,"threshold_uncertainty_score":0.576418},"labels":[],"label_agreement":null},{"id":"W2131700130","doi":"10.1109/sipnn.1994.344786","title":"Comparison of three different architectures for MOS-compatible quadratic synapses","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Software; Quadratic equation; Electronics; Computer architecture; Artificial intelligence; Computer engineering; Computer hardware; Electrical engineering; Mathematics; Engineering; Programming language","score_opus":0.06569824609943513,"score_gpt":0.2949000399528547,"score_spread":0.22920179385341954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131700130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87722737,0.0003626807,0.121011436,0.000015662445,0.000109444714,0.00016488525,0.0000024347082,0.00017501549,0.00093108934],"genre_scores_gemma":[0.9970518,0.0000022479635,0.0028218722,0.00001298763,0.000041452196,0.000011021606,0.0000014347061,0.000015279094,0.000041957814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945563,0.0000059882746,0.00021278416,0.00009410155,0.000067529814,0.00016398996],"domain_scores_gemma":[0.9995966,0.00020484839,0.000029094299,0.00012358252,0.0000124478,0.000033411507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000017452003,0.0001143551,0.00023914431,0.000041176943,0.000044254146,0.0000063264033,0.000092310846,0.000025472691,0.000077955796],"category_scores_gemma":[0.000017444847,0.000088985966,0.000057661324,0.000055237764,0.00001850446,0.000015694563,0.0000145692,0.00007978464,0.0000038524995],"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":[0.000014564172,0.00009930704,0.00808255,0.00043938778,0.000049503455,6.7617736e-7,0.00049633946,0.8816281,0.082278565,0.0012702517,0.00055301125,0.025087748],"study_design_scores_gemma":[0.00018608679,0.00009846896,0.0013602149,0.000027954842,0.0000099300305,0.0000017904284,0.000031716612,0.6825781,0.3144081,0.001106498,0.00007881093,0.000112331494],"about_ca_topic_score_codex":0.0000016806115,"about_ca_topic_score_gemma":0.00003418608,"teacher_disagreement_score":0.23212954,"about_ca_system_score_codex":0.000012815538,"about_ca_system_score_gemma":6.249238e-7,"threshold_uncertainty_score":0.36287424},"labels":[],"label_agreement":null},{"id":"W2132829787","doi":"10.1109/ahs.2013.6604238","title":"Hardware realization of GALS based cortical column systems","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Asynchronous communication; Realization (probability); Pooling; Software; Von Neumann architecture; Scheme (mathematics); Computer architecture; Artificial intelligence; Embedded system; Computer hardware","score_opus":0.013975512351436322,"score_gpt":0.21366576037561275,"score_spread":0.19969024802417643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132829787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43441412,0.00006722615,0.5572428,0.000009970237,0.00037947838,0.00025809812,0.0000023385135,0.00035837147,0.007267574],"genre_scores_gemma":[0.99921703,0.0000021438361,0.0004988319,0.000020356876,0.000041341562,0.000008059609,0.000004686077,0.000009818853,0.00019770763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962443,0.000013986744,0.0001504809,0.00005708307,0.00006259166,0.00009144668],"domain_scores_gemma":[0.99975955,0.000059609054,0.00001583155,0.00008697426,0.000043081567,0.000034943896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034499062,0.00004924247,0.00009277109,0.000019736292,0.000019855828,0.00001010954,0.000037476522,0.000027213775,0.00012086334],"category_scores_gemma":[0.000026309983,0.00004533932,0.000016885984,0.00007067103,0.000009066909,0.000074739684,0.0000060338434,0.000042816024,0.000026119069],"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":[0.0000032250207,0.000013298939,0.0009971848,0.00032851732,0.000009845422,0.0000020669609,0.000031316806,0.8185244,0.17131299,0.0032686933,0.004524812,0.0009836241],"study_design_scores_gemma":[0.00016251109,0.000027647115,0.0021700682,0.00006163197,0.00000507522,0.0000022179674,0.00004857468,0.9045294,0.092216484,0.00005135912,0.00062316365,0.000101896265],"about_ca_topic_score_codex":0.0000137688685,"about_ca_topic_score_gemma":0.0000011509135,"teacher_disagreement_score":0.56480294,"about_ca_system_score_codex":0.000011137691,"about_ca_system_score_gemma":0.0000033435401,"threshold_uncertainty_score":0.18488838},"labels":[],"label_agreement":null},{"id":"W2138584260","doi":"10.1109/irds.2002.1041386","title":"An architecture for a VLSI sensory-motor system for autonomous robots","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pixel; Computer science; Very-large-scale integration; Robot; Artificial intelligence; Obstacle avoidance; Computer vision; Sensory system; Robotics; Motion planning; Mobile robot; Embedded system; Neuroscience","score_opus":0.01752057423011711,"score_gpt":0.2473561824658886,"score_spread":0.2298356082357715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138584260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10477906,0.000066021224,0.8916809,0.000012360962,0.00044703536,0.00067679,0.000018425815,0.00086078193,0.0014586423],"genre_scores_gemma":[0.90944123,4.7026748e-7,0.08963538,0.000044565237,0.00016015033,0.000074614,0.0000048968964,0.000044447428,0.0005942359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935335,0.000013461895,0.00014582751,0.00017503582,0.000038030023,0.00027429799],"domain_scores_gemma":[0.99959105,0.00011993623,0.000018004983,0.00016591427,0.000024723695,0.00008037078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081639024,0.00013765058,0.0001559898,0.00003790973,0.0000976092,0.000019182879,0.00007293166,0.000055823624,0.000004019077],"category_scores_gemma":[0.000024954994,0.00012348511,0.000074465075,0.000041329695,0.0000067101264,0.0000621878,0.000003657063,0.00007244377,0.0000033013753],"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":[0.000023548202,0.000010306973,0.0000038363446,0.00044747096,0.000018218781,0.0000030332822,0.0001075974,0.7539989,0.23062204,0.00558273,0.00006798956,0.009114382],"study_design_scores_gemma":[0.0010152517,0.00029892096,0.000013313428,0.00004915247,0.000025237163,0.000090171954,0.00023712516,0.32152447,0.65330434,0.0007121507,0.02226397,0.00046587933],"about_ca_topic_score_codex":3.7138307e-7,"about_ca_topic_score_gemma":0.0000031552006,"teacher_disagreement_score":0.80466217,"about_ca_system_score_codex":0.000044000986,"about_ca_system_score_gemma":0.0000077874365,"threshold_uncertainty_score":0.5035576},"labels":[],"label_agreement":null},{"id":"W2139911369","doi":"10.1109/tnano.2012.2226747","title":"Design and Architectural Assessment of 3-D Resistive Memory Technologies in FPGAs","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":48,"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":"Connaught Fund; Agence Nationale de la Recherche","keywords":"Random access; Field-programmable gate array; Computer science; Resistive random-access memory; Benchmark (surveying); Non-volatile memory; Resistive touchscreen; Flash (photography); Stack (abstract data type); Parallel computing; Computer engineering; Embedded system; Computer hardware; Electrical engineering; Engineering; Operating system","score_opus":0.018802533384418674,"score_gpt":0.26051282454401414,"score_spread":0.24171029115959547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139911369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45227575,0.00017692547,0.546744,0.000062698615,0.00014234261,0.00013025792,0.0000015302368,0.0004315041,0.000035034216],"genre_scores_gemma":[0.9821307,0.00009151963,0.017705228,0.0000057665184,0.000005229349,0.000035329977,1.640712e-7,0.000015690443,0.000010369637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999307,0.000031495892,0.00019166681,0.00013580013,0.000060365826,0.00027370517],"domain_scores_gemma":[0.99955773,0.00020593846,0.000029552486,0.00017724633,0.000009912478,0.00001960539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117377545,0.00013484912,0.00020219102,0.00040062304,0.000050968465,0.0000023529558,0.000105062085,0.00019125246,0.000003683645],"category_scores_gemma":[0.000008397876,0.00013076527,0.00002639388,0.00030498812,0.00015181408,0.00008426508,0.0000025887825,0.0004885698,0.0000014589222],"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":[0.00002838673,0.00005215087,0.000023589846,0.00004410635,0.0000220044,0.0000054566553,0.00014007097,0.48714277,0.3218216,0.00017039943,0.0000021293893,0.19054732],"study_design_scores_gemma":[0.0002852672,0.000118185766,0.00015152141,0.000040611874,0.000010333849,0.000043674387,0.00022381607,0.014198978,0.98423797,0.00053277635,0.00002112791,0.00013576646],"about_ca_topic_score_codex":0.0000019281515,"about_ca_topic_score_gemma":0.000004955957,"teacher_disagreement_score":0.66241634,"about_ca_system_score_codex":0.00005992091,"about_ca_system_score_gemma":0.000007983503,"threshold_uncertainty_score":0.5332453},"labels":[],"label_agreement":null},{"id":"W2140479193","doi":"10.1109/icnn.1996.549011","title":"A modular architecture for hybrid VLSI neural networks and its application in a smart photosensor","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Very-large-scale integration; Computer science; Scalability; Modular design; Block (permutation group theory); Neuromorphic engineering; CMOS; Robustness (evolution); Computer architecture; Artificial neural network; Computer hardware; Embedded system; Electronic engineering; Engineering; Artificial intelligence","score_opus":0.024368561284608414,"score_gpt":0.2384320765011076,"score_spread":0.2140635152164992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140479193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98849833,0.0004144874,0.008247839,0.0005435751,0.00039367573,0.0007721785,0.00001541688,0.0001835735,0.0009309108],"genre_scores_gemma":[0.99855644,0.0002638586,0.00030271176,0.00022107922,0.00038637788,0.00013079343,0.000019613828,0.000048080863,0.00007106529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839836,0.000008093665,0.0004735268,0.0004686528,0.00022764681,0.00042373102],"domain_scores_gemma":[0.99926156,0.00012156297,0.00017261348,0.00008558665,0.00024597117,0.00011270556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012456776,0.00033393066,0.00033840103,0.00019034196,0.00007948583,0.000076600605,0.00035270385,0.00010940094,0.00001824703],"category_scores_gemma":[0.00006620305,0.00033697064,0.000093399925,0.00020621305,0.00005016009,0.0003086255,0.00007763746,0.00055358885,0.0000012855759],"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":[0.00024675782,0.00007579323,0.00082197855,0.00013334653,0.00004296306,0.000006730282,0.00013449838,0.92025167,0.03537028,0.0034712767,0.00018474646,0.03925996],"study_design_scores_gemma":[0.00076660595,0.0001448355,0.00033824757,0.00013134824,0.00001130721,0.000048748596,0.000029584788,0.98953164,0.007827707,0.0006420364,0.00022862238,0.0002993314],"about_ca_topic_score_codex":0.0000027086344,"about_ca_topic_score_gemma":0.000004875969,"teacher_disagreement_score":0.069279954,"about_ca_system_score_codex":0.000049673632,"about_ca_system_score_gemma":0.0000024753317,"threshold_uncertainty_score":0.9999082},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6825201,0.0000407059,0.3166181,0.000014241498,0.00045974914,0.000146747,0.000006544029,0.00012989022,0.000063913845],"genre_scores_gemma":[0.99828094,0.000071860224,0.001470898,0.00002830204,0.000094262745,0.000009450721,0.000002872147,0.000032403583,0.000009019399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992159,0.000029690744,0.00026029002,0.00016693055,0.00008952517,0.00023765415],"domain_scores_gemma":[0.9994978,0.0001633902,0.000072527,0.00014842642,0.000050483846,0.000067415924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006624723,0.00016475303,0.00021674181,0.000046230783,0.00009415616,0.000011218228,0.00006782728,0.0000736485,0.000017446971],"category_scores_gemma":[0.0000027476178,0.00015121128,0.00005094047,0.00027694146,0.000088501016,0.00014953439,0.0000012272443,0.0005367301,4.7362423e-7],"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":[0.0001046523,0.000019292454,0.00003422892,0.000031474385,0.000020125466,0.000007933528,0.000024179371,0.8936014,0.053262196,0.0000017493168,0.000011469191,0.052881286],"study_design_scores_gemma":[0.0005804603,0.00032175798,0.0007137409,0.00015598224,0.00007012376,0.00007942506,0.000011362624,0.76392996,0.23383611,0.00005299913,0.000015664269,0.00023241466],"about_ca_topic_score_codex":0.0000026816344,"about_ca_topic_score_gemma":0.000058179994,"teacher_disagreement_score":0.31576085,"about_ca_system_score_codex":0.0000045249058,"about_ca_system_score_gemma":0.0000048272113,"threshold_uncertainty_score":0.6166217},"labels":[],"label_agreement":null},{"id":"W2143597877","doi":"10.1186/1471-2202-15-s1-p21","title":"A digital hardware design for real-time simulation of large neural-system models in physical settings","year":2014,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Artificial intelligence; Computer architecture","score_opus":0.0354756712189686,"score_gpt":0.2703695318783316,"score_spread":0.23489386065936302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143597877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4568131,0.0000014317515,0.5426879,0.000002280381,0.00007127707,0.00017265607,0.000007596281,0.00012410576,0.000119636126],"genre_scores_gemma":[0.99868256,2.570324e-7,0.0011971382,0.000014629124,0.00004507697,0.000009816986,0.0000013193071,0.000017537786,0.00003168653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919784,0.000029334416,0.0001772363,0.00022657633,0.00012549473,0.00024351654],"domain_scores_gemma":[0.9993205,0.0004239481,0.00004876727,0.0001408558,0.000023999928,0.000041910047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015398527,0.00010557998,0.00016072784,0.000051947347,0.000054037595,0.00002663914,0.00014930236,0.000020892016,1.3235187e-7],"category_scores_gemma":[0.00016709503,0.00010310158,0.000045208042,0.00021329927,0.00002327558,0.00048637151,0.000033544387,0.000060722068,0.0000013763691],"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":[0.000009916487,0.000010218664,0.000027017644,0.000082261526,1.2563834e-7,5.859537e-7,0.00005878965,0.890997,0.10803755,0.00013241637,0.00000385262,0.00064030325],"study_design_scores_gemma":[0.00021554783,0.00006172656,0.00017295608,0.000037742673,0.0000016555928,0.0000019803254,0.000009776152,0.9816289,0.01756771,0.0001748364,0.000030809002,0.00009638992],"about_ca_topic_score_codex":1.7609533e-7,"about_ca_topic_score_gemma":1.9019208e-7,"teacher_disagreement_score":0.54186946,"about_ca_system_score_codex":0.000018526203,"about_ca_system_score_gemma":0.000007090663,"threshold_uncertainty_score":0.42043602},"labels":[],"label_agreement":null},{"id":"W2143806831","doi":"10.1109/ccece.1997.608277","title":"Harmonic distortion in switched-current memory cell","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Total harmonic distortion; THD analyzer; Upper and lower bounds; Distortion (music); Current (fluid); Measure (data warehouse); Harmonic; Harmonic analysis; Mathematics; Topology (electrical circuits); Control theory (sociology); Computer science; Mathematical analysis; Nonlinear distortion; Physics; Telecommunications; Acoustics; Electrical engineering; Combinatorics; Engineering; Voltage; Artificial intelligence; Bandwidth (computing)","score_opus":0.025637795035284645,"score_gpt":0.22092602540847994,"score_spread":0.1952882303731953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143806831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9829072,0.0021601906,0.0061827623,0.000016709077,0.00041191748,0.000065919055,2.7992516e-7,0.00025066026,0.0080043785],"genre_scores_gemma":[0.99922246,0.00013941443,0.00012915084,0.000016284344,0.000053490465,0.000004039093,8.956377e-7,0.000010943398,0.00042334717],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995791,0.0000044573885,0.000121746765,0.00009377922,0.00005102288,0.00014989408],"domain_scores_gemma":[0.99985,0.000017744296,0.000010134958,0.00008544853,0.0000038994203,0.00003280384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003112055,0.00007435496,0.00006887256,0.00003868695,0.000019934998,0.0000053490967,0.000048476675,0.000019199402,0.00016996959],"category_scores_gemma":[0.0000036177005,0.000072871495,0.000021888764,0.00009405011,0.00000478321,0.000098898316,0.000012284695,0.00014286098,0.00012091426],"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":[0.0000063486978,0.00015390215,0.00067651103,0.00026082198,0.0000049433916,0.00003483125,0.0012466946,0.2121837,0.17923526,0.00006124233,0.0022946,0.6038411],"study_design_scores_gemma":[0.0009039786,0.0000441805,0.0029303813,0.000058685702,0.0000074828195,0.000007067059,0.00009546962,0.2663263,0.7199573,0.00025797007,0.008887295,0.00052390643],"about_ca_topic_score_codex":7.1511556e-7,"about_ca_topic_score_gemma":0.000002888462,"teacher_disagreement_score":0.6033172,"about_ca_system_score_codex":0.000058261747,"about_ca_system_score_gemma":8.134454e-7,"threshold_uncertainty_score":0.29716134},"labels":[],"label_agreement":null},{"id":"W2145526734","doi":"10.1149/ma2008-02/30/2118","title":"High Speed Unipolar Switching with Very Low Reset Current Resistance RAM (RRAM) for Non-Volatile Memory Application","year":2008,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SAIT Polytechnic","funders":"","keywords":"Resistive random-access memory; Reset (finance); Non-volatile memory; Current (fluid); Materials science; Optoelectronics; Electrical engineering; Engineering; Voltage; Business","score_opus":0.014734944944540782,"score_gpt":0.2361285590729874,"score_spread":0.22139361412844663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145526734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98843116,0.0004129588,0.00661693,0.000032839456,0.00045586104,0.00050523813,0.0000107176775,0.00044905927,0.0030852258],"genre_scores_gemma":[0.9906167,0.00004053344,0.008598801,0.000019682606,0.0004979611,0.000026268972,0.000039154103,0.000075771975,0.000085122105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985269,0.00001917649,0.00040029557,0.00036325943,0.00023576501,0.00045461097],"domain_scores_gemma":[0.9989423,0.00033651126,0.00017236284,0.00034221224,0.00008144778,0.00012517259],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031450117,0.0002646712,0.00026762433,0.000081699895,0.0003930143,0.000031105632,0.00019887096,0.000079864774,0.0000016373536],"category_scores_gemma":[0.00013012704,0.00026449905,0.00005803136,0.00020851861,0.000032600037,0.0002516127,0.00002898793,0.00043491612,0.000016935763],"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":[0.00010571898,0.000032570315,0.00015754717,0.00035021873,0.000017158285,0.000018416342,0.0002907418,0.86258715,0.13437176,0.000006221863,0.00045238063,0.0016100996],"study_design_scores_gemma":[0.0017827263,0.00009486554,0.006640921,0.0013187225,0.000043769272,0.000039886574,0.000097798955,0.0814377,0.89755565,0.00061915634,0.00938561,0.0009831871],"about_ca_topic_score_codex":0.000012863321,"about_ca_topic_score_gemma":0.000017870589,"teacher_disagreement_score":0.78114945,"about_ca_system_score_codex":0.000115026756,"about_ca_system_score_gemma":0.000038302773,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W2145585442","doi":"10.1109/iscas.2000.857205","title":"Low-power data-driven dynamic logic (D/sup 3/L) [CMOS devices]","year":2000,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Toronto","funders":"","keywords":"Pass transistor logic; Dynamic logic (digital electronics); Logic gate; Computer science; Logic family; CMOS; Sequential logic; Logic level; Synchronization (alternating current); Asynchronous circuit; Dissipation; Dynamic demand; Logic optimization; Pull-up resistor; Power (physics); Clock synchronization; Electronic engineering; Logic synthesis; Clock signal; Electrical engineering; Synchronous circuit; Electronic circuit; Algorithm; Digital electronics; Engineering; Physics; Transistor; Telecommunications; Voltage","score_opus":0.019657951119076936,"score_gpt":0.25866118090209406,"score_spread":0.23900322978301713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145585442","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9363186,0.00021940228,0.019118348,0.00006263953,0.0002054685,0.00015392023,0.000015845155,0.0011494466,0.042756334],"genre_scores_gemma":[0.9947187,0.000047165,0.003463971,0.00027909406,0.000047609003,0.0000021464605,0.000040418374,0.0000278403,0.0013730853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920315,0.000012029759,0.00016935602,0.00025000132,0.00008880473,0.0002766658],"domain_scores_gemma":[0.99937034,0.000045505687,0.00001136307,0.0004945293,0.000009238581,0.00006905383],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000047930487,0.00015038045,0.00013829551,0.000027699074,0.00006545554,0.000022413331,0.00036376325,0.000050292543,0.0023320587],"category_scores_gemma":[0.0000064247356,0.00013338844,0.000028566912,0.00012765106,0.000017783432,0.0003067293,0.000067416564,0.0001699727,0.000813416],"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":[0.000012999659,0.00003392316,0.000060303966,0.00008655416,0.000036389032,0.00007603483,0.000101507736,0.90652764,0.0073658978,0.00019726803,0.001421907,0.084079586],"study_design_scores_gemma":[0.00031224775,0.00003691506,0.0005263615,0.00004743513,0.000013046481,0.00003974187,0.000047700447,0.97597873,0.0030341079,0.0003597782,0.019186204,0.00041773473],"about_ca_topic_score_codex":0.0000011547459,"about_ca_topic_score_gemma":0.000010913446,"teacher_disagreement_score":0.083661854,"about_ca_system_score_codex":0.000025636728,"about_ca_system_score_gemma":0.000004702274,"threshold_uncertainty_score":0.9999646},"labels":[],"label_agreement":null},{"id":"W2145930696","doi":"10.3389/fnins.2015.00380","title":"RETRACTED: Benchmarking neuromorphic systems with Nengo","year":2015,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Benchmark (surveying); Computer science; Suite; Benchmarking; Neuromorphic engineering; Software; Test suite; Artificial intelligence; Computer architecture; Machine learning; Artificial neural network; Test case; Programming language","score_opus":0.037450791238175254,"score_gpt":0.20995310315403432,"score_spread":0.17250231191585907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145930696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7982661,0.00044667738,0.18953556,0.000029284663,0.008998668,0.00024073318,0.000002180968,0.000396701,0.0020840862],"genre_scores_gemma":[0.9966627,0.000027213113,0.0030226286,0.00009004816,0.000112512615,0.0000042810566,6.8661103e-7,0.000022981321,0.000056946854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885523,0.000039975122,0.00017348134,0.0002998204,0.00026988852,0.00036157272],"domain_scores_gemma":[0.9995592,0.000024204504,0.000036252877,0.00021215348,0.000023150957,0.00014499208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020523655,0.00014598649,0.0001662099,0.00014098054,0.000063037936,0.000058878104,0.00027550207,0.00004545895,3.7631133e-7],"category_scores_gemma":[0.0000736094,0.00013338264,0.0000143443385,0.00072116294,0.000087307184,0.000363231,0.0000356634,0.0003923936,0.0000017157846],"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":[0.00002052147,0.000011453245,0.010162985,0.000038017733,0.0000010524701,0.00030848235,0.00016944128,0.97425586,0.009141307,0.00003996011,0.0019655656,0.0038853483],"study_design_scores_gemma":[0.0003624268,0.0001676658,0.0049834503,0.00008672279,0.0000035181,0.00015560094,0.0001149942,0.9846452,0.0015466796,0.000072432835,0.007565586,0.00029575732],"about_ca_topic_score_codex":0.000004361435,"about_ca_topic_score_gemma":0.0000012786035,"teacher_disagreement_score":0.1983966,"about_ca_system_score_codex":0.00006061486,"about_ca_system_score_gemma":0.000023965078,"threshold_uncertainty_score":0.5439186},"labels":[],"label_agreement":null},{"id":"W2146745371","doi":"10.1007/s10470-013-0176-x","title":"Special issue on IEEE MWSCAS 2012","year":2013,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Sandia National Laboratories; Grinnell College; University of Alberta; New Mexico State University; Natural Sciences and Engineering Research Council of Canada; Johns Hopkins University; California Institute of Technology","keywords":"Globe; Library science; Task (project management); Computer science; Operations research; Telecommunications; Management; Engineering; Psychology","score_opus":0.01582727979539067,"score_gpt":0.22781294174619635,"score_spread":0.21198566195080568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146745371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81637776,0.0018462044,0.03197358,0.00014283204,0.0013221843,0.00047364322,0.000009741834,0.00091076014,0.14694329],"genre_scores_gemma":[0.9955885,0.000039017035,0.000058626192,0.00027441254,0.0033458676,0.000007951376,0.000006584051,0.000032250984,0.0006467989],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990513,0.000019777524,0.0002266938,0.00023889076,0.00012491229,0.00033845694],"domain_scores_gemma":[0.9996264,0.00005002997,0.000040351733,0.0000797798,0.00008006473,0.00012335178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006918034,0.0002260478,0.00021125513,0.00010436357,0.00020220951,0.0001416751,0.00010280885,0.00009575583,0.00061277667],"category_scores_gemma":[0.000009215288,0.00018617406,0.00003713913,0.00024629862,0.00005101571,0.0005004221,0.000008537969,0.00039119858,0.00015892468],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035703417,0.00001506304,0.000042966814,0.00006578842,0.000016038182,0.000017674458,0.00022737354,0.0048180274,0.03353149,0.00006462763,0.008213816,0.95298356],"study_design_scores_gemma":[0.002227565,0.0010031954,0.0026019448,0.002233335,0.00015367898,0.00034212938,0.0023066816,0.4628189,0.31708908,0.009687288,0.1960029,0.0035333193],"about_ca_topic_score_codex":0.000009818219,"about_ca_topic_score_gemma":0.0000043438167,"teacher_disagreement_score":0.94945025,"about_ca_system_score_codex":0.000030878688,"about_ca_system_score_gemma":0.000015894138,"threshold_uncertainty_score":0.7591958},"labels":[],"label_agreement":null},{"id":"W2148005189","doi":"10.1007/978-1-4614-6675-8_506","title":"Large-Scale Neural Networks: Vision","year":2015,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Scale (ratio); Computer science; Artificial intelligence; Artificial neural network; Computer vision; Geography; Cartography","score_opus":0.01756066870552178,"score_gpt":0.25757856260974266,"score_spread":0.24001789390422087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148005189","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068915477,0.0023507448,0.31659803,0.00009159252,0.0073662475,0.0006305146,0.0001346878,0.0008296074,0.665107],"genre_scores_gemma":[0.8144891,0.0014694729,0.008836825,0.00090216816,0.0025447828,0.000017691478,0.00026464343,0.00047021973,0.17100506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982089,0.00001611794,0.00046401768,0.0004064848,0.0005926348,0.0003118366],"domain_scores_gemma":[0.9991408,0.00017293236,0.00016376693,0.00021754032,0.00014074608,0.00016419386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015848898,0.00032680706,0.00034978054,0.00014253178,0.000103176935,0.00002286214,0.00038121315,0.00012514181,0.000031788648],"category_scores_gemma":[0.000035833655,0.00034121313,0.00010976465,0.00011832578,0.0001490108,0.00026227205,0.00014689754,0.00045201342,0.000015523869],"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":[0.0000060611237,0.000009118459,0.0000056886142,0.000029083485,0.000001859156,0.000017976878,0.00004938894,0.9917667,0.000036717393,0.0025698408,0.0021862152,0.0033213706],"study_design_scores_gemma":[0.00019476752,0.00010962163,0.00017266539,0.000073694966,0.000012371374,0.000031495747,0.0000037686755,0.8800695,0.000014144253,0.0151327625,0.103828974,0.00035621284],"about_ca_topic_score_codex":1.333877e-7,"about_ca_topic_score_gemma":8.6648396e-7,"teacher_disagreement_score":0.8075976,"about_ca_system_score_codex":0.000038037586,"about_ca_system_score_gemma":0.00004534881,"threshold_uncertainty_score":0.999904},"labels":[],"label_agreement":null},{"id":"W2148344725","doi":"10.1088/0268-1242/26/7/075019","title":"Bipolar and unipolar resistive switching behaviors of sol–gel-derived SrTiO<sub>3</sub>thin films with different compliance currents","year":2011,"lang":"en","type":"article","venue":"Semiconductor Science and Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Geological Survey of Canada","funders":"","keywords":"Materials science; Thin film; Resistive touchscreen; Compliance (psychology); Condensed matter physics; Insulator (electricity); Substrate (aquarium); Current (fluid); Metal; Optoelectronics; Nanotechnology; Electrical engineering; Psychology; Physics","score_opus":0.02892716981162265,"score_gpt":0.23630679468311644,"score_spread":0.20737962487149378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148344725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997849,0.0010195606,0.0005818853,0.000014941576,0.00014769187,0.0001467433,0.0000068504596,0.00021155833,0.000021758799],"genre_scores_gemma":[0.99888164,0.0001831432,0.00089478435,0.000009821347,0.0000071498825,0.000006412984,9.253174e-7,0.000014684452,0.0000014276623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892074,0.00001084648,0.00019807446,0.00036839375,0.00015465192,0.00034726664],"domain_scores_gemma":[0.9994659,0.00002493771,0.00008470506,0.00023370537,0.00010531303,0.000085438325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012052418,0.00019110944,0.000245043,0.00029487544,0.00024012882,0.00001453171,0.00027331212,0.00009251198,0.0000021013363],"category_scores_gemma":[0.00005435177,0.00015752802,0.000013172107,0.00050410326,0.00084923493,0.00032442855,0.00015524674,0.00033453916,8.2393103e-7],"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":[0.000007776799,0.000016642098,0.0076018455,0.0000389084,0.000006766738,0.000005357606,0.00053365435,0.0000112624175,0.9821413,0.00027231034,0.0000013579572,0.009362806],"study_design_scores_gemma":[0.00022626032,0.00013755201,0.0071209823,0.00014905423,0.000016252701,0.00004096605,0.0005632986,0.0012284648,0.98966676,0.0006460823,0.000008109438,0.00019619732],"about_ca_topic_score_codex":0.0000066736,"about_ca_topic_score_gemma":0.00001223659,"teacher_disagreement_score":0.009166609,"about_ca_system_score_codex":0.000027674107,"about_ca_system_score_gemma":0.00002493902,"threshold_uncertainty_score":0.64238065},"labels":[],"label_agreement":null},{"id":"W2152352529","doi":"10.1117/12.569405","title":"Calibrating grayscale direct write bimetallic photomasks to create 3D photoresist structures","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Simon Fraser University","funders":"","keywords":"Grayscale; Photomask; Photoresist; Computer science; Computer graphics (images); Artificial intelligence; Pixel; Materials science; Resist; Nanotechnology","score_opus":0.010890979016708513,"score_gpt":0.2254098807386153,"score_spread":0.21451890172190677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152352529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99545026,0.00016499187,0.00019409548,0.0003317027,0.0003423824,0.00057309016,0.000050036655,0.00035505986,0.0025383555],"genre_scores_gemma":[0.8568747,0.000066404915,0.14213416,0.00012301469,0.0004874951,0.00010977023,0.000008343268,0.0001137862,0.000082320876],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997758,1.5343284e-8,0.0007176506,0.00043130497,0.0005508504,0.00054221525],"domain_scores_gemma":[0.9988473,0.0001430564,0.00018871484,0.00007168342,0.0005406972,0.00020852448],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028199036,0.00043782967,0.0005065473,0.00013046672,0.00013944333,0.00013142529,0.000808054,0.00017062874,0.00001141222],"category_scores_gemma":[0.00037818623,0.00038041626,0.0005283043,0.0004802637,0.00013101473,0.00056074635,0.00016466588,0.00038090008,0.0000022342667],"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":[0.000058760288,0.000038541097,0.00006752517,0.0005380771,0.00028128814,3.8621462e-7,0.0002699273,0.022266278,0.91723984,0.05836988,0.0006039212,0.0002655903],"study_design_scores_gemma":[0.000888225,0.0002214502,0.00037787948,0.00040533283,0.0001088349,0.00001890943,0.00044901058,0.018779673,0.9742379,0.0016537402,0.0023210987,0.00053790555],"about_ca_topic_score_codex":0.000010881842,"about_ca_topic_score_gemma":4.3340324e-7,"teacher_disagreement_score":0.14194006,"about_ca_system_score_codex":0.00021879244,"about_ca_system_score_gemma":0.000023654318,"threshold_uncertainty_score":0.99986476},"labels":[],"label_agreement":null},{"id":"W2153710474","doi":"10.3390/s110504572","title":"Recent Advances in Neural Recording Microsystems","year":2011,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Interfacing; Microsystem; Computer science; Power management; Embedded system; Computer hardware; Power (physics); Nanotechnology","score_opus":0.05507193891870061,"score_gpt":0.3036286557852001,"score_spread":0.2485567168664995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153710474","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029394496,0.995106,0.000011609116,0.0000010247231,0.0020377587,0.00034462335,0.00000691824,0.00027118722,0.0019269226],"genre_scores_gemma":[0.00006737625,0.9993414,0.00010542722,0.0000041141984,0.0002528642,0.000016223063,0.000011349687,0.000090982445,0.000110230445],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985865,0.000089754096,0.00056066044,0.000306971,0.000062324856,0.0003938041],"domain_scores_gemma":[0.99945015,0.00011707519,0.0001144935,0.00024053974,0.000013298962,0.00006442924],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000115451876,0.00038525736,0.0010323793,0.00018248303,0.000038791954,0.000014491769,0.00019031545,0.00016736871,0.000019735691],"category_scores_gemma":[0.00003135703,0.00034607583,0.00017584051,0.0003482566,0.00001814562,0.00011818872,0.000037729995,0.00057467417,0.00007378499],"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":[0.0000011566352,0.0000026062958,9.4937525e-7,0.0056746844,0.000008049968,0.000034263932,0.000030249761,0.003161235,0.0000020765226,0.00000759221,0.000018053654,0.99105906],"study_design_scores_gemma":[0.00005307118,0.000010297784,1.8540545e-7,0.006450332,0.000028972783,0.00008326842,0.000014111994,0.00071209186,0.000018754503,0.000012048315,0.99225736,0.00035952646],"about_ca_topic_score_codex":0.0000013681505,"about_ca_topic_score_gemma":0.000011262405,"teacher_disagreement_score":0.9922393,"about_ca_system_score_codex":0.00012233088,"about_ca_system_score_gemma":0.000010503619,"threshold_uncertainty_score":0.99989915},"labels":[],"label_agreement":null},{"id":"W2154208623","doi":"10.1109/ijcnn.1991.155217","title":"CMOS implementation of analog Hebbian synaptic learning circuits","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hebbian theory; Computer science; Synapse; Analogue electronics; Artificial neural network; Electronic circuit; Very-large-scale integration; CMOS; Electronic engineering; Artificial intelligence; Electrical engineering; Neuroscience; Engineering; Embedded system","score_opus":0.022644718394464865,"score_gpt":0.24830292283624394,"score_spread":0.2256582044417791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154208623","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9764258,0.00011722691,0.01736616,0.0000133022495,0.00010573238,0.00005847982,5.7674174e-7,0.00021766331,0.0056950185],"genre_scores_gemma":[0.99955076,0.000018631574,0.00023324176,0.000016281107,0.000025569947,0.0000013278577,0.0000018524513,0.000009644314,0.00014271845],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962324,0.000009595078,0.00012897459,0.000066914305,0.000051880517,0.00011937355],"domain_scores_gemma":[0.9998597,0.000030385441,0.000019648158,0.00005485318,0.000010656342,0.000024752215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034408382,0.00005560741,0.00007445247,0.000036720492,0.00003370081,0.000004231798,0.00003847859,0.000016258076,0.00050477404],"category_scores_gemma":[0.00000740527,0.000057166726,0.000022340642,0.00010323203,0.0000065118456,0.000083051535,0.0000086330265,0.00007967876,0.000023546188],"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":[8.427595e-7,0.00001305638,0.00443945,0.00013936927,0.000064504275,0.000013017518,0.0010248222,0.31724045,0.26319706,0.0006604457,0.00038613676,0.41282082],"study_design_scores_gemma":[0.0010489641,0.00028582144,0.0091129495,0.00006889774,0.00004310109,0.00004520153,0.0022264314,0.41530666,0.5678515,0.00033370135,0.0030662674,0.00061051664],"about_ca_topic_score_codex":0.0000025744619,"about_ca_topic_score_gemma":0.0000053834483,"teacher_disagreement_score":0.41221032,"about_ca_system_score_codex":0.000012849792,"about_ca_system_score_gemma":7.8007474e-7,"threshold_uncertainty_score":0.5526922},"labels":[],"label_agreement":null},{"id":"W2154515525","doi":"10.1109/ijcnn.1992.226922","title":"Architectural synthesis for digital neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial neural network; Computer architecture; Very-large-scale integration; Backpropagation; Digital signal processing; Implementation; Physical neural network; Artificial intelligence; Embedded system; Computer engineering; Computer hardware; Time delay neural network; Types of artificial neural networks; Software engineering","score_opus":0.011627763684727597,"score_gpt":0.2071285885359028,"score_spread":0.1955008248511752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154515525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47277406,0.0001000452,0.5183949,0.000015225073,0.0003541558,0.00013489382,0.0000025630432,0.00047276303,0.0077514085],"genre_scores_gemma":[0.99744886,9.591291e-7,0.00224627,0.000037210455,0.00008261214,0.000012695038,0.0000013008046,0.00001995679,0.00015010564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995627,0.0000048023217,0.00009097962,0.000091315575,0.00003082637,0.00021942153],"domain_scores_gemma":[0.9996135,0.00024913574,0.000007250384,0.00007829434,0.000007046096,0.000044720626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026208923,0.00009265058,0.000086147105,0.000018552264,0.00004974749,0.000029680728,0.00005183729,0.00002322799,0.000017540084],"category_scores_gemma":[0.00007024121,0.000079180296,0.000056384815,0.000053601772,0.00001033074,0.00010018583,0.0000064743663,0.0000715608,0.00000310968],"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":[0.000004925142,0.0000026257594,0.00005263302,0.000013053351,0.000007676433,0.0000017289623,0.000010590857,0.90876776,0.00048452555,0.0006284469,0.00009388751,0.08993215],"study_design_scores_gemma":[0.00015304353,0.000025520898,0.00006286251,0.00000880713,0.000006684786,0.00005110133,0.000022754533,0.96409655,0.03141733,0.0007201947,0.003193961,0.0002412101],"about_ca_topic_score_codex":7.1488365e-8,"about_ca_topic_score_gemma":4.0723145e-7,"teacher_disagreement_score":0.52467483,"about_ca_system_score_codex":0.0000087155195,"about_ca_system_score_gemma":0.0000010889236,"threshold_uncertainty_score":0.32288787},"labels":[],"label_agreement":null},{"id":"W2155437302","doi":"10.1523/jneurosci.23-06-02434.2003","title":"Bimodal Locomotion Elicited by Electrical Stimulation of the Midbrain in the Salamander<i>Notophthalmus viridescens</i>","year":2003,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":135,"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 du Québec à Montréal; Université de Montréal","funders":"Canadian Institutes of Health Research; Institut National de la Santé et de la Recherche Médicale","keywords":"Notophthalmus viridescens; Midbrain; Salamander; Stimulation; Caudata; Microstimulation; Anatomy; Neuroscience; Amphibian; Biology; Chemistry; Central nervous system; Zoology; Ecology; Regeneration (biology)","score_opus":0.016402989716612436,"score_gpt":0.2452728701695355,"score_spread":0.22886988045292306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155437302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907502,0.0000683281,0.008612808,0.000097145006,0.00029162568,0.000088081484,4.4417206e-7,0.0000068148947,0.00008458243],"genre_scores_gemma":[0.99966115,0.000014594923,0.00008466585,0.00020403767,0.000024001114,4.098281e-7,4.757527e-8,0.0000054543893,0.000005611682],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907154,0.00014072699,0.00029141788,0.0000728075,0.0002793494,0.00014413307],"domain_scores_gemma":[0.99954635,0.00016587539,0.00013043349,0.00010090027,0.00003130458,0.000025147401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040471833,0.00007172678,0.00010111111,0.0000657359,0.00006344967,0.000017387569,0.0002542081,0.000024091665,8.930436e-7],"category_scores_gemma":[0.00032207603,0.00004262515,0.00005203882,0.000686111,0.000046761215,0.00017395696,0.00000956752,0.00031042492,1.7723528e-7],"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":[0.0000056784447,0.000029477924,0.001925587,0.0000046992755,3.7850927e-7,0.000007741147,0.00005890007,0.11122554,0.88604003,0.00006320651,0.000080242105,0.000558499],"study_design_scores_gemma":[0.00095512724,0.00041699925,0.22042444,0.000108808075,0.000013840319,0.0010581844,0.000046599518,0.17323233,0.60126543,0.0013060089,0.00096850743,0.00020370929],"about_ca_topic_score_codex":3.5494105e-7,"about_ca_topic_score_gemma":2.2636085e-7,"teacher_disagreement_score":0.2847746,"about_ca_system_score_codex":0.000025030045,"about_ca_system_score_gemma":0.000014347919,"threshold_uncertainty_score":0.17382032},"labels":[],"label_agreement":null},{"id":"W2156210242","doi":"10.1557/opl.2012.937","title":"Time Voltage Dependency in Resistance Switching TiO<sub>2</sub>","year":2012,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université de Sherbrooke","funders":"","keywords":"Materials science; Voltage; Optoelectronics; Insulator (electricity); Non-volatile memory; Nanotechnology; Electrical engineering; Engineering","score_opus":0.008348365132881227,"score_gpt":0.20613276847343523,"score_spread":0.197784403340554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156210242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907245,0.000498118,0.00072485144,0.000018267026,0.0001967168,0.0001247686,6.6677563e-7,0.0004208177,0.007291268],"genre_scores_gemma":[0.9990473,0.000025859015,0.000381153,0.000055143177,0.0002602921,0.000012489359,0.000001155539,0.000045586046,0.00017101366],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989315,0.0000028216114,0.00022817445,0.00017952248,0.00014447577,0.00051345496],"domain_scores_gemma":[0.99972665,0.000033185253,0.000040963518,0.00007009432,0.000026658012,0.00010243792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023452338,0.00017745601,0.0001699216,0.000106187144,0.00008256969,0.000027186736,0.00013931196,0.00007648566,0.000008018653],"category_scores_gemma":[0.00006524124,0.00019228486,0.000033299697,0.00033013985,0.0000133402,0.00075740565,0.000045768527,0.0003405843,0.00011995338],"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":[0.000010556768,0.000014200893,0.0016413319,0.00012800143,0.000004891584,0.000004931823,0.00090699474,0.0003491396,0.991734,0.00038076635,0.00039012206,0.00443502],"study_design_scores_gemma":[0.00037275403,0.000016532214,0.004666271,0.0002555066,0.00001064855,0.000021237494,0.00016954914,0.0040909527,0.9864244,0.00141365,0.0020322928,0.00052621413],"about_ca_topic_score_codex":5.038234e-7,"about_ca_topic_score_gemma":0.0000026838125,"teacher_disagreement_score":0.008322783,"about_ca_system_score_codex":0.00009264597,"about_ca_system_score_gemma":0.000005103506,"threshold_uncertainty_score":0.7841149},"labels":[],"label_agreement":null},{"id":"W2158395626","doi":"10.1109/spdp.1990.143580","title":"On linear speedup of a class of neighborhood functions in an array processor","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; York University","funders":"","keywords":"Speedup; Computer science; Parallel computing; Block (permutation group theory); Class (philosophy); Algorithm; Theoretical computer science; Mathematics; Combinatorics; Artificial intelligence","score_opus":0.02869555971723966,"score_gpt":0.24359319110449376,"score_spread":0.2148976313872541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158395626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97153616,0.00003382257,0.009765932,0.000019835697,0.00007730482,0.000069640366,0.0000019607892,0.00008063963,0.018414708],"genre_scores_gemma":[0.99905777,0.000003260122,0.00065231894,0.000017654686,0.000031633717,0.0000015888736,8.8915607e-7,0.000010056404,0.00022481498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960583,0.000006212968,0.00015794966,0.00007789002,0.000056570392,0.00009551606],"domain_scores_gemma":[0.99979436,0.000038921175,0.00002116465,0.00010085744,0.000018853003,0.000025836667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025851741,0.00006336788,0.00010900989,0.00006454278,0.000010319849,0.0000014277506,0.000051039846,0.000028131233,0.0001202953],"category_scores_gemma":[0.000019251782,0.0000572074,0.000020891126,0.00018874904,0.000010569873,0.00008868228,0.0000043529103,0.00009846002,0.000009903715],"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":[0.000025466225,0.00023247281,0.0014624167,0.00018551586,0.0000112509115,0.0000031801842,0.000473865,0.6762359,0.3116399,0.0013343523,0.000076132616,0.0083195325],"study_design_scores_gemma":[0.0006380132,0.00041612814,0.0013084261,0.00010296959,0.0000073705396,0.000003494474,0.00021478035,0.3453719,0.6509776,0.00057920424,0.0001834524,0.00019667442],"about_ca_topic_score_codex":6.4426865e-7,"about_ca_topic_score_gemma":0.000006926809,"teacher_disagreement_score":0.33933768,"about_ca_system_score_codex":0.000008677148,"about_ca_system_score_gemma":0.0000017865151,"threshold_uncertainty_score":0.233285},"labels":[],"label_agreement":null},{"id":"W2158711852","doi":"10.1049/el.2013.2788","title":"Is memristor a dynamic element?","year":2013,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Calgary","funders":"","keywords":"Memristor; Electrical element; Element (criminal law); Electronic circuit; Finite element method; Electronic engineering; Charge (physics); Electrical network; Electrical engineering; Topology (electrical circuits); Computer science; Control theory (sociology); Physics; Engineering; Artificial intelligence; Quantum mechanics; Law; Structural engineering","score_opus":0.004469237723631606,"score_gpt":0.2016645464692789,"score_spread":0.19719530874564728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158711852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9778018,0.0007931344,0.018541884,0.0017363643,0.0001924771,0.0001528333,8.851586e-7,0.0003560874,0.00042450614],"genre_scores_gemma":[0.9960468,0.00006227699,0.0007474834,0.0028870613,0.000057480414,0.000019757623,0.0000039062375,0.000032805696,0.00014245097],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992364,0.000008083824,0.00012122452,0.00013496925,0.0000837423,0.00041554967],"domain_scores_gemma":[0.9997571,0.000019519155,0.000017215294,0.00015211749,0.000008486705,0.000045545974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032203585,0.00012141329,0.00009307558,0.00003984865,0.000055970344,0.000021443255,0.00011849288,0.00002515865,0.00015568968],"category_scores_gemma":[0.0000031190652,0.00012768457,0.000044974666,0.00009577047,0.0000111474765,0.00013851452,0.000015396696,0.00022641606,0.00023353692],"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":[0.0000019317401,0.000005832099,0.000031598,0.000026007505,0.000036402074,0.0000042107727,0.00014519191,0.013062139,0.95334405,0.00010148064,0.021295616,0.011945538],"study_design_scores_gemma":[0.0010528484,0.00016524128,0.0008886258,0.000046905825,0.000040797117,0.00004569804,0.000054470514,0.29150653,0.50936925,0.0021263016,0.19337708,0.0013262633],"about_ca_topic_score_codex":0.0000014458332,"about_ca_topic_score_gemma":0.0000032289372,"teacher_disagreement_score":0.44397482,"about_ca_system_score_codex":0.00016516546,"about_ca_system_score_gemma":0.000005384958,"threshold_uncertainty_score":0.5206826},"labels":[],"label_agreement":null},{"id":"W2161357031","doi":"10.3389/neuro.11.007.2009","title":"Python scripting in the Nengo simulator","year":2009,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"","keywords":"Python (programming language); Computer science; Scripting language; Java; Spiking neural network; Graphical user interface; Artificial neural network; Population; Neural coding; Artificial intelligence; Machine learning; Programming language","score_opus":0.008408233837252529,"score_gpt":0.20788396957110938,"score_spread":0.19947573573385685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161357031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9621598,0.000260844,0.03141941,0.00011613556,0.0010399347,0.00026528974,0.0000010976167,0.00020927319,0.0045282273],"genre_scores_gemma":[0.99311775,0.000048184087,0.0059495694,0.0008072061,0.00005366988,0.0000015635982,0.0000017052071,0.000011043647,0.000009280167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916095,0.000022523504,0.00034223293,0.000065150634,0.00012449162,0.00028463066],"domain_scores_gemma":[0.99968255,0.000055163786,0.000034200886,0.00019426488,0.000005898444,0.000027918779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018426935,0.00012474721,0.00014646875,0.00012749298,0.00004714436,0.000035148296,0.00022707728,0.000039640112,6.931401e-7],"category_scores_gemma":[0.000071397255,0.000104061226,0.000030962816,0.00033962834,0.000013002065,0.00036168605,0.000014024209,0.0003912184,0.000004332204],"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":[0.0000062367176,0.000008581714,0.000726853,0.00003712558,0.0000013415043,0.000028579112,0.0031530003,0.96301985,0.00019664368,0.00010702001,0.0020649692,0.030649828],"study_design_scores_gemma":[0.0003062907,0.000036262387,0.0026219855,0.000044221368,0.000002297877,0.000010654715,0.0009913476,0.9896845,0.0005817619,0.00084485364,0.004725657,0.0001501312],"about_ca_topic_score_codex":2.5710006e-7,"about_ca_topic_score_gemma":6.757737e-7,"teacher_disagreement_score":0.030957991,"about_ca_system_score_codex":0.00003576672,"about_ca_system_score_gemma":0.0000047638505,"threshold_uncertainty_score":0.42434934},"labels":[],"label_agreement":null},{"id":"W2162113562","doi":"10.1109/mwscas.1989.101899","title":"VLSI design of optically coupled neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Resistor; Massively parallel; Artificial neural network; Computer science; Very-large-scale integration; Electronic circuit; Biological neural network; CMOS; Construct (python library); Operational amplifier; Electronic engineering; Amplifier; Artificial intelligence; Voltage; Electrical engineering; Engineering; Embedded system; Parallel computing; Machine learning; Computer network","score_opus":0.019925995434891777,"score_gpt":0.22033055926522532,"score_spread":0.20040456383033356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162113562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13690057,0.0000916718,0.8604784,0.0000032719256,0.00015650118,0.000068179615,5.3817214e-8,0.00015344571,0.002147915],"genre_scores_gemma":[0.98049426,0.0000101765545,0.019375384,0.000030171568,0.000023396582,0.0000015001615,3.044297e-7,0.000013252055,0.000051561135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999562,0.000017332246,0.00013711254,0.00007151464,0.000048770016,0.0001632368],"domain_scores_gemma":[0.9997144,0.00012201045,0.0000114822515,0.000093477436,0.000016154196,0.000042482698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007818111,0.00007664138,0.00010648909,0.000016298463,0.000021304948,0.000006261117,0.00005397388,0.00003174588,0.000063545296],"category_scores_gemma":[0.000030672625,0.00006863711,0.000025265042,0.00009215156,0.000014753051,0.000070921305,0.0000069143584,0.00009433471,0.0000032325263],"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":[0.000003561573,0.000003921011,0.000018609504,0.0000069442963,0.0000047469057,0.0000037450761,0.000007851576,0.98198026,0.01626192,0.00088426133,0.00002146317,0.00080270774],"study_design_scores_gemma":[0.00013481423,0.000029293133,0.000040261173,0.0000046500973,0.000003974756,0.0000083851555,0.000011592109,0.97249746,0.02707826,0.00008331254,0.00002747794,0.00008050183],"about_ca_topic_score_codex":1.7201442e-7,"about_ca_topic_score_gemma":1.4551017e-7,"teacher_disagreement_score":0.84359366,"about_ca_system_score_codex":0.000006729366,"about_ca_system_score_gemma":0.000002516205,"threshold_uncertainty_score":0.27989402},"labels":[],"label_agreement":null},{"id":"W2162841553","doi":"10.1109/ijcnn.2011.6033271","title":"Learning algorithms for a specific configuration of the quantron","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Ceiling (cloud); Computer science; Activation function; Nonlinear system; Algorithm; Function (biology); Artificial intelligence; Artificial neural network; Physics","score_opus":0.059649808094676605,"score_gpt":0.24604821080523975,"score_spread":0.18639840271056315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162841553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3003151,0.000059709717,0.6902515,0.000007265638,0.00031257712,0.00016609476,5.7755597e-7,0.00012424373,0.0087628625],"genre_scores_gemma":[0.99599653,0.0000056238405,0.003699742,0.0000050802737,0.000038527374,0.0000030732176,5.608011e-7,0.000006932264,0.00024392961],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977416,0.000005921054,0.000081389815,0.00004318448,0.00002781147,0.00006753249],"domain_scores_gemma":[0.999874,0.00003141715,0.000017425178,0.000054770015,0.000013805278,0.000008587372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039581275,0.000037728296,0.00004761539,0.000009174804,0.000037019716,0.0000020140171,0.00004478394,0.000014486105,0.000031363295],"category_scores_gemma":[0.000008862425,0.00002610291,0.000028567487,0.00003683532,0.0000124288545,0.00004039357,0.0000055306064,0.00005200964,0.0000024153103],"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":[0.00003942125,0.000019458936,0.00020484588,0.000101068035,0.000023678222,6.5828226e-7,0.0024120116,0.075919405,0.7334937,0.024148,0.0002894047,0.16334833],"study_design_scores_gemma":[0.00013245556,0.000042691383,0.0005783518,0.000010010296,0.0000026026073,0.0000012747753,0.0001650061,0.04284653,0.95353127,0.00045089584,0.0021875435,0.0000513755],"about_ca_topic_score_codex":6.140475e-7,"about_ca_topic_score_gemma":4.563946e-7,"teacher_disagreement_score":0.6956814,"about_ca_system_score_codex":0.000005006833,"about_ca_system_score_gemma":0.0000013842558,"threshold_uncertainty_score":0.106444575},"labels":[],"label_agreement":null},{"id":"W2162940434","doi":"10.1016/j.neucom.2005.11.011","title":"The oscillatory dynamic link matcher for spiking-neuron-based pattern recognition","year":2006,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Pattern recognition (psychology); Stimulus (psychology); Invariant (physics); Artificial neural network; DEVS; Affine transformation; Link (geometry); Pattern matching; Artificial intelligence; Mathematics; Simulation","score_opus":0.011596783908816138,"score_gpt":0.21929120272428654,"score_spread":0.2076944188154704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162940434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7901772,0.0001503114,0.20630035,0.00020464692,0.0011334197,0.00042748693,0.000006042906,0.00087352813,0.00072700914],"genre_scores_gemma":[0.99755937,0.0000035091925,0.0013601682,0.0002929648,0.00062848354,0.000018180437,0.000013561645,0.000081403334,0.00004233778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881876,0.00003681916,0.00031711787,0.00027149587,0.00012283216,0.00043298336],"domain_scores_gemma":[0.99901783,0.00062319724,0.00007997654,0.00019482772,0.000044106695,0.000040039373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016733039,0.00020704442,0.00014284103,0.00004812586,0.00043685734,0.000075787044,0.0001703026,0.00005926901,0.00000192569],"category_scores_gemma":[0.000032536802,0.00018490937,0.00010454623,0.00013116667,0.000028461991,0.00006616768,0.000032787015,0.00027459272,0.000017046135],"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":[0.000009429823,0.00000851705,0.00022975476,0.00010681168,0.0000050036183,0.000011446516,0.000012568578,0.44562662,0.020326793,0.0000053385256,0.0002132127,0.5334445],"study_design_scores_gemma":[0.00046313443,0.000049498674,0.0028404258,0.00007078857,0.000012370976,0.000012035688,0.0000068762665,0.9725008,0.015445864,0.00046135174,0.007847578,0.00028928297],"about_ca_topic_score_codex":0.0000016602797,"about_ca_topic_score_gemma":0.000007773476,"teacher_disagreement_score":0.53315526,"about_ca_system_score_codex":0.00004295493,"about_ca_system_score_gemma":0.00000938772,"threshold_uncertainty_score":0.7540385},"labels":[],"label_agreement":null},{"id":"W2163270290","doi":"10.1109/tnano.2011.2111460","title":"On the Reliability of Computational Structures Using Majority Logic","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"National Science Foundation","keywords":"Reliability (semiconductor); Fault tree analysis; Computer science; Tree (set theory); Logic gate; Fault tolerance; Algorithm; Theoretical computer science; Artificial intelligence; Reliability engineering; Mathematics; Engineering; Combinatorics; Distributed computing; Physics","score_opus":0.039347159469616706,"score_gpt":0.24603759847267245,"score_spread":0.20669043900305575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163270290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.537146,0.000006003551,0.4623005,0.000028889555,0.00021459078,0.00006942601,0.0000060695465,0.00016266787,0.000065886736],"genre_scores_gemma":[0.9943704,0.0000042834754,0.005552809,0.00004662467,0.0000065184818,0.0000042903575,2.5554965e-7,0.000011186944,0.0000036574438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994451,0.000027920352,0.00017375719,0.00013933882,0.000075617776,0.00013826141],"domain_scores_gemma":[0.99951226,0.00018167276,0.000034366712,0.00022708002,0.000029103889,0.000015518493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006513464,0.0001086249,0.0001278192,0.00009624123,0.00011918857,0.0000018930965,0.00014726033,0.00013603171,0.00007362198],"category_scores_gemma":[0.000010884215,0.0000823789,0.00005658392,0.00020129094,0.00014504377,0.000037483394,0.0000011953642,0.00038905427,0.000005185403],"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":[0.00002875161,0.000034025343,0.0000025407917,0.0000137712605,0.000012293777,0.0000019747633,0.000060229184,0.9730744,0.01701334,0.0056599914,0.0000032166392,0.004095508],"study_design_scores_gemma":[0.00014251292,0.00011720575,0.00005586911,0.0000136564095,0.000012666032,0.000016820446,0.00003217655,0.045735262,0.8143059,0.13945661,0.000010545702,0.00010075941],"about_ca_topic_score_codex":0.0000061051446,"about_ca_topic_score_gemma":0.0000029667394,"teacher_disagreement_score":0.9273391,"about_ca_system_score_codex":0.000037244383,"about_ca_system_score_gemma":0.000008088041,"threshold_uncertainty_score":0.3359314},"labels":[],"label_agreement":null},{"id":"W2164610565","doi":"10.3389/fnsyn.2012.00002","title":"Spike-Timing-Dependent Plasticity: A Comprehensive Overview","year":2012,"lang":"en","type":"article","venue":"Frontiers in Synaptic Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":380,"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 Health Centre; Montreal General Hospital","funders":"","keywords":"Spike (software development); Spike-timing-dependent plasticity; Neuroscience; Computer science; Plasticity; Psychology; Synaptic plasticity; Biology","score_opus":0.048697845868543234,"score_gpt":0.26980505163503504,"score_spread":0.22110720576649182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164610565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8376792,0.0019215684,0.15360044,0.000019148223,0.005865581,0.00020075888,0.0000032830928,0.00025014638,0.0004598886],"genre_scores_gemma":[0.9955137,0.0001729752,0.0038322625,0.00032034153,0.00008664968,0.000009548301,3.4539218e-7,0.000022235006,0.000041968728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864334,0.000037608865,0.00022790828,0.00025211135,0.0002199494,0.0006191079],"domain_scores_gemma":[0.99955195,0.00007504885,0.000036627112,0.00017203893,0.000012933978,0.00015141888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010138567,0.0001840595,0.00023520914,0.00013011452,0.00009197207,0.000024025681,0.00028176082,0.000044144897,0.0000061893766],"category_scores_gemma":[0.00008865254,0.0001872316,0.000039181006,0.00038718205,0.00011235179,0.00041672704,0.000097026554,0.00028097152,0.000015405878],"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":[0.00005086816,0.00022023583,0.035053663,0.00051771296,0.00002309097,0.00028713926,0.0015867839,0.5624762,0.36644396,0.0011516805,0.0026022363,0.02958638],"study_design_scores_gemma":[0.0017623598,0.0003153131,0.07052505,0.0005169012,0.000072009396,0.00053863483,0.000820696,0.799733,0.08905933,0.0011839672,0.033209167,0.0022635937],"about_ca_topic_score_codex":0.0000010815331,"about_ca_topic_score_gemma":4.1530075e-7,"teacher_disagreement_score":0.27738464,"about_ca_system_score_codex":0.0000810941,"about_ca_system_score_gemma":0.000009502837,"threshold_uncertainty_score":0.7635083},"labels":[],"label_agreement":null},{"id":"W2164874302","doi":"10.7554/elife.09457","title":"Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning","year":2015,"lang":"en","type":"article","venue":"eLife","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"National Institute on Deafness and Other Communication Disorders; Fundação para a Ciência e a Tecnologia; Biotechnology and Biological Sciences Research Council; Medical Research Council; Albert Einstein College of Medicine, Yeshiva University; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; European Commission; Canada Foundation for Innovation; Alfred P. Sloan Foundation","keywords":"Postsynaptic potential; Nonsynaptic plasticity; Neuroscience; Plasticity; Synaptic plasticity; Spike-timing-dependent plasticity; Post-tetanic potentiation; Metaplasticity; Postsynaptic density; Neuroplasticity; Homosynaptic plasticity; Biology; Inhibitory postsynaptic potential; Synaptic augmentation; Excitatory postsynaptic potential; Physics","score_opus":0.02255732682463166,"score_gpt":0.24597406245203093,"score_spread":0.22341673562739928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164874302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99428016,0.0008361555,0.0035190817,0.000010738692,0.000117473806,0.00006830856,5.357888e-7,0.00036615838,0.0008013901],"genre_scores_gemma":[0.99858266,0.00014047272,0.00064669736,0.000029294028,0.00008065901,0.0000021506435,0.0000022071215,0.000019502817,0.00049632805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945354,0.000011062185,0.000109441426,0.00014137862,0.00008647145,0.00019810135],"domain_scores_gemma":[0.9996889,0.00007033737,0.000019212624,0.000061664774,0.000025224781,0.00013460752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009401908,0.000107825945,0.00012565596,0.000040034898,0.00008277368,0.00003280872,0.000042617336,0.0000441866,0.0000038739636],"category_scores_gemma":[0.0000906901,0.000105843625,0.000009312391,0.00007306411,0.000028475828,0.00015212076,0.000058743393,0.00018655142,0.000008233318],"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":[0.00019428218,0.000035168658,0.10275819,0.0006157641,0.00008157801,0.000119682285,0.0021905296,0.7936384,0.08929377,0.0006362959,0.000580239,0.009856066],"study_design_scores_gemma":[0.0042426568,0.0011180986,0.24419813,0.0007700781,0.00012822733,0.00045784595,0.0006120361,0.383276,0.3539679,0.0006815117,0.008660742,0.0018867828],"about_ca_topic_score_codex":0.0000046502228,"about_ca_topic_score_gemma":0.0000027625788,"teacher_disagreement_score":0.41036245,"about_ca_system_score_codex":0.00001825612,"about_ca_system_score_gemma":0.000008843696,"threshold_uncertainty_score":0.43161777},"labels":[],"label_agreement":null},{"id":"W2165433905","doi":"10.1109/icnn.1994.374520","title":"Analog hardware tolerance of soft competitive learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"CMC Microsystems","keywords":"CMOS; Computer science; Competitive learning; Noise (video); Transistor; Artificial neural network; Artificial intelligence; Electronic engineering; Computer engineering; Electrical engineering; Engineering","score_opus":0.015944524119168954,"score_gpt":0.20722835363201914,"score_spread":0.19128382951285017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165433905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7646008,0.0009038344,0.08973367,0.000031691765,0.0002551052,0.00009446341,0.0000024644226,0.0007249264,0.14365306],"genre_scores_gemma":[0.9979564,0.000024296107,0.0009994026,0.000026452317,0.00003928128,8.4175156e-7,8.7771133e-7,0.000009864497,0.00094259914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964786,0.000008579871,0.000103526865,0.00007343349,0.000051497158,0.000115077964],"domain_scores_gemma":[0.99982756,0.000053726748,0.00001580657,0.00005893569,0.00001898375,0.000024991106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000021149786,0.00006552523,0.000106828535,0.000023577919,0.00003452247,0.0000035214248,0.000051332117,0.000019260257,0.00031872027],"category_scores_gemma":[0.000015782442,0.00006412333,0.000029414234,0.00008947217,0.000012325007,0.00006990408,0.000012704535,0.00012718009,0.000035370555],"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":[0.0000027392293,0.0000103149105,0.00088248483,0.00006952248,0.000013092145,0.000010943786,0.00048345432,0.95686847,0.022037672,0.0018521867,0.0002361931,0.017532934],"study_design_scores_gemma":[0.0003773861,0.00008054683,0.0018824348,0.00011706001,0.000007842706,0.000015253705,0.0003035724,0.82991254,0.15382509,0.00017282028,0.012993446,0.0003119861],"about_ca_topic_score_codex":6.596222e-7,"about_ca_topic_score_gemma":0.0000014726575,"teacher_disagreement_score":0.2333556,"about_ca_system_score_codex":0.000007725077,"about_ca_system_score_gemma":5.57489e-7,"threshold_uncertainty_score":0.34897634},"labels":[],"label_agreement":null},{"id":"W2166191096","doi":"10.1111/j.1468-0394.2009.00535.x","title":"The polychronic economy","year":2009,"lang":"en","type":"article","venue":"Expert Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Biological neuron model; Neuron; Spiking neural network; Turing; Neuroscience; Artificial intelligence; Artificial neural network; Cognitive science; Psychology","score_opus":0.009885987672847539,"score_gpt":0.22926488720582647,"score_spread":0.21937889953297893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166191096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4386373,0.18830931,0.045369994,0.0014348667,0.017584248,0.0012860813,0.0000021850387,0.0040526795,0.30332333],"genre_scores_gemma":[0.99870867,0.0000664097,0.000018495544,0.00008541154,0.00051849766,0.0000071612444,3.8500153e-7,0.000008626196,0.00058635586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995987,0.00001093176,0.00011644503,0.000070126174,0.000032048516,0.00017179114],"domain_scores_gemma":[0.99974465,0.000049635542,0.000012917714,0.00015496329,0.0000055829446,0.00003227393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005271584,0.000071024646,0.00007226108,0.000010495196,0.00013129547,0.000039967897,0.00010384101,0.000021367334,0.0000024845215],"category_scores_gemma":[0.000004279489,0.00004947828,0.00002590565,0.00003694346,0.0000076671295,0.00007169751,0.000005305642,0.00006685376,0.000050928662],"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":[0.000043056963,0.0000465458,0.00007087402,0.00012684277,0.00013589815,0.000086544416,0.004387961,0.3462097,0.14578758,0.059048213,0.13640252,0.30765426],"study_design_scores_gemma":[0.00018901675,0.000046296987,0.00006480488,0.00004983764,0.0000012124709,0.000049861726,0.0002753873,0.06451435,0.014954182,0.00029341146,0.9193434,0.00021821719],"about_ca_topic_score_codex":0.0000017950423,"about_ca_topic_score_gemma":5.4304616e-7,"teacher_disagreement_score":0.7829409,"about_ca_system_score_codex":0.000041037994,"about_ca_system_score_gemma":0.000003458346,"threshold_uncertainty_score":0.20176657},"labels":[],"label_agreement":null},{"id":"W2166644473","doi":"10.1109/saci.2011.5873060","title":"ASIPs for artificial neural networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Uniprocessor system; Artificial neural network; Initialization; Content-addressable memory; Parallel computing; Instruction set; Activation function; Embedded system; Computer architecture; Multiprocessing; Artificial intelligence","score_opus":0.0659051758029185,"score_gpt":0.236694099967538,"score_spread":0.1707889241646195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166644473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18728967,0.00007249043,0.7890702,0.000011468317,0.0011584929,0.0001872474,9.5128576e-7,0.0007795711,0.021429915],"genre_scores_gemma":[0.9966915,0.0000012229738,0.0028383166,0.000059945032,0.00023849038,0.0000067271685,0.0000013380743,0.000014807882,0.00014765548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996573,0.0000013619077,0.0000878789,0.00007039473,0.000020299603,0.0001627691],"domain_scores_gemma":[0.99985707,0.00002891854,0.000006806811,0.00006612028,0.0000085341135,0.00003257981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003084575,0.00006218156,0.000060062543,0.000013400949,0.000042053056,0.00000529872,0.000050834467,0.000026631697,0.00006055088],"category_scores_gemma":[0.0000059498534,0.00005674545,0.000033204542,0.00004045988,0.000007249656,0.00006557967,0.000009097667,0.00006283577,0.0000059647964],"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":[0.00007896323,0.000026248483,0.00010073461,0.00004352061,0.000025759442,0.000011962286,0.00030645553,0.49646384,0.0074583045,0.01476983,0.0023332757,0.4783811],"study_design_scores_gemma":[0.00007627385,0.00003674857,0.00014210514,0.0000024819174,0.000004360098,0.0000042114343,0.000022475037,0.9562466,0.040802658,0.0017764937,0.0007648135,0.00012073986],"about_ca_topic_score_codex":4.818968e-7,"about_ca_topic_score_gemma":0.000002969464,"teacher_disagreement_score":0.8094018,"about_ca_system_score_codex":0.000004674506,"about_ca_system_score_gemma":6.9791605e-7,"threshold_uncertainty_score":0.23140123},"labels":[],"label_agreement":null},{"id":"W2167024970","doi":"10.1242/jeb.02163","title":"Neuronal networks and synaptic plasticity: understanding complex system dynamics by interfacing neurons with silicon technologies","year":2006,"lang":"en","type":"review","venue":"Journal of Experimental Biology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Calgary","funders":"","keywords":"Interfacing; Neuroscience; Synaptic plasticity; Computer science; Sensory system; Biology","score_opus":0.046079958287193905,"score_gpt":0.2869670655428322,"score_spread":0.24088710725563828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167024970","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007244824,0.9638257,0.028017217,0.0000055063483,0.00042751763,0.00019021277,0.000022112103,0.0001847458,0.00008216404],"genre_scores_gemma":[0.74040705,0.2592578,0.00015599743,0.000005835049,0.00009065628,0.0000043946698,0.000019249297,0.000057458357,0.0000015669415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867874,0.0000774753,0.00064365694,0.00021523121,0.000070596536,0.00031429628],"domain_scores_gemma":[0.9991298,0.00030933437,0.00040347056,0.000089174704,0.0000138421865,0.000054388078],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006261617,0.00040043597,0.0011784049,0.00019238194,0.00010178076,0.00003361602,0.00021121673,0.00023819036,0.0000013416641],"category_scores_gemma":[0.000009928574,0.00029531444,0.000120872945,0.0001312015,0.0001808115,0.00006562832,0.00014531342,0.0008405648,4.7127182e-7],"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":[0.0006226145,0.00047208447,0.000111297035,0.03472141,0.005148495,0.002138901,0.00028793962,0.1466758,0.08584627,0.012219872,0.0028320455,0.7089233],"study_design_scores_gemma":[0.0065227463,0.017196435,0.000004825011,0.081633486,0.0039739497,0.06666089,0.017326558,0.66082764,0.016754244,0.0002871616,0.1209701,0.007841994],"about_ca_topic_score_codex":7.753791e-7,"about_ca_topic_score_gemma":0.0000012947255,"teacher_disagreement_score":0.7331622,"about_ca_system_score_codex":0.0005558586,"about_ca_system_score_gemma":0.000015458967,"threshold_uncertainty_score":0.9999499},"labels":[],"label_agreement":null},{"id":"W2168816712","doi":"10.1109/pacrim.1989.48445","title":"A data flow processor for edge tracing stored binary images","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Tracing; Computer science; Binary number; Binary data; TRACE (psycholinguistics); Object (grammar); Data flow diagram; Image processing; Computer graphics (images); Computer hardware; Artificial intelligence; Image (mathematics); Operating system; Arithmetic; Database","score_opus":0.05126650652157005,"score_gpt":0.28488330084634705,"score_spread":0.233616794324777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168816712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15942034,0.0012175001,0.82696253,0.000056599187,0.0006941349,0.00055204524,0.00009230947,0.001138004,0.009866552],"genre_scores_gemma":[0.92727435,0.000011992202,0.071882874,0.000043292956,0.00010210688,0.000014846263,0.000032437052,0.000032780135,0.00060529605],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994637,0.0000069756547,0.00011226168,0.00017746963,0.000043503063,0.00019609401],"domain_scores_gemma":[0.9996068,0.000078901125,0.000011771396,0.00024599358,0.000016753887,0.000039762625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010479703,0.00009776537,0.00010050188,0.000027475122,0.00007586462,0.00001600545,0.00015213867,0.000026376378,0.000019063455],"category_scores_gemma":[0.00009344424,0.000089027235,0.00001982363,0.00008571502,0.000008481409,0.00031012672,0.000024581219,0.00007557196,0.000006398588],"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":[0.000063778934,0.00009995629,0.0000584093,0.0016446691,0.00008501768,0.000041642386,0.000607197,0.41175443,0.46031457,0.0006646233,0.030040326,0.094625354],"study_design_scores_gemma":[0.0007137564,0.00006393737,0.000036550377,0.00006130987,0.000021699234,0.000027696824,0.0002626513,0.5486352,0.42339075,0.00044580942,0.025933664,0.0004069869],"about_ca_topic_score_codex":1.0832142e-7,"about_ca_topic_score_gemma":0.0000011582188,"teacher_disagreement_score":0.76785403,"about_ca_system_score_codex":0.000012049136,"about_ca_system_score_gemma":0.000008157067,"threshold_uncertainty_score":0.36304253},"labels":[],"label_agreement":null},{"id":"W2170184326","doi":"10.1109/ijcnn.2006.247076","title":"Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Benchmark (surveying); Recurrent neural network; Stochastic neural network; Artificial neural network; Inverted pendulum; Artificial intelligence; Construct (python library); Spiking neural network; Outcome (game theory); Machine learning; Mathematics","score_opus":0.026099613010765927,"score_gpt":0.25352625071448004,"score_spread":0.2274266377037141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170184326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991664,0.00017768226,0.0019744597,0.0005434543,0.0006426327,0.0002239308,0.000009157475,0.00018308831,0.0045816093],"genre_scores_gemma":[0.9986439,0.000065376116,0.0001578898,0.000104137514,0.00074908684,0.00000791844,0.0000171805,0.000028274822,0.00022624063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983742,0.000024152605,0.00047803635,0.00032243147,0.00036639493,0.0004347869],"domain_scores_gemma":[0.99926084,0.00014394349,0.00017336733,0.00011889646,0.00023446957,0.00006849603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029437832,0.0002828276,0.00041897126,0.00019618247,0.00013706647,0.00012505898,0.00033588655,0.000073302144,0.00004985088],"category_scores_gemma":[0.00001738234,0.00022595933,0.00014873905,0.00055399217,0.00012884758,0.0002056498,0.000084718675,0.0004878397,0.0000042508336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007804003,0.000019167992,0.001919503,0.000015484407,0.00016603683,0.0000048952506,0.00006717901,0.97687,0.009824263,0.004737857,0.0010093449,0.00528822],"study_design_scores_gemma":[0.00020279331,0.00008494243,0.006113934,0.00009510698,0.00011447036,0.00001623207,0.000031433457,0.9893694,0.0031460882,0.0004945377,0.0001050872,0.00022601102],"about_ca_topic_score_codex":0.000021584343,"about_ca_topic_score_gemma":0.000008303657,"teacher_disagreement_score":0.012499355,"about_ca_system_score_codex":0.00004908033,"about_ca_system_score_gemma":0.0000058115293,"threshold_uncertainty_score":0.9214354},"labels":[],"label_agreement":null},{"id":"W2173873847","doi":"10.3389/fnins.2015.00464","title":"Closed-Loop Neuromorphic Benchmarks","year":2015,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Air Force Office of Scientific Research; Mitacs; Canada Research Chairs; Office of Naval Research; Canada Foundation for Innovation; Ontario Innovation Trust","keywords":"Neuromorphic engineering; Benchmark (surveying); Computer science; Task (project management); Set (abstract data type); Hardware-in-the-loop simulation; Control (management); Computer engineering; Computer architecture; Artificial intelligence; Artificial neural network; Simulation; Engineering","score_opus":0.0410495683758058,"score_gpt":0.23315701806199096,"score_spread":0.19210744968618515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2173873847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9508398,0.00034831464,0.03506451,0.00007650381,0.008585861,0.00014767924,0.0000022761808,0.00025918384,0.0046758642],"genre_scores_gemma":[0.99723095,0.000037385653,0.0022018596,0.0003024594,0.00006106787,0.00000479586,0.0000011148658,0.000016771557,0.0001435793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989797,0.000030435269,0.00016739362,0.00027583135,0.00019349912,0.00035313496],"domain_scores_gemma":[0.9995884,0.000018822093,0.000021520354,0.00020544352,0.000014386304,0.00015145693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015352179,0.0001297066,0.00013951407,0.00012801103,0.000050584902,0.000029756995,0.0003370146,0.00003592294,0.0000025794254],"category_scores_gemma":[0.0001493426,0.00013554565,0.000024281084,0.00058294367,0.00009785699,0.00029678544,0.000060290684,0.00027057782,0.000004516346],"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":[0.00003979081,0.00006974207,0.029204292,0.000060565606,0.0000021012518,0.0006657582,0.00058281684,0.77827144,0.10579966,0.00030830377,0.05200036,0.03299515],"study_design_scores_gemma":[0.0010781141,0.00023251554,0.0194466,0.000058060097,0.0000066626253,0.00009960713,0.00020706217,0.90195006,0.03122802,0.0031400197,0.04174989,0.0008034069],"about_ca_topic_score_codex":0.0000010527801,"about_ca_topic_score_gemma":8.069012e-7,"teacher_disagreement_score":0.12367858,"about_ca_system_score_codex":0.000044905748,"about_ca_system_score_gemma":0.000020445297,"threshold_uncertainty_score":0.5527391},"labels":[],"label_agreement":null},{"id":"W2178393517","doi":"10.1139/cjp-2013-0569","title":"The role of biasing electric field in intrinsic resistive switching characteristics of highly silicon-rich a-SiO<sub><i>x</i></sub> films","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dangling bond; Biasing; Percolation (cognitive psychology); Electric field; Silicon; Materials science; Condensed matter physics; Voltage; Electroforming; X-ray photoelectron spectroscopy; Optoelectronics; Nanotechnology; Physics; Nuclear magnetic resonance; Layer (electronics)","score_opus":0.005395597753129476,"score_gpt":0.18371823051544828,"score_spread":0.1783226327623188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178393517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972914,0.00036414215,0.0018413814,0.000023883347,0.00022159792,0.000044069286,0.0000025239008,0.000005522516,0.00020547841],"genre_scores_gemma":[0.9995954,0.000052953168,0.000095631374,0.000031620788,0.00020467277,4.3615256e-7,5.0970897e-7,0.000017998389,7.6948567e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990866,0.00004693359,0.0004364169,0.000069607486,0.00010450277,0.00025594333],"domain_scores_gemma":[0.9988528,0.00051462353,0.0002601126,0.00012845272,0.000114530136,0.00012951886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023364283,0.00011244239,0.00027040107,0.000129073,0.00008392561,0.00001650271,0.00019085615,0.0000465858,7.6093096e-7],"category_scores_gemma":[0.0002869196,0.000100291276,0.00006638936,0.00034465044,0.000018414998,0.000115931296,0.000010554606,0.00041948407,6.1660234e-7],"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":[0.000017950813,0.0000055134446,0.0016028822,0.00003811142,0.000023241497,0.000010809341,0.0004088893,0.0067335037,0.7315992,0.00067920884,0.000026566326,0.25885412],"study_design_scores_gemma":[0.00017729914,0.00011098096,0.008974418,0.0002152467,0.000018474046,0.00001125741,0.0001323192,0.0047020935,0.98306054,0.0023208796,0.00015682526,0.00011965113],"about_ca_topic_score_codex":0.00007133446,"about_ca_topic_score_gemma":0.0004164683,"teacher_disagreement_score":0.25873446,"about_ca_system_score_codex":0.00006062934,"about_ca_system_score_gemma":0.00013654049,"threshold_uncertainty_score":0.40897596},"labels":[],"label_agreement":null},{"id":"W2180261481","doi":"10.1023/b:aire.0000006611.32608.f2","title":"Three-Dimensional Feedforward Neural Networks and Their Realization by Nano-Devices","year":2003,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hypercube; Computer science; Realization (probability); Embedding; Binary number; Feedforward neural network; Artificial neural network; Feed forward; Topology (electrical circuits); Binary tree; Theoretical computer science; Algorithm; Parallel computing; Artificial intelligence; Mathematics; Combinatorics; Arithmetic","score_opus":0.037847750030204685,"score_gpt":0.27439843928766167,"score_spread":0.23655068925745698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180261481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022303697,0.32507655,0.6508063,0.0001521117,0.00054267404,0.0004929504,0.000004340019,0.00028370967,0.00033763118],"genre_scores_gemma":[0.9759871,0.022979192,0.00028327986,0.0006086613,0.000076675184,0.0000129530545,0.000015035031,0.000028908984,0.000008201544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990607,0.000045156503,0.00036440865,0.0002084528,0.00007794864,0.00024331831],"domain_scores_gemma":[0.9995717,0.00011209326,0.000054412354,0.00014582626,0.000037608912,0.00007836567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024551974,0.00018307952,0.0002484834,0.000021604132,0.00012207736,0.000027383523,0.00008541108,0.000048155518,0.000057094174],"category_scores_gemma":[0.00006315276,0.00014871314,0.000054009288,0.000256326,0.00004183153,0.00016132425,0.000019955363,0.00014290522,0.000016577846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052611053,0.000021362115,0.000071104536,0.0007985582,0.000024481655,0.0000039561496,0.00003932679,0.17157729,0.0037848158,0.008378184,0.001098675,0.814197],"study_design_scores_gemma":[0.000028680684,0.000083501596,0.000016551421,0.0020371594,0.00005373533,0.00007648396,0.00004278721,0.89132875,0.06536729,0.010886285,0.029369948,0.00070883235],"about_ca_topic_score_codex":0.0000036884524,"about_ca_topic_score_gemma":0.00002450679,"teacher_disagreement_score":0.9536834,"about_ca_system_score_codex":0.000015261809,"about_ca_system_score_gemma":0.0000047460276,"threshold_uncertainty_score":0.6064346},"labels":[],"label_agreement":null},{"id":"W2193426143","doi":"10.1038/nnano.2015.215","title":"Naturally random","year":2015,"lang":"en","type":"letter","venue":"Nature Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Nanotechnology; Materials science; Computer science","score_opus":0.007990323517817548,"score_gpt":0.2282824939067084,"score_spread":0.22029217038889085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2193426143","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070651174,0.061526533,0.0034672995,0.880019,0.021267153,0.0012875709,0.00010571209,0.0161078,0.009153809],"genre_scores_gemma":[0.07978038,0.0004992843,0.0033681665,0.90058917,0.011010983,0.0000669963,0.0005126366,0.0004937945,0.0036785705],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983474,0.00003369591,0.00028857435,0.00043278234,0.00027894715,0.0006186021],"domain_scores_gemma":[0.9990348,0.000159527,0.0000780767,0.00058324856,0.000104725346,0.000039600112],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00010826837,0.00047993814,0.00061950396,0.0003593308,0.00006078547,0.000018674196,0.00071148964,0.009457563,0.000022400754],"category_scores_gemma":[0.00021604508,0.0004379834,0.00013956107,0.00037172178,0.00009310101,0.00007828155,0.00011674175,0.017269114,0.00011502225],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011909453,0.0000017161665,3.143286e-7,0.0000966075,0.000045285276,0.0007648774,0.000007748623,0.00059977535,0.00248944,0.000055401288,0.98749053,0.008436372],"study_design_scores_gemma":[0.0006562183,0.00003251689,2.6188815e-7,0.00005795816,0.00002766553,0.0002620518,0.0000022289084,0.00034583014,0.017045015,0.0034653046,0.97764176,0.00046319864],"about_ca_topic_score_codex":3.5282068e-7,"about_ca_topic_score_gemma":0.0000014730232,"teacher_disagreement_score":0.07271526,"about_ca_system_score_codex":0.00015034864,"about_ca_system_score_gemma":0.000036288617,"threshold_uncertainty_score":0.9998072},"labels":[],"label_agreement":null},{"id":"W2211622316","doi":"10.1109/iccv.2015.289","title":"Contractive Rectifier Networks for Nonlinear Maximum Margin Classification","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"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":"MNIST database; Margin (machine learning); Nonlinear system; Support vector machine; Rectifier (neural networks); Contraction (grammar); Computer science; Mathematical optimization; Mathematics; Algorithm; Control theory (sociology); Artificial intelligence; Machine learning; Artificial neural network; Recurrent neural network","score_opus":0.05858663890322559,"score_gpt":0.2797400539479395,"score_spread":0.22115341504471392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211622316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033114877,0.000104715895,0.9555062,0.00012760679,0.0008296628,0.00028202118,0.0000033951321,0.0003908388,0.009640697],"genre_scores_gemma":[0.97941315,0.00001104045,0.019155396,0.00010413928,0.0004935941,0.000029945044,0.000025150319,0.00002924722,0.0007383514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995714,0.0000090536205,0.00011021185,0.00010840572,0.000043851334,0.00015711277],"domain_scores_gemma":[0.9996565,0.00010354016,0.00001878986,0.000096122414,0.000057989208,0.000067082554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113625094,0.000078400204,0.000087543056,0.00002026536,0.00003425183,0.000013399623,0.000050015213,0.00005299958,0.000008773302],"category_scores_gemma":[0.000043926175,0.0000729964,0.000027525406,0.00006772196,0.00000904135,0.00012393175,0.000007462115,0.00010624518,0.0000127615185],"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":[0.0001987134,0.00003866838,0.00008814498,0.00003458227,0.000049800135,0.0000046525706,0.00020318161,0.7932786,0.012863002,0.0021306947,0.0141095985,0.1770004],"study_design_scores_gemma":[0.0004123155,0.000034750818,0.000105309984,0.0000069663415,0.000006651616,0.000004417454,0.00014219196,0.9542478,0.0118958065,0.0013067316,0.0317058,0.00013126168],"about_ca_topic_score_codex":6.5394363e-7,"about_ca_topic_score_gemma":0.0000031393693,"teacher_disagreement_score":0.94629824,"about_ca_system_score_codex":0.000043182434,"about_ca_system_score_gemma":0.0000064817145,"threshold_uncertainty_score":0.2976707},"labels":[],"label_agreement":null},{"id":"W2258411813","doi":"10.1038/srep21525","title":"Spatially resolved TiOx phases in switched RRAM devices using soft X-ray spectromicroscopy","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":31,"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; Brockhouse Institute for Materials Research","funders":"Engineering and Physical Sciences Research Council; University of Southampton","keywords":"Materials science; Resistive random-access memory; Transmission electron microscopy; Lamella (surface anatomy); Resistive touchscreen; Orthorhombic crystal system; Rutile; Electrode; Nanotechnology; Optoelectronics; Joule heating; Diffraction; Optics; Chemical engineering; Chemistry; Computer science; Composite material","score_opus":0.022341294236089604,"score_gpt":0.2682813460052556,"score_spread":0.245940051769166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258411813","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96959454,0.00016638084,0.02547885,0.000022848551,0.0040675104,0.00016863433,0.000001284265,0.00024185436,0.00025809027],"genre_scores_gemma":[0.9960534,0.0000033550052,0.0034271094,0.00001198626,0.000118468764,0.0000035015444,0.000003893683,0.000025554435,0.00035270717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983826,0.000014977075,0.0004757983,0.00050342095,0.00021385567,0.0004092935],"domain_scores_gemma":[0.99923146,0.00006627112,0.00012396017,0.00045177736,0.00004423838,0.00008229079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005497732,0.0001660407,0.00020174314,0.00016974281,0.00015468108,0.00011326647,0.000118362055,0.000051200994,0.00007788091],"category_scores_gemma":[0.00011685053,0.00012932968,0.000055650973,0.0003583282,0.00009071248,0.00035632568,0.000046370566,0.00010031761,0.0000105716545],"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":[0.000006377555,0.00001502153,0.0017361128,0.000018598845,0.000004777232,0.00027555024,0.00008765897,0.014066147,0.9802107,0.0000031274587,0.00011384701,0.003462079],"study_design_scores_gemma":[0.00023599686,0.0000152105895,0.0006068275,0.00023482298,0.0000077737095,0.000096047064,0.000020991118,0.004679641,0.98844033,0.0013667583,0.004033872,0.00026173147],"about_ca_topic_score_codex":0.000008067465,"about_ca_topic_score_gemma":0.000060216353,"teacher_disagreement_score":0.026458874,"about_ca_system_score_codex":0.00011440598,"about_ca_system_score_gemma":0.000054146574,"threshold_uncertainty_score":0.52739114},"labels":[],"label_agreement":null},{"id":"W2262010595","doi":"10.1038/srep20659","title":"Boosting the Transparency of Thin Layers by Coatings of Opposing Susceptibility: How Metals Help See Through Dielectrics","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Transparency (behavior); Boosting (machine learning); Dielectric; Materials science; Computer science; Optoelectronics; Artificial intelligence; Computer security","score_opus":0.025506861570972147,"score_gpt":0.24252653271444705,"score_spread":0.2170196711434749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2262010595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9503718,0.0006659197,0.04634162,0.00014086961,0.0015691132,0.00022939428,0.0000064950063,0.00011596234,0.00055883825],"genre_scores_gemma":[0.9984925,0.000015001437,0.00095408806,0.0000078739495,0.000025525957,0.0000030661936,0.00000448652,0.000018826402,0.00047863266],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831325,0.000049138187,0.000580455,0.00037698203,0.0003739253,0.00030627815],"domain_scores_gemma":[0.99871397,0.0002515658,0.000330964,0.0005430814,0.00011724217,0.00004314789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013010561,0.0001496104,0.00026478534,0.00004774513,0.00019927957,0.00004399533,0.00017306018,0.000048429494,0.000018180795],"category_scores_gemma":[0.0003810603,0.00009219987,0.000111272144,0.00047529434,0.00023349118,0.0003391457,0.00003499891,0.000116684365,6.7957507e-7],"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":[0.0000025827403,0.000010797743,0.0005113532,0.00007854133,0.000013028304,0.000010606862,0.00081227894,0.0028657888,0.9902109,0.000027463067,0.0010138489,0.004442848],"study_design_scores_gemma":[0.00007529577,0.000027067897,0.00004010996,0.00012688396,0.000024178038,0.00003163751,0.00018079142,0.00095437287,0.99504155,0.0018625841,0.0015080059,0.00012749906],"about_ca_topic_score_codex":0.000006123418,"about_ca_topic_score_gemma":0.0000055033242,"teacher_disagreement_score":0.04812071,"about_ca_system_score_codex":0.000032585016,"about_ca_system_score_gemma":0.000029524206,"threshold_uncertainty_score":0.37598017},"labels":[],"label_agreement":null},{"id":"W2264952008","doi":"10.1091/mbc.e15-04-0257","title":"Endosomal Na<sup>+</sup>/H<sup>+</sup>exchanger NHE5 influences MET recycling and cell migration","year":2015,"lang":"en","type":"article","venue":"Molecular Biology of the Cell","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Endosome; Cell biology; Biology; Transferrin receptor; Endocytosis; CDC42; Signal transduction; Receptor tyrosine kinase; RAC1; Phosphatidylinositol; Kinase; Receptor; Biochemistry; Intracellular","score_opus":0.017029801062112906,"score_gpt":0.2378399780181225,"score_spread":0.2208101769560096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2264952008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916989,0.00277207,0.004273936,0.0000937495,0.00016373525,0.0001963523,0.000008729245,0.00008806345,0.00070442306],"genre_scores_gemma":[0.99656063,0.00006799548,0.0029488658,0.00017588695,0.00007796394,0.000008836623,0.000011949461,0.000028418548,0.00011946385],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988287,0.0001763101,0.00028856087,0.00028260815,0.00010604272,0.0003177682],"domain_scores_gemma":[0.99935913,0.00010381581,0.00009358502,0.00030097834,0.000050456285,0.000092035174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024768827,0.00022969242,0.00026904448,0.000077970966,0.00008841427,0.000013331475,0.00027259186,0.00017168943,0.0000069662697],"category_scores_gemma":[0.000074164316,0.00018204463,0.0000953143,0.00016346088,0.00013099644,0.00008206079,0.00014841388,0.00026408734,0.000010953997],"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":[0.000022744653,0.00001703429,0.0013219664,0.000063584805,0.000023050188,0.000008632996,0.00095585675,0.33616117,0.6598661,0.000034589288,0.00006898212,0.0014562921],"study_design_scores_gemma":[0.0004892935,0.00013432196,0.000048445716,0.00003377883,0.000038149028,0.000013973177,0.00027693974,0.051047206,0.94528127,0.00072949514,0.0016845108,0.00022264551],"about_ca_topic_score_codex":0.000016921098,"about_ca_topic_score_gemma":0.000002098159,"teacher_disagreement_score":0.2854151,"about_ca_system_score_codex":0.000025302146,"about_ca_system_score_gemma":0.000016597683,"threshold_uncertainty_score":0.7423565},"labels":[],"label_agreement":null},{"id":"W2267425405","doi":"10.1149/ma2011-01/39/1868","title":"Probing Electrochemical Charge Transfer at Surfaces Using Graphene Transistors","year":2011,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal; McGill University; Université de Montréal","funders":"","keywords":"Graphene; Transistor; Charge (physics); Materials science; Electrochemistry; Nanotechnology; Optoelectronics; Transfer (computing); Electrode; Electrical engineering; Voltage; Physics; Computer science; Engineering; Quantum mechanics","score_opus":0.02993866676466657,"score_gpt":0.21913624889169553,"score_spread":0.18919758212702897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2267425405","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9838518,0.00040846533,0.00036739162,0.0000045589554,0.0002899493,0.00012794665,0.0000016561714,0.00054494076,0.014403315],"genre_scores_gemma":[0.99728566,0.000020291493,0.0024746826,0.000017181765,0.00010271876,0.0000035416003,0.000003787198,0.00006486995,0.000027250162],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865276,0.000020830319,0.00035792778,0.00027554418,0.00014943744,0.0005434833],"domain_scores_gemma":[0.99960005,0.000064719025,0.000033821478,0.00014632297,0.000024544686,0.00013055044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020548937,0.00025313048,0.00022983507,0.000069175614,0.00019461024,0.000013623098,0.0001274786,0.00010971757,0.000037955688],"category_scores_gemma":[0.0000233047,0.00026769767,0.00010019333,0.00017150897,0.000036058926,0.00016355689,0.000013260827,0.00032360203,0.000019768015],"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":[0.000020733076,0.000017239185,0.00018395804,0.00007933973,0.000019932368,0.000016889153,0.0011119257,0.049209345,0.9492588,0.0000033595295,0.000007356448,0.00007109104],"study_design_scores_gemma":[0.0001986285,0.000020775205,0.0003951759,0.00011725279,0.00002502649,0.000034764777,0.00002913143,0.0047205626,0.99395126,0.000050647057,0.00014707042,0.00030972998],"about_ca_topic_score_codex":0.000010483286,"about_ca_topic_score_gemma":0.0000054187303,"teacher_disagreement_score":0.044692405,"about_ca_system_score_codex":0.00008709619,"about_ca_system_score_gemma":0.0000101984715,"threshold_uncertainty_score":0.9999775},"labels":[],"label_agreement":null},{"id":"W2267461739","doi":"10.1149/ma2008-02/1/140","title":"N-Doping Nature and Electrochemical Performance of Ordered TiO2-xNx Nanotubes Synthesized via the Anodizing Process and Plasma Treatment","year":2008,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Anodizing; Doping; Materials science; Electrochemistry; Plasma; Chemical engineering; Nanotechnology; Process (computing); Optoelectronics; Metallurgy; Chemistry; Physical chemistry; Electrode; Computer science; Physics; Aluminium","score_opus":0.009380423267618341,"score_gpt":0.21990828553409933,"score_spread":0.210527862266481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2267461739","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972374,0.0010917793,0.000009555077,0.000031212407,0.000058724836,0.0001250608,6.965291e-7,0.000114886774,0.0013306745],"genre_scores_gemma":[0.9988072,0.0003509369,0.0007121405,0.000012306517,0.00007368759,0.000006702453,0.0000014848979,0.00002223268,0.000013339446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922955,0.000014264281,0.00022468684,0.0001793709,0.00010604885,0.00024608176],"domain_scores_gemma":[0.9994386,0.00031156378,0.00007219803,0.00010027165,0.000028920264,0.000048459664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102953076,0.00017451048,0.00020172971,0.000033401244,0.00024163342,0.000013012464,0.000072235474,0.0000928209,8.112886e-7],"category_scores_gemma":[0.00010282309,0.00013093512,0.000023606259,0.00009459614,0.00006540793,0.00011098031,0.000018555353,0.0002635029,6.7278313e-7],"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":[0.00005004388,0.000016162576,0.0013306936,0.0002228163,0.000029714554,0.000010918085,0.0008563787,0.036473475,0.95887566,9.018407e-7,0.000003223772,0.0021300174],"study_design_scores_gemma":[0.00030184112,0.00006012095,0.0027202915,0.00020714909,0.000015435444,0.00019622265,0.000060545957,0.025800463,0.9704136,0.000016061505,0.000064751424,0.00014352147],"about_ca_topic_score_codex":0.0000022134602,"about_ca_topic_score_gemma":0.0000013701188,"teacher_disagreement_score":0.011537943,"about_ca_system_score_codex":0.000025521744,"about_ca_system_score_gemma":0.000014063825,"threshold_uncertainty_score":0.53393793},"labels":[],"label_agreement":null},{"id":"W2276570126","doi":"10.1371/journal.pone.0149928","title":"Optimizing Semantic Pointer Representations for Symbol-Like Processing in Spiking Neural Networks","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Compute Canada","keywords":"Computer science; Pointer (user interface); Artificial neural network; Computation; Artificial intelligence; Heuristic; Transformation (genetics); Neuromorphic engineering; Latency (audio); Theoretical computer science; Algorithm","score_opus":0.04410978073342397,"score_gpt":0.253967972887517,"score_spread":0.20985819215409302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276570126","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7959697,0.0002486446,0.20293246,0.00010913436,0.00009807853,0.0002461968,9.0935936e-7,0.00024631582,0.00014850963],"genre_scores_gemma":[0.99176127,0.000016096412,0.007824415,0.000056237877,0.00018802162,0.000036969075,0.0000019468978,0.00003829201,0.00007678231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999207,0.000012204318,0.00021731285,0.0001847339,0.00007904323,0.00029967233],"domain_scores_gemma":[0.9996486,0.0001244951,0.000034295426,0.00012321431,0.000029006536,0.00004038887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007501493,0.0001128329,0.00016567436,0.00007353917,0.00007449188,0.000033138534,0.00008816846,0.00003764976,0.000006749408],"category_scores_gemma":[0.000050230617,0.0000962383,0.000035755103,0.0001304574,0.000015109378,0.0003767858,0.000031729804,0.000116709074,0.000002348667],"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":[0.00005970709,0.0001597353,0.0033458713,0.00057544495,0.000053742173,0.000026465106,0.0008133594,0.613872,0.34962377,0.000042231863,0.00003172544,0.031395964],"study_design_scores_gemma":[0.0006405303,0.000024794634,0.0004949086,0.0009821632,0.000027208684,0.000003912707,0.000078493424,0.9582714,0.039133836,0.00014789774,0.000004600524,0.00019027761],"about_ca_topic_score_codex":6.451988e-7,"about_ca_topic_score_gemma":0.0000057738976,"teacher_disagreement_score":0.3443994,"about_ca_system_score_codex":0.000038787297,"about_ca_system_score_gemma":0.000003284005,"threshold_uncertainty_score":0.3924484},"labels":[],"label_agreement":null},{"id":"W2278346737","doi":"","title":"A reminder system for memory loss","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer security; Business","score_opus":0.024873053353677555,"score_gpt":0.22099438973475624,"score_spread":0.19612133638107868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278346737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76576674,0.00014085116,0.21129562,0.000013744069,0.00071955245,0.0001773178,0.0000016044795,0.0010424351,0.020842116],"genre_scores_gemma":[0.99548376,0.0000033562485,0.0032835857,0.000027236101,0.00013885683,0.000008744332,9.421323e-7,0.000015972955,0.0010375783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965894,0.0000025578543,0.00008801258,0.00007694258,0.000038207043,0.00013531446],"domain_scores_gemma":[0.9998118,0.000047358302,0.000007202284,0.000088142515,0.000012812652,0.00003265565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000029549923,0.00006335469,0.000080006845,0.000017736022,0.00007034052,0.0000023820467,0.000047157086,0.000026405041,0.0000076198353],"category_scores_gemma":[0.0000061983847,0.000056816305,0.000033805823,0.00004273903,0.000009495294,0.000053661053,0.000007925065,0.000046807076,0.000019798354],"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":[0.00011624691,0.000047660633,0.00048024824,0.0026950338,0.00015093677,0.0005325108,0.0018703956,0.6775077,0.24788386,0.008093991,0.027035551,0.03358585],"study_design_scores_gemma":[0.0008318261,0.00004142427,0.00026030862,0.00006764091,0.000010135583,0.0006419737,0.00027371212,0.08398066,0.90568775,0.00010990685,0.007703267,0.00039139375],"about_ca_topic_score_codex":3.0769897e-7,"about_ca_topic_score_gemma":3.4350865e-7,"teacher_disagreement_score":0.6578039,"about_ca_system_score_codex":0.000024286039,"about_ca_system_score_gemma":0.0000031059244,"threshold_uncertainty_score":0.23169017},"labels":[],"label_agreement":null},{"id":"W2290537806","doi":"10.1109/iedm.2015.7409670","title":"1Kbit FinFET Dielectric (FIND) RRAM in pure 16nm FinFET CMOS logic process","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"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":"Institute of Population and Public Health; Taiwan Semiconductor Manufacturing Company","keywords":"Resistive random-access memory; CMOS; Reset (finance); Materials science; Transistor; Voltage; Optoelectronics; Reliability (semiconductor); Logic gate; Electrical engineering; Electronic engineering; Engineering; Physics; Power (physics)","score_opus":0.034244457156083326,"score_gpt":0.2698177432402315,"score_spread":0.23557328608414818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290537806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95855314,0.0006324307,0.008338362,0.00013969168,0.00027325648,0.00026437562,0.000001618294,0.0006849375,0.031112185],"genre_scores_gemma":[0.99843967,0.000032621243,0.00048064842,0.00020441391,0.00010672011,0.000017707664,0.000005876706,0.000026133886,0.0006862044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901813,0.000015955977,0.00022425974,0.00020760317,0.0001371693,0.00039685683],"domain_scores_gemma":[0.99961823,0.000039685834,0.000025730926,0.0001640417,0.00003924945,0.00011307656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117794596,0.00018610731,0.00020213952,0.00010759377,0.000034457615,0.000021696911,0.00017117047,0.000097066186,0.00005777812],"category_scores_gemma":[0.00011030809,0.00016553464,0.000033391254,0.00050206104,0.000013196132,0.00021491195,0.000034319553,0.00033291514,0.000092648246],"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":[0.000029637555,0.00009776037,0.0017556099,0.00023763723,0.000021239388,0.00016941529,0.00092014845,0.9285511,0.009763872,0.0022922712,0.0033411041,0.052820235],"study_design_scores_gemma":[0.0055503817,0.0010760237,0.01065958,0.0003084556,0.00004605538,0.00025715271,0.00066624035,0.71040875,0.19549826,0.057807807,0.014489178,0.0032321159],"about_ca_topic_score_codex":0.0000021864064,"about_ca_topic_score_gemma":0.000023599752,"teacher_disagreement_score":0.21814232,"about_ca_system_score_codex":0.0000630895,"about_ca_system_score_gemma":0.000030189163,"threshold_uncertainty_score":0.6750307},"labels":[],"label_agreement":null},{"id":"W2294111665","doi":"10.14778/2732951.2732960","title":"Scalable logging through emerging non-volatile memory","year":2014,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":206,"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":"Commit; Scalability; Computer science; Logging; Dram; Bottleneck; Embedded system; Overhead (engineering); Cache; Operating system; Computer hardware; Database; Forestry","score_opus":0.009201764651845435,"score_gpt":0.2140710501752543,"score_spread":0.20486928552340886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294111665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93868697,0.00017060743,0.0029936065,0.00016682343,0.00066026096,0.00029328512,0.0000010982696,0.00027859045,0.056748763],"genre_scores_gemma":[0.9965774,0.000023531016,0.0027802605,0.00009665233,0.00017426004,0.000015871929,3.5789773e-7,0.00003306024,0.00029863088],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990234,0.0000033004628,0.00026340704,0.00018547502,0.00020221664,0.00032224128],"domain_scores_gemma":[0.9996544,0.000041771935,0.000095654534,0.00011935652,0.000046263198,0.000042524945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001940666,0.00017410413,0.00020763825,0.00003844881,0.00015506144,0.000022632346,0.00031835583,0.000038797836,0.000019970512],"category_scores_gemma":[0.00004329647,0.00013455709,0.00008979337,0.00018769012,0.000038390892,0.00027723875,0.00015832955,0.00019821149,0.000010447451],"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":[0.000014485829,0.000030463498,0.00091348856,0.0006078284,0.00004886808,3.573366e-7,0.0014106685,0.050189327,0.93157315,0.0017589668,0.0026234898,0.010828932],"study_design_scores_gemma":[0.00032718136,0.000030218405,0.00024181975,0.00019594416,0.00002127989,0.0000074566774,0.00022397116,0.043345474,0.9474207,0.0033793969,0.004614752,0.00019177303],"about_ca_topic_score_codex":0.0000062430413,"about_ca_topic_score_gemma":3.6681053e-7,"teacher_disagreement_score":0.05789041,"about_ca_system_score_codex":0.0000529348,"about_ca_system_score_gemma":0.0000041957805,"threshold_uncertainty_score":0.5487079},"labels":[],"label_agreement":null},{"id":"W2295689585","doi":"10.1109/embc.2015.7318819","title":"Low-power adaptive spike detector based on a sigma-delta control loop","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Microcontroller; Detector; Delta-sigma modulation; Thresholding; SIGNAL (programming language); Computer science; Spike (software development); Power (physics); Noise (video); Sigma; Electronic engineering; Computer hardware; Artificial intelligence; Engineering; Physics; CMOS; Telecommunications","score_opus":0.02023885378216347,"score_gpt":0.22406693267019917,"score_spread":0.2038280788880357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295689585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4086977,0.00007089244,0.5701023,0.000060360922,0.0006686929,0.0003147069,0.000010207022,0.0010016403,0.019073509],"genre_scores_gemma":[0.998186,3.0669565e-7,0.0010463118,0.00047377317,0.00008936274,0.00000837456,0.0000012716832,0.00003118268,0.0001633957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926865,0.00002127602,0.00013956934,0.0001710979,0.00014415436,0.00025526845],"domain_scores_gemma":[0.9994548,0.00013718828,0.000018743578,0.00019380101,0.000038264003,0.00015722489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009340582,0.00016410282,0.0001634452,0.00005120815,0.000033809167,0.000013875499,0.00009943437,0.00005273768,0.00010049545],"category_scores_gemma":[0.000057104546,0.00014097884,0.000052064748,0.0000924317,0.000016205124,0.00008953249,0.000012356467,0.00017795447,0.0001271559],"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":[0.0001854873,0.000030299027,0.000032549775,0.00001140437,0.000014635948,0.000043263677,0.000051385636,0.97803193,0.016944764,0.00015214845,0.0007836402,0.0037185142],"study_design_scores_gemma":[0.0019504181,0.00041209164,0.00009732042,0.00004025839,0.000009399516,0.0000033138504,0.00005856029,0.8665617,0.12931773,0.000115272815,0.001124634,0.00030926205],"about_ca_topic_score_codex":0.0000016977232,"about_ca_topic_score_gemma":0.0000053084345,"teacher_disagreement_score":0.5894883,"about_ca_system_score_codex":0.000059024893,"about_ca_system_score_gemma":0.000015989097,"threshold_uncertainty_score":0.574895},"labels":[],"label_agreement":null},{"id":"W2296127811","doi":"10.1016/j.neucom.2016.01.003","title":"Effect of spike-timing-dependent plasticity on neural assembly computing","year":2016,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Spiking neural network; Spike-timing-dependent plasticity; Memorization; Artificial intelligence; Spike (software development); Neural ensemble; Convergence (economics); Similarity (geometry); Pattern recognition (psychology); Synaptic plasticity; Machine learning; Mathematics; Biology","score_opus":0.013760659247500723,"score_gpt":0.25569310630174963,"score_spread":0.2419324470542489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296127811","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95455295,0.000020714495,0.042631485,0.00002321823,0.00083147414,0.0002437887,0.0000029102391,0.000671209,0.0010222504],"genre_scores_gemma":[0.999127,0.0000024820295,0.00034905842,0.00006150556,0.00037242947,0.000002022096,6.435343e-7,0.00006883715,0.000016057706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803776,0.00015606129,0.000500124,0.00042021344,0.0003100084,0.0005758585],"domain_scores_gemma":[0.99695575,0.0025040363,0.00014940972,0.00023140086,0.000036654917,0.00012277221],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031917795,0.00036511503,0.00044957435,0.00013874799,0.00015988907,0.000023817232,0.0002988115,0.00008057931,0.0000077426375],"category_scores_gemma":[0.00024894153,0.00026807727,0.00013856658,0.00020292266,0.000044611377,0.00012912384,0.00015655176,0.0003455686,0.000031149524],"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":[0.000069976326,0.000018238865,0.00088070636,0.00019057088,0.000016329337,0.000049875867,0.000047540656,0.37173134,0.41040862,0.000062779625,0.000031742635,0.21649227],"study_design_scores_gemma":[0.0010302127,0.00076465827,0.001985599,0.00038244523,0.000020476407,0.00005005962,0.000003864386,0.24076848,0.7545939,0.00001446462,0.00006439781,0.00032142265],"about_ca_topic_score_codex":0.0000011208517,"about_ca_topic_score_gemma":3.4697987e-7,"teacher_disagreement_score":0.3441853,"about_ca_system_score_codex":0.00005650791,"about_ca_system_score_gemma":0.000006420703,"threshold_uncertainty_score":0.9999772},"labels":[],"label_agreement":null},{"id":"W2298974907","doi":"10.3389/fncom.2016.00015","title":"Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights","year":2016,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Mean field theory; Computer science; Artificial neural network; Type (biology); Mathematics; Topology (electrical circuits); Algorithm; Physics; Combinatorics; Artificial intelligence","score_opus":0.014513276073952058,"score_gpt":0.24175385490024254,"score_spread":0.22724057882629048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298974907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2558963,0.000028352717,0.7429451,0.00006407498,0.00080126274,0.000164201,0.000005287497,0.00005900415,0.000036464313],"genre_scores_gemma":[0.94671357,0.000009649034,0.05308099,0.00010984694,0.000042715994,0.0000063381303,0.000004384895,0.000018670497,0.000013844463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989304,0.000024152223,0.00028434212,0.0002924872,0.00018298031,0.00028566486],"domain_scores_gemma":[0.9991524,0.0005177078,0.000076053126,0.0001183634,0.0000693541,0.00006612459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000112197355,0.00014430989,0.00020073014,0.00013643336,0.00007898145,0.000018491006,0.00023325671,0.000037575926,8.8928317e-7],"category_scores_gemma":[0.00014124042,0.000113822876,0.000039219485,0.000400609,0.000097639284,0.00022668479,0.000033721295,0.00013266478,1.17366326e-7],"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":[0.00011305317,0.000018935436,0.0053681005,0.000032240863,0.000004598951,0.000012798829,0.000035462814,0.981881,0.0014046874,0.0006446818,0.000053113396,0.010431327],"study_design_scores_gemma":[0.00044507717,0.00019481721,0.0036112492,0.00012230127,0.0000075119715,0.000008709688,0.000008184319,0.9926063,0.0005044723,0.0023070853,0.000034814755,0.0001494784],"about_ca_topic_score_codex":4.813021e-7,"about_ca_topic_score_gemma":0.0000039452543,"teacher_disagreement_score":0.69081724,"about_ca_system_score_codex":0.000044348762,"about_ca_system_score_gemma":0.000038792954,"threshold_uncertainty_score":0.46415624},"labels":[],"label_agreement":null},{"id":"W2301108683","doi":"10.4018/ijcini.2015100101","title":"Chaotic Liquid State Machine","year":2015,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Chaotic; Classifier (UML); Signature (topology); Artificial intelligence; Class (philosophy); Set (abstract data type); Finite-state machine; Connection (principal bundle); Pattern recognition (psychology); Machine learning; Algorithm; Programming language","score_opus":0.02324992972675511,"score_gpt":0.29024002296903717,"score_spread":0.26699009324228207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301108683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85077775,0.0016964163,0.1450556,0.00004937525,0.0015889482,0.00005182923,0.000010658833,0.000026644193,0.0007427819],"genre_scores_gemma":[0.9977801,0.00057853735,0.0012906513,0.00018976473,0.00012221494,4.006227e-7,0.0000035655614,0.0000068420186,0.00002791398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990958,0.000009964029,0.00047196934,0.000031318352,0.00028138765,0.00010958943],"domain_scores_gemma":[0.9988317,0.00016846752,0.00016361699,0.000027215769,0.00070330454,0.00010570728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019854326,0.000103646096,0.000129891,0.0001422829,0.000019766003,0.00004836054,0.00017854788,0.000022305008,0.000007287446],"category_scores_gemma":[0.00023025915,0.000082458624,0.00004180741,0.00007221127,0.000045328536,0.00058081397,0.000050147795,0.0003076776,0.0000108240065],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016872257,0.00007848645,0.00036654796,0.00011304948,0.00069222273,0.00037019572,0.0176483,0.12645158,0.0019285432,0.0012430768,0.0002929651,0.8491278],"study_design_scores_gemma":[0.0020133224,0.0016940546,0.00052020425,0.0018164634,0.000084886764,0.004491868,0.00738895,0.6522637,0.31260026,0.011521518,0.004624076,0.000980731],"about_ca_topic_score_codex":7.475298e-7,"about_ca_topic_score_gemma":9.770874e-7,"teacher_disagreement_score":0.8481471,"about_ca_system_score_codex":0.000036308717,"about_ca_system_score_gemma":0.000019654772,"threshold_uncertainty_score":0.33625653},"labels":[],"label_agreement":null},{"id":"W2312305522","doi":"10.3938/jkps.57.1248","title":"Leakage Current Characteristics of the Multiple Metal Alloy Nanodot Memory","year":2010,"lang":"en","type":"article","venue":"Journal of the Korean Physical Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Hatch (Canada)","funders":"","keywords":"Materials science; Nanodot; Alloy; Metal; Current (fluid); Metallurgy; Optoelectronics; Electrical engineering","score_opus":0.010062940018841615,"score_gpt":0.22676991219945142,"score_spread":0.2167069721806098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312305522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975841,0.000046809262,0.00023145787,0.00009961988,0.0018942801,0.00006650337,0.000008690474,0.000017368662,0.00005115285],"genre_scores_gemma":[0.99872255,0.000013581172,0.00023007838,0.000034145858,0.0009567159,4.2997766e-7,3.1640522e-7,0.00001834319,0.000023845749],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992047,0.000034182503,0.0002778456,0.00006558459,0.0002590194,0.00015869005],"domain_scores_gemma":[0.99924916,0.00014924396,0.0002536422,0.0002266882,0.000065193475,0.000056043045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001718857,0.00012534544,0.00024781137,0.0000059161857,0.000114040544,0.000010625956,0.00044976026,0.000037135473,0.0000036806996],"category_scores_gemma":[0.00009493886,0.00006574027,0.00071625056,0.00011349133,0.00011546696,0.00010684707,0.00012278918,0.0009963003,0.000001387998],"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":[0.00000764304,0.00009014476,0.00038666688,0.000049870658,0.000069284295,6.354945e-7,0.0008730293,0.0026829848,0.9857833,0.00007885418,0.00045336768,0.009524268],"study_design_scores_gemma":[0.00066637184,0.00004269872,0.02903359,0.00013304928,0.0001554573,0.00003621666,0.00012484075,0.028497992,0.93677354,0.0009618035,0.003341609,0.00023284747],"about_ca_topic_score_codex":5.0943567e-7,"about_ca_topic_score_gemma":6.086895e-7,"teacher_disagreement_score":0.049009725,"about_ca_system_score_codex":0.00002519997,"about_ca_system_score_gemma":0.000017469756,"threshold_uncertainty_score":0.43284842},"labels":[],"label_agreement":null},{"id":"W2315015477","doi":"10.1557/opl.2013.584","title":"Multipodal and Multilayer TiO<sub>2</sub> Nanotube Arrays: Hierarchical Structures for Energy Harvesting and Sensing","year":2013,"lang":"en","type":"article","venue":"MRS Proceedings","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"","keywords":"Materials science; Photocurrent; Nanotube; Optoelectronics; Absorption (acoustics); Nanotechnology; Carbon nanotube; Composite material","score_opus":0.01100849691589563,"score_gpt":0.2048709195515962,"score_spread":0.19386242263570058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315015477","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9864719,0.00014879956,0.012574336,0.00005835283,0.00012865338,0.00023166822,0.0000019177965,0.00029544876,0.0000889226],"genre_scores_gemma":[0.977602,0.000022837514,0.021912193,0.00010759717,0.0002632119,0.000016932532,0.0000021735252,0.000051185572,0.000021892734],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990177,0.0000039114025,0.00020806435,0.00030768142,0.000090814414,0.00037183816],"domain_scores_gemma":[0.99956495,0.00013055859,0.000042569616,0.000047773585,0.000074838936,0.00013930039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007033912,0.00022549114,0.00020450316,0.000074612195,0.0002279848,0.00013592905,0.000055736742,0.00009780918,9.997594e-7],"category_scores_gemma":[0.0001278434,0.00021448535,0.000032015858,0.00008416479,0.00007159802,0.0004170881,0.000058693866,0.00019528306,8.1489156e-7],"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":[0.000009846094,0.0000023974737,0.0001612201,0.0001430014,0.0000116395995,0.0000012133078,0.00039984475,0.000635104,0.8759004,0.0003237837,0.00007336678,0.122338176],"study_design_scores_gemma":[0.0005173544,0.000048132148,0.0016252601,0.00008702068,0.0000137775205,0.00007891312,0.00015469438,0.23295957,0.76003814,0.0035392623,0.0006016992,0.0003361632],"about_ca_topic_score_codex":0.000006786424,"about_ca_topic_score_gemma":0.0000023476277,"teacher_disagreement_score":0.23232447,"about_ca_system_score_codex":0.000020082589,"about_ca_system_score_gemma":0.0000042950533,"threshold_uncertainty_score":0.8746459},"labels":[],"label_agreement":null},{"id":"W2324765487","doi":"10.3389/fnbot.2016.00001","title":"Serendipitous Offline Learning in a Neuromorphic Robot","year":2016,"lang":"en","type":"article","venue":"Frontiers in Neurorobotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Technische Universität München; Engineering and Physical Sciences Research Council; Deutsche Forschungsgemeinschaft","keywords":"Neuromorphic engineering; Computer science; Artificial intelligence; Robot; Human–computer interaction; Robotics; Robot learning; Machine learning; Artificial neural network; Mobile robot","score_opus":0.02234455521372696,"score_gpt":0.20486023380526466,"score_spread":0.1825156785915377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324765487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72367007,0.0004322861,0.27056336,0.00039971873,0.0037076685,0.00022570655,0.0000024715246,0.0005987781,0.00039992045],"genre_scores_gemma":[0.9924003,0.00027071682,0.0067574917,0.00008140782,0.000095248404,0.0000046769683,0.0000016173456,0.00006209957,0.00032642842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876565,0.00007281517,0.000326977,0.00026585467,0.0001190414,0.00044964225],"domain_scores_gemma":[0.999575,0.00011005923,0.000038302,0.00019211687,0.000011954342,0.00007252046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009820782,0.00020302001,0.00027367065,0.00023590727,0.00003478935,0.000012583014,0.0001921747,0.000075559576,0.0000073911124],"category_scores_gemma":[0.00013229124,0.00018013146,0.000041525756,0.00036095807,0.000039843235,0.00016879977,0.00005559578,0.00046441986,0.000012546013],"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":[0.0000187521,0.000019848567,0.033925317,0.000033510794,0.000004183978,0.00028821835,0.00006157591,0.92680746,0.018672002,0.000018869208,0.00046755123,0.019682731],"study_design_scores_gemma":[0.0057256254,0.0004980191,0.11627035,0.0008190595,0.00003457314,0.0002111318,0.00015600481,0.8273321,0.0346215,0.0026461617,0.009730998,0.0019544945],"about_ca_topic_score_codex":0.000001080285,"about_ca_topic_score_gemma":0.000009275548,"teacher_disagreement_score":0.26873022,"about_ca_system_score_codex":0.0000785106,"about_ca_system_score_gemma":0.000010444013,"threshold_uncertainty_score":0.7345548},"labels":[],"label_agreement":null},{"id":"W2327275214","doi":"10.7567/apex.9.051501","title":"Suppression of relaxation effect in HfO<sub>2</sub> resistive random access memory array by improved program operations","year":2016,"lang":"en","type":"article","venue":"Applied Physics Express","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"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":"Institute of Population and Public Health","keywords":"Resistive random-access memory; Relaxation (psychology); Resistive touchscreen; Materials science; Ion; Optoelectronics; Computer science; Atomic physics; Electrical engineering; Physics; Voltage; Engineering; Psychology","score_opus":0.007179634817176922,"score_gpt":0.240157335194413,"score_spread":0.23297770037723609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327275214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9239217,0.000046056393,0.07401464,0.0000078772255,0.00013918325,0.0010397314,0.000021759784,0.00021838676,0.00059066917],"genre_scores_gemma":[0.99874043,0.000026511167,0.00057405187,0.000009611154,0.00010245745,0.00045504683,0.000035502308,0.000043252243,0.000013143292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989631,0.000058197696,0.00030066934,0.00028329348,0.0001392011,0.00025550314],"domain_scores_gemma":[0.99920803,0.000347428,0.00007969424,0.00027542716,0.00003586838,0.000053571526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013332057,0.00021525276,0.0003041287,0.000042688986,0.000076743534,0.000026265318,0.00021062787,0.00008433969,0.0000022318784],"category_scores_gemma":[0.000031646596,0.00016558146,0.000056210607,0.00020167558,0.00005209683,0.00036578908,0.00005972709,0.000175762,0.000004187434],"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":[0.00019341281,0.00005083912,0.000015557955,0.00008234052,0.000014556595,4.7195724e-7,0.0001879934,0.021470651,0.91827196,0.00006131966,0.00011061255,0.059540287],"study_design_scores_gemma":[0.002291458,0.00004446697,0.00009547136,0.00015118935,0.000014084117,1.8367035e-7,0.00001492299,0.0041872137,0.992466,0.0004929861,0.000031475654,0.0002105741],"about_ca_topic_score_codex":0.0000035988764,"about_ca_topic_score_gemma":0.000002723225,"teacher_disagreement_score":0.07481873,"about_ca_system_score_codex":0.000045639943,"about_ca_system_score_gemma":0.000009840844,"threshold_uncertainty_score":0.67522156},"labels":[],"label_agreement":null},{"id":"W2338434056","doi":"10.1109/tvlsi.2015.2474706","title":"Racetrack Memory-Based Nonvolatile Storage Elements for Multicontext FPGAs","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University","funders":"","keywords":"Non-volatile memory; Field-programmable gate array; Computer science; Embedded system; Parallel computing; Very-large-scale integration; Semiconductor memory; Non-volatile random-access memory; Computer hardware; Memory refresh; Computer architecture; Computer memory","score_opus":0.03265050515637805,"score_gpt":0.2663978201855771,"score_spread":0.23374731502919907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338434056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15345602,0.00011464403,0.84024817,0.000023164008,0.004161452,0.0009772695,0.00021729682,0.00051859044,0.0002833881],"genre_scores_gemma":[0.99582136,0.0000046206087,0.002266859,0.00007381034,0.0002275766,0.0003058305,0.000049311977,0.00007959706,0.0011710648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981082,0.00010502072,0.00063999457,0.00036291667,0.00033856006,0.0004453037],"domain_scores_gemma":[0.99884325,0.0002102643,0.00011386568,0.00037607717,0.00023554217,0.00022100504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046663563,0.00034722223,0.00037689798,0.0002192531,0.00027255891,0.00009976942,0.00018321323,0.00017803257,0.000038817623],"category_scores_gemma":[0.000023478457,0.00033863063,0.00019716361,0.00025719387,0.000027629269,0.0004898139,0.0000010783258,0.00039348827,0.00011070618],"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":[0.00013244379,0.00016471856,0.000011415977,0.0001328122,0.00004863805,0.0000041407984,0.00066036836,0.9756558,0.01712621,0.000013762132,0.0010622249,0.0049874615],"study_design_scores_gemma":[0.002010666,0.00019012143,0.000006081792,0.00018792196,0.00003949345,0.0000070847336,0.0011606076,0.8476954,0.14477736,0.000009735724,0.0035814887,0.00033404768],"about_ca_topic_score_codex":0.000015768352,"about_ca_topic_score_gemma":0.00014875508,"teacher_disagreement_score":0.8423653,"about_ca_system_score_codex":0.00033440665,"about_ca_system_score_gemma":0.000049779683,"threshold_uncertainty_score":0.9999066},"labels":[],"label_agreement":null},{"id":"W2340581119","doi":"10.1109/lascas.2016.7451011","title":"A modified synapse model for neuromorphic circuits","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Neuromorphic engineering; Computer science; Implementation; Biological neural network; Synapse; Biological neuron model; Transmission (telecommunications); Computer architecture; Behavioral modeling; Artificial neural network; Neuroscience; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.06964378572002056,"score_gpt":0.24055874935444943,"score_spread":0.17091496363442887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340581119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2951422,0.000015018454,0.7032135,0.000073020856,0.00008676777,0.00009973506,0.0000044961203,0.00036211225,0.0010031098],"genre_scores_gemma":[0.9979198,0.000007460369,0.00071024534,0.00008876015,0.000046523335,0.00001695221,4.867612e-7,0.000022464537,0.0011873225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995627,0.000003255977,0.000096031916,0.00011446527,0.00003826697,0.00018530483],"domain_scores_gemma":[0.99971783,0.00009489694,0.000008762355,0.000116581316,0.000014911036,0.000047020207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033571134,0.000081947794,0.00008220737,0.000024092147,0.000034938515,0.000005147396,0.00007373184,0.000028046323,0.000008925108],"category_scores_gemma":[0.000026797119,0.00005615798,0.00003629276,0.000035433564,0.0000091324355,0.00009896528,0.0000123746195,0.000034624853,0.0000135097325],"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":[0.0000054400443,0.00000437269,0.0000036926529,0.00003302753,0.000006818498,0.0000029143348,0.000023498478,0.59214574,0.3891081,0.003862934,0.00036035277,0.01444315],"study_design_scores_gemma":[0.0003295944,0.00001632547,0.000011255055,0.000016443399,0.000003465397,0.000006074364,9.614491e-7,0.9360618,0.060326368,0.0030081028,0.0001059098,0.00011371033],"about_ca_topic_score_codex":8.2725464e-8,"about_ca_topic_score_gemma":5.6964683e-7,"teacher_disagreement_score":0.70277756,"about_ca_system_score_codex":0.000012564424,"about_ca_system_score_gemma":0.0000038135224,"threshold_uncertainty_score":0.22900559},"labels":[],"label_agreement":null},{"id":"W2393540806","doi":"10.1145/2902961.2903016","title":"8T1R","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"Arizona State University","keywords":"Computer science","score_opus":0.007738636599890636,"score_gpt":0.18320341867692372,"score_spread":0.1754647820770331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2393540806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6688608,0.00004147735,0.23732123,0.000098212826,0.00025173713,0.000022262899,3.001974e-7,0.0008187382,0.09258525],"genre_scores_gemma":[0.99734926,0.0000059368895,0.00062040886,0.000029644858,0.000040849663,4.096518e-7,2.6916432e-8,0.0000041324643,0.0019493572],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987525,9.433022e-7,0.00002580655,0.00002677732,0.000014246148,0.000056953195],"domain_scores_gemma":[0.9999241,0.000018951261,0.0000012688563,0.000039217273,0.0000020076538,0.000014455789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000008538388,0.00002204588,0.000019555428,0.0000061318983,0.000008097062,0.0000011906651,0.00002107301,0.0000065076993,0.00011439386],"category_scores_gemma":[0.0000034823126,0.000012338131,0.000007292553,0.000016356846,0.0000030767726,0.00004139145,0.0000047992435,0.000011043491,0.00015081922],"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":[9.750279e-7,0.000001269693,0.00013827009,0.000004837079,0.0000028035972,0.000003804439,0.000010075345,0.0011624626,0.68443954,0.0022101107,0.0013012467,0.31072462],"study_design_scores_gemma":[0.00013415067,0.000008117874,0.00048287975,0.000012252559,7.256198e-7,0.000005498476,0.0000035590308,0.000614797,0.95320237,0.001532936,0.043910827,0.00009188339],"about_ca_topic_score_codex":2.470586e-8,"about_ca_topic_score_gemma":1.8391583e-7,"teacher_disagreement_score":0.32848844,"about_ca_system_score_codex":0.0000043037503,"about_ca_system_score_gemma":3.537089e-7,"threshold_uncertainty_score":0.19385263},"labels":[],"label_agreement":null},{"id":"W2398014893","doi":"","title":"Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks","year":2013,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":36,"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":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Innovation Trust","keywords":"Spiking neural network; Artificial intelligence; Computer science; Unsupervised learning; Supervised learning; Learning rule; Machine learning; Artificial neural network; Pattern recognition (psychology)","score_opus":0.011779493402470502,"score_gpt":0.19680701297525896,"score_spread":0.18502751957278846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398014893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99412286,0.00034912195,0.003278773,0.000025324654,0.000063752,0.00029850143,0.00007798053,0.00043465686,0.001349054],"genre_scores_gemma":[0.99925137,0.000018699553,0.00027515925,0.00009292502,0.00006451128,0.000016935786,0.00016437471,0.000055256263,0.000060785536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986868,0.00006114933,0.0004163394,0.00029794947,0.00010933093,0.00042844776],"domain_scores_gemma":[0.998741,0.00092008995,0.000053001637,0.00009134603,0.00003214718,0.00016242497],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006574466,0.00026647805,0.0003062484,0.00014214322,0.00010001125,0.0002622347,0.00014723244,0.00014130693,0.00019690914],"category_scores_gemma":[0.00035735787,0.00024571316,0.0000707114,0.00044213852,0.00007984984,0.0018634881,0.00014136691,0.00070252136,0.00006330131],"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":[0.00013674461,0.00009209899,0.4084784,0.00013133873,0.000044510474,0.00006822472,0.000081790786,0.5191798,0.0063634673,0.00007339577,0.000020060746,0.06533015],"study_design_scores_gemma":[0.0011042077,0.00025836058,0.016465668,0.0003135277,0.000014724843,0.00002873152,0.00027664634,0.97725016,0.002652477,0.00063771446,0.0004156416,0.00058215787],"about_ca_topic_score_codex":0.0000017830521,"about_ca_topic_score_gemma":7.645747e-7,"teacher_disagreement_score":0.4580703,"about_ca_system_score_codex":0.000013320589,"about_ca_system_score_gemma":0.000008599589,"threshold_uncertainty_score":0.9999995},"labels":[],"label_agreement":null},{"id":"W2401504277","doi":"10.1021/acsami.6b01922","title":"Photolithographically Patterned TiO<sub>2</sub> Films for Electrolyte-Gated Transistors","year":2016,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; CMC Microsystems","keywords":"Materials science; Transistor; Electrolyte; Optoelectronics; Nanotechnology; Thin-film transistor; Semiconductor; Threshold voltage; Flexible electronics; Voltage; Electrode; Electrical engineering; Layer (electronics)","score_opus":0.00832978909798013,"score_gpt":0.20366225166119575,"score_spread":0.19533246256321563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401504277","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909368,0.0000662514,0.0070110117,0.000057118403,0.0005184688,0.00053006556,0.00008130837,0.00067090825,0.0001280452],"genre_scores_gemma":[0.9990623,0.00009957139,0.00032645103,0.00010471823,0.00012527272,0.00016019403,0.00001804374,0.000087067994,0.000016380422],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853843,0.000022029739,0.00044208183,0.0003647614,0.000115168994,0.00051750924],"domain_scores_gemma":[0.9994244,0.00014836156,0.000082475075,0.00023708453,0.000036796406,0.0000708662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016868989,0.00032452808,0.000388102,0.00009152035,0.000090522866,0.000056634002,0.00024889756,0.00013978126,0.000055386634],"category_scores_gemma":[0.000014615639,0.00024666448,0.000037557566,0.00010569354,0.00005887544,0.00012848942,0.0000311332,0.000085156986,0.00004370172],"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":[0.00023948176,0.000014367107,0.000002342705,0.00011042256,0.00005832772,0.0000018338519,0.00007636725,0.0001913944,0.9934987,0.00018441808,0.0001940572,0.005428292],"study_design_scores_gemma":[0.0007458092,0.00009380602,0.000041805903,0.0000900398,0.000028725977,0.00000557536,0.000014996882,0.0000074599548,0.9975917,0.0008403399,0.00018370923,0.00035605126],"about_ca_topic_score_codex":8.5700657e-7,"about_ca_topic_score_gemma":0.000005191713,"teacher_disagreement_score":0.008125478,"about_ca_system_score_codex":0.00003220777,"about_ca_system_score_gemma":0.00000785818,"threshold_uncertainty_score":0.99999857},"labels":[],"label_agreement":null},{"id":"W2402457549","doi":"","title":"Definition and Categorization of Dew Computing","year":2016,"lang":"en","type":"article","venue":"RonPub -- Research Online Publishing (RonPub UG)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Prince Edward Island","funders":"","keywords":"Dew; Cloud computing; Computer science; Categorization; Dew point; Data science; Key (lock); World Wide Web; Computer security; Artificial intelligence; Operating system; Meteorology; Geography","score_opus":0.10849774393364162,"score_gpt":0.33279864690079325,"score_spread":0.22430090296715163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402457549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9302605,0.0010039501,0.06301214,0.0018255741,0.00038823133,0.00033041742,0.000068695306,0.0004824801,0.002628018],"genre_scores_gemma":[0.9917845,0.00034292083,0.007130405,0.000031519718,0.00046847796,0.000006142199,0.00008294231,0.00005877443,0.00009428239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975463,0.00020565967,0.0004893975,0.00038873724,0.0006194295,0.0007504789],"domain_scores_gemma":[0.9978719,0.0010309776,0.00009318835,0.00032642,0.00045154858,0.00022596211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016704586,0.00020790948,0.00028736755,0.00043077843,0.00025272727,0.00032399155,0.0003790431,0.00015548947,0.00003122898],"category_scores_gemma":[0.0021224294,0.00016846313,0.00005119485,0.0006941702,0.00016433415,0.0023544002,0.0003077458,0.0006327643,0.000007993226],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109680754,0.00027378727,0.008535509,0.0012388114,0.00012965381,0.000041623953,0.0011207503,0.008705482,0.23282494,0.044007774,0.006274537,0.69673747],"study_design_scores_gemma":[0.015689157,0.0023161822,0.139156,0.0074915853,0.00017952164,0.00047985223,0.004383738,0.30191982,0.2612743,0.20595191,0.05537826,0.0057796757],"about_ca_topic_score_codex":0.00003317715,"about_ca_topic_score_gemma":0.00005246387,"teacher_disagreement_score":0.6909578,"about_ca_system_score_codex":0.00017704397,"about_ca_system_score_gemma":0.000068706795,"threshold_uncertainty_score":0.68697274},"labels":[],"label_agreement":null},{"id":"W2404672146","doi":"10.1063/1.4950963","title":"Plasmonic engineering of metal-oxide nanowire heterojunctions in integrated nanowire rectification units","year":2016,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Nanowire; Materials science; Heterojunction; Optoelectronics; Nanotechnology; Femtosecond; Oxide; Graphene; Wetting; Laser; Plasmon; Composite material; Optics","score_opus":0.014214689866839584,"score_gpt":0.19474552103947448,"score_spread":0.1805308311726349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404672146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9574716,0.000014812103,0.04178311,0.00004951976,0.00020292154,0.0001374514,0.0000059201316,0.00021439283,0.000120254575],"genre_scores_gemma":[0.99919957,0.000011034745,0.00060431246,0.000047099373,0.00004162512,0.00003304092,0.0000101112155,0.000039877257,0.000013346187],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924964,0.000010779774,0.00024220535,0.00018436753,0.00009007988,0.00022295157],"domain_scores_gemma":[0.9995655,0.00011869514,0.00004936892,0.00021124443,0.000021934606,0.00003327161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049799757,0.0001724549,0.00019279157,0.00008738539,0.000031771906,0.0000061584647,0.00011487108,0.000044638233,0.0000028067852],"category_scores_gemma":[0.000012878644,0.00014895665,0.00003895917,0.00058413576,0.000027430617,0.00014821069,0.000019875115,0.0001612702,0.000013614459],"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":[0.0000067145197,0.0000074439654,0.000035507976,0.00002550662,0.000019757419,0.0000010444103,0.00006391557,0.15006383,0.8403801,0.0005198048,0.000040299205,0.008836111],"study_design_scores_gemma":[0.00029115716,0.000006351498,0.00027269177,0.00009259002,0.000010050637,0.000001470809,0.000029058105,0.0014590526,0.99728316,0.000044417917,0.00032486542,0.00018510516],"about_ca_topic_score_codex":0.000004481193,"about_ca_topic_score_gemma":0.0000030089468,"teacher_disagreement_score":0.15690312,"about_ca_system_score_codex":0.00009464466,"about_ca_system_score_gemma":0.000008462461,"threshold_uncertainty_score":0.6074276},"labels":[],"label_agreement":null},{"id":"W2405525175","doi":"","title":"A Survey on Spiking Neural Networks in Image Processing.","year":2014,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spiking neural network; Computer science; Artificial neural network; Image processing; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Computer vision","score_opus":0.014240225651795217,"score_gpt":0.2302463236801481,"score_spread":0.21600609802835288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405525175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8177665,0.00006239775,0.17660485,0.0000046389446,0.000315924,0.00015585661,0.0000025067066,0.00037884983,0.0047085015],"genre_scores_gemma":[0.99932176,0.0000048311776,0.00040497194,0.00011238845,0.00007997471,0.000010836724,0.000044425728,0.00001649066,0.0000043061204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908674,0.000052535066,0.00037917262,0.00008289574,0.00010909373,0.00028957805],"domain_scores_gemma":[0.99959767,0.00010178844,0.000082548846,0.000120386874,0.00005274637,0.000044881297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042558936,0.0001543495,0.00016235939,0.00015658159,0.00010985835,0.00012339538,0.000110943016,0.00006762497,0.0000039069305],"category_scores_gemma":[0.0001939047,0.00015484987,0.000026267098,0.00034337942,0.000033058674,0.0015884086,0.000023499764,0.00023270832,0.000017811635],"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":[0.000018446979,0.0000038388457,0.0013329708,0.00013790232,0.0000020045409,0.0000011971894,0.0006166268,0.69792324,0.00014744973,0.00004985299,0.00004541044,0.29972103],"study_design_scores_gemma":[0.00019346268,0.000040675466,0.041849468,0.00015604788,0.0000015543545,0.000005354757,0.00004297208,0.95634466,0.00086976466,0.00015553394,0.00016088966,0.0001795873],"about_ca_topic_score_codex":0.000013391838,"about_ca_topic_score_gemma":0.000024593077,"teacher_disagreement_score":0.29954144,"about_ca_system_score_codex":0.00011386199,"about_ca_system_score_gemma":0.0000055645946,"threshold_uncertainty_score":0.6314594},"labels":[],"label_agreement":null},{"id":"W24057010","doi":"10.1039/c3nr03472k","title":"安定性の限界値(Limits of stability)を用いた高齢者のバランス能に関する研究(7.2要旨,7.平成17年度修士論文)","year":2006,"lang":"en","type":"article","venue":"Canadian parliamentary review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Stability (learning theory); Environmental science; Economics; Internal medicine; Medicine; Computer science","score_opus":0.02480327649985357,"score_gpt":0.22919160527355542,"score_spread":0.20438832877370186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W24057010","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54223824,0.42105007,0.0004587215,0.0010463824,0.0014065115,0.0018724982,0.0003346938,0.00042280983,0.031170063],"genre_scores_gemma":[0.9849883,0.0119928885,0.00077585864,0.0017243377,0.0002165171,0.000030542058,0.000103468716,0.00005690219,0.00011122773],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865955,0.000042126314,0.0004954343,0.00022806105,0.00012839057,0.00044644764],"domain_scores_gemma":[0.99920475,0.000052587657,0.000060606086,0.0003750872,0.000036674657,0.0002703042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015999629,0.00022226034,0.00042287336,0.0000831751,0.00006594446,0.000009127668,0.0002153328,0.00004756932,0.0005201338],"category_scores_gemma":[0.00001718055,0.00023194856,0.00012538655,0.00033615567,0.000048714744,0.00015765133,0.000013265609,0.00017435185,0.000062295985],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029011411,0.00021894104,0.026987277,0.042096347,0.00044200075,0.00070695975,0.00027732286,0.026982961,0.037323963,0.0045754933,0.3502838,0.5100759],"study_design_scores_gemma":[0.0006227183,0.00011461786,0.0032786084,0.0061161965,0.000217265,0.00008320058,0.00008030748,0.0009380146,0.048414372,0.0008041339,0.93808824,0.0012423408],"about_ca_topic_score_codex":0.008263467,"about_ca_topic_score_gemma":0.04390836,"teacher_disagreement_score":0.58780444,"about_ca_system_score_codex":0.00024993642,"about_ca_system_score_gemma":0.00008584511,"threshold_uncertainty_score":0.9983406},"labels":[],"label_agreement":null},{"id":"W2426931417","doi":"10.1093/neuonc/now076.92","title":"MB-96IMPAIRED NEURAL FUNCTION DURING VISUAL-MOTOR PERFORMANCE IN CHILDREN TREATED FOR BRAIN TUMOURS","year":2016,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Magnetoencephalography; Motor cortex; Medicine; Audiology; Sick child; Visual cortex; Motor function; Psychology; Neuroscience; Pediatrics; Physical medicine and rehabilitation; Electroencephalography; Internal medicine","score_opus":0.011286423622696344,"score_gpt":0.2500631970886195,"score_spread":0.23877677346592316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2426931417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975249,0.000037775797,0.00064750924,0.00020520152,0.00050379446,0.00050223235,0.0000076033402,0.0004359971,0.00013494262],"genre_scores_gemma":[0.9991796,0.000022969818,0.00007425713,0.00019574401,0.0003192918,0.00007116519,0.000006353515,0.00005517785,0.00007542186],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99876225,0.00007112963,0.00029993022,0.00031014174,0.00007158806,0.00048493396],"domain_scores_gemma":[0.99934316,0.00034540784,0.00006209788,0.00014013717,0.000022624008,0.00008656903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010833707,0.000212837,0.0002749337,0.00016049051,0.00012120967,0.000008628193,0.00011965874,0.000119406395,0.000014923696],"category_scores_gemma":[0.00010006502,0.00017806966,0.0000707886,0.00016797305,0.00004889496,0.0002576648,0.0000392065,0.00019944902,0.000017752956],"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":[0.0003565558,0.000031615808,0.030220704,0.000028205932,0.000009544915,0.000013967534,0.000033618668,0.005540977,0.93459314,0.0000062596378,0.0000689337,0.029096503],"study_design_scores_gemma":[0.006121337,0.0027642057,0.49472764,0.000063494794,0.000028905546,0.00027382103,0.000018494133,0.038373977,0.45481947,0.00004769974,0.002222718,0.0005382345],"about_ca_topic_score_codex":0.0000014500314,"about_ca_topic_score_gemma":0.000006634879,"teacher_disagreement_score":0.47977364,"about_ca_system_score_codex":0.00022630946,"about_ca_system_score_gemma":0.000018357065,"threshold_uncertainty_score":0.726147},"labels":[],"label_agreement":null},{"id":"W24566811","doi":"10.1093/bja/aet585","title":"ESAM 495 Introduction to Computational Neuroscience Fall Quarter 2008","year":2008,"lang":"en","type":"article","venue":"British Journal of Anaesthesia","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computational neuroscience; Class (philosophy); Computer science; Quarter (Canadian coin); Neuroscience; Cognitive science; Artificial intelligence; Psychology; Geography","score_opus":0.01234402127516012,"score_gpt":0.21585384813283243,"score_spread":0.2035098268576723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W24566811","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95294094,0.00033459897,0.04559909,0.000510941,0.0004688255,0.000045727404,9.844493e-7,0.000049158116,0.000049719605],"genre_scores_gemma":[0.99298835,0.00017083196,0.0057734353,0.00029040806,0.00072807603,5.098019e-7,6.851417e-7,0.000014232194,0.000033493543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991944,0.000024995787,0.0002832435,0.00010906825,0.00021491968,0.00017339543],"domain_scores_gemma":[0.9996445,0.000014440574,0.00006340727,0.000056608693,0.000098395445,0.00012262454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001189277,0.00008159602,0.00014528651,0.00009075841,0.00013948194,0.000032521126,0.00014395047,0.000024043882,0.0000068737872],"category_scores_gemma":[0.000045940313,0.000099063465,0.000059099533,0.00017186519,0.00003786327,0.00031779797,0.000009576041,0.00021572459,0.000010823332],"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":[0.00001557892,0.00002997831,0.0004589919,0.000008563137,0.0000046888276,0.0013066394,0.00017890672,0.9164069,0.0032798129,0.00002005539,0.028553078,0.04973679],"study_design_scores_gemma":[0.002559161,0.0026731307,0.5000399,0.00049240433,0.000056381665,0.25723052,0.00026146616,0.06907258,0.0107755875,0.0019010857,0.15322597,0.0017118258],"about_ca_topic_score_codex":0.000003067754,"about_ca_topic_score_gemma":0.000003335074,"teacher_disagreement_score":0.8473343,"about_ca_system_score_codex":0.000030570962,"about_ca_system_score_gemma":0.000020329091,"threshold_uncertainty_score":0.40396908},"labels":[],"label_agreement":null},{"id":"W2460057774","doi":"10.1109/tdmr.2016.2582211","title":"Investigation of Set/Reset Operations in CMOS-Logic-Compatible Contact Backfilled RRAMs","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Device and Materials Reliability","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"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":"Institute of Population and Public Health; Taiwan Semiconductor Manufacturing Company","keywords":"Resistive random-access memory; Reset (finance); Etching (microfabrication); Stack (abstract data type); Materials science; Electrical contacts; Silicon; Resistive touchscreen; Chemical vapor deposition; Optoelectronics; Deposition (geology); Process (computing); CMOS; Electrical engineering; Electronic engineering; Layer (electronics); Computer science; Nanotechnology; Engineering; Voltage","score_opus":0.03148561774533466,"score_gpt":0.25609542228042065,"score_spread":0.224609804535086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460057774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9304749,0.000013709302,0.068619475,0.00010965008,0.00033090275,0.00025958885,0.00007009442,0.000088773326,0.000032947613],"genre_scores_gemma":[0.9990186,0.00009225922,0.0007638124,0.00004465496,0.000017331648,0.00003333074,0.00000394565,0.000011924988,0.00001413786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990862,0.00009623116,0.0003743687,0.00019552115,0.0000763587,0.00017133268],"domain_scores_gemma":[0.99948764,0.00018361215,0.000028789047,0.0001930784,0.00004429378,0.00006257637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029708262,0.00013144981,0.00025099886,0.000068429596,0.00006630871,0.000016020813,0.000063179235,0.00007328182,0.000094696785],"category_scores_gemma":[0.00001766568,0.00009775747,0.000028843575,0.00013293044,0.0000509315,0.0002643441,0.0000014570484,0.00007444819,0.0000107088445],"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":[0.000049000766,0.000017890326,0.00009401571,0.00013399225,0.0000074396826,7.8741573e-7,0.00019621714,0.024818856,0.97293246,0.00006473183,0.000007293486,0.0016773322],"study_design_scores_gemma":[0.00052803295,0.00008454425,0.0016933103,0.00013272077,0.000010570564,0.0000025511802,0.000038490245,0.0014115609,0.99530196,0.0006253274,0.000039478535,0.00013145046],"about_ca_topic_score_codex":0.000080153346,"about_ca_topic_score_gemma":0.00012792787,"teacher_disagreement_score":0.06854374,"about_ca_system_score_codex":0.00006678958,"about_ca_system_score_gemma":0.000017112348,"threshold_uncertainty_score":0.39864337},"labels":[],"label_agreement":null},{"id":"W2468089099","doi":"10.1016/j.neuroimage.2017.01.072","title":"Statistical power and prediction accuracy in multisite resting-state fMRI connectivity","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroRx Research (Canada); Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies; McGill University; Alzheimer's Association; Sanofi; Courtois Foundation; Consortium canadien en neurodégénérescence associée au vieillissement","keywords":"Resting state fMRI; Functional connectivity; Computer science; Statistical power; Artificial intelligence; Machine learning; Statistics; Psychology; Neuroscience; Mathematics","score_opus":0.0216595656851934,"score_gpt":0.2828193820645391,"score_spread":0.2611598163793457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468089099","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898392,0.000025550022,0.008381902,0.000036090518,0.00025915253,0.00011283337,0.000026664624,0.00016516476,0.0011534231],"genre_scores_gemma":[0.9990752,0.000021769165,0.0007999919,0.000026694428,0.000030421923,0.000002935536,0.0000017870157,0.000018874538,0.000022362285],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9993806,0.000029829169,0.00013047073,0.00020222462,0.00006658796,0.00019028211],"domain_scores_gemma":[0.99933416,0.0003278104,0.000036927315,0.00023200341,0.000011660417,0.00005744123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010055589,0.0001035654,0.00011400336,0.00003422965,0.00016298251,0.00007638949,0.00008313435,0.000021017804,0.000006425515],"category_scores_gemma":[0.0008648293,0.00011031607,0.000011508083,0.000025902864,0.00005465351,0.0003910685,0.000071073846,0.00026658698,0.000006928954],"study_design_candidate":"observational","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":[0.0002203965,0.00009941172,0.22737664,0.00030123172,0.000019834806,0.0026076075,0.00087710767,0.03913926,0.59001344,0.00048322533,0.00062891736,0.13823295],"study_design_scores_gemma":[0.0004990269,0.00004478382,0.92854524,0.000033148943,0.0000029013122,0.00003878157,0.0000055102064,0.0608199,0.009134064,0.0004383073,0.00031371886,0.00012464763],"about_ca_topic_score_codex":0.000010910778,"about_ca_topic_score_gemma":0.000020369655,"teacher_disagreement_score":0.7011686,"about_ca_system_score_codex":0.000013838836,"about_ca_system_score_gemma":0.0000027125402,"threshold_uncertainty_score":0.44985586},"labels":[],"label_agreement":null},{"id":"W2472097956","doi":"10.1002/adfm.201601143","title":"Plasmonic‐Radiation‐Enhanced Metal Oxide Nanowire Heterojunctions for Controllable Multilevel Memory","year":2016,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":73,"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":"Materials science; Plasmon; Optoelectronics; Nanowire; Electroforming; Heterojunction; Nanotechnology; Memristor; Resistive random-access memory; Oxide; Non-volatile memory; Femtosecond; Voltage; Laser; Electronic engineering; Optics; Layer (electronics); Electrical engineering","score_opus":0.015304988127181032,"score_gpt":0.22211838055194533,"score_spread":0.2068133924247643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2472097956","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8090137,0.00014114614,0.185167,0.00008855619,0.004006057,0.00054591754,0.000265113,0.0005676777,0.0002048018],"genre_scores_gemma":[0.99318707,0.000056801135,0.003546708,0.00012658165,0.00041312163,0.00044616804,0.000057335565,0.00007325098,0.0020929717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998471,0.000031266252,0.0005147366,0.00037014205,0.00016663733,0.00044622138],"domain_scores_gemma":[0.9989238,0.0004949929,0.000120200995,0.00023929056,0.00012035435,0.00010138641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001364474,0.00029397235,0.00039244286,0.00009473631,0.00025615894,0.000030338031,0.000114322385,0.00009903776,0.0003060239],"category_scores_gemma":[0.00022473748,0.00023290662,0.0001192113,0.00009395404,0.000045594497,0.0006652819,0.000028774324,0.00006119851,0.0001428125],"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":[0.00027357874,0.000016083543,0.0000019530469,0.00003799655,0.00006959607,9.904359e-7,0.000010449208,0.053453587,0.9317544,0.00036093715,0.0002834138,0.013737],"study_design_scores_gemma":[0.0026651307,0.00007400117,0.000283372,0.00005655276,0.000032266977,0.0000106517855,0.000015880853,0.00028875726,0.98977387,0.0016497286,0.004821411,0.00032836027],"about_ca_topic_score_codex":0.0000010797107,"about_ca_topic_score_gemma":0.0000024346155,"teacher_disagreement_score":0.18417333,"about_ca_system_score_codex":0.00014337282,"about_ca_system_score_gemma":0.000025187512,"threshold_uncertainty_score":0.9497656},"labels":[],"label_agreement":null},{"id":"W2510046743","doi":"10.1109/iscas.2016.7539134","title":"Analog cellular neural network for application in physical unclonable functions","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Physical unclonable function; Computer science; Hardware security module; Artificial neural network; Hamming distance; Authentication (law); CMOS; Electronic engineering; Key (lock); Cryptography; Algorithm; Engineering; Artificial intelligence","score_opus":0.011758822592392592,"score_gpt":0.22364878296425544,"score_spread":0.21188996037186286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510046743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42995077,0.000027246004,0.56901,0.00005506385,0.00010522102,0.00012564391,0.0000015015345,0.00015465223,0.0005698663],"genre_scores_gemma":[0.9982468,0.0000018438343,0.00087239593,0.000026692043,0.00035106033,0.000044912744,0.0000047730146,0.000012045358,0.00043947122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960047,0.000004858464,0.00008045794,0.00010569645,0.000029447145,0.00017904918],"domain_scores_gemma":[0.9997614,0.00010156108,0.0000087297785,0.00009263674,0.000009578694,0.000026082665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000036178128,0.00006338009,0.00007425597,0.00001826694,0.00004018681,0.0000037845828,0.000042817046,0.000020026715,0.0000053490467],"category_scores_gemma":[0.000005800238,0.000045451707,0.000030817635,0.00011988381,0.0000079368165,0.00008657266,0.000009698715,0.000041380514,0.000017543847],"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":[0.000011185602,0.000014019319,0.0006138555,0.000015862239,0.000004541096,7.3218456e-7,0.000013043255,0.75775677,0.19267169,0.0028071022,0.0011328121,0.04495839],"study_design_scores_gemma":[0.00059604633,0.0000577248,0.0010561785,0.000018743483,0.000008827979,0.0000016680834,0.000014972719,0.9115725,0.061858334,0.008913333,0.01565798,0.00024365797],"about_ca_topic_score_codex":0.0000014507549,"about_ca_topic_score_gemma":0.0000107555725,"teacher_disagreement_score":0.568296,"about_ca_system_score_codex":0.000023794686,"about_ca_system_score_gemma":0.0000018708042,"threshold_uncertainty_score":0.18534668},"labels":[],"label_agreement":null},{"id":"W2515101082","doi":"10.1109/iscas.2016.7527472","title":"Memristor-based 4:2 compressor cells design","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; Memistor; Bottleneck; CMOS; Computer science; Scalability; Von Neumann architecture; Resistive random-access memory; Gas compressor; Electronic engineering; Electronic circuit; Computer architecture; Electrical engineering; Embedded system; Engineering; Voltage","score_opus":0.025673707450629663,"score_gpt":0.20343192437536933,"score_spread":0.17775821692473967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515101082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019942457,0.000054684166,0.9758678,0.00004880807,0.0002663223,0.00007151471,0.0000010838351,0.0006000143,0.0031473201],"genre_scores_gemma":[0.9793395,0.000005396399,0.019747088,0.00009334572,0.00005445684,0.0000034680086,1.8852656e-7,0.000017890934,0.0007386988],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960846,0.000013658993,0.0000859482,0.00008812596,0.000052093626,0.00015170843],"domain_scores_gemma":[0.99962884,0.00018622403,0.000008832679,0.00012022908,0.000009927426,0.000045951154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044279903,0.00007973282,0.00007504199,0.00002316025,0.000030526127,0.0000048527313,0.00007843176,0.000023861112,0.00016321987],"category_scores_gemma":[0.000006399867,0.000051639166,0.000024777784,0.000041389965,0.000013747601,0.00006233656,0.000008208992,0.000037819264,0.0001448255],"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":[0.0000065414147,0.000004289803,0.0000046842597,0.000013084273,0.0000033537742,0.000004873051,0.000006165329,0.12061762,0.86807436,0.00006109716,0.0030331968,0.008170744],"study_design_scores_gemma":[0.0002345828,0.000016349846,0.0000118268,0.00001723489,0.000001993604,7.34151e-7,0.0000015553154,0.020728005,0.9683432,0.0001124989,0.0104250815,0.00010696529],"about_ca_topic_score_codex":2.619318e-7,"about_ca_topic_score_gemma":1.8168315e-7,"teacher_disagreement_score":0.959397,"about_ca_system_score_codex":0.000023658638,"about_ca_system_score_gemma":0.0000035030127,"threshold_uncertainty_score":0.21057841},"labels":[],"label_agreement":null},{"id":"W2515328120","doi":"10.1007/978-3-319-44778-0_41","title":"Real-Time FPGA Simulation of Surrogate Models of Large Spiking Networks","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Field-programmable gate array; Artificial neural network; Spiking neural network; Forcing (mathematics); Range (aeronautics); Scale (ratio); Gate array; Field (mathematics); Real-time simulation; Artificial intelligence; Simulation; Embedded system","score_opus":0.017615056907300627,"score_gpt":0.24868618578385165,"score_spread":0.231071128876551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515328120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034386914,0.00017025734,0.9936809,0.000005460709,0.00047247336,0.00015125365,0.00000664402,0.00009854203,0.001975771],"genre_scores_gemma":[0.985548,0.000044393237,0.014072904,0.000019079185,0.00022845251,5.9536393e-7,0.0000025057052,0.000035248515,0.000048782847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985608,0.000011619626,0.00042796205,0.0003713039,0.00028857155,0.00033973635],"domain_scores_gemma":[0.9987685,0.0005125541,0.00018991856,0.00036965398,0.00010943246,0.000049925955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034007314,0.00025919662,0.0004193532,0.00025117456,0.000054943117,0.000014298805,0.00041199592,0.00017928732,0.000012199879],"category_scores_gemma":[0.0000211391,0.00022608181,0.00008187652,0.0001781658,0.00015767239,0.00024539235,0.00019129399,0.00028158905,0.0000021107167],"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":[0.000004920204,0.0000027068281,0.0000024019434,0.0000491896,0.0000046347714,0.000004660152,0.00008547064,0.91059357,0.002737894,0.00051396433,3.157813e-7,0.08600026],"study_design_scores_gemma":[0.00014193171,0.0000347725,0.0000062882013,0.00072464964,0.0000056583663,0.0000026417936,3.3385692e-8,0.9600933,0.011407321,0.02735321,0.000016775837,0.00021336843],"about_ca_topic_score_codex":9.171986e-7,"about_ca_topic_score_gemma":0.000002572957,"teacher_disagreement_score":0.98210937,"about_ca_system_score_codex":0.00007468018,"about_ca_system_score_gemma":0.000031474552,"threshold_uncertainty_score":0.92193484},"labels":[],"label_agreement":null},{"id":"W2518386427","doi":"10.1109/iscas.2016.7539100","title":"FPGA minimal components SKAN model for classical and operant conditioning","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","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":"Université du Québec à Montréal","funders":"","keywords":"Field-programmable gate array; Operant conditioning; Computer science; Process (computing); Kernel (algebra); Architecture; Spiking neural network; Artificial neural network; Computer architecture; Conditioning; Artificial intelligence; Embedded system; Engineering; Mathematics","score_opus":0.03745321956766039,"score_gpt":0.2544908667870374,"score_spread":0.21703764721937702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518386427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.575169,0.00001539168,0.42413265,0.000092629765,0.000050630046,0.00006340941,0.000006611122,0.00011169379,0.00035797796],"genre_scores_gemma":[0.9912242,0.000005438602,0.008080657,0.00006119156,0.00005051795,0.000008286348,0.0000025094819,0.000012897009,0.0005542548],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996166,0.0000034414256,0.000094523246,0.000103923114,0.000035991252,0.00014551944],"domain_scores_gemma":[0.9997877,0.000092943934,0.000007246708,0.000049494527,0.000010414077,0.000052207906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000029812247,0.0000719129,0.00008598223,0.000016920323,0.00006487689,0.000009710649,0.00003509856,0.000025582754,0.000010280472],"category_scores_gemma":[0.000009745788,0.00004897887,0.000020293463,0.0000142353165,0.000022143247,0.00011716819,0.000017913902,0.00003459653,0.0000044238545],"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":[0.00003642368,0.000012953347,0.00007820166,0.000044490742,0.000017550985,0.000003234071,0.00010787083,0.047267966,0.9257371,0.0037472062,0.00068172225,0.022265317],"study_design_scores_gemma":[0.00064617035,0.000024321693,0.00021175598,0.000033672408,0.0000042926627,0.000007434547,0.000009236243,0.9263684,0.071280725,0.00080945203,0.00048811472,0.00011643426],"about_ca_topic_score_codex":1.2155992e-7,"about_ca_topic_score_gemma":7.6786483e-7,"teacher_disagreement_score":0.87910044,"about_ca_system_score_codex":0.000012208257,"about_ca_system_score_gemma":0.0000021490316,"threshold_uncertainty_score":0.19973004},"labels":[],"label_agreement":null},{"id":"W2525885841","doi":"10.1109/irps.2016.7574568","title":"Extensive reliability investigation of a-VMCO nonfilamentary RRAM: Relaxation, retention and key differences to filamentary switching","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Institute of Population and Public Health","keywords":"Resistive random-access memory; Materials science; Reliability (semiconductor); Optoelectronics; Rectification; Electrical conductor; Data retention; Quantum tunnelling; Relaxation (psychology); Scalability; Work (physics); Parametric statistics; Thermal conduction; Electrode; Electrical engineering; Computer science; Engineering; Physics; Composite material","score_opus":0.017182583758145933,"score_gpt":0.21504763247808356,"score_spread":0.19786504871993763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525885841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96119523,0.000030864776,0.037767738,0.0004002381,0.00016433887,0.00017835978,0.0000039603124,0.0001222869,0.00013701007],"genre_scores_gemma":[0.99382114,0.00003132529,0.0058920556,0.00014353856,0.000036986374,0.0000059688164,0.000003948688,0.000009997042,0.00005501613],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992711,0.000031866646,0.0002648696,0.00019695683,0.000110096466,0.00012511792],"domain_scores_gemma":[0.9995226,0.00015319845,0.00005467718,0.00014139744,0.000054229735,0.00007388764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013697064,0.00010873917,0.00013529103,0.00005745977,0.00005745772,0.000008365525,0.000054088516,0.00003541213,0.000031434396],"category_scores_gemma":[0.00007647526,0.000077544515,0.000023568824,0.00009740364,0.000030698702,0.00029756557,0.000040746858,0.000062922525,0.0000041956096],"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":[0.000020190886,0.0000045235047,0.021583548,0.00006313826,0.000010854156,9.2518866e-7,0.00057526,0.0010458108,0.96246046,0.00019475416,0.00018634452,0.013854208],"study_design_scores_gemma":[0.0004103808,0.00017173792,0.364501,0.00038768805,0.000021351885,0.0000067965047,0.00037412098,0.0030121491,0.6227949,0.00796162,0.00007803511,0.00028022326],"about_ca_topic_score_codex":0.000019116362,"about_ca_topic_score_gemma":0.0000109686835,"teacher_disagreement_score":0.34291744,"about_ca_system_score_codex":0.000044145527,"about_ca_system_score_gemma":0.000004681902,"threshold_uncertainty_score":0.31621736},"labels":[],"label_agreement":null},{"id":"W2527798464","doi":"10.3389/fncom.2017.00024","title":"Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation","year":2017,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":460,"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; Concordia University; Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Samsung; Université de Montréal; Compute Canada; Canadian Institute for Advanced Research","keywords":"Backpropagation; Computer science; Artificial neural network; Propagation of uncertainty; Algorithm; Computation; Hebbian theory; Error function; Artificial intelligence","score_opus":0.04478735180542688,"score_gpt":0.26324462530636716,"score_spread":0.2184572735009403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527798464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16949151,0.000061368366,0.828969,0.00052732043,0.00061538495,0.00009741015,0.000004700765,0.00006998504,0.00016332077],"genre_scores_gemma":[0.99138635,0.000004309105,0.008271312,0.0001885272,0.00010809435,0.000007662836,0.000004771958,0.000012186972,0.000016784295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913687,0.000032424072,0.00017165567,0.00024605307,0.0002207719,0.00019222825],"domain_scores_gemma":[0.99958766,0.00007728717,0.0000743326,0.00018408436,0.000029719256,0.00004693218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017169048,0.00011116904,0.000107532636,0.00007200874,0.000390942,0.00018048265,0.00031911657,0.00002462148,3.3297633e-7],"category_scores_gemma":[0.000057637106,0.00009745575,0.000020197243,0.0001253824,0.00022306839,0.00065518083,0.000069294976,0.0001344252,3.528173e-7],"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":[0.0000029900375,0.0000028392328,0.0020556804,0.000012697548,6.85142e-7,0.0000035668675,0.000021829288,0.98749983,0.0011998079,0.0005429606,0.00011545038,0.008541652],"study_design_scores_gemma":[0.00016586292,0.00001363003,0.016355978,0.000025226003,0.0000023334449,0.0000037637901,0.0000051443403,0.96526796,0.0021968754,0.015776375,0.00007769758,0.000109130044],"about_ca_topic_score_codex":0.0000017665578,"about_ca_topic_score_gemma":5.3279376e-7,"teacher_disagreement_score":0.8218948,"about_ca_system_score_codex":0.000029729888,"about_ca_system_score_gemma":0.00002767755,"threshold_uncertainty_score":0.39741302},"labels":[],"label_agreement":null},{"id":"W2528063041","doi":"10.1109/tcsi.2016.2598161","title":"A CORDIC Based Digital Hardware For Adaptive Exponential Integrate and Fire Neuron","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":78,"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":"CORDIC; Field-programmable gate array; Computer science; Artificial neural network; Computer hardware; Bifurcation; Algorithm; Artificial intelligence","score_opus":0.01975104331958326,"score_gpt":0.20393128039884137,"score_spread":0.18418023707925812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528063041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3013836,0.00024302685,0.69668233,0.000042864052,0.0007653175,0.00040530757,0.00014351124,0.00019607607,0.00013792464],"genre_scores_gemma":[0.99938744,0.00004210394,0.000022297092,0.000019764448,0.000059017555,0.000053322812,0.0000017676435,0.00003558613,0.0003786858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992439,0.000023660843,0.00017706487,0.00025460214,0.00009327108,0.0002074709],"domain_scores_gemma":[0.999531,0.00015970164,0.000029480942,0.00012687917,0.00002592126,0.00012704926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055329663,0.00018336573,0.00020094437,0.000056606266,0.00017234856,0.000060028273,0.000048476784,0.00006809894,0.0000042141182],"category_scores_gemma":[0.000006308935,0.00013624954,0.000072528754,0.000060028022,0.00005462869,0.0001857306,5.7484704e-7,0.00008872109,0.0000018271105],"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":[0.0001138488,0.000036752823,0.000012896813,0.00033582782,0.00011858515,0.00002270789,0.0002241527,0.054392308,0.24070673,0.00009745722,0.00008402666,0.70385474],"study_design_scores_gemma":[0.013221974,0.0034174977,0.0005529755,0.004599505,0.00040880838,0.0005703041,0.0026484034,0.76254284,0.18108527,0.00018552347,0.027483268,0.0032836238],"about_ca_topic_score_codex":0.0000021262852,"about_ca_topic_score_gemma":0.000002319504,"teacher_disagreement_score":0.7081505,"about_ca_system_score_codex":0.000031668536,"about_ca_system_score_gemma":0.000011405788,"threshold_uncertainty_score":0.5556095},"labels":[],"label_agreement":null},{"id":"W2529958663","doi":"10.1109/tcad.2017.2681075","title":"FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"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":"Defense Advanced Research Projects Agency; Microelectronics Advanced Research Corporation; Canadian Institute for Advanced Research; Intel Corporation; Semiconductor Research Corporation; National Science Foundation","keywords":"Computer science; Classifier (UML); Artificial intelligence; Scalability; Pattern recognition (psychology); Machine learning; Contextual image classification; Modular design; AdaBoost; Data mining; Image (mathematics); Database","score_opus":0.05555985660566954,"score_gpt":0.2549462936519125,"score_spread":0.19938643704624298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529958663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024548758,0.00008140178,0.9730903,0.000017390646,0.0012945399,0.0005600355,0.00009161461,0.00019594951,0.00012000603],"genre_scores_gemma":[0.9968844,0.00005255237,0.0027287945,0.000011652648,0.000112992384,0.0000993997,0.000013788368,0.000036250898,0.00006015408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989154,0.00008575293,0.00033078084,0.00031933005,0.00012480438,0.00022396272],"domain_scores_gemma":[0.998915,0.00023404167,0.00018495799,0.00028541312,0.0002998476,0.00008073333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001746729,0.00024405876,0.000338347,0.00015988792,0.00045249827,0.00014676513,0.00017262487,0.0001585146,0.0000016789231],"category_scores_gemma":[0.000008968824,0.00022400354,0.000093876326,0.00011152379,0.00006410657,0.00019066817,0.0000010156026,0.00023688046,0.0000022479087],"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":[0.000052744756,0.000066148474,0.0000010918944,0.00014406156,0.00011576723,0.000003397234,0.00017885458,0.58661216,0.27511913,0.000114437484,0.00025312728,0.13733912],"study_design_scores_gemma":[0.00055810245,0.00027738762,0.000033366523,0.00032541988,0.000040018334,0.000030809166,0.000090043184,0.8230873,0.17519532,0.00006972332,0.00007661238,0.00021590007],"about_ca_topic_score_codex":0.000016867598,"about_ca_topic_score_gemma":0.0000033211807,"teacher_disagreement_score":0.97233564,"about_ca_system_score_codex":0.00009224614,"about_ca_system_score_gemma":0.00002856031,"threshold_uncertainty_score":0.91345996},"labels":[],"label_agreement":null},{"id":"W2531297735","doi":"","title":"Engineering of Light-Gated Artificial Ion Channels","year":2006,"lang":"en","type":"article","venue":"Qucosa (Saxon State and University Library Dresden)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Ústav analytické chemie, Akademie Věd České Republiky; National Institutes of Health; National Institute of Standards and Technology; Universität Bremen; University of Toronto; Technische Universität Dresden","keywords":"Chemistry; Computer science","score_opus":0.009152894852070239,"score_gpt":0.17531487510211974,"score_spread":0.1661619802500495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531297735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99345756,0.00014483657,0.0033230935,0.00007266563,0.00024183007,0.000090432375,0.000022031909,0.00029549684,0.0023520486],"genre_scores_gemma":[0.9976639,0.00007756898,0.00039606352,0.00000952158,0.00011029409,1.0188213e-7,0.000020247528,0.000019698164,0.0017026004],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941105,0.000015803296,0.00014348933,0.00015136531,0.00007418695,0.0002041317],"domain_scores_gemma":[0.9997617,0.000033424833,0.000034034478,0.00009644321,0.000010130767,0.00006429185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025343295,0.00013430163,0.000165741,0.00014944519,0.00007158383,0.000020452451,0.00009108302,0.000052281623,0.000030989213],"category_scores_gemma":[0.0000017564726,0.00015506054,0.000042063202,0.00022111317,0.000017703303,0.00074238534,0.000060815575,0.00012774643,0.000008971955],"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":[0.00036592982,0.000118375116,0.0010691647,0.00062562444,0.000119657074,0.00058096676,0.0012429004,0.43254647,0.52574354,0.016761117,0.0049304585,0.015895784],"study_design_scores_gemma":[0.00054468884,0.000078901554,0.0010061434,0.000102605976,0.000023449049,0.000010441096,0.00022521357,0.02146709,0.8927314,0.0008936345,0.0824854,0.0004309852],"about_ca_topic_score_codex":0.000008242553,"about_ca_topic_score_gemma":0.000003314632,"teacher_disagreement_score":0.41107938,"about_ca_system_score_codex":0.000011392819,"about_ca_system_score_gemma":0.000007796319,"threshold_uncertainty_score":0.6323185},"labels":[],"label_agreement":null},{"id":"W2531627096","doi":"10.1002/aelm.201600253","title":"Design Criteria for Ultrathin Single‐Layer Flash Memristors from an Organic Polyradical","year":2016,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Western University; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Materials science; Flash memory; Cathode; Memristor; Anode; Work function; Flash (photography); Organic electronics; Nanotechnology; Optoelectronics; Active layer; Layer (electronics); Thin-film transistor; Transistor; Voltage; Computer science; Electrical engineering; Electrode; Chemistry","score_opus":0.024059096991070183,"score_gpt":0.27193941773644575,"score_spread":0.24788032074537558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531627096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6822041,0.00027597058,0.31568792,0.00007034811,0.00074480195,0.00035934392,0.000041509848,0.0005965926,0.000019347124],"genre_scores_gemma":[0.99167836,0.000095387375,0.0072254976,0.00012378051,0.0004963284,0.0000745383,0.00003318853,0.00013079448,0.00014213086],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997993,0.000109369284,0.00043982003,0.00045611567,0.00012692543,0.0008747773],"domain_scores_gemma":[0.9990346,0.00033260667,0.000084629086,0.00036842722,0.000037560043,0.000142174],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024756885,0.00033784405,0.0004127056,0.00005484564,0.00012187336,0.00005010367,0.00028220797,0.00013436934,0.0006050305],"category_scores_gemma":[0.000110490204,0.0002723273,0.000053649484,0.000093012444,0.000041569314,0.0005440785,0.000023824154,0.00010139845,0.000058536058],"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":[0.00019223873,0.000023315824,7.191515e-7,0.000019879151,0.000028316575,0.0000034200914,0.00007189584,0.0005317413,0.99024624,0.0002535077,0.0001271786,0.008501574],"study_design_scores_gemma":[0.00093190616,0.00040059513,0.000010939707,0.000054990443,0.000024607481,0.000011876433,0.000008059647,0.00013425812,0.98420703,0.008943046,0.004858848,0.00041386217],"about_ca_topic_score_codex":0.0000016412938,"about_ca_topic_score_gemma":0.0000073423143,"teacher_disagreement_score":0.3094742,"about_ca_system_score_codex":0.0003024998,"about_ca_system_score_gemma":0.000042913343,"threshold_uncertainty_score":0.9999729},"labels":[],"label_agreement":null},{"id":"W25370123","doi":"10.1038/ncomms6397","title":"An Energy Complexity Measure for Threshold Circuits that is Motivated by Biological Data on Cortical Computations (計算機科学基礎理論とその応用 研究集会報告集)","year":2005,"lang":"en","type":"article","venue":"数理解析研究所講究録","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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 Institutes of Health Research","keywords":"Measure (data warehouse); Computation; Computer science; Energy (signal processing); Electronic circuit; Algorithm; Theoretical computer science; Mathematics; Data mining; Statistics; Engineering; Electrical engineering","score_opus":0.20581335457717942,"score_gpt":0.3228138979470471,"score_spread":0.1170005433698677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W25370123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6869618,0.0002834534,0.3089353,0.00058625324,0.00031668306,0.0003481222,0.0007342442,0.0009848814,0.00084922486],"genre_scores_gemma":[0.99531233,0.000017412116,0.0023415547,0.0011406332,0.00024583816,0.0000147346145,0.00085617293,0.000042646563,0.000028670456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851114,0.00005062669,0.0002733105,0.00050961727,0.00019757121,0.00045774083],"domain_scores_gemma":[0.9988712,0.00022638886,0.000044117554,0.00060274557,0.000049266157,0.00020627744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016120341,0.00026399488,0.00028239662,0.00005185267,0.00031184728,0.00005191149,0.0005330616,0.00014409923,0.00006501858],"category_scores_gemma":[0.0000598453,0.00024046972,0.00006104303,0.00015502793,0.00009516128,0.000302438,0.0000739946,0.0003037379,0.000021781012],"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":[0.00022196452,0.0012316345,0.0016552303,0.0001402703,0.00038333389,0.00003738643,0.0010951164,0.37694767,0.35779545,0.024237568,0.05692647,0.17932793],"study_design_scores_gemma":[0.001121593,0.00032680054,0.0021818455,0.00005506267,0.000034466422,0.000019810752,0.000059966653,0.89982706,0.06660571,0.0016187023,0.027469242,0.0006797248],"about_ca_topic_score_codex":0.000004262176,"about_ca_topic_score_gemma":0.000017557699,"teacher_disagreement_score":0.5228794,"about_ca_system_score_codex":0.000056891262,"about_ca_system_score_gemma":0.000012346801,"threshold_uncertainty_score":0.98060703},"labels":[],"label_agreement":null},{"id":"W2546925628","doi":"10.1109/ccece.2016.7726661","title":"A novel hybrid CMOS-memristor logic circuit using Memristor Ratioed Logic","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; Pass transistor logic; Logic gate; Adder; CMOS; Computer science; Logic family; Inverter; Electronic engineering; AND-OR-Invert; Electronic circuit; Transistor; Logic synthesis; Digital electronics; Electrical engineering; Engineering; Voltage","score_opus":0.06699873423230461,"score_gpt":0.2534542834553237,"score_spread":0.1864555492230191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546925628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17361759,0.00017494007,0.8194383,0.00009249733,0.00074462924,0.00016869624,0.0000086375,0.00077480264,0.004979951],"genre_scores_gemma":[0.987042,0.000014728787,0.011186946,0.0002874732,0.00030054033,0.0000066471935,0.0000022254544,0.000047252055,0.001112204],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988291,0.000017387363,0.00029823458,0.0003008406,0.0001451939,0.00040919593],"domain_scores_gemma":[0.9993648,0.00013801319,0.000053748252,0.00028336214,0.000042492287,0.00011757004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011322044,0.00024220967,0.000248077,0.00007563746,0.00013104476,0.00002173776,0.00018714849,0.000058214006,0.00029678593],"category_scores_gemma":[0.00007700836,0.0001729426,0.000089204426,0.0001287734,0.000046034238,0.0002678204,0.000048985035,0.00013416089,0.000121097546],"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":[0.000007859446,0.000018478851,0.000021669523,0.0000303364,0.000017388336,0.00002717323,0.00003055045,0.01158701,0.98089737,0.0020296217,0.00042695273,0.004905616],"study_design_scores_gemma":[0.003190413,0.00021596746,0.00025038773,0.00026215447,0.00008318399,0.0006447229,0.000098523924,0.07776203,0.8810171,0.012224402,0.02213434,0.0021167744],"about_ca_topic_score_codex":0.000004631831,"about_ca_topic_score_gemma":0.0000045926945,"teacher_disagreement_score":0.8134244,"about_ca_system_score_codex":0.00023338808,"about_ca_system_score_gemma":0.000019437704,"threshold_uncertainty_score":0.7052395},"labels":[],"label_agreement":null},{"id":"W2547321793","doi":"10.1109/icm.2014.7071833","title":"Resistorless memristor based oscillator","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Guelph","funders":"","keywords":"Memristor; Resistor; Oscillation (cell signaling); Memistor; Computer science; Electronic engineering; Electrical engineering; Control theory (sociology); Engineering; Voltage; Resistive random-access memory; Artificial intelligence; Control (management)","score_opus":0.005559799691997434,"score_gpt":0.17561573599904257,"score_spread":0.17005593630704513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547321793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48693588,0.00011925716,0.4046901,0.00009748847,0.0010732588,0.00009706301,0.0000011570083,0.0017814174,0.10520437],"genre_scores_gemma":[0.99454623,7.881468e-7,0.004598818,0.00017105248,0.00012046112,0.000001907611,7.644368e-7,0.000016713668,0.00054326816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996248,0.000011169034,0.00008372416,0.0000901511,0.000060374605,0.00012981724],"domain_scores_gemma":[0.9997235,0.0000655616,0.000008109305,0.00014002665,0.000009771768,0.000053061245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006601413,0.00007317944,0.00008095662,0.000028101149,0.000046851514,0.000006694396,0.000068021145,0.000019478643,0.00007018132],"category_scores_gemma":[0.0000227844,0.000067412635,0.000027750923,0.000060546485,0.000009882842,0.000042253887,0.000008011874,0.00006476355,0.000061722785],"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":[0.000036588794,0.000029288889,0.0005990201,0.0003208003,0.000023470262,0.000015655502,0.00009801971,0.6346991,0.27894366,0.009637411,0.029684832,0.045912165],"study_design_scores_gemma":[0.0004124627,0.000033961285,0.0005711666,0.00002428247,0.000006759415,0.0000031936331,0.000010579997,0.34588143,0.24579288,0.00032420037,0.40657476,0.0003643169],"about_ca_topic_score_codex":6.538496e-7,"about_ca_topic_score_gemma":0.0000032957378,"teacher_disagreement_score":0.5076103,"about_ca_system_score_codex":0.000028098255,"about_ca_system_score_gemma":0.0000028277827,"threshold_uncertainty_score":0.27490076},"labels":[],"label_agreement":null},{"id":"W2549035524","doi":"10.1109/ccece.2016.7726743","title":"A cellular automata based Izhikevich neuron model","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Cellular automaton; Biological neuron model; Computer science; Automaton; Neuron; State (computer science); Finite-state machine; Artificial intelligence; Biological system; Algorithm; Artificial neural network; Neuroscience","score_opus":0.016822731167688763,"score_gpt":0.20670626491945654,"score_spread":0.18988353375176778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549035524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2802898,0.00002192969,0.7132933,0.00011012241,0.00010871114,0.000050223032,0.000001557647,0.00095720735,0.0051671267],"genre_scores_gemma":[0.9943474,0.000003876652,0.004724803,0.00014549101,0.000037477443,0.0000028345669,6.5293966e-7,0.000023214094,0.0007142654],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995325,0.000006849986,0.00009631894,0.00011980983,0.00006596967,0.00017852423],"domain_scores_gemma":[0.99968994,0.000048242324,0.000008136122,0.00019367135,0.0000085662305,0.00005145997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003616897,0.000092490875,0.00007572808,0.000028114935,0.000029625297,0.000006538929,0.00009360836,0.000026333599,0.000055285396],"category_scores_gemma":[0.000010631084,0.00006199475,0.000030232402,0.000054996814,0.000008586257,0.000109025066,0.000019663516,0.000048754973,0.000084302585],"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":[0.0000033144856,0.000005540529,0.000010231367,0.000019318468,0.0000020480354,0.000011115623,0.0000057050656,0.340345,0.64612406,0.00033334782,0.00040857954,0.012731748],"study_design_scores_gemma":[0.00018878577,0.000008889875,0.000012994644,0.000013702905,0.0000019073861,0.00000128437,6.326869e-7,0.73022765,0.26847148,0.00020386778,0.0007818802,0.00008693426],"about_ca_topic_score_codex":2.0228761e-7,"about_ca_topic_score_gemma":4.104076e-7,"teacher_disagreement_score":0.71405756,"about_ca_system_score_codex":0.000015568987,"about_ca_system_score_gemma":0.0000052403357,"threshold_uncertainty_score":0.2528073},"labels":[],"label_agreement":null},{"id":"W2550772441","doi":"10.1101/086066","title":"Cracking the Barcodes of Fullerene-Like Cortical Microcolumns","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Biological neural network; Computer science; Neuroscience; Topology (electrical circuits); Spiking neural network; Electronic circuit; Robustness (evolution); Artificial neural network; Cortical neurons; Biological system; Physics; Biology; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.013936318289019952,"score_gpt":0.2169567451477555,"score_spread":0.20302042685873556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550772441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97030014,0.001514539,0.024835303,0.00009860701,0.0017915731,0.00059500785,0.00010314779,0.00072631944,0.000035378278],"genre_scores_gemma":[0.99700195,0.00019929542,0.0019599686,0.00008820687,0.0005275246,0.00007334547,8.0706684e-8,0.00014527891,0.000004371165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978249,0.00010862771,0.00062873383,0.00055728544,0.00028476998,0.00059566484],"domain_scores_gemma":[0.99801105,0.00029771647,0.00022545482,0.0010828677,0.00021658732,0.00016634566],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039318055,0.00050537713,0.00056019507,0.00012406654,0.00019843463,0.00007898017,0.0007256488,0.00046242622,0.000039622904],"category_scores_gemma":[0.00013539784,0.0004003859,0.00020001177,0.0002561064,0.00019173499,0.00011819607,0.0005048424,0.0013163424,0.000035065266],"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":[0.000014432411,0.00002341983,0.00064017717,0.00040910585,0.00010819598,0.000021736047,0.00001053859,0.002668987,0.9955055,0.00036391898,0.00022036055,0.0000135914315],"study_design_scores_gemma":[0.00034401647,0.000029161482,0.010801575,0.00093698764,0.000091442686,5.1229378e-8,0.000003860934,0.0022876505,0.97976893,0.000015254584,0.0049917903,0.00072926667],"about_ca_topic_score_codex":0.0000026059865,"about_ca_topic_score_gemma":0.0000010084632,"teacher_disagreement_score":0.026701804,"about_ca_system_score_codex":0.00013034155,"about_ca_system_score_gemma":0.00011851911,"threshold_uncertainty_score":0.9998448},"labels":[],"label_agreement":null},{"id":"W2552660288","doi":"10.1109/ijcnn.2016.7727201","title":"A digital neuromorphic circuit for neural-glial interaction","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"CMC Microsystems","keywords":"Neuromorphic engineering; Computer science; Astrocyte; Field-programmable gate array; Synapse; Biological neural network; Artificial neural network; Neuron; Neuroscience; Spiking neural network; Computer architecture; Computer hardware; Artificial intelligence; Biology; Machine learning","score_opus":0.046142156961018116,"score_gpt":0.23832560332264358,"score_spread":0.19218344636162546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552660288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80484015,0.000009311124,0.18783988,0.00012169291,0.0010021988,0.0001469188,0.000010243645,0.00059708033,0.005432549],"genre_scores_gemma":[0.9986402,0.0000021809296,0.00006159772,0.000050990668,0.00023969907,0.000008294165,0.0000017221191,0.000019439382,0.0009758915],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996015,0.0000027300023,0.00010226046,0.00010682606,0.000038114336,0.00014855711],"domain_scores_gemma":[0.99971527,0.00014033876,0.000012223005,0.000079656566,0.000014083983,0.000038409387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000016247002,0.000079068675,0.000068382586,0.000027385851,0.000032069085,0.000024735156,0.000054209515,0.000020342635,0.000035513585],"category_scores_gemma":[0.00003954674,0.00005497011,0.00004467516,0.000037952854,0.000008933343,0.00039966806,0.000012543426,0.000043921307,0.00003788941],"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":[0.000033175023,0.0000114723525,0.00009656197,0.000032550528,0.000011906965,0.000008864397,0.000029857822,0.0062383963,0.42900354,0.0006157952,0.0007426275,0.56317526],"study_design_scores_gemma":[0.0035393515,0.0006200527,0.000980853,0.00017071693,0.00003122386,0.00032920294,0.00006953372,0.1461378,0.68910754,0.0060017575,0.15171117,0.0013007802],"about_ca_topic_score_codex":8.052389e-8,"about_ca_topic_score_gemma":3.955609e-7,"teacher_disagreement_score":0.56187445,"about_ca_system_score_codex":0.000017272154,"about_ca_system_score_gemma":0.0000016244023,"threshold_uncertainty_score":0.2241616},"labels":[],"label_agreement":null},{"id":"W2552737632","doi":"10.1038/ncomms13276","title":"Random synaptic feedback weights support error backpropagation for deep learning","year":2016,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":851,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Vetenskapsrådet","keywords":"Backpropagation; Computer science; Mechanism (biology); Constraint (computer-aided design); Mistake; Artificial intelligence; Blame; Artificial neural network; Synaptic weight; Mathematics","score_opus":0.019334053238969338,"score_gpt":0.27869359986216247,"score_spread":0.2593595466231931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552737632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30563033,0.013311439,0.64916897,0.0072424016,0.0021667401,0.002692105,0.00005054013,0.0034681978,0.016269306],"genre_scores_gemma":[0.98371696,0.0003167998,0.015360287,0.00006281456,0.00008310643,0.00006512364,0.000046979952,0.000032839034,0.00031509],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993472,0.000054259275,0.00020020349,0.00012900296,0.00007394598,0.00019535693],"domain_scores_gemma":[0.9983657,0.00082160847,0.000052276355,0.00061509194,0.000093403185,0.000051920248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016282659,0.000121416415,0.00013933169,0.00006044486,0.00031191533,0.000016027392,0.0004408282,0.0001546021,0.000026136533],"category_scores_gemma":[0.00024158598,0.00009193808,0.00007414469,0.00013186646,0.000049523725,0.00019366763,0.00007640135,0.00046509455,0.000053045],"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":[0.00039905155,0.00022489576,0.0023232915,0.00039295058,0.00057962147,0.000006322924,0.0018467958,0.03437373,0.44521022,0.051176555,0.0062375683,0.457229],"study_design_scores_gemma":[0.010007955,0.0003433772,0.00502016,0.00053663715,0.00025773546,0.00008573907,0.00033581493,0.24276917,0.091002956,0.009359598,0.63849956,0.001781328],"about_ca_topic_score_codex":1.9746818e-7,"about_ca_topic_score_gemma":0.000021518554,"teacher_disagreement_score":0.67808664,"about_ca_system_score_codex":0.00005736767,"about_ca_system_score_gemma":0.000009546193,"threshold_uncertainty_score":0.3749126},"labels":[],"label_agreement":null},{"id":"W2552954910","doi":"10.1109/ijcnn.2016.7727198","title":"High accuracy implementation of Adaptive Exponential integrated and fire neuron model","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Implementation; Biological neuron model; Artificial neural network; Exponential function; Scale (ratio); Artificial intelligence; Computer engineering; Computer architecture; Mathematics","score_opus":0.02101635734548983,"score_gpt":0.2595558956964372,"score_spread":0.23853953835094738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552954910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83576304,0.000014013786,0.16396822,0.000021688482,0.00005316683,0.000054103886,0.0000067546644,0.000075212585,0.000043823504],"genre_scores_gemma":[0.99747694,0.00003103143,0.0024136775,0.000010949498,0.000016226633,0.0000022134923,0.0000018428373,0.000008399458,0.00003871291],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996835,0.0000069929115,0.0001120068,0.000076552424,0.000038421873,0.00008253396],"domain_scores_gemma":[0.9998392,0.00005159316,0.000020953112,0.000050495073,0.000016380674,0.000021388178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000019125446,0.00006275174,0.000068333036,0.00001789613,0.000017963752,0.0000029887221,0.00002940729,0.000015045981,0.000038890674],"category_scores_gemma":[0.0000070361807,0.000042441174,0.000012284201,0.00003487027,0.000011229859,0.00018539648,0.000018476032,0.000030663257,0.0000013695646],"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":[0.000024977087,0.0000040413,0.000052854568,0.000012450328,0.000009964538,0.0000012260613,0.00012326914,0.027746055,0.7259965,0.0010368625,0.00010073755,0.24489106],"study_design_scores_gemma":[0.0005419173,0.00006407693,0.0009007891,0.000023069453,0.0000069149846,0.000002112486,0.00015547159,0.19809029,0.7993843,0.00070751214,0.000021323027,0.00010221626],"about_ca_topic_score_codex":0.000011965835,"about_ca_topic_score_gemma":0.000005568343,"teacher_disagreement_score":0.24478884,"about_ca_system_score_codex":0.000009892102,"about_ca_system_score_gemma":0.0000038116211,"threshold_uncertainty_score":0.17307009},"labels":[],"label_agreement":null},{"id":"W2554571367","doi":"10.1007/s40820-016-0116-2","title":"Resistive Switching Memory of TiO2 Nanowire Networks Grown on Ti Foil by a Single Hydrothermal Method","year":2016,"lang":"en","type":"article","venue":"Nano-Micro Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Nanowire; Materials science; Electroforming; FOIL method; Electrode; Hydrothermal circulation; Optoelectronics; Nanotechnology; Resistive random-access memory; Resistive touchscreen; Evaporation; Titanium; Electrical engineering; Composite material; Layer (electronics); Chemical engineering; Metallurgy; Chemistry","score_opus":0.008199887514456687,"score_gpt":0.21339492611061875,"score_spread":0.20519503859616206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554571367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76245046,0.00039876375,0.23369832,0.0007594459,0.0005443272,0.00020000408,0.000015078306,0.00030728578,0.0016263198],"genre_scores_gemma":[0.99372935,0.00001398707,0.0040876926,0.0012701049,0.00023660973,0.000009096489,0.0000036484835,0.00008128118,0.0005682495],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870557,0.000091970025,0.00031297174,0.00031249668,0.00015293168,0.0004240706],"domain_scores_gemma":[0.99911565,0.00042329458,0.00010229281,0.00026673166,0.000019950967,0.00007207534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020559986,0.00026149547,0.0002967792,0.00008446168,0.00010170519,0.000014094761,0.00021014982,0.000103573,0.000023998164],"category_scores_gemma":[0.00003804575,0.00021008267,0.00011472676,0.00014966616,0.000043638556,0.00016994603,0.00004507912,0.0002169945,0.00001551058],"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":[0.00005446361,0.0000170738,0.000014503807,0.000035131037,0.00003665067,0.000012607586,0.00012145061,0.011414309,0.95158744,0.000012569426,0.0066554574,0.030038359],"study_design_scores_gemma":[0.00060124043,0.000047992824,0.00003418837,0.00032238293,0.00001752824,0.0000092501805,0.000012618485,0.0008180309,0.99328,0.000024011315,0.0045323544,0.00030043692],"about_ca_topic_score_codex":0.000004249373,"about_ca_topic_score_gemma":0.0000026313264,"teacher_disagreement_score":0.23127888,"about_ca_system_score_codex":0.00013395037,"about_ca_system_score_gemma":0.000006409454,"threshold_uncertainty_score":0.85669225},"labels":[],"label_agreement":null},{"id":"W2555443672","doi":"10.1109/ijcnn.2016.7727816","title":"Hardware implementation of deep brain stimulator on a biophysical neural population model","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Field-programmable gate array; Software; Population; Artificial neural network; Neurophysiology; Artificial intelligence; Neural activity; Computer hardware; Neuroscience; Medicine; Operating system","score_opus":0.0182168469204593,"score_gpt":0.28850131311000854,"score_spread":0.27028446618954927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555443672","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8597716,0.000001924702,0.13984866,0.000074254785,0.000049937164,0.00006675757,0.0000051675497,0.00012003014,0.00006166192],"genre_scores_gemma":[0.9989472,4.6924328e-7,0.000907655,0.000053695185,0.000040547355,0.0000035445735,0.00000463333,0.000012293951,0.000029980132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995972,0.00000741026,0.00012954063,0.00008786644,0.0000741454,0.0001038188],"domain_scores_gemma":[0.99980855,0.000053949698,0.000019830457,0.00007981479,0.0000105010395,0.000027373351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000020774993,0.000071040195,0.00007864943,0.00003167241,0.000020547131,0.000002853311,0.000036303063,0.000018192308,0.000038358612],"category_scores_gemma":[0.000007895697,0.00004989128,0.000031872813,0.00004836168,0.0000050324484,0.00012975902,0.000010393501,0.000029847435,0.0000053920944],"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":[0.000011070213,0.000005608633,0.00029857812,0.000013241288,0.000004327785,4.5388512e-7,0.00004464791,0.6038422,0.32295913,0.0010843937,0.000049453185,0.07168693],"study_design_scores_gemma":[0.0003372763,0.00004771501,0.004274394,0.000010719333,0.0000029095206,4.7037236e-7,0.000016863427,0.81146306,0.18318306,0.00057676475,0.0000053984304,0.00008137758],"about_ca_topic_score_codex":0.0000022843994,"about_ca_topic_score_gemma":0.000005706478,"teacher_disagreement_score":0.20762089,"about_ca_system_score_codex":0.00002412433,"about_ca_system_score_gemma":0.0000013590119,"threshold_uncertainty_score":0.20345074},"labels":[],"label_agreement":null},{"id":"W2555477762","doi":"10.1109/ijcnn.2016.7727892","title":"Robotic implementation of classical and Operant Conditioning as a single STDP learning process","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Université du Québec à Montréal","funders":"","keywords":"Operant conditioning; Computer science; Spiking neural network; Adaptation (eye); Spike-timing-dependent plasticity; Process (computing); Conditioning; Field-programmable gate array; Artificial intelligence; Artificial neural network; Robot; Kernel (algebra); Classical conditioning; Neuroscience; Embedded system; Synaptic plasticity; Engineering; Psychology","score_opus":0.014521847714345724,"score_gpt":0.28337748294223125,"score_spread":0.2688556352278855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555477762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9400299,0.000025633886,0.059142876,0.00004804169,0.000033075237,0.000055655026,3.6011178e-7,0.00010115837,0.00056327245],"genre_scores_gemma":[0.9994653,0.000005903232,0.0003803982,0.000012148088,0.000021610234,0.0000028150562,0.0000011753917,0.000009230007,0.000101420614],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996387,0.000009977351,0.00011712377,0.00007947395,0.000052813062,0.000101953556],"domain_scores_gemma":[0.99983966,0.00006424417,0.000019755933,0.000029237697,0.000018684505,0.00002839115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003615658,0.000055579203,0.00007941498,0.00002508294,0.000044090633,0.000007956415,0.000022677497,0.000015719916,0.000085487896],"category_scores_gemma":[0.000017715574,0.000039814528,0.000010724275,0.00004432873,0.00001910308,0.00016272068,0.000011114142,0.00004580097,0.000003934937],"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":[0.0000054173543,0.0000070785172,0.0013155389,0.00006735838,0.000011725255,0.0000029682133,0.00035005633,0.050876956,0.903374,0.00078582,0.000008268459,0.043194797],"study_design_scores_gemma":[0.000661733,0.00022029085,0.0014151774,0.00011197476,0.000010510686,0.000023776576,0.0010429685,0.010224439,0.9855063,0.00056256814,0.000076775155,0.00014351906],"about_ca_topic_score_codex":0.0000011844635,"about_ca_topic_score_gemma":0.0000029866567,"teacher_disagreement_score":0.08213225,"about_ca_system_score_codex":0.000013485735,"about_ca_system_score_gemma":0.000004718431,"threshold_uncertainty_score":0.16235892},"labels":[],"label_agreement":null},{"id":"W2556038295","doi":"10.1109/ijcnn.2016.7727228","title":"Evolving Spiking Neural Networks of artificial creatures using Genetic Algorithm","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Creatures; Artificial neural network; Computer science; Spiking neural network; Genetic algorithm; Artificial life; Artificial intelligence; Evolutionary acquisition of neural topologies; Evolutionary algorithm; Algorithm; Machine learning; Time delay neural network; Biology; Natural (archaeology)","score_opus":0.01788638510068326,"score_gpt":0.2403065535334725,"score_spread":0.22242016843278922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556038295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38507164,0.00033673702,0.61392266,0.0000036937934,0.00031752166,0.000043274573,7.4488e-7,0.00013852274,0.00016523054],"genre_scores_gemma":[0.95522153,0.000011711568,0.044310853,0.000015653966,0.00039032823,8.0093116e-7,3.124599e-7,0.000025987505,0.000022827355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992654,0.000015037386,0.00022920119,0.00013754024,0.00008601984,0.00026682904],"domain_scores_gemma":[0.9996505,0.00010218674,0.00003653175,0.00013407948,0.000027928389,0.00004876963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049869126,0.00012751919,0.00015327503,0.000052803043,0.000060017224,0.000012686898,0.00009071547,0.000055547465,0.000053221596],"category_scores_gemma":[0.000019457053,0.0000922866,0.000056028795,0.00011843904,0.000029443196,0.00013373552,0.00003789294,0.00009243966,0.0000013691725],"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":[0.000002716952,0.0000031646014,0.00014533647,0.000009864763,0.000008067793,0.000008528516,0.000014977811,0.5723916,0.16120385,0.000027799451,0.000009383504,0.2661747],"study_design_scores_gemma":[0.00008397679,0.000016023776,0.0009011363,0.000057243622,0.000009442778,0.000018513178,0.000012678627,0.92386776,0.07472103,0.00016233746,0.00001543288,0.00013440661],"about_ca_topic_score_codex":0.0000033885149,"about_ca_topic_score_gemma":0.0000012272394,"teacher_disagreement_score":0.5701499,"about_ca_system_score_codex":0.000025863255,"about_ca_system_score_gemma":0.000003474279,"threshold_uncertainty_score":0.37633386},"labels":[],"label_agreement":null},{"id":"W2563740559","doi":"10.1109/sips.2016.61","title":"Stochastic Computing Can Improve Upon Digital Spiking Neural Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stochastic computing; Computer science; Spiking neural network; Artificial neural network; Implementation; Artificial intelligence; Theoretical computer science; Distributed computing; Computer architecture","score_opus":0.008618456077861082,"score_gpt":0.20650645799566916,"score_spread":0.1978880019178081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563740559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44208166,0.000035314206,0.555995,0.00003200076,0.0005798674,0.00006835804,0.0000019048621,0.00054693315,0.000658945],"genre_scores_gemma":[0.9990764,0.0000010431895,0.00024055583,0.000054087235,0.00046896024,0.0000012363613,0.0000017265304,0.000037700236,0.00011824465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990889,0.0000064946807,0.00019928582,0.0001976968,0.00008335581,0.00042429255],"domain_scores_gemma":[0.9995235,0.00018422998,0.000029473738,0.00014735384,0.000017856724,0.000097575066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045521512,0.00017578655,0.00014783297,0.000037810336,0.00008911873,0.000053522108,0.00012131013,0.000045566223,0.000013845936],"category_scores_gemma":[0.000032753764,0.00012489475,0.000053158084,0.00009541089,0.000018924406,0.00023464717,0.000075091106,0.00015215387,0.000009917907],"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":[0.000004370551,0.0000028073503,0.00007011777,0.000007694264,0.000007610991,0.000008802597,0.000025013342,0.7462305,0.009179547,0.00009889551,0.000018953422,0.24434568],"study_design_scores_gemma":[0.0003041497,0.000037853053,0.00012610748,0.000056103512,0.0000047676103,0.000024360485,0.00002233826,0.994864,0.004052731,0.00017215488,0.00006384089,0.00027160344],"about_ca_topic_score_codex":0.0000011486261,"about_ca_topic_score_gemma":0.0000039442793,"teacher_disagreement_score":0.5569948,"about_ca_system_score_codex":0.00005031303,"about_ca_system_score_gemma":0.0000032229025,"threshold_uncertainty_score":0.509306},"labels":[],"label_agreement":null},{"id":"W2566165147","doi":"10.1109/irws.2003.1283331","title":"Magnetoresistive random access memory (MRAM) and reliability","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Magnetoresistive random-access memory; Reliability (semiconductor); Computer science; Random access memory; Perspective (graphical); Magnetoresistance; Reliability engineering; Engineering; Computer hardware; Artificial intelligence; Magnetic field; Physics","score_opus":0.011116865520556194,"score_gpt":0.2427630051028775,"score_spread":0.2316461395823213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566165147","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9650376,0.00020958921,0.02414993,0.00006480577,0.00017649718,0.00013565835,0.0000011524063,0.00038233984,0.009842445],"genre_scores_gemma":[0.99758834,0.000035656816,0.0020726703,0.00007413569,0.000061032217,0.0000026370305,8.7996676e-7,0.000011388168,0.00015328167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999543,0.000009029462,0.000110174784,0.00014286608,0.00005418885,0.00014072693],"domain_scores_gemma":[0.9997208,0.00007943543,0.000009629058,0.000116720206,0.0000148224,0.000058584523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007448846,0.00009455858,0.00011616389,0.000021615075,0.000058448604,0.000024393723,0.00007775467,0.000031570566,0.00003785427],"category_scores_gemma":[0.000043584067,0.00008137567,0.000022760236,0.00007145177,0.0000360424,0.00021286128,0.000047111098,0.00010500823,0.000008405033],"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":[0.00017234168,0.000033393262,0.0005266317,0.00024925332,0.000019580066,0.00004392643,0.00035924738,0.9556392,0.021638434,0.00087232154,0.00023198716,0.020213682],"study_design_scores_gemma":[0.00958745,0.00015755589,0.029673122,0.00012008121,0.000049908514,0.00005938512,0.00022374906,0.0125031425,0.9119115,0.03197933,0.0026159617,0.001118804],"about_ca_topic_score_codex":0.000008497587,"about_ca_topic_score_gemma":0.0000043332357,"teacher_disagreement_score":0.94313604,"about_ca_system_score_codex":0.000029461662,"about_ca_system_score_gemma":0.000005140499,"threshold_uncertainty_score":0.33184037},"labels":[],"label_agreement":null},{"id":"W2566199700","doi":"10.1109/nmdc.2016.7777083","title":"HfO<sub>x</sub> complementary resistive switches","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Resistive touchscreen; Fabrication; Computer science; Electrical engineering; Engineering","score_opus":0.025859783090445666,"score_gpt":0.24618309903311655,"score_spread":0.22032331594267088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566199700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84048223,0.0006085489,0.12652966,0.00027415485,0.0021715176,0.0005335615,0.000118388154,0.0018987975,0.027383141],"genre_scores_gemma":[0.99750495,0.00012706111,0.001436444,0.00013345503,0.0005292481,0.000024534696,0.00004926341,0.00006571113,0.00012934748],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987511,0.000021529975,0.00032166924,0.00039338754,0.0001503761,0.0003619235],"domain_scores_gemma":[0.9992714,0.00014044737,0.00006046125,0.00039580246,0.000035621208,0.00009625469],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009734047,0.0003520178,0.00033425383,0.0000841152,0.00008525777,0.000025886791,0.00027340653,0.00013715387,0.000076030075],"category_scores_gemma":[0.00001611446,0.00029535915,0.00012312236,0.000052053325,0.00003475696,0.000077630975,0.0005397609,0.00050045585,0.00011259557],"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":[0.00004857583,0.000026832935,0.00021176225,0.00074607704,0.00029526924,0.00006635167,0.00021976676,0.031724114,0.88337576,0.0010660397,0.014481737,0.0677377],"study_design_scores_gemma":[0.00025144068,0.000018589939,0.00062754354,0.00036874885,0.00002986051,0.000005763749,0.000046425226,0.0015428875,0.98103726,0.01316896,0.0023190423,0.00058347615],"about_ca_topic_score_codex":0.0000025042652,"about_ca_topic_score_gemma":0.000014803905,"teacher_disagreement_score":0.15702271,"about_ca_system_score_codex":0.00012158557,"about_ca_system_score_gemma":0.00001792255,"threshold_uncertainty_score":0.9999499},"labels":[],"label_agreement":null},{"id":"W2568104936","doi":"10.1039/c6ra25618j","title":"The development of sol–gel derived TiO<sub>2</sub> thin films and corresponding memristor architectures","year":2017,"lang":"en","type":"article","venue":"RSC Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CMC Microsystems (Canada)","funders":"Università degli Studi di Trento","keywords":"Memristor; Sol-gel; Thin film; Materials science; Chemical engineering; Nanotechnology; Electronic engineering; Engineering","score_opus":0.015235466241063231,"score_gpt":0.2511649401584084,"score_spread":0.23592947391734517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568104936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9871862,0.002816965,0.008734396,0.000025440588,0.00046626286,0.00010686639,0.0000021204398,0.00009236734,0.00056941295],"genre_scores_gemma":[0.99105996,0.00022928599,0.008596011,0.00001121566,0.000047915662,0.000009088949,0.0000010169789,0.00001827903,0.000027237276],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992711,0.00001723043,0.00021778983,0.00015328976,0.000116324714,0.00022427176],"domain_scores_gemma":[0.9993285,0.00024951776,0.00012214786,0.00023098562,0.00001848892,0.00005035307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001531327,0.00014535646,0.00016041029,0.00003370031,0.00082923874,0.000044898603,0.00024337236,0.000031374137,0.0000014166898],"category_scores_gemma":[0.00012029428,0.00010780126,0.000029988474,0.000026120935,0.00012495668,0.00014922,0.00009608005,0.00015166358,0.0000025455092],"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":[0.000053176107,0.0000053455005,0.00023954599,0.00009850968,0.000025405081,0.0000047808553,0.0014767235,0.034000453,0.682771,0.00007719859,0.000026058917,0.28122178],"study_design_scores_gemma":[0.00019734261,0.000019049487,0.0037882302,0.00009652616,0.000006538011,0.0000071926065,0.00019874125,0.004503337,0.9863145,0.0008483176,0.0038367815,0.0001834725],"about_ca_topic_score_codex":3.231282e-7,"about_ca_topic_score_gemma":0.00004311312,"teacher_disagreement_score":0.30354342,"about_ca_system_score_codex":0.000017390266,"about_ca_system_score_gemma":0.000014532175,"threshold_uncertainty_score":0.6377918},"labels":[],"label_agreement":null},{"id":"W2571711614","doi":"10.1021/acsami.6b14206","title":"Reliable and Low-Power Multilevel Resistive Switching in TiO<sub>2</sub> Nanorod Arrays Structured with a TiO<sub><i>x</i></sub> Seed Layer","year":2017,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Nanorod; Electroforming; Tin oxide; Optoelectronics; Resistive random-access memory; Layer (electronics); Substrate (aquarium); Nanotechnology; Doping; Voltage; Electrical engineering","score_opus":0.008959443332714825,"score_gpt":0.21430981029893828,"score_spread":0.20535036696622344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571711614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99768066,0.00008584541,0.00043539517,0.000024006978,0.00058320025,0.0005389668,0.000032705368,0.00026754412,0.00035165364],"genre_scores_gemma":[0.9989513,0.00007421991,0.0005760742,0.000057017434,0.00013474993,0.000073851785,0.000011157434,0.00011193521,0.000009674546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99804,0.000028115475,0.0005083791,0.00062383054,0.0002046642,0.0005949722],"domain_scores_gemma":[0.99893016,0.00008081814,0.00027594122,0.0005670215,0.000050652474,0.000095419695],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027550364,0.0005106899,0.00060995773,0.00011574427,0.00039499465,0.00036267852,0.00037642056,0.00019703794,0.000008414792],"category_scores_gemma":[0.000042971777,0.00045792712,0.000018430312,0.00006706546,0.00010335851,0.0004632195,0.00025227762,0.00033657785,0.000029017328],"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":[0.00043070354,0.000017870027,0.000054232012,0.00019311249,0.000052977815,0.000024179106,0.00081871654,0.006244688,0.99101025,0.000090862115,0.000027128264,0.0010352958],"study_design_scores_gemma":[0.0011588838,0.00006084943,0.004193036,0.0004283749,0.000028801773,0.000016715258,0.00020915571,0.000071786606,0.99245715,0.0008033656,0.000010521172,0.00056134834],"about_ca_topic_score_codex":0.00001401055,"about_ca_topic_score_gemma":0.000081908154,"teacher_disagreement_score":0.0061729015,"about_ca_system_score_codex":0.0000771995,"about_ca_system_score_gemma":0.000020828944,"threshold_uncertainty_score":0.9997873},"labels":[],"label_agreement":null},{"id":"W2574346082","doi":"","title":"Neural Cognitive Modelling: A Biologically Constrained Spiking Neuron Model of the Tower of Hanoi Task","year":2011,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"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":"Action selection; Basal ganglia; Neuroscience; Set (abstract data type); Task (project management); Computer science; Working memory; Cognition; Artificial intelligence; Cognitive science; Psychology; Engineering","score_opus":0.04284843029976032,"score_gpt":0.20294020909720623,"score_spread":0.1600917787974459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574346082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9648915,0.000098533164,0.024114154,0.000015085383,0.00010226461,0.00023754768,0.0007499913,0.00024613316,0.009544816],"genre_scores_gemma":[0.99834716,0.0000076731185,0.0013647532,0.000118179145,0.000028288841,0.0000050676163,0.000030699237,0.000048962087,0.000049196326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987138,0.00003906257,0.000490436,0.00025909345,0.00016914854,0.00032844162],"domain_scores_gemma":[0.9993326,0.0001407011,0.0001451137,0.00023197044,0.0000432828,0.0001063456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006830216,0.0002619174,0.00030218516,0.00006931125,0.00006471871,0.00005236104,0.00039872713,0.00011016677,0.00004663969],"category_scores_gemma":[0.000107173655,0.00019234365,0.00020029681,0.00026803074,0.00020541818,0.0011057731,0.00019810224,0.00042605188,0.000011090007],"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":[0.0009690419,0.0005300578,0.029640533,0.0005079468,0.00022292262,0.000060155202,0.000701517,0.74836063,0.19385283,0.004426252,0.00007870986,0.020649388],"study_design_scores_gemma":[0.00087421027,0.00023908635,0.0010236667,0.00035729862,0.00005260153,0.000021501097,0.000073918956,0.70720726,0.27213,0.017189411,0.00018724418,0.0006437784],"about_ca_topic_score_codex":3.3488388e-7,"about_ca_topic_score_gemma":7.8362284e-8,"teacher_disagreement_score":0.07827719,"about_ca_system_score_codex":0.0000069249522,"about_ca_system_score_gemma":0.00002610406,"threshold_uncertainty_score":0.7843546},"labels":[],"label_agreement":null},{"id":"W2579313944","doi":"10.1162/neco_a_00929","title":"Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights","year":2017,"lang":"en","type":"article","venue":"Neural Computation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Spiking neural network; Backpropagation; Artificial neural network; Deep learning; Artificial intelligence; Feed forward; Piecewise; Learning rule; Algorithm; Mathematics","score_opus":0.011074848314723447,"score_gpt":0.24111409935215755,"score_spread":0.2300392510374341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579313944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93071353,0.000090779744,0.068216056,0.00009189605,0.00014701184,0.000101269754,2.6140563e-7,0.0002861756,0.0003530375],"genre_scores_gemma":[0.99804294,0.0000139137655,0.0017783116,0.000044016815,0.00006570329,0.0000020072257,0.0000053682747,0.000029226032,0.000018517547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993929,0.000022002292,0.00011495669,0.00019237337,0.000090951726,0.00018679324],"domain_scores_gemma":[0.99967504,0.000061280276,0.000072700386,0.000099083605,0.000024491903,0.00006740881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000030510433,0.00014167126,0.00012537008,0.00003296534,0.0005188119,0.00013538059,0.000095536125,0.00003256598,0.000002436252],"category_scores_gemma":[0.000023919865,0.00012972359,0.00001979375,0.00004113023,0.000041170122,0.00036887577,0.000044143944,0.00025764082,0.000004896349],"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":[0.000018721244,0.0000022301847,0.0015204404,0.00004686801,0.0000060628495,0.000021146674,0.00020079172,0.9007451,0.0050681387,0.000017579297,0.0000021537296,0.09235076],"study_design_scores_gemma":[0.0003705991,0.00009619654,0.049005046,0.000031147203,0.000009617434,0.000031544754,0.000025300185,0.94931465,0.00082703464,0.00008860412,0.000043366075,0.00015689908],"about_ca_topic_score_codex":0.0000018892379,"about_ca_topic_score_gemma":0.000009153175,"teacher_disagreement_score":0.09219386,"about_ca_system_score_codex":0.000014009968,"about_ca_system_score_gemma":0.0000021507565,"threshold_uncertainty_score":0.5289975},"labels":[],"label_agreement":null},{"id":"W2584537058","doi":"10.1016/j.bpj.2016.11.1361","title":"The Isolated Voltage Sensing Domain of the Shaker Potassium Channel forms a Cation Channel","year":2017,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Shaker; Chemistry; Ion channel; Membrane potential; Biophysics; Potassium channel; Depolarization; Gating; Ion; Relaxation (psychology); KcsA potassium channel; Coupling (piping); Materials science; Biochemistry; Physics; Vibration","score_opus":0.014327258647614116,"score_gpt":0.23677237136669915,"score_spread":0.22244511271908504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584537058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96919066,0.0000286485,0.02896131,0.0005046777,0.00092573295,0.000091963935,0.000002691063,0.00003458687,0.00025975128],"genre_scores_gemma":[0.99924463,0.000018357214,0.000049644485,0.000028581788,0.0005875841,5.7421977e-7,3.5757907e-7,0.000016964723,0.000053332376],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993073,0.000020887715,0.00019613723,0.00007910471,0.00015223495,0.0002443468],"domain_scores_gemma":[0.9993765,0.000049626135,0.00017751899,0.00028347713,0.000052609605,0.000060284747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015040087,0.00011267993,0.00012243848,0.000014912374,0.0011311254,0.000108313296,0.0003074783,0.000043563898,8.622164e-7],"category_scores_gemma":[0.00004829899,0.000059701877,0.00011933803,0.000057960533,0.0000921943,0.00019473048,0.000078276644,0.00037616518,0.0000041804105],"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":[0.000029526633,0.000012712875,0.000007107868,0.000014198713,0.000030622985,0.000009767696,0.00029575778,0.004853162,0.9871502,0.0002471836,0.000092876384,0.0072568753],"study_design_scores_gemma":[0.0010588828,0.00013884358,0.013413527,0.00033489926,0.00004995373,0.00035692606,0.00040108035,0.43469432,0.5320579,0.016048739,0.0009774088,0.00046752207],"about_ca_topic_score_codex":0.000001770448,"about_ca_topic_score_gemma":0.0000023838504,"teacher_disagreement_score":0.4550923,"about_ca_system_score_codex":0.00003347462,"about_ca_system_score_gemma":0.000010209331,"threshold_uncertainty_score":0.8699816},"labels":[],"label_agreement":null},{"id":"W2587648566","doi":"","title":"Modeling The Fan Effect Using Dynamically Structured Holographic Memory","year":2008,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Carleton University","funders":"","keywords":"Computer science; Holography; Noise (video); Artificial intelligence; Physics; Optics","score_opus":0.014803550714758968,"score_gpt":0.20650625316160018,"score_spread":0.1917027024468412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587648566","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852762,0.00042372922,0.011021453,0.00003447425,0.00025280056,0.0002785097,0.00015746897,0.001143409,0.0014119147],"genre_scores_gemma":[0.9984275,0.000020704336,0.0009388605,0.00016826432,0.00019512056,0.00000740728,0.00008974785,0.00012474434,0.000027650145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983466,0.000085468884,0.00038820168,0.00035943597,0.00026533456,0.000554947],"domain_scores_gemma":[0.9991454,0.00018175361,0.000045306475,0.00041738554,0.000017350141,0.00019279937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000112560956,0.00039228637,0.0003121528,0.00013797461,0.0004247435,0.00029172577,0.0005098447,0.00015355865,0.000033378074],"category_scores_gemma":[0.000095500414,0.00028940433,0.00022357728,0.0005324382,0.00012617021,0.0020115494,0.00017080783,0.00086101814,0.00009146364],"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":[0.00015256203,0.000037731243,0.023719298,0.0001709752,0.00013363449,0.00032087378,0.00008460329,0.93615294,0.026007269,0.00015391762,0.00008807864,0.012978148],"study_design_scores_gemma":[0.0010382931,0.000121595695,0.0020234939,0.0001625808,0.00005294456,0.0006274342,0.0000327189,0.95334166,0.03499501,0.00522477,0.0011493904,0.0012301324],"about_ca_topic_score_codex":5.9601905e-7,"about_ca_topic_score_gemma":3.5758217e-7,"teacher_disagreement_score":0.021695804,"about_ca_system_score_codex":0.000029504,"about_ca_system_score_gemma":0.000022746312,"threshold_uncertainty_score":0.99995583},"labels":[],"label_agreement":null},{"id":"W2589998820","doi":"10.1016/s1452-3981(23)06736-6","title":"Low-Temperature Formed Quaternary NiZrSiGe Nanocrystal Memory","year":2015,"lang":"en","type":"article","venue":"International Journal of Electrochemical Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"NSCAD University; Ministry of Science and Technology, Taiwan; Clinical Trial Center, China Medical University Hospital","keywords":"Nanocrystal; Flash memory; Materials science; Annealing (glass); Non-volatile memory; Zirconium; Chemical engineering; Nanotechnology; Optoelectronics; Metallurgy; Embedded system; Computer science","score_opus":0.010295592354056615,"score_gpt":0.2571873913658494,"score_spread":0.24689179901179278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589998820","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99386156,0.00019043192,0.0035829265,0.00016500398,0.001187797,0.000029514917,7.3881404e-7,0.00004128312,0.00094072695],"genre_scores_gemma":[0.99828774,0.000015750973,0.0009473727,0.00013480274,0.00056465965,5.9074114e-7,0.000001014513,0.000008806438,0.00003926542],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859345,0.000008597828,0.00028263146,0.00011364942,0.00073715387,0.0002645023],"domain_scores_gemma":[0.99909484,0.00004429714,0.0000895358,0.00006943065,0.0004743525,0.00022756436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031031875,0.000104570136,0.00012319052,0.00013191157,0.00003899464,0.000062990825,0.00077099266,0.000039544022,0.000010211936],"category_scores_gemma":[0.00024027936,0.00008626845,0.00006254897,0.00024845416,0.00010844932,0.0006605974,0.00006324215,0.0003374794,0.0000059664653],"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":[0.00005171446,0.000017212153,0.000030694744,0.0000028696923,0.000012194478,0.000057393445,0.000102496495,0.0015713301,0.9962452,0.000057421832,0.00025358307,0.0015979116],"study_design_scores_gemma":[0.00035590582,0.00007987074,0.00003168279,0.00004324063,0.0000030751992,0.00068662403,0.000040435494,0.0019433316,0.99506176,0.0013483777,0.0003016515,0.00010402738],"about_ca_topic_score_codex":4.1238147e-7,"about_ca_topic_score_gemma":3.4297557e-7,"teacher_disagreement_score":0.00442616,"about_ca_system_score_codex":0.00029141127,"about_ca_system_score_gemma":0.00012826028,"threshold_uncertainty_score":0.3517925},"labels":[],"label_agreement":null},{"id":"W2590500368","doi":"10.1109/icci-cc.2016.7862055","title":"Robotic implementation of classical and operant conditioning within a single SNN architecture","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Operant conditioning; Spiking neural network; Computer science; Classical conditioning; Adaptation (eye); Spike-timing-dependent plasticity; Artificial intelligence; Artificial neural network; Spike (software development); Conditioning; Kernel (algebra); Neuroscience; Synaptic plasticity; Psychology; Mathematics","score_opus":0.014669792933220576,"score_gpt":0.25144098318634067,"score_spread":0.2367711902531201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590500368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7945141,0.000025271733,0.20499833,0.0001078587,0.000052984586,0.00004968,0.0000013975642,0.00006664597,0.00018368773],"genre_scores_gemma":[0.99569386,0.0000023858045,0.004197548,0.000028647271,0.000025259078,0.0000015858033,0.0000010982013,0.000007346958,0.00004226276],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996853,0.000008615067,0.000114506634,0.00006895599,0.000040764113,0.00008184798],"domain_scores_gemma":[0.9998501,0.000057469042,0.000015803422,0.000042039777,0.0000084805815,0.000026103224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000029673995,0.000053022,0.000073498435,0.000023995544,0.000025585467,0.0000052626433,0.000021569265,0.00001505445,0.000044102693],"category_scores_gemma":[0.0000072129174,0.00003441831,0.000011651375,0.000033634326,0.00002239435,0.00006984862,0.000012197836,0.00003749523,0.0000013387921],"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":[0.0000034191933,0.0000035468925,0.00018428087,0.000023730376,0.000008023043,0.0000017458829,0.00023208038,0.027756214,0.937329,0.0010442989,0.000021421716,0.03339222],"study_design_scores_gemma":[0.00055354746,0.00009469881,0.001194307,0.000075272954,0.000008537906,0.0000193088,0.00021263416,0.0038622904,0.992027,0.0017811166,0.00005780774,0.00011350261],"about_ca_topic_score_codex":0.0000010155549,"about_ca_topic_score_gemma":0.000011960055,"teacher_disagreement_score":0.20117971,"about_ca_system_score_codex":0.000011013537,"about_ca_system_score_gemma":0.0000027759672,"threshold_uncertainty_score":0.1403538},"labels":[],"label_agreement":null},{"id":"W2591671750","doi":"10.1109/mwscas.2016.7870144","title":"Efficient mixed-signal synapse multipliers for multi-layer feed-forward neural networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Very-large-scale integration; Artificial neural network; Multiplication (music); Multiplier (economics); Modular design; Modularity (biology); CMOS; Scalability; Mixed-signal integrated circuit; Topology (electrical circuits); Integrated circuit; Electronic engineering; Artificial intelligence; Electrical engineering; Mathematics; Embedded system; Engineering","score_opus":0.02813523670066142,"score_gpt":0.2545008857816687,"score_spread":0.22636564908100726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591671750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35232127,0.00007458558,0.6462844,0.000030871102,0.0005447952,0.00023405069,0.0000047281915,0.00041849184,0.000086793305],"genre_scores_gemma":[0.99036866,0.000004918258,0.00883049,0.00008912594,0.00022646648,0.000032896827,0.0000024800295,0.00005027933,0.00039467274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988533,0.000015434325,0.00024278389,0.00026511948,0.000091744936,0.00053160614],"domain_scores_gemma":[0.9993497,0.00026137166,0.000030911404,0.0001768658,0.000036990063,0.000144174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011449317,0.00022370386,0.00019642305,0.000054143573,0.00012471127,0.00001709349,0.00015190477,0.00008772271,0.00004084096],"category_scores_gemma":[0.00003450536,0.00015123365,0.00012843599,0.00009463095,0.000037933674,0.000067113026,0.00004848416,0.00012141395,0.000021925622],"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":[0.000033412918,0.000015806676,0.00005755651,0.000018297796,0.000020112991,0.000004305182,0.000024875671,0.9188148,0.040355615,0.00010969321,0.00033752766,0.040208012],"study_design_scores_gemma":[0.0010991744,0.000044234865,0.00023349961,0.000025906722,0.000011211716,0.000007890466,0.000027357808,0.9550509,0.042856876,0.0000119582755,0.00036859614,0.00026239335],"about_ca_topic_score_codex":6.713998e-7,"about_ca_topic_score_gemma":0.0000036500805,"teacher_disagreement_score":0.6380474,"about_ca_system_score_codex":0.000057253834,"about_ca_system_score_gemma":0.0000038780136,"threshold_uncertainty_score":0.61671287},"labels":[],"label_agreement":null},{"id":"W2599944644","doi":"10.4018/ijcini.2017040101","title":"Single SNN Architecture for Classical and Operant Conditioning using Reinforcement Learning","year":2017,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Operant conditioning; Reinforcement learning; Spiking neural network; Field-programmable gate array; Artificial neural network; Scalability; Adaptation (eye); Artificial intelligence; Reinforcement; Computer hardware; Neuroscience","score_opus":0.032468470560981864,"score_gpt":0.31825227471501955,"score_spread":0.2857838041540377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599944644","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54527694,0.0002769995,0.4536622,0.000045824818,0.00047651632,0.000069520895,0.000003529022,0.000008734439,0.00017975869],"genre_scores_gemma":[0.9939608,0.00016341923,0.0055576377,0.0001145767,0.0001769556,9.2548794e-7,0.000003688819,0.0000071205754,0.00001488306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992819,0.0000070069696,0.000387913,0.000043975655,0.00016375916,0.00011545135],"domain_scores_gemma":[0.9989707,0.00027748835,0.0002861501,0.00003219319,0.00038169915,0.00005177273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014467361,0.000104516206,0.00014016076,0.0000936206,0.00022392423,0.0002207281,0.00015502002,0.000034645494,0.0000035635535],"category_scores_gemma":[0.00041802385,0.0000865344,0.00004745452,0.000015638669,0.00010004946,0.0006141299,0.000068201814,0.00032793536,3.8470247e-7],"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":[0.00034924826,0.00002052828,0.00033216947,0.00013251747,0.0003476262,0.000040541272,0.003704102,0.30070785,0.016745172,0.0014737481,0.000016193551,0.6761303],"study_design_scores_gemma":[0.0006563265,0.000293005,0.0002687755,0.0011900766,0.000045024353,0.00057645363,0.0013292502,0.89072406,0.10100135,0.0027121902,0.0009530793,0.0002503853],"about_ca_topic_score_codex":6.544121e-7,"about_ca_topic_score_gemma":8.3541397e-7,"teacher_disagreement_score":0.6758799,"about_ca_system_score_codex":0.000030161938,"about_ca_system_score_gemma":0.00001073457,"threshold_uncertainty_score":0.35287702},"labels":[],"label_agreement":null},{"id":"W2600834343","doi":"10.1149/ma2008-01/3/71","title":"Multi-Valued Analog Information Storage using Self-Assembled Nanoparticle Films","year":2008,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nanoparticle; Nanotechnology; Materials science; Information storage; Computer science; Information retrieval","score_opus":0.02654701040230765,"score_gpt":0.2468347178702183,"score_spread":0.22028770746791065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600834343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947165,0.00006805796,0.0008907811,0.000003845633,0.0003846633,0.00012555982,0.000001342276,0.00084178644,0.0029674356],"genre_scores_gemma":[0.9606852,0.000011297189,0.039156854,0.000037732178,0.00007164069,0.0000027375943,0.0000038025098,0.000024252382,0.000006513926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891025,0.00002675168,0.0003980639,0.00012858116,0.00016991807,0.00036646245],"domain_scores_gemma":[0.99949616,0.00006365895,0.00011120622,0.00016632282,0.00005149032,0.000111138885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023845868,0.00017395845,0.00016772539,0.0000814743,0.00029335346,0.00003435771,0.00010796624,0.00007333925,0.000005890324],"category_scores_gemma":[0.00012854012,0.000187909,0.000048658425,0.00020791279,0.000009463265,0.0007023277,0.00003099345,0.00022713872,0.00007544082],"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":[0.0000030333879,0.000011592153,0.00016762874,0.000027391641,0.0000071491577,0.000020373458,0.0004499143,0.59406686,0.40517455,6.6531095e-7,0.000029688375,0.000041160845],"study_design_scores_gemma":[0.00032552835,0.000012171151,0.004463302,0.00004468524,0.0000085056545,0.000048829123,0.000066777415,0.50585985,0.4888771,0.0000036069375,0.00011826083,0.00017142628],"about_ca_topic_score_codex":0.000014830785,"about_ca_topic_score_gemma":0.0000016598424,"teacher_disagreement_score":0.08820703,"about_ca_system_score_codex":0.00007129389,"about_ca_system_score_gemma":0.000022097704,"threshold_uncertainty_score":0.76627064},"labels":[],"label_agreement":null},{"id":"W2604145830","doi":"10.1063/1.4978664","title":"Fully inkjet printed flexible resistive memory","year":2017,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Bayerische Forschungsallianz; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Resistive random-access memory; Materials science; PEDOT:PSS; Polystyrene sulfonate; Non-volatile memory; Polystyrene; Optoelectronics; Nanotechnology; Voltage; Sintering; Layer (electronics); Electrical conductor; Electrical engineering; Composite material; Polymer","score_opus":0.019467535339041615,"score_gpt":0.23910579825943154,"score_spread":0.21963826292038993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604145830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93406117,0.0000115888915,0.034611173,0.00019672468,0.00035724626,0.00019533248,0.0000041491776,0.0005129976,0.030049648],"genre_scores_gemma":[0.9976052,0.0000029198688,0.0011706891,0.0006432056,0.00043374093,0.00001942765,0.000006957251,0.000041378244,0.00007647584],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992692,0.0000058752225,0.00012529586,0.00021237336,0.000111092464,0.00027619526],"domain_scores_gemma":[0.9992993,0.000042717253,0.00006492626,0.0005244721,0.000012230415,0.00005636961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005121343,0.00017465671,0.00016670524,0.000019808278,0.00032329117,0.00006320742,0.00028917327,0.000034392662,0.0000070092788],"category_scores_gemma":[0.000007757096,0.0001879566,0.000049856077,0.00004827088,0.000079192825,0.00015204391,0.000096548414,0.0002601576,0.00009383964],"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":[0.000020058918,0.00000825337,0.000028889486,0.000039694307,0.000034317018,0.000010524825,0.00015860924,0.059792623,0.91994256,0.0025751728,0.0015371754,0.015852118],"study_design_scores_gemma":[0.0005016395,0.000008657794,0.0024466598,0.00003450099,0.00001827945,0.0000017900027,0.00003518761,0.0012060638,0.9919373,0.0012505122,0.0021442368,0.00041518634],"about_ca_topic_score_codex":0.0000021436454,"about_ca_topic_score_gemma":5.684129e-7,"teacher_disagreement_score":0.07199472,"about_ca_system_score_codex":0.000037416805,"about_ca_system_score_gemma":0.000004883677,"threshold_uncertainty_score":0.76646477},"labels":[],"label_agreement":null},{"id":"W2605768265","doi":"10.1149/ma2007-02/23/1239","title":"Solution Processed Organic Memory Devices","year":2007,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Process engineering; Environmental science; Engineering","score_opus":0.014727436394741451,"score_gpt":0.23848393171730164,"score_spread":0.2237564953225602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605768265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9317856,0.00033481,0.0008734843,0.000022375445,0.00045548,0.00008231187,3.2251526e-7,0.0007492168,0.06569643],"genre_scores_gemma":[0.9977361,0.0000060856974,0.0017267439,0.000052240663,0.00036218055,0.0000012729838,0.0000031352126,0.000036434576,0.00007580887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989916,0.000007917442,0.0002941576,0.00017008891,0.00014392144,0.00039228905],"domain_scores_gemma":[0.99950725,0.00016459616,0.00007285046,0.00012025387,0.000041056413,0.0000940056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050964084,0.00014796578,0.000121152254,0.00006257447,0.00014574859,0.000032538384,0.00011788403,0.000074760115,0.000013266996],"category_scores_gemma":[0.0001591084,0.00015605366,0.000029537177,0.00019501626,0.000016288961,0.00023906433,0.00002215869,0.00024746376,0.000086765845],"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":[0.0000063497164,0.000010524159,0.00010588462,0.00012956721,0.0000075030007,0.00002961008,0.00020097585,0.15746062,0.83891684,0.0000010009111,0.000067240144,0.0030638685],"study_design_scores_gemma":[0.00014975775,0.000013523563,0.006270948,0.00011379276,0.000008354966,0.00002212334,0.00014980999,0.0024787276,0.9896686,0.000063570486,0.00085601915,0.00020474182],"about_ca_topic_score_codex":0.0000037091784,"about_ca_topic_score_gemma":0.00002662912,"teacher_disagreement_score":0.15498188,"about_ca_system_score_codex":0.00005173143,"about_ca_system_score_gemma":0.000012374536,"threshold_uncertainty_score":0.6363684},"labels":[],"label_agreement":null},{"id":"W2609318638","doi":"10.1109/tnano.2017.2698205","title":"Fabrication of Planar Back End of Line Compatible HfO$_x$ Complementary Resistive Switches","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Resistive touchscreen; Fabrication; Planar; Topology (electrical circuits); Electrical engineering; Physics; Computer science; Engineering; Operating system","score_opus":0.03450606104422275,"score_gpt":0.2698778702580119,"score_spread":0.23537180921378917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609318638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49812075,0.00004484315,0.50086474,0.00017096476,0.00026417003,0.00014262847,0.00006959197,0.000121793,0.0002004878],"genre_scores_gemma":[0.9965412,0.000071154274,0.0033013439,0.0000108823915,0.000014645944,0.0000073713836,0.0000048207544,0.000015545013,0.000033047116],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932444,0.000011063836,0.00028526783,0.0001495177,0.00007964267,0.00015005455],"domain_scores_gemma":[0.99926525,0.00008455173,0.00014116624,0.00043941347,0.00004773247,0.00002186065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053410175,0.000119837714,0.00024797017,0.00016892265,0.00015795861,0.0000037501,0.0002686889,0.000110380875,0.000054848315],"category_scores_gemma":[0.0000076753295,0.00012768812,0.00005513526,0.0001021209,0.00018011281,0.00009102165,0.0000022691095,0.00022724019,0.000011321143],"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":[0.00010618447,0.0001130097,0.000069036505,0.00014508251,0.00009708416,0.0000027699293,0.00011216271,0.107580684,0.8334987,0.00027636052,0.000075434575,0.057923485],"study_design_scores_gemma":[0.00043208216,0.00018244631,0.00031478546,0.000060229027,0.000023979961,0.000004348712,0.00005906247,0.0051628826,0.99278176,0.00055775273,0.0003153312,0.00010535605],"about_ca_topic_score_codex":0.00003817786,"about_ca_topic_score_gemma":0.0000898665,"teacher_disagreement_score":0.49842042,"about_ca_system_score_codex":0.000031886462,"about_ca_system_score_gemma":0.000009081301,"threshold_uncertainty_score":0.52069706},"labels":[],"label_agreement":null},{"id":"W2609450303","doi":"10.1021/acsami.7b03527","title":"Oxygen Vacancies Control Transition of Resistive Switching Mode in Single-Crystal TiO<sub>2</sub> Memory Device","year":2017,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","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":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Research Chairs","keywords":"Materials science; X-ray photoelectron spectroscopy; Oxygen; Resistive random-access memory; Thin film; Sputter deposition; Valence (chemistry); Sputtering; Optoelectronics; Photoemission spectroscopy; Single crystal; Analytical Chemistry (journal); Nanotechnology; Chemical engineering; Crystallography; Voltage; Electrical engineering","score_opus":0.014587812418626648,"score_gpt":0.23064946598798203,"score_spread":0.2160616535693554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609450303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963634,0.00010948519,0.0021200057,0.000031552623,0.0003526551,0.00030789786,0.00004599798,0.00012960908,0.0005393913],"genre_scores_gemma":[0.99956065,0.000026645806,0.00016860073,0.000033720655,0.00012741123,0.000032695385,0.0000069590055,0.000040732404,0.0000025867196],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884826,0.000032710654,0.00045446557,0.0002486045,0.0001260957,0.00028985864],"domain_scores_gemma":[0.9993539,0.000094502444,0.00019938029,0.00028446908,0.0000339829,0.000033774068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023056757,0.00023529975,0.00043363392,0.00007877828,0.00014810928,0.000092561524,0.00027768358,0.00009879097,0.000007057644],"category_scores_gemma":[0.000036002286,0.0002413197,0.000018842607,0.00003794125,0.000055970697,0.00030552378,0.000059556514,0.00013519896,0.000007895923],"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":[0.00024187833,0.000017889612,0.0000021098244,0.00022516254,0.000031316125,0.000004833037,0.0009675739,0.032402683,0.96417093,0.00007452294,0.000006561595,0.0018545114],"study_design_scores_gemma":[0.00072949723,0.00003251115,0.00037401225,0.00023832053,0.000025191503,0.0000032829835,0.00030854729,0.00015653395,0.9971726,0.0007223747,0.0000029945172,0.0002341124],"about_ca_topic_score_codex":0.000027663107,"about_ca_topic_score_gemma":0.0000787032,"teacher_disagreement_score":0.033001665,"about_ca_system_score_codex":0.00006642217,"about_ca_system_score_gemma":0.000010257313,"threshold_uncertainty_score":0.98407316},"labels":[],"label_agreement":null},{"id":"W2611142117","doi":"10.3389/fninf.2017.00033","title":"Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator","year":2017,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Air Force Office of Scientific Research; Ontario Innovation Trust","keywords":"Computer science; Python (programming language); Implementation; Computation; Software; Artificial neural network; Parallel computing; Computer architecture simulator; Graph; Algorithm; Artificial intelligence; Operating system; Programming language; Theoretical computer science","score_opus":0.011748015582656743,"score_gpt":0.22729980486234164,"score_spread":0.21555178927968488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611142117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.868193,0.000059487415,0.12924455,0.00006857742,0.0016000508,0.00037813882,0.0000015517048,0.00005755342,0.00039710643],"genre_scores_gemma":[0.9894243,0.000022249342,0.010397356,0.00010306955,0.00003478472,0.0000031901457,0.0000020389714,0.000011313106,0.0000017165357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918395,0.000045651148,0.00038662436,0.000053850956,0.0001447536,0.00018517928],"domain_scores_gemma":[0.9993657,0.00008884842,0.00017615825,0.00034063208,0.000013086846,0.000015611824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018623365,0.000107790715,0.00015467827,0.000057064204,0.00017599015,0.000050869217,0.00044046226,0.000037303496,6.0128593e-7],"category_scores_gemma":[0.000099923476,0.000075079675,0.000046154095,0.00020124264,0.00005844084,0.00038766145,0.00005478874,0.00024363367,3.6425018e-7],"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":[0.0000023956743,0.0000044762146,0.0037371772,0.000060983533,0.0000023796808,0.0000019258996,0.00060725835,0.9904024,0.000002998197,0.00001898724,0.00034497265,0.0048140283],"study_design_scores_gemma":[0.0002549007,0.000011980923,0.01350997,0.000053552776,0.0000053317012,0.000004830325,0.00012286942,0.9854194,0.000039025952,0.00048093544,0.000024345592,0.00007285],"about_ca_topic_score_codex":9.54874e-7,"about_ca_topic_score_gemma":0.0000020495222,"teacher_disagreement_score":0.1212313,"about_ca_system_score_codex":0.000018599585,"about_ca_system_score_gemma":0.0000064889127,"threshold_uncertainty_score":0.30616602},"labels":[],"label_agreement":null},{"id":"W2612782654","doi":"10.1063/1.4983175","title":"Thermodynamics of self-oscillations in VO2 for spiking solid-state neurons","year":2017,"lang":"en","type":"article","venue":"AIP Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Research Chairs","keywords":"Capacitance; Physics; Electronic circuit; Self consistent; Power (physics); Statistical physics; Thermodynamics; Chemistry; Condensed matter physics; Quantum mechanics; Quantum electrodynamics","score_opus":0.01737394123263371,"score_gpt":0.29162786040589217,"score_spread":0.2742539191732585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612782654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95539546,0.000333496,0.041692298,0.000036519777,0.00039186046,0.00018142589,0.000012146403,0.00012448084,0.0018322879],"genre_scores_gemma":[0.9956712,0.00020214594,0.004022417,0.00001303994,0.000045192803,0.000008404452,0.0000014072052,0.00001868593,0.000017464727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994839,0.000005951736,0.00016883772,0.000114760645,0.0000487032,0.00017786726],"domain_scores_gemma":[0.9995581,0.00011451109,0.00007767923,0.00020623062,0.00002080951,0.000022669592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060161994,0.00008939879,0.00013899295,0.000042618274,0.00015305936,0.000016207961,0.00016400185,0.000020427005,0.0000010089699],"category_scores_gemma":[0.000059442213,0.00009075036,0.000037638147,0.00004263695,0.000028162976,0.00034638125,0.000030148534,0.000079960555,8.6582435e-7],"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":[0.000012703513,0.000018062388,0.0068973345,0.00015111727,0.00001239135,0.0000045962424,0.00038183457,0.9082061,0.035488244,0.0008788702,0.0000034578918,0.047945276],"study_design_scores_gemma":[0.0007924218,0.0000992852,0.048337508,0.00017103793,0.000017691349,0.00000526923,0.00008399924,0.8870048,0.0334662,0.02339042,0.006213034,0.00041836052],"about_ca_topic_score_codex":0.0000016583507,"about_ca_topic_score_gemma":0.00007165235,"teacher_disagreement_score":0.04752692,"about_ca_system_score_codex":0.000019488827,"about_ca_system_score_gemma":0.0000067651786,"threshold_uncertainty_score":0.37006924},"labels":[],"label_agreement":null},{"id":"W2618779684","doi":"","title":"A Spiking Neuron Model of Serial-Order Recall","year":2010,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"","keywords":"Computer science; Encoding (memory); Spiking neural network; Recall; Population; Theoretical computer science; Artificial neural network; Algorithm; Artificial intelligence; Psychology","score_opus":0.014686604990014717,"score_gpt":0.20520101829596293,"score_spread":0.1905144133059482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618779684","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9816444,0.000040371826,0.0040522977,0.00005284609,0.00036887763,0.00014253559,0.00029847393,0.00074485136,0.012655352],"genre_scores_gemma":[0.9928698,0.0000071729073,0.006429136,0.000129116,0.00017621653,0.0000052296023,0.000067052235,0.00010676909,0.00020948517],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878246,0.000014713491,0.00038769917,0.00026755998,0.00017773536,0.0003698128],"domain_scores_gemma":[0.99930537,0.000085986365,0.00006727594,0.00033721517,0.000026117877,0.00017803772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007467549,0.00025343476,0.00024841202,0.000099083685,0.00006602304,0.0002202851,0.0003361856,0.0001356046,0.00009385832],"category_scores_gemma":[0.00015003396,0.00025143617,0.000104083185,0.00030561734,0.00005129222,0.002400553,0.00014685829,0.0007424726,0.00014947454],"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":[0.00015561415,0.00013376013,0.0081085935,0.00036351747,0.00004714206,0.000041233354,0.00006485811,0.20341183,0.7471173,0.003046255,0.0006096764,0.036900263],"study_design_scores_gemma":[0.0012287624,0.00013687692,0.0010185036,0.00020030157,0.000032405256,0.000049059767,0.000017318474,0.2683529,0.659305,0.027103757,0.041127782,0.0014273811],"about_ca_topic_score_codex":1.5714848e-7,"about_ca_topic_score_gemma":4.8064834e-7,"teacher_disagreement_score":0.087812304,"about_ca_system_score_codex":0.000006843476,"about_ca_system_score_gemma":0.00002658236,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W2619254848","doi":"10.1016/j.ijdevneu.2015.04.260","title":"ISDN2014_0313: The development of functional connectivity in the neural network supporting waking","year":2015,"lang":"en","type":"article","venue":"International Journal of Developmental Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Citation; Library science; Computer science; World Wide Web","score_opus":0.06330920099057372,"score_gpt":0.2936115993735194,"score_spread":0.23030239838294567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619254848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98724777,0.000038083694,0.009639912,0.00023351492,0.00238144,0.00006232079,6.0848555e-7,0.000011048766,0.00038530238],"genre_scores_gemma":[0.9975158,0.0000027619687,0.0019732425,0.0003249037,0.00016759921,0.0000018001972,5.3081504e-7,0.0000064111227,0.0000069774946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983845,0.00006034386,0.00057289674,0.000096726944,0.0006919678,0.000193613],"domain_scores_gemma":[0.9993043,0.00022132763,0.00024368703,0.000051147246,0.00013491824,0.00004459838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288418,0.000100264224,0.000114546056,0.00008184399,0.00009692314,0.000044316057,0.0006504853,0.000017304583,0.00000414584],"category_scores_gemma":[0.00026647726,0.00006315167,0.000041632145,0.00026890778,0.00007573807,0.0003584028,0.000102336984,0.0002800609,0.0000013284781],"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":[0.000100299985,0.000076455035,0.034732077,0.000010214606,0.000023170174,0.00013897759,0.0052921767,0.8676097,0.0626751,0.0003452276,0.00050068274,0.02849594],"study_design_scores_gemma":[0.0026345495,0.00023268198,0.7577702,0.00038201883,0.000018209888,0.0059059495,0.006733233,0.0924695,0.11815347,0.0027346152,0.012196021,0.000769554],"about_ca_topic_score_codex":6.6942704e-7,"about_ca_topic_score_gemma":0.000009582272,"teacher_disagreement_score":0.77514017,"about_ca_system_score_codex":0.00011074077,"about_ca_system_score_gemma":0.000152263,"threshold_uncertainty_score":0.25752503},"labels":[],"label_agreement":null},{"id":"W2620317457","doi":"10.1007/978-3-319-59876-5_21","title":"An Event-Based Optical Flow Algorithm for Dynamic Vision Sensors","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Optical flow; Computer science; Event (particle physics); Computer vision; Detector; Artificial intelligence; Algorithm; Motion (physics); Real-time computing; Image (mathematics); Telecommunications","score_opus":0.011878551598841576,"score_gpt":0.27901535749979917,"score_spread":0.26713680590095756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620317457","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004192017,0.00012927658,0.9970384,0.00004580251,0.0016698008,0.00033556786,0.000016470678,0.00021706423,0.0001284144],"genre_scores_gemma":[0.24774005,0.000008805841,0.751403,0.0001492722,0.0005133592,0.0000062271683,0.000025050289,0.00007549064,0.000078771416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822026,0.000008332662,0.00027134424,0.0007115028,0.00031658635,0.00047195595],"domain_scores_gemma":[0.998736,0.00024384416,0.000080159356,0.00071458414,0.00008220593,0.00014315742],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026615747,0.00038617454,0.00036106352,0.00024324862,0.000298396,0.00017626128,0.0008105755,0.0002432183,0.000005839075],"category_scores_gemma":[0.000038264094,0.00036726028,0.00011113511,0.000055822937,0.00028353423,0.00026779567,0.00010120429,0.0005410338,0.0000076016213],"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":[0.0000019840545,0.0000037003986,1.4241178e-7,0.000021188745,0.0000016469767,0.000017079372,0.000017317245,0.5181406,0.00086959027,0.00001698063,8.1110437e-7,0.48090893],"study_design_scores_gemma":[0.00024251165,0.00019506829,0.0000103615475,0.0003029626,0.000007651891,0.000018435829,5.534783e-8,0.9823799,0.010624599,0.005566922,0.00022578189,0.00042575636],"about_ca_topic_score_codex":4.2497086e-7,"about_ca_topic_score_gemma":0.000009431218,"teacher_disagreement_score":0.48048317,"about_ca_system_score_codex":0.00016656495,"about_ca_system_score_gemma":0.000074793265,"threshold_uncertainty_score":0.9998779},"labels":[],"label_agreement":null},{"id":"W2621136797","doi":"10.1109/irps.2017.7936322","title":"Kinetic defect distribution approach for modeling the transient, endurance and retention of a-VMCO RRAM","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"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":"Institute of Population and Public Health","keywords":"Resistive random-access memory; Transient (computer programming); Transient analysis; Computer science; Distribution (mathematics); Materials science; Electrical engineering; Transient response; Engineering; Mathematics; Voltage","score_opus":0.0421421844571065,"score_gpt":0.2512730139557505,"score_spread":0.20913082949864403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621136797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43997133,0.00009056499,0.55940604,0.000009053194,0.000039778894,0.00011852643,0.0000046731375,0.000027786082,0.00033221208],"genre_scores_gemma":[0.99747294,0.000027609376,0.0024187977,0.0000025818856,0.000031839718,0.000010937523,0.000011460695,0.0000068526642,0.000016957569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996695,0.0000060263574,0.00010005387,0.00008885699,0.000038288497,0.0000972855],"domain_scores_gemma":[0.9997679,0.000023929308,0.000026146148,0.00014930224,0.000017783757,0.000014917287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009488369,0.00005963157,0.00007559485,0.0000062425706,0.00018186664,0.000018226117,0.000075808406,0.000023799834,5.3898236e-7],"category_scores_gemma":[0.00002374289,0.000042743948,0.000046634086,0.000015570637,0.000024848026,0.00009853846,0.0000098429355,0.000054478915,8.838908e-8],"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":[0.000024344214,0.00000783046,0.000055851084,0.00024264128,0.000015285947,1.4782023e-7,0.00008366656,0.8932012,0.08911928,0.0015141637,0.00001666997,0.015718928],"study_design_scores_gemma":[0.00020422184,0.000015109288,0.0009747283,0.000017126365,0.00001507696,0.0000028632242,0.000021847196,0.981712,0.01665234,0.00028327556,0.00004538706,0.000056024946],"about_ca_topic_score_codex":0.0000020704833,"about_ca_topic_score_gemma":0.0000010015722,"teacher_disagreement_score":0.5575016,"about_ca_system_score_codex":0.000005829536,"about_ca_system_score_gemma":0.0000010668525,"threshold_uncertainty_score":0.17430477},"labels":[],"label_agreement":null},{"id":"W2725753878","doi":"10.1145/3060579","title":"Improving Performance under Process and Voltage Variations in Near-Threshold Computing Using 3D ICs","year":2017,"lang":"en","type":"article","venue":"ACM Journal on Emerging Technologies in Computing Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Voltage; Threshold voltage; Die (integrated circuit); Three-dimensional integrated circuit; Chip; Voltage drop; Process corners; Integrated circuit; Transistor; Electronic engineering; Power network design; Integrated circuit design; Engineering; Low voltage; Sensitivity (control systems); Power (physics); Process variation; Electrical engineering; Physics","score_opus":0.03254818440203598,"score_gpt":0.2986155316724781,"score_spread":0.2660673472704421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2725753878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9214214,0.00063406344,0.07550524,0.000086350556,0.0014107975,0.00020948655,7.4475355e-7,0.0006454913,0.0000863928],"genre_scores_gemma":[0.99199927,0.00007986835,0.0076818243,0.0000124176,0.00016924753,0.0000019175593,4.4629473e-7,0.00005189551,0.0000031424986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787384,0.000034282584,0.00078706356,0.00037062482,0.00025958373,0.0006746075],"domain_scores_gemma":[0.99849737,0.00024629067,0.0005063338,0.0006320197,0.000068454094,0.000049523347],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010550886,0.0003283525,0.00045767892,0.00043155625,0.0013824985,0.0005165792,0.0010222,0.00021199152,3.674084e-7],"category_scores_gemma":[0.00061567005,0.00033086806,0.00004212313,0.00033495002,0.000135033,0.00054062874,0.0005977912,0.0015627336,0.0000010707923],"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":[0.000006451658,0.000014544767,0.031143576,0.00024259835,0.000014484936,0.00006059838,0.00031156334,0.92776567,0.002239713,0.00017489765,0.0000027012443,0.03802322],"study_design_scores_gemma":[0.00052030245,0.00004962644,0.0036910977,0.00192427,0.000007734601,0.0002861524,0.0009794259,0.99068034,0.001029225,0.00045288628,0.000018864965,0.00036009576],"about_ca_topic_score_codex":0.000018918161,"about_ca_topic_score_gemma":0.0000044047915,"teacher_disagreement_score":0.07057781,"about_ca_system_score_codex":0.00030860052,"about_ca_system_score_gemma":0.000037737824,"threshold_uncertainty_score":0.99991757},"labels":[],"label_agreement":null},{"id":"W2734669038","doi":"10.1109/ijcnn.2017.7966284","title":"STDP-based unsupervised learning of memristive spiking neural network by Morris-Lecar model","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Spiking neural network; Artificial intelligence; Memristor; Spike-timing-dependent plasticity; Artificial neural network; Unsupervised learning; Biological neuron model; Synaptic plasticity; Machine learning; Biology; Engineering","score_opus":0.018947212065820264,"score_gpt":0.24376058303949222,"score_spread":0.22481337097367196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734669038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7468982,0.00020629467,0.24522758,0.000029928417,0.00018428481,0.000117624266,0.0000035033165,0.00034552984,0.006987075],"genre_scores_gemma":[0.99496025,0.00000921969,0.0046039913,0.00006275939,0.000090387344,0.0000038760822,0.000007228487,0.000041522897,0.00022079195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990555,0.000022684791,0.00023539018,0.00020015507,0.0001352992,0.00035095136],"domain_scores_gemma":[0.99938697,0.000084417006,0.00009584584,0.00031660803,0.00003902956,0.00007714881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011957591,0.00019115691,0.0002571938,0.000028361474,0.00037423734,0.000040618626,0.00028640177,0.00006414747,0.000021255597],"category_scores_gemma":[0.000050728082,0.00018848275,0.00008204847,0.000056442634,0.000051599054,0.00022060752,0.00006533051,0.0003230957,0.0000034629381],"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":[0.000015537355,0.0000054755337,0.00088326697,0.00003606866,0.000011627699,0.0000040619457,0.00004277399,0.96620005,0.02904016,0.00009908691,0.00026728914,0.0033946186],"study_design_scores_gemma":[0.0003637366,0.00003620011,0.00023706467,0.000047206497,0.0000129165965,8.2797163e-7,0.0000253337,0.94293416,0.05587375,0.00011331391,0.00015710384,0.000198375],"about_ca_topic_score_codex":0.000011092791,"about_ca_topic_score_gemma":0.000003018033,"teacher_disagreement_score":0.24806203,"about_ca_system_score_codex":0.000024949335,"about_ca_system_score_gemma":0.000009599042,"threshold_uncertainty_score":0.76861036},"labels":[],"label_agreement":null},{"id":"W2737280394","doi":"10.1109/tcsii.2017.2729499","title":"Logic Design on Mirrored Memristive Crossbars","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; Computer science; Computation; Computer architecture; Logic gate; Crossbar switch; Logic family; Logic synthesis; Logic optimization; Pass transistor logic; Computer engineering; Electronic engineering; Theoretical computer science; Algorithm; Engineering; Digital electronics; Electrical engineering; Electronic circuit","score_opus":0.06353006716985847,"score_gpt":0.2769255906329196,"score_spread":0.21339552346306112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737280394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054882392,0.00015967102,0.93646395,0.000031137923,0.004024691,0.0007252641,0.00008143391,0.0008062128,0.0028252387],"genre_scores_gemma":[0.9980744,0.00003312872,0.00018766274,0.000060504583,0.00018244152,0.00014875393,0.0000018940539,0.00008888886,0.0012223334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981001,0.00010566823,0.00042378987,0.0005109888,0.0003265671,0.00053286436],"domain_scores_gemma":[0.9983676,0.00024789857,0.0001497789,0.00097311666,0.00007541243,0.00018616027],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00021418158,0.000409475,0.00044650203,0.00013096386,0.0018115775,0.00023565604,0.0005520524,0.00019152215,0.000026274874],"category_scores_gemma":[0.000020744503,0.00041566926,0.0001604453,0.00009274471,0.00012293395,0.0004221246,0.0000034806721,0.0005411897,0.00011182657],"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":[0.000044610537,0.00008354461,0.0000016785433,0.00011327004,0.000082138125,0.00006770781,0.0006360088,0.89233017,0.09814251,0.00013810054,0.00035795983,0.008002289],"study_design_scores_gemma":[0.0027095661,0.0007964667,0.00036175808,0.0014850183,0.0001254369,0.00018211339,0.00033267238,0.04914745,0.934644,0.0002998946,0.0081255,0.0017901332],"about_ca_topic_score_codex":0.0000460162,"about_ca_topic_score_gemma":0.000005416285,"teacher_disagreement_score":0.943192,"about_ca_system_score_codex":0.00014999237,"about_ca_system_score_gemma":0.00002672023,"threshold_uncertainty_score":0.99982953},"labels":[],"label_agreement":null},{"id":"W2739348581","doi":"10.1088/1361-6528/aa8150","title":"Improving the electrical contact at a Pt/TiO<sub>2</sub>nanowire interface by selective application of focused femtosecond laser irradiation","year":2017,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"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":"National Natural Science Foundation of China","keywords":"Materials science; Oxide; Schottky barrier; Laser; Optoelectronics; Electrode; Fluence; Irradiation; Femtosecond; Heterojunction; Nanowire; Auger electron spectroscopy; Nanotechnology; Optics; Diode","score_opus":0.005723835911390434,"score_gpt":0.21650610440979698,"score_spread":0.21078226849840653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739348581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9583216,0.00030856777,0.040439826,0.00016318525,0.0001135628,0.0003122178,0.000006175876,0.0002773865,0.00005748114],"genre_scores_gemma":[0.9997157,0.000033628796,0.00010368127,0.00002355106,0.000033249944,0.000045926445,0.0000045363768,0.000026647265,0.000013080674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991746,0.000022557126,0.00022041655,0.00024031596,0.00007817269,0.00026396147],"domain_scores_gemma":[0.9991663,0.00011161371,0.00020413639,0.0004483553,0.00004431873,0.000025249692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010059355,0.00015116044,0.00020577585,0.00006852673,0.00030424487,0.000016938373,0.00035154054,0.00025744626,0.0000016017577],"category_scores_gemma":[0.00015678526,0.00013087573,0.00004764441,0.00013142545,0.00007717199,0.00014226497,0.00009566011,0.0003816013,0.00001458573],"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":[0.000024253295,0.000009201524,0.00012987514,0.000013567693,0.000016831924,6.7473997e-7,0.000042719443,0.00062737055,0.8640167,0.00013524505,0.000090032685,0.1348935],"study_design_scores_gemma":[0.00038509627,0.000110975096,0.0005759593,0.000008360038,0.000013643589,0.000010466328,0.000008860659,0.027023917,0.97096115,0.00034779866,0.00042493513,0.00012885183],"about_ca_topic_score_codex":0.000007672078,"about_ca_topic_score_gemma":0.00004481072,"teacher_disagreement_score":0.13476466,"about_ca_system_score_codex":0.00016866582,"about_ca_system_score_gemma":0.000012462938,"threshold_uncertainty_score":0.53369576},"labels":[],"label_agreement":null},{"id":"W2743204949","doi":"10.1088/1757-899x/224/1/012054","title":"Implementation of Fixed-point Neuron Models with Threshold, Ramp and Sigmoid Activation Functions","year":2017,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Regina","funders":"","keywords":"Sigmoid function; Activation function; Field-programmable gate array; Computer science; Artificial neuron; Artificial neural network; Fixed point; Transfer function; Neuron; Point (geometry); Biological neuron model; Function (biology); Computer hardware; Artificial intelligence; Mathematics; Neuroscience; Electrical engineering; Engineering","score_opus":0.024608486580534997,"score_gpt":0.24340601470859877,"score_spread":0.21879752812806377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743204949","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9837192,0.000011831121,0.01569549,0.000047636313,0.00019169548,0.00011646084,0.0000061462983,0.000078682904,0.00013280171],"genre_scores_gemma":[0.99906194,0.000057806115,0.00081376074,0.0000052968226,0.00003117772,0.0000102714175,0.000001872814,0.000010918743,0.0000069307657],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993559,0.0000030175831,0.00014623706,0.00017466272,0.0001291856,0.0001909956],"domain_scores_gemma":[0.9996189,0.000011598878,0.000060410108,0.00017669826,0.000079178935,0.00005321822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015922403,0.00012149816,0.00014081487,0.00006784424,0.00029327424,0.0002389739,0.00011979556,0.000022283752,0.000009651394],"category_scores_gemma":[0.000020391868,0.0001087116,0.0000058837218,0.00006426889,0.00016640627,0.0020437543,0.000072102324,0.00005169743,3.051675e-7],"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":[0.000010775129,0.0000012666184,0.000036030753,0.00007510041,0.0000029983407,7.0095774e-7,0.00024645514,0.028688377,0.9670082,0.0016377459,0.0000010405329,0.002291328],"study_design_scores_gemma":[0.00016069601,0.00008121406,0.0065500177,0.00007115155,0.0000059630934,0.000012987487,0.0003580048,0.015500303,0.9769145,0.00019085224,0.000015940243,0.00013836249],"about_ca_topic_score_codex":0.000018535444,"about_ca_topic_score_gemma":0.000007660525,"teacher_disagreement_score":0.015342709,"about_ca_system_score_codex":0.000015281661,"about_ca_system_score_gemma":0.00002467535,"threshold_uncertainty_score":0.44331306},"labels":[],"label_agreement":null},{"id":"W2743937810","doi":"10.1155/2017/7863095","title":"CMOS Realization of All-Positive Pinched Hysteresis Loops","year":2017,"lang":"en","type":"article","venue":"Complexity","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"NMOS logic; Realization (probability); Hysteresis; Electronic circuit; CMOS; Triode; Transistor; Nonlinear system; Control theory (sociology); Computer science; Electronic engineering; Topology (electrical circuits); Physics; Electrical engineering; Voltage; Mathematics; Optoelectronics; Condensed matter physics; Engineering; Capacitor; Quantum mechanics; Control (management)","score_opus":0.10615229853081096,"score_gpt":0.31339406822279536,"score_spread":0.2072417696919844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743937810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92840415,0.000053320884,0.06366884,0.000034223478,0.00030959366,0.00012202224,0.00001845426,0.00014501852,0.0072443746],"genre_scores_gemma":[0.99898887,0.000007952729,0.00085177715,0.000021152055,0.000059495094,0.0000015771276,0.000014020149,0.000011865965,0.00004331023],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955267,0.000017374692,0.00013701343,0.00010464731,0.00006661776,0.000121683435],"domain_scores_gemma":[0.99947655,0.000033220513,0.00007531073,0.0003411431,0.000037655922,0.000036121062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058249385,0.0000829383,0.00014260526,0.000019573206,0.00014533613,0.000018844685,0.00019169567,0.000030311683,0.000018484989],"category_scores_gemma":[0.000053643747,0.00008649115,0.000035266174,0.000025838171,0.00007451194,0.00014047872,0.00006768085,0.00006791299,0.0000078241765],"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":[0.00022989509,0.00019658716,0.010679331,0.0014115471,0.00039035548,0.00007462442,0.0037549182,0.056830175,0.79240745,0.06299997,0.0031165942,0.06790855],"study_design_scores_gemma":[0.0009902575,0.00009743835,0.25999826,0.00026292133,0.000039188424,0.0000140657785,0.000077217126,0.07567743,0.6328513,0.02839393,0.0010639945,0.00053396344],"about_ca_topic_score_codex":0.000014431663,"about_ca_topic_score_gemma":0.000016055468,"teacher_disagreement_score":0.24931894,"about_ca_system_score_codex":0.00002131744,"about_ca_system_score_gemma":0.000003306142,"threshold_uncertainty_score":0.35270068},"labels":[],"label_agreement":null},{"id":"W2753029429","doi":"10.1167/17.10.336","title":"Parieto-occipital alpha power dynamics selectively code for the storage of spatial locations in visual working memory","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Working memory; Psychology; Alpha (finance); Task (project management); Power (physics); Electroencephalography; Interval (graph theory); Neuroscience; Cognitive psychology; Cognition; Physics; Developmental psychology; Mathematics; Engineering","score_opus":0.016658564295457,"score_gpt":0.30536164028680035,"score_spread":0.28870307599134337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753029429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75723845,0.00017082377,0.24165682,0.00011037733,0.00061570795,0.00010122439,0.0000031244253,0.000008443919,0.00009501806],"genre_scores_gemma":[0.99887305,0.000029363026,0.00090085424,0.000009974578,0.00015566105,9.968371e-7,5.7585436e-7,0.000015136514,0.000014392329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933183,0.000018904335,0.00033159708,0.00006178521,0.00013046555,0.00012540122],"domain_scores_gemma":[0.999189,0.00027318735,0.0003021802,0.00011783204,0.0000868943,0.000030857445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038048174,0.000080863465,0.00016461979,0.00006778336,0.00018881503,0.000031885014,0.00021164607,0.00004476015,0.0000033996453],"category_scores_gemma":[0.00018966875,0.000060975508,0.00007583123,0.000044470933,0.000033236047,0.00022820763,0.000035846253,0.00026508482,3.4331399e-7],"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":[0.00039329505,0.00012508871,0.0024190692,0.000077806915,0.00008104462,0.000024616813,0.0010242971,0.83276856,0.049269684,0.00021370151,0.0001457958,0.113457054],"study_design_scores_gemma":[0.0011222108,0.000468519,0.10043989,0.00041216414,0.00002980633,0.000035279954,0.00034531986,0.8841222,0.011921662,0.00048222672,0.00046581193,0.0001549258],"about_ca_topic_score_codex":0.0000039315187,"about_ca_topic_score_gemma":0.000078238394,"teacher_disagreement_score":0.24163459,"about_ca_system_score_codex":0.00008546179,"about_ca_system_score_gemma":0.00002337724,"threshold_uncertainty_score":0.24865091},"labels":[],"label_agreement":null},{"id":"W2753730591","doi":"10.1016/j.radmeas.2018.08.008","title":"Preliminary characterization of the response of an organic field effect transistor to ionizing radiation","year":2018,"lang":"en","type":"article","venue":"Radiation Measurements","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Irradiation; Optoelectronics; Substrate (aquarium); Threshold voltage; Ionizing radiation; Pentacene; Thin-film transistor; Transistor; Radiochemistry; Voltage; Chemistry; Layer (electronics); Nanotechnology; Electrical engineering","score_opus":0.018150050744829227,"score_gpt":0.2425931661714477,"score_spread":0.22444311542661846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753730591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98710865,0.0000217734,0.012021001,0.00003672585,0.00046119397,0.00028444742,0.000002951462,0.000044428984,0.000018815927],"genre_scores_gemma":[0.9996934,0.0000021445474,0.00012311676,0.000047455273,0.000099539364,0.000005525122,0.000005469398,0.000014137157,0.000009201211],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925846,0.00015952729,0.00020986632,0.00009459591,0.00018886867,0.00008870462],"domain_scores_gemma":[0.9995921,0.000064217136,0.00007826206,0.00016815132,0.00006363391,0.00003363255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004071083,0.00007628204,0.00010023395,0.00006767518,0.000066789406,0.0000048638835,0.00009773762,0.00003979721,0.000015769572],"category_scores_gemma":[0.00021921877,0.0000680027,0.000029615769,0.00024772628,0.000008979303,0.00015977703,0.0000072366965,0.000048479116,0.0000035687478],"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":[0.0002940446,0.000008308238,0.0011343039,0.000035674962,0.000009421343,6.746498e-8,0.0007711592,0.0021763311,0.97532314,8.345226e-7,0.000007524572,0.020239212],"study_design_scores_gemma":[0.00024675604,0.00039775294,0.12708709,0.000045591773,0.000013777527,6.152166e-7,0.0000053118765,0.0031632967,0.8689105,0.0000023875612,0.000071331495,0.00005563282],"about_ca_topic_score_codex":8.254726e-7,"about_ca_topic_score_gemma":0.0000012254538,"teacher_disagreement_score":0.12595278,"about_ca_system_score_codex":0.00006451401,"about_ca_system_score_gemma":0.000014104119,"threshold_uncertainty_score":0.27730697},"labels":[],"label_agreement":null},{"id":"W2757810720","doi":"10.1109/iscas.2017.8050985","title":"A population-level approach to temperature robustness in neuromorphic systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Office of Naval Research","keywords":"Robustness (evolution); Neuromorphic engineering; Computer science; Population; Silicon; Decoding methods; Biological system; Artificial neural network; Control theory (sociology); Materials science; Algorithm; Artificial intelligence; Optoelectronics; Chemistry; Biology","score_opus":0.08719073282278801,"score_gpt":0.25380046723635225,"score_spread":0.16660973441356425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757810720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9829905,0.000033117354,0.012028001,0.000029656674,0.00049268623,0.00020874542,0.0000024305732,0.0001682097,0.004046668],"genre_scores_gemma":[0.99788684,0.0000014604926,0.0015006758,0.000024574456,0.000106802705,0.000014183241,0.00000445343,0.000019291816,0.0004417349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994606,0.000011702281,0.00013253944,0.00015351188,0.00007149403,0.00017014956],"domain_scores_gemma":[0.99959385,0.000015411162,0.000018717756,0.00030326002,0.000012786991,0.00005597178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006887526,0.000103997656,0.00014241789,0.000053799205,0.00013464341,0.00009922263,0.0001941634,0.000050441373,0.0000018367691],"category_scores_gemma":[0.000035511854,0.00009461162,0.0000170764,0.000068633715,0.0000054891393,0.00018444295,0.000040318155,0.00016009797,0.0000058613164],"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":[0.0000024373242,0.0000066511643,0.001082689,0.000052316725,0.0000018059961,0.000007686879,0.000028321496,0.9938516,0.00410971,0.000476716,0.000076430966,0.00030361203],"study_design_scores_gemma":[0.0004116887,0.000016250786,0.13739459,0.000115956864,0.0000037543302,0.000053546788,0.00007208272,0.8592695,0.002056881,0.00003231745,0.00017347351,0.00039996306],"about_ca_topic_score_codex":0.000031406278,"about_ca_topic_score_gemma":0.000015887023,"teacher_disagreement_score":0.1363119,"about_ca_system_score_codex":0.00002187724,"about_ca_system_score_gemma":0.000002397345,"threshold_uncertainty_score":0.385815},"labels":[],"label_agreement":null},{"id":"W2759700740","doi":"10.1109/mwscas.2017.8053199","title":"A hybrid memristor-CMOS multiplier design based on memristive universal logic gates","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; CMOS; Logic gate; Pass transistor logic; Logic family; Computer science; Electronic engineering; Multiplier (economics); Digital electronics; Logic synthesis; Very-large-scale integration; Electronic circuit; AND-OR-Invert; Memistor; Computer architecture; Electrical engineering; Engineering; Embedded system; Resistive random-access memory; Voltage","score_opus":0.036641393102730485,"score_gpt":0.24860928352807354,"score_spread":0.21196789042534306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759700740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07599483,0.00004649935,0.87364435,0.00019234199,0.00070390594,0.00040989762,0.0000068089957,0.0010047975,0.047996584],"genre_scores_gemma":[0.9878028,0.000004269496,0.011533251,0.00014326246,0.00008291932,0.0000050249114,0.0000017156809,0.000027345292,0.00039939262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992985,0.000025145375,0.00011010052,0.00021402573,0.00010196051,0.00025027388],"domain_scores_gemma":[0.9992308,0.00021161366,0.00004544008,0.00037882724,0.000026552125,0.00010678846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009435661,0.00018229753,0.00015940933,0.000048976613,0.00034545272,0.000043840344,0.0002554606,0.0000387606,0.000111736386],"category_scores_gemma":[0.0001322603,0.00016222213,0.000054744636,0.00002263043,0.000043875363,0.00015922346,0.000036190795,0.00018239616,0.00008789309],"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":[0.00011632597,0.000031770956,0.00007935476,0.000028018123,0.000024164865,0.0001900803,0.000056131055,0.9670543,0.024869146,0.0011228088,0.0016618604,0.004766048],"study_design_scores_gemma":[0.00067652576,0.00010359584,0.00037028725,0.000037885333,0.000012169033,0.000004250789,0.000028133745,0.6997992,0.29690507,0.0006431922,0.0011222794,0.0002974247],"about_ca_topic_score_codex":0.000005576863,"about_ca_topic_score_gemma":0.0000015216166,"teacher_disagreement_score":0.911808,"about_ca_system_score_codex":0.00008325953,"about_ca_system_score_gemma":0.000013463279,"threshold_uncertainty_score":0.6615226},"labels":[],"label_agreement":null},{"id":"W2762482777","doi":"10.1021/acsami.7b07971","title":"High-Performance Single-Active-Layer Memristor Based on an Ultrananocrystalline Oxygen-Deficient TiO<sub><i>x</i></sub> Film","year":2017,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Electroforming; Optoelectronics; Memristor; Joule heating; Active matrix; Non-volatile memory; Nanotechnology; Resistive random-access memory; Voltage; Electrical engineering; Layer (electronics)","score_opus":0.019626924898235035,"score_gpt":0.22577889597924242,"score_spread":0.20615197108100738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762482777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961916,0.000022505315,0.00050285156,0.00003629961,0.0013402856,0.00034116604,0.000075435964,0.00045390136,0.001035924],"genre_scores_gemma":[0.9989569,0.000034632714,0.00036735222,0.00012812957,0.00029237027,0.000056382276,0.00004712471,0.00009810397,0.00001899675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99833155,0.000024596862,0.0004302036,0.00048331553,0.00023583649,0.0004945042],"domain_scores_gemma":[0.9988101,0.000051382292,0.00021947935,0.00078008027,0.00004293204,0.00009598941],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020315894,0.00042691923,0.00043795982,0.00008611615,0.00045466918,0.0002521875,0.0005534083,0.00013179914,0.00006866398],"category_scores_gemma":[0.000018296638,0.00040496088,0.00002136212,0.0000512945,0.000098277764,0.0003643116,0.00008096371,0.00019187003,0.000107484986],"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":[0.00029108944,0.00006911503,0.0000011787089,0.00011018977,0.000017898663,0.0000051388292,0.00009123462,0.13275827,0.86279,0.000058251127,0.000051710493,0.003755942],"study_design_scores_gemma":[0.0006093385,0.00024090297,0.00028522455,0.000115196744,0.000021989137,0.0000030865706,0.00003548222,0.0006293108,0.9973721,0.000055447665,0.00016393092,0.00046801183],"about_ca_topic_score_codex":0.0000053430203,"about_ca_topic_score_gemma":0.000006933056,"teacher_disagreement_score":0.1345821,"about_ca_system_score_codex":0.00010555112,"about_ca_system_score_gemma":0.000012024998,"threshold_uncertainty_score":0.9998402},"labels":[],"label_agreement":null},{"id":"W2766895107","doi":"10.1007/978-3-319-68600-4_40","title":"Model Derived Spike Time Dependent Plasticity","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Spike (software development); Learning rule; Spike-timing-dependent plasticity; Postsynaptic potential; Synaptic weight; Spiking neural network; Node (physics); Artificial neural network; Bidirectional associative memory; Artificial intelligence; Content-addressable memory","score_opus":0.024035353547537042,"score_gpt":0.2392908017464042,"score_spread":0.21525544819886716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766895107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002980449,0.00013151085,0.99072194,0.00002003682,0.00076290173,0.00017182095,0.0000072256494,0.00024165958,0.004962438],"genre_scores_gemma":[0.915685,0.000031776206,0.08266776,0.00020903302,0.00055305066,0.0000031825186,0.000004061726,0.00007351159,0.00077265536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825317,0.00000522871,0.00026114078,0.00063212303,0.00038145107,0.00046689092],"domain_scores_gemma":[0.99901366,0.00017901327,0.000099613964,0.000529468,0.000057179448,0.00012103795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015710127,0.0004034061,0.00038355886,0.00021171974,0.00026709042,0.00014495965,0.0010364316,0.0002111849,0.00002000999],"category_scores_gemma":[0.00005764442,0.00039153904,0.00007464303,0.00004174833,0.0002883409,0.00024865818,0.0004086122,0.00075415644,0.00006760329],"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":[0.0000031567433,0.000002528013,0.0000014449339,0.000026002623,0.00000416729,0.000045819233,0.00006529736,0.90122664,0.0098961685,0.0000746054,0.0000032993876,0.088650875],"study_design_scores_gemma":[0.00012192088,0.000027219381,0.00001067175,0.0002297715,0.0000071094814,0.000030762272,1.3326203e-8,0.9530726,0.023385234,0.022624262,0.00006836275,0.00042206558],"about_ca_topic_score_codex":0.0000011363556,"about_ca_topic_score_gemma":0.000018632254,"teacher_disagreement_score":0.9127045,"about_ca_system_score_codex":0.00017024932,"about_ca_system_score_gemma":0.00008395217,"threshold_uncertainty_score":0.9998537},"labels":[],"label_agreement":null},{"id":"W2767003055","doi":"","title":"A General Purpose Architecture for Building Spiking Neuron Models of Biological Cognition - eScholarship","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Meeting of the Cognitive Science Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Cognitive architecture; Object (grammar); Phrase; Set (abstract data type); Artificial intelligence; Cognition; Cognitive model; Cognitive science; Psychology; Programming language; Neuroscience","score_opus":0.028970608555352044,"score_gpt":0.26149861051059703,"score_spread":0.23252800195524498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767003055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969227,0.00005845283,0.0015626122,0.00007549998,0.00016794543,0.0006781691,0.000026302832,0.000049110156,0.00045925824],"genre_scores_gemma":[0.99215055,0.000009871914,0.0076293065,0.00008905503,0.00007227665,0.000025413945,3.6494197e-7,0.00001598327,0.000007192377],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986876,0.000014815825,0.00033490584,0.00026559626,0.00033909047,0.0003579661],"domain_scores_gemma":[0.99828905,0.00024746914,0.00032728724,0.000078621415,0.0010094057,0.000048145477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088443,0.00016799259,0.00023254997,0.000041669777,0.00036632112,0.000035187506,0.0007252057,0.000069278896,0.0000010123761],"category_scores_gemma":[0.0012715647,0.00010520995,0.00029082398,0.0006414088,0.00079698954,0.00062345096,0.00038776643,0.00031123997,1.7169462e-7],"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":[0.00001433801,0.000016313845,0.00042029112,0.00017684666,0.000014006543,1.1347362e-8,0.001450532,0.0066041322,0.9880143,0.00039982746,0.000013638006,0.0028757663],"study_design_scores_gemma":[0.00024377619,0.000084409185,0.002001464,0.00065081095,0.00003544063,0.000004633282,0.0020350243,0.019837206,0.95382595,0.021141723,0.0000041080552,0.00013544486],"about_ca_topic_score_codex":0.0000033327326,"about_ca_topic_score_gemma":1.14540725e-7,"teacher_disagreement_score":0.034188338,"about_ca_system_score_codex":0.000026730682,"about_ca_system_score_gemma":0.000023628996,"threshold_uncertainty_score":0.42903373},"labels":[],"label_agreement":null},{"id":"W2767101381","doi":"10.1038/s41598-017-15395-5","title":"A Silk Fibroin Bio-Transient Solution Processable Memristor","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":71,"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":"University of Melbourne; RMIT University; Centre of Excellence for Integrative Brain Function, Australian Research Council; Australian Research Council; Ontario Ministry of Natural Resources and Forestry","keywords":"Fibroin; SILK; Materials science; Environmentally friendly; Electrical conductor; Biocompatible material; Nanotechnology; Electrode; Memristor; Resistive touchscreen; Computer science; Composite material; Chemistry; Electronic engineering; Biomedical engineering","score_opus":0.019674899219992197,"score_gpt":0.24795701685213103,"score_spread":0.22828211763213885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767101381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96915215,0.0003252245,0.008529635,0.000082420716,0.012067264,0.00024129501,0.000001304773,0.00044823036,0.009152501],"genre_scores_gemma":[0.9959984,0.000002988391,0.0007543211,0.0000049478654,0.00010120803,0.000008260261,0.0000075710636,0.00001671715,0.003105577],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988035,0.000006751796,0.00026069584,0.0003933664,0.00023241433,0.00030325283],"domain_scores_gemma":[0.9988976,0.000008613702,0.00012931129,0.00081377337,0.000059675,0.00009102784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044029436,0.00011763651,0.0001245117,0.00006090782,0.0009984738,0.0002997958,0.00016498372,0.00004241778,0.000026482701],"category_scores_gemma":[0.00009664868,0.00011264805,0.00005691503,0.00009658024,0.00013218484,0.0004457798,0.000045909615,0.00010760977,0.000025236308],"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":[0.000008157169,0.00003682045,0.00068517815,0.00027093367,0.000016245916,0.000533662,0.00054622395,0.03093211,0.9269781,0.00004300246,0.027829468,0.012120097],"study_design_scores_gemma":[0.00017612633,0.000027246726,0.0011116741,0.00016503321,0.000018987343,0.00034948418,0.00003820443,0.022882681,0.7985428,0.003952273,0.17228304,0.00045248077],"about_ca_topic_score_codex":0.000003923351,"about_ca_topic_score_gemma":0.000012408554,"teacher_disagreement_score":0.14445357,"about_ca_system_score_codex":0.0000502768,"about_ca_system_score_gemma":0.000029072124,"threshold_uncertainty_score":0.7679555},"labels":[],"label_agreement":null},{"id":"W2767108711","doi":"","title":"ACT-R models of a delayed match-to sample task - eScholarship","year":2014,"lang":"en","type":"article","venue":"Proceedings of the Annual Meeting of the Cognitive Science Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Task (project management); Sample (material); Cognition; Psychology; Object (grammar); Cognitive psychology; Computer science; Artificial intelligence; Management","score_opus":0.01631619629148564,"score_gpt":0.24747417005531383,"score_spread":0.23115797376382818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767108711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99580586,0.000020122505,0.0015111278,0.00007488233,0.000164503,0.00025924927,0.000038754544,0.000049348626,0.0020761765],"genre_scores_gemma":[0.99538434,0.0000048413226,0.004400675,0.00013185214,0.000040805695,0.0000052952414,1.6778431e-7,0.000017448878,0.0000146013335],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998527,0.000011532276,0.00033238973,0.00024651567,0.000533598,0.00034895074],"domain_scores_gemma":[0.9981958,0.00034593474,0.00028261103,0.00013077822,0.00097096054,0.00007389886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016449771,0.0001579942,0.00024489962,0.000039318595,0.00031964266,0.00002343022,0.0010887261,0.00005026422,0.000001088886],"category_scores_gemma":[0.0023995913,0.000106537555,0.00022560146,0.0010641918,0.0006112295,0.0005883361,0.0005881443,0.00027402234,7.678112e-7],"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":[0.000040689672,0.000026925305,0.0010025058,0.00023574986,0.000032107328,1.1325778e-8,0.011452641,0.017337458,0.9668413,0.0010133558,0.00006920138,0.0019480888],"study_design_scores_gemma":[0.00021496051,0.000092759874,0.0011856661,0.0007035768,0.00004049079,0.000002057602,0.005033656,0.018660538,0.9614829,0.012403565,0.000018741917,0.0001610797],"about_ca_topic_score_codex":0.00001077959,"about_ca_topic_score_gemma":6.0512633e-7,"teacher_disagreement_score":0.011390209,"about_ca_system_score_codex":0.00003858581,"about_ca_system_score_gemma":0.000033414635,"threshold_uncertainty_score":0.43444753},"labels":[],"label_agreement":null},{"id":"W2768754295","doi":"10.1109/ecctd.2017.8093297","title":"A novel CVNS adder with memristive analog memory","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Adder; Computation; Computer science; Modulo; Electronic engineering; Arithmetic; Algorithm; Mathematics; Engineering; Telecommunications","score_opus":0.020621147018391944,"score_gpt":0.2389915166598632,"score_spread":0.21837036964147127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768754295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45386305,0.000075328,0.2790903,0.000119867436,0.0003852982,0.00018410078,0.0000068667455,0.000676279,0.26559892],"genre_scores_gemma":[0.9922541,0.0000034708246,0.006081752,0.00006281492,0.0001054854,0.0000036662634,0.0000011650587,0.000018817105,0.0014687225],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956715,0.0000023197151,0.000070729315,0.00012391165,0.00006557637,0.00017033864],"domain_scores_gemma":[0.9995851,0.000027936016,0.000025866933,0.00028612488,0.000020885109,0.000054068787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033427,0.00010475898,0.00010636703,0.000021455895,0.00021058653,0.000036075442,0.00015419003,0.000025822701,0.000071855124],"category_scores_gemma":[0.000019700467,0.000079619036,0.000022325341,0.00002565424,0.000042684922,0.00019218639,0.000037366102,0.00012048926,0.000026093925],"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":[0.00022820522,0.00016245208,0.0024777981,0.00029473327,0.00054744625,0.00046755132,0.0015906534,0.31960326,0.53608567,0.0064425836,0.007889848,0.12420982],"study_design_scores_gemma":[0.0038550368,0.00033860028,0.051412307,0.0002629013,0.00010534358,0.00028149693,0.00075146055,0.1248,0.80616206,0.00076675677,0.009375737,0.0018883216],"about_ca_topic_score_codex":0.000010772029,"about_ca_topic_score_gemma":0.00004436489,"teacher_disagreement_score":0.53839105,"about_ca_system_score_codex":0.000013566364,"about_ca_system_score_gemma":0.0000055321098,"threshold_uncertainty_score":0.32467702},"labels":[],"label_agreement":null},{"id":"W2779019997","doi":"10.1088/2053-1591/aaa30b","title":"Ion beam synthesis of indium-oxide nanocrystals for improvement of oxide resistive random-access memories","year":2017,"lang":"en","type":"article","venue":"Materials Research Express","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Indium; Materials science; Nanocrystal; Oxide; Optoelectronics; Ion implantation; Nanotechnology; Indium tin oxide; Ion beam; Resistive random-access memory; Ion; Layer (electronics); Voltage; Chemistry; Electrical engineering","score_opus":0.06364096533998469,"score_gpt":0.35856002602307724,"score_spread":0.29491906068309254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779019997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99665576,0.0000837611,0.0013824792,0.000021807338,0.00032310808,0.00095896836,0.0003064475,0.00005299687,0.00021466332],"genre_scores_gemma":[0.99846524,0.00009754799,0.0007799269,0.000002262755,0.0001285122,0.000352891,0.00000767406,0.0000444745,0.00012145131],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817026,0.000106077045,0.00055278995,0.00027318666,0.00041381936,0.0004838635],"domain_scores_gemma":[0.99773663,0.00096638117,0.0002436389,0.0006714743,0.00030265294,0.00007922293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015997436,0.0001831739,0.0005759222,0.00015882695,0.00036369383,0.00016380072,0.00086588267,0.00008854656,0.000033953325],"category_scores_gemma":[0.0018010803,0.00016531999,0.00007880626,0.00006557902,0.00024268214,0.00044949012,0.00046786963,0.00010478777,0.0000014422043],"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":[0.0009804664,0.000026800866,0.000019728246,0.0015171585,0.000054292545,0.000004029767,0.00012450958,0.0011414719,0.9949169,0.000047626378,0.00017665075,0.0009903596],"study_design_scores_gemma":[0.0008356946,0.00008474599,0.0004203223,0.00043297387,0.000013549146,7.02342e-7,0.00013330794,0.00004578754,0.996934,0.00072547945,0.00022121255,0.00015222696],"about_ca_topic_score_codex":0.00009942119,"about_ca_topic_score_gemma":0.0000067786214,"teacher_disagreement_score":0.0020170917,"about_ca_system_score_codex":0.000049605398,"about_ca_system_score_gemma":0.000028523507,"threshold_uncertainty_score":0.6741553},"labels":[],"label_agreement":null},{"id":"W2783721012","doi":"10.1039/c7tc04529h","title":"Tungsten oxide ion gel-gated transistors: how structural and electrochemical properties affect the doping mechanism","year":2018,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; CMC Microsystems","keywords":"Materials science; Transistor; Doping; Electrolyte; Electrochemistry; Nanotechnology; Tungsten oxide; Oxide; Tungsten; Ion; Optoelectronics; Mechanism (biology); Electronics; Electrode; Electrical engineering; Metallurgy; Physical chemistry; Voltage; Chemistry","score_opus":0.011093450116252745,"score_gpt":0.2033597518539694,"score_spread":0.19226630173771667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783721012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99868727,0.00027313383,0.00050124765,0.00014169914,0.0002740985,0.00005464069,0.000001308751,0.000046500703,0.000020085568],"genre_scores_gemma":[0.9987603,0.000034263256,0.00032470634,0.000021443944,0.0008169439,8.5058537e-7,9.153491e-7,0.000019436422,0.00002113911],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934417,0.000025491649,0.00023229359,0.00008900036,0.00011941753,0.00018965354],"domain_scores_gemma":[0.9996572,0.000027776952,0.000114505725,0.00008507094,0.000060889906,0.000054546228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001726166,0.00014691798,0.000223072,0.000013540882,0.000094076175,0.00007350138,0.0001383976,0.00007083203,0.000018415794],"category_scores_gemma":[0.00004734946,0.00009340432,0.000042676635,0.000041933927,0.00006004481,0.00014781742,0.000021111957,0.00016992359,4.9926854e-7],"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":[0.00006887413,0.0000015298943,0.0000014089296,0.00016365894,0.000028334427,0.0000067555106,0.00012899637,0.0000394153,0.9994524,0.000002922672,0.000030191699,0.00007550583],"study_design_scores_gemma":[0.00024249037,0.00004052468,0.000024011997,0.00018040733,0.000025387282,0.0006084261,0.00006038781,0.00020457793,0.9982679,0.00014355485,0.000085420106,0.000116913805],"about_ca_topic_score_codex":3.6495365e-7,"about_ca_topic_score_gemma":1.2822734e-7,"teacher_disagreement_score":0.0011845086,"about_ca_system_score_codex":0.000045181514,"about_ca_system_score_gemma":0.000011189072,"threshold_uncertainty_score":0.38089177},"labels":[],"label_agreement":null},{"id":"W2785784536","doi":"10.1109/iedm.2017.8268341","title":"Fast, energy-efficient, robust, and reproducible mixed-signal neuromorphic classifier based on embedded NOR flash memory technology","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":191,"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":"Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research","keywords":"Neuromorphic engineering; Computer science; Bottleneck; Computer hardware; MNIST database; Flash memory; Parallel computing; Artificial neural network; Algorithm; Artificial intelligence; Embedded system","score_opus":0.03190262939632871,"score_gpt":0.22751406881277883,"score_spread":0.19561143941645012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785784536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9012832,0.00010944941,0.07358652,0.0003839635,0.0008891446,0.00019742157,0.0000066321036,0.0011362361,0.022407405],"genre_scores_gemma":[0.9950295,0.000010363008,0.0027984993,0.00015130751,0.00014266504,0.000014561859,0.0000033182573,0.00005015191,0.0017996422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985664,0.000026346615,0.00022910717,0.00063006603,0.00014704547,0.0004010725],"domain_scores_gemma":[0.99858654,0.00008095199,0.00007942938,0.0011120075,0.000036105954,0.00010496222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016767628,0.00026054346,0.00025756843,0.00020760742,0.0004369222,0.00007707075,0.00033400467,0.0001405622,0.00006229679],"category_scores_gemma":[0.00009765708,0.00024269232,0.00005288125,0.00013819554,0.0001476114,0.00011136018,0.00012989677,0.0003463353,0.000025519688],"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":[0.00003422622,0.000044906905,0.00018497862,0.000042839252,0.000013316815,0.000090039386,0.000016682246,0.8974864,0.07221434,0.00063256215,0.00097339944,0.02826634],"study_design_scores_gemma":[0.0005422345,0.00009790313,0.0003676566,0.00004411303,0.000012137401,0.000031815936,0.000032823886,0.70893663,0.288266,0.00012182122,0.0012619775,0.0002848997],"about_ca_topic_score_codex":0.0000023752318,"about_ca_topic_score_gemma":0.0000077317145,"teacher_disagreement_score":0.21605165,"about_ca_system_score_codex":0.00002451427,"about_ca_system_score_gemma":0.00001533052,"threshold_uncertainty_score":0.9896705},"labels":[],"label_agreement":null},{"id":"W2786448627","doi":"10.15781/t2hx16701","title":"Cerium oxide based resistive random access memory devices","year":2017,"lang":"en","type":"dissertation","venue":"Texas ScholarWorks (Texas Digital Library)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"York University","keywords":"Resistive random-access memory; Cerium oxide; Cerium; Random access; Resistive touchscreen; Oxide; Materials science; Computer science; Optoelectronics; Electrical engineering; Operating system; Engineering; Metallurgy","score_opus":0.016353727483478957,"score_gpt":0.2564595214281298,"score_spread":0.2401057939446508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786448627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61552185,0.0057806475,0.0006343402,0.00007992288,0.0028404722,0.0012236692,0.00043340545,0.0032400812,0.3702456],"genre_scores_gemma":[0.9297994,0.00014281158,0.00047449963,0.00027055942,0.00080198783,0.00004483644,0.004203108,0.00054284435,0.06371998],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966016,0.000078514546,0.000856266,0.0010000678,0.0005613875,0.0009021761],"domain_scores_gemma":[0.9973367,0.00050607004,0.00051727274,0.0011967559,0.000095984484,0.00034724938],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00015393163,0.0011199273,0.00109513,0.00043790962,0.00049421575,0.004574526,0.0022020761,0.00077799626,0.0005469831],"category_scores_gemma":[0.00040158848,0.001133129,0.00048446233,0.00040768864,0.00010206099,0.011764292,0.00020986922,0.0021267722,0.0004734328],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016019657,0.0012565478,0.044515286,0.019379603,0.003912728,0.0050216946,0.0005058677,0.23878513,0.0026580128,0.0010105292,0.19374606,0.47318888],"study_design_scores_gemma":[0.013366216,0.00045432345,0.26459992,0.020240145,0.0011362752,0.00006611795,0.0003159075,0.014778924,0.39335045,0.0064167064,0.27063227,0.014642754],"about_ca_topic_score_codex":0.000005681547,"about_ca_topic_score_gemma":0.000014050014,"teacher_disagreement_score":0.45854613,"about_ca_system_score_codex":0.00008105291,"about_ca_system_score_gemma":0.00018749251,"threshold_uncertainty_score":0.9991119},"labels":[],"label_agreement":null},{"id":"W2786576904","doi":"10.1109/icecs.2017.8292094","title":"Hybrid memristor-CMOS based linear feedback shift register design","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"CMOS; Memristor; Computer science; Electronic engineering; Logic gate; Scheme (mathematics); Shift register; Electronic circuit; Electrical engineering; Engineering; Mathematics","score_opus":0.050070453255664685,"score_gpt":0.2635529171439124,"score_spread":0.2134824638882477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786576904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08230967,0.000067732995,0.89314914,0.00025154866,0.00074461946,0.00020685507,0.0000023690243,0.000717543,0.02255055],"genre_scores_gemma":[0.976642,0.0000036819802,0.021529194,0.00022581036,0.00022463378,0.0000048723396,0.0000017744344,0.000035600846,0.0013324057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932396,0.00001674892,0.00014803965,0.00017740586,0.000092521026,0.00024133433],"domain_scores_gemma":[0.9991796,0.00007831159,0.000043747004,0.0005753168,0.000015943282,0.000107063424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012210217,0.00014971072,0.00014764379,0.000027009317,0.00026435594,0.000058892303,0.00029471997,0.000035558085,0.00014482242],"category_scores_gemma":[0.000084126856,0.0001377581,0.000057262365,0.000016259188,0.00003844542,0.0002188078,0.000039902752,0.00016057996,0.00015999487],"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":[0.00017323907,0.000065220964,0.0004579212,0.00032521936,0.000076778226,0.00041239467,0.00020142531,0.88849324,0.050917678,0.000713113,0.0260501,0.032113645],"study_design_scores_gemma":[0.00083903066,0.00006500889,0.0007234422,0.0000705351,0.000016662809,0.000013294244,0.000007842181,0.43489775,0.53600156,0.0005998483,0.026238212,0.000526782],"about_ca_topic_score_codex":0.0000025564302,"about_ca_topic_score_gemma":0.0000017549377,"teacher_disagreement_score":0.89433235,"about_ca_system_score_codex":0.000030394956,"about_ca_system_score_gemma":0.000011331938,"threshold_uncertainty_score":0.56176126},"labels":[],"label_agreement":null},{"id":"W2786738752","doi":"10.48550/arxiv.1807.04587","title":"Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Toronto","funders":"","keywords":"MNIST database; Scalability; Backpropagation; Computer science; Artificial intelligence; Deep learning; Machine learning; Artificial neural network; Biological network; Mathematics","score_opus":0.04911571001263108,"score_gpt":0.2076081279926062,"score_spread":0.15849241797997513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786738752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9141746,0.000037076374,0.08498684,0.0000056714152,0.00005148627,0.000051654522,3.6327083e-7,0.00012821957,0.0005640929],"genre_scores_gemma":[0.99949896,0.000013961137,0.00040648459,0.000010888888,0.00004163677,6.0352356e-8,6.210072e-7,0.000006781674,0.0000206235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948156,0.00007783769,0.00008478781,0.00018256121,0.000021548132,0.00015170813],"domain_scores_gemma":[0.9995379,0.00022568574,0.0000359746,0.00012376333,0.00003796859,0.00003873559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001368867,0.000092323324,0.0001092325,0.00003486858,0.00017713838,0.000013184806,0.00012208818,0.00004529218,0.000013338131],"category_scores_gemma":[0.00007837244,0.000069704074,0.000031167314,0.000255556,0.00027300196,0.00008046252,0.00007503017,0.00019964657,0.0000021357428],"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":[0.00003635116,0.000027947328,0.038808808,0.00006624717,0.00005999834,0.000029722973,0.00062020554,0.83671135,0.07120564,0.0013837982,0.0000025008208,0.05104742],"study_design_scores_gemma":[0.0003495611,0.00016053306,0.09014942,0.000043277058,0.000028006525,0.000011798159,0.00060085993,0.8528747,0.050310202,0.0050919894,0.00013392758,0.00024575388],"about_ca_topic_score_codex":0.0000033134172,"about_ca_topic_score_gemma":0.000002983669,"teacher_disagreement_score":0.085324354,"about_ca_system_score_codex":0.00001569149,"about_ca_system_score_gemma":0.0000032497476,"threshold_uncertainty_score":0.28424495},"labels":[],"label_agreement":null},{"id":"W2789840809","doi":"10.1016/j.bpj.2017.11.2982","title":"Mode Shift of Shaker Isolated-Voltage Sensing Domain","year":2018,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Shaker; Biophysics; Depolarization; Chemistry; Hyperpolarization (physics); Mutant; Materials science; Physics; Acoustics; Biochemistry; Vibration; Biology; Stereochemistry","score_opus":0.011611710595399295,"score_gpt":0.2479108654918413,"score_spread":0.23629915489644202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789840809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80810726,0.000012510442,0.19090357,0.000019866724,0.00032524593,0.000023504479,0.0000017533996,0.00006608264,0.0005402275],"genre_scores_gemma":[0.9961681,0.0000046664113,0.0026196588,0.00003727539,0.0011372769,8.030865e-8,4.2690073e-7,0.000020480667,0.000012046836],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933803,0.000016602262,0.00020971075,0.000087738765,0.00012300906,0.00022490305],"domain_scores_gemma":[0.9996826,0.00003494927,0.00004926556,0.0000992253,0.000037869206,0.000096060416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005744464,0.00011071848,0.00016232733,0.000039422703,0.00010164549,0.00002003503,0.0000909101,0.000043712596,0.000018498491],"category_scores_gemma":[0.000008909441,0.000095825155,0.00008499012,0.00012306805,0.00006969941,0.00012938459,0.000027525755,0.00028053072,0.000030172285],"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":[0.000023125456,0.000011033891,0.000007613531,0.000010857026,0.000017534416,0.000030647432,0.00021749218,0.0022499221,0.9915414,0.00022960515,0.000077514196,0.005583259],"study_design_scores_gemma":[0.0004537018,0.00024264427,0.0004699696,0.00012927725,0.000018331133,0.00019543427,0.000053771033,0.14211391,0.849395,0.0057534934,0.00088427414,0.00029019656],"about_ca_topic_score_codex":7.449156e-7,"about_ca_topic_score_gemma":5.4796084e-7,"teacher_disagreement_score":0.18828392,"about_ca_system_score_codex":0.000022445063,"about_ca_system_score_gemma":0.00000678829,"threshold_uncertainty_score":0.39076364},"labels":[],"label_agreement":null},{"id":"W2790071460","doi":"10.1007/s11265-018-1332-4","title":"A Rank Decomposed Statistical Error Compensation Technique for Robust Convolutional Neural Networks in the Near Threshold Voltage Regime","year":2018,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research; Semiconductor Research Corporation","keywords":"MNIST database; Computer science; Convolutional neural network; Redundancy (engineering); Estimator; Pattern recognition (psychology); Compensation (psychology); Artificial intelligence; Algorithm; Artificial neural network; Mathematics; Statistics","score_opus":0.0358262283067966,"score_gpt":0.2798921470510717,"score_spread":0.24406591874427508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790071460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048892867,0.0007063632,0.9494688,0.00005447779,0.00036845155,0.0004116193,0.000004068713,0.00003828558,0.000055079694],"genre_scores_gemma":[0.9927543,0.0000017686222,0.0061075496,0.000060041068,0.0010224762,0.000017261993,0.0000044036688,0.000025716867,0.0000065117347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986774,0.000071959636,0.00059512595,0.00012060366,0.00026394337,0.00027097244],"domain_scores_gemma":[0.99900097,0.00034206832,0.0002816988,0.000076198296,0.00024109495,0.000057983027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010565404,0.00015764326,0.0002974339,0.00008639783,0.0002372973,0.00014035503,0.00023313782,0.00008772011,0.000004078736],"category_scores_gemma":[0.000037843634,0.00011464749,0.00006278967,0.00020511223,0.00011331645,0.00030525343,0.00001188536,0.00043547456,7.3786276e-7],"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":[0.00019664517,0.000025556596,0.00015261465,0.0002306622,0.000015271773,0.000033421373,0.00025274578,0.9814939,0.015270746,0.0002547851,0.00056546024,0.0015081909],"study_design_scores_gemma":[0.0006310184,0.00023219502,0.00025253883,0.00042374968,0.00001971068,0.00050930935,0.00016199882,0.9965794,0.0005715943,0.0002668478,0.00021696856,0.00013468802],"about_ca_topic_score_codex":0.0000015390999,"about_ca_topic_score_gemma":0.000002028151,"teacher_disagreement_score":0.9438614,"about_ca_system_score_codex":0.00008094037,"about_ca_system_score_gemma":0.000049592963,"threshold_uncertainty_score":0.4675189},"labels":[],"label_agreement":null},{"id":"W2791116020","doi":"10.1109/led.2018.2799973","title":"Regular and Inverted Operating Regimes in TiN/a-Si/TiOx/TiN RRAM Devices","year":2018,"lang":"en","type":"article","venue":"IEEE Electron Device Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"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":"Institute of Population and Public Health","keywords":"Tin; Materials science; Resistive random-access memory; Reliability (semiconductor); Optoelectronics; Silicon; Electrical conductor; Condensed matter physics; Voltage; Electrical engineering; Power (physics); Thermodynamics; Physics; Composite material; Engineering; Metallurgy","score_opus":0.010834063542022062,"score_gpt":0.23522852347308723,"score_spread":0.22439445993106516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791116020","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99592566,0.00044351697,0.0014367102,0.0011038849,0.00024082478,0.00016997708,4.9602266e-7,0.00030719026,0.00037172396],"genre_scores_gemma":[0.9937998,0.000022858885,0.0013516775,0.00429541,0.00042034578,0.000011606646,0.0000031991744,0.00004698173,0.000048117858],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986509,0.000064036576,0.00028011648,0.000329975,0.00012653452,0.00054840924],"domain_scores_gemma":[0.99955994,0.000092616756,0.00004982609,0.00020160516,0.000025994532,0.000070021866],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018676619,0.00023916426,0.00023141119,0.0001283969,0.0001382497,0.000059387577,0.0001658046,0.00007701568,0.000009069322],"category_scores_gemma":[0.000030502342,0.00025055357,0.000030332814,0.00035536409,0.0000676677,0.00032991686,0.000026414758,0.00037402753,0.000025561667],"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":[0.000013939462,0.000008039143,0.0022659292,0.00009570695,0.000026366413,0.00002988421,0.00057511753,0.0061926814,0.9833062,0.000039170318,0.0015401278,0.0059068194],"study_design_scores_gemma":[0.00068068976,0.00012412506,0.0026820537,0.00023236002,0.000020271273,0.000063345055,0.000103108876,0.041466136,0.9475077,0.00007082433,0.0064364937,0.000612943],"about_ca_topic_score_codex":0.000017295064,"about_ca_topic_score_gemma":0.0001411826,"teacher_disagreement_score":0.03579857,"about_ca_system_score_codex":0.00007664128,"about_ca_system_score_gemma":0.000010363533,"threshold_uncertainty_score":0.9999947},"labels":[],"label_agreement":null},{"id":"W2792110548","doi":"10.1002/adfm.201706230","title":"UV‐Induced Multilevel Current Amplification Memory Effect in Zinc Oxide Rods Resistive Switching Devices","year":2018,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":86,"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":"Materials science; Optoelectronics; Ultraviolet; Resistive random-access memory; Electrode; Resistive touchscreen; Semiconductor; Rod; Oxide; Zinc","score_opus":0.027251647301892203,"score_gpt":0.28169592362266027,"score_spread":0.25444427632076805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792110548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97697794,0.00015593795,0.019035242,0.000014072689,0.0029564304,0.00035742688,0.00001439841,0.00032069138,0.0001678697],"genre_scores_gemma":[0.99785584,0.0000146989605,0.0012562984,0.00004862879,0.0006231512,0.00009378742,0.000040210787,0.000045186083,0.000022225113],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853164,0.00009986452,0.0004485135,0.00038985308,0.00018561022,0.0003445279],"domain_scores_gemma":[0.99920577,0.00030766416,0.00011333552,0.00022503812,0.0000792135,0.000068993155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034990467,0.0002739,0.00031096852,0.00014062058,0.00017473781,0.000038000053,0.00012652963,0.00007588125,0.00006863718],"category_scores_gemma":[0.0001914392,0.00026665133,0.000042909553,0.00018173394,0.000029312852,0.00046999555,0.000050284692,0.00018027684,0.00012505872],"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":[0.00022569098,0.00001599965,0.00013878006,0.00011289999,0.000012215192,0.0000026005441,0.00006718679,0.009738309,0.96122164,0.0001858262,0.000015606081,0.028263222],"study_design_scores_gemma":[0.0006494876,0.00007779161,0.06578226,0.00022127491,0.000011630287,0.0000051293177,0.000025976127,0.0006538146,0.9312197,0.0008532735,0.00020589326,0.0002937484],"about_ca_topic_score_codex":0.000007720123,"about_ca_topic_score_gemma":0.000015444333,"teacher_disagreement_score":0.06564348,"about_ca_system_score_codex":0.0001516319,"about_ca_system_score_gemma":0.000015614383,"threshold_uncertainty_score":0.99997854},"labels":[],"label_agreement":null},{"id":"W2792165231","doi":"10.20982/tqmp.14.1.p001","title":"Spike Neural Models Part II: Abstract Neural Models","year":2018,"lang":"en","type":"article","venue":"The Quantitative Methods for Psychology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Spike (software development); Neuroscience; Artificial neural network; Computer science; Artificial intelligence; Biology","score_opus":0.2791482294850553,"score_gpt":0.49349133686880586,"score_spread":0.21434310738375056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792165231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.223849,0.0006016113,0.7682343,0.00033431328,0.0022263944,0.00038679098,0.00001570742,0.0002811643,0.004070704],"genre_scores_gemma":[0.68698156,0.00003758407,0.31145376,0.00080325786,0.0004215319,0.0000671007,0.000007154674,0.000076280536,0.0001517544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818623,0.00029608357,0.00042353143,0.0004156288,0.00008598812,0.00059253233],"domain_scores_gemma":[0.9981053,0.0011099064,0.00010797407,0.00047503348,0.00011744452,0.000084371444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010886966,0.0003054906,0.00038337935,0.00009054015,0.0004298341,0.00001916418,0.0004441195,0.00011292558,0.000038370817],"category_scores_gemma":[0.00011544168,0.00023032968,0.00016929275,0.00023340288,0.00035626034,0.00035712877,0.00007166821,0.00039964262,0.000019403275],"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":[0.0006632807,0.00008816794,0.0000033590625,0.00007239082,0.00018422687,0.000007631284,0.00555438,0.44886068,0.07690073,0.11524429,0.0025794616,0.34984142],"study_design_scores_gemma":[0.00047082503,0.0006282449,0.00006821012,0.000012615596,0.000029066467,0.000031319036,0.00013417607,0.76407117,0.006404532,0.22377893,0.0040958617,0.00027504773],"about_ca_topic_score_codex":0.000002362662,"about_ca_topic_score_gemma":0.0000036113083,"teacher_disagreement_score":0.4631326,"about_ca_system_score_codex":0.000019493667,"about_ca_system_score_gemma":0.0000062349136,"threshold_uncertainty_score":0.93925714},"labels":[],"label_agreement":null},{"id":"W2793122622","doi":"10.1002/cta.2452","title":"Simple MOS‐based circuit designed to show pinched hysteresis behavior","year":2018,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hysteresis; Simple (philosophy); Transistor; CMOS; Electronic engineering; Computer science; Electrical engineering; Engineering; Physics; Voltage; Condensed matter physics","score_opus":0.02500193677026204,"score_gpt":0.2917550629560294,"score_spread":0.26675312618576735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793122622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29376054,0.000064267224,0.70501816,0.00002556559,0.00016992516,0.00014926467,0.0000152582625,0.000046080055,0.0007509231],"genre_scores_gemma":[0.99863535,0.000007166969,0.00022680213,0.00033787233,0.000683143,0.000038098144,0.0000046055584,0.000019664894,0.000047280504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991839,0.000043430904,0.00032550754,0.00012099044,0.00018955207,0.00013660092],"domain_scores_gemma":[0.9991075,0.00025437234,0.00010242408,0.00013313958,0.00027378363,0.0001288147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032441886,0.00011439575,0.00014017762,0.00015538676,0.000104255094,0.00005301783,0.00034687802,0.000041064024,0.000097924756],"category_scores_gemma":[0.00005460307,0.000113018956,0.00006481008,0.00013038897,0.000060389502,0.00015967338,0.000025046696,0.00012825236,0.00002262975],"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":[0.00011763112,0.00014637598,0.00065010646,0.000027528271,0.00016358227,0.000029810504,0.0005115453,0.0052985167,0.54616624,0.24901105,0.00025981935,0.19761775],"study_design_scores_gemma":[0.002587011,0.0005484646,0.012697136,0.00027136834,0.00028235762,0.00057949877,0.0005977573,0.0010515,0.5239657,0.40845984,0.047879696,0.0010796345],"about_ca_topic_score_codex":3.3658483e-7,"about_ca_topic_score_gemma":0.0000010810088,"teacher_disagreement_score":0.7048748,"about_ca_system_score_codex":0.00005393997,"about_ca_system_score_gemma":0.000022298604,"threshold_uncertainty_score":0.46087793},"labels":[],"label_agreement":null},{"id":"W2794022367","doi":"10.1039/c7nr09335g","title":"Oxygen vacancy migration/diffusion induced synaptic plasticity in a single titanate nanobelt","year":2018,"lang":"en","type":"article","venue":"Nanoscale","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Materials science; Titanate; Oxygen; Diffusion; Nanotechnology; Vacancy defect; Plasticity; Chemical engineering; Chemistry; Computer science; Composite material; Artificial neural network; Crystallography; Ceramic","score_opus":0.018745676535013182,"score_gpt":0.2333474243512934,"score_spread":0.21460174781628022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794022367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951844,0.000057202582,0.0027201197,0.000033229295,0.0004398285,0.00011536217,0.000002267878,0.00020883576,0.001238709],"genre_scores_gemma":[0.99922526,0.000008182711,0.0004367508,0.00005412661,0.00016562984,0.0000066700413,0.0000023248278,0.000022473287,0.00007861275],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923664,0.000019022527,0.00019677194,0.00017959018,0.000104685394,0.00026326234],"domain_scores_gemma":[0.99971235,0.00005969008,0.00002580536,0.0001131814,0.000030502822,0.000058483954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040680094,0.00012891968,0.00013989033,0.00008848384,0.000081740385,0.000014523105,0.00010304401,0.00008968174,0.00004452945],"category_scores_gemma":[0.000058247344,0.0001286236,0.000027868402,0.00027618906,0.00002391799,0.00013359314,0.00003712787,0.0001533424,0.000085190935],"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":[0.00001340627,0.000032697208,0.000454034,0.000022145903,0.000005436191,0.000009944424,0.0002773251,0.00089066074,0.99376947,0.000036915153,0.00011058129,0.0043773893],"study_design_scores_gemma":[0.00068449497,0.0002797929,0.010844789,0.00017551884,0.000010832544,0.000013364122,0.000058297028,0.055376437,0.9297089,0.0005240204,0.0019486047,0.0003749478],"about_ca_topic_score_codex":0.000014317849,"about_ca_topic_score_gemma":0.00036640832,"teacher_disagreement_score":0.06406056,"about_ca_system_score_codex":0.00008715717,"about_ca_system_score_gemma":0.0000094812,"threshold_uncertainty_score":0.5245118},"labels":[],"label_agreement":null},{"id":"W2794796749","doi":"10.1088/1361-6641/aaba26","title":"Multistate storage nonvolatile memory device based on ferroelectricity and resistive switching effects of SrBi <sub>2</sub> Ta <sub>2</sub> O <sub>9</sub> films","year":2018,"lang":"en","type":"article","venue":"Semiconductor Science and Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada","funders":"National Natural Science Foundation of China","keywords":"Ferroelectricity; Non-volatile memory; Materials science; Resistive touchscreen; Optoelectronics; Electrical engineering; Engineering; Dielectric","score_opus":0.007399901892654406,"score_gpt":0.2195433129645153,"score_spread":0.21214341107186088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794796749","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99569654,0.0004332303,0.0020816168,0.00006251452,0.0005212463,0.0005435308,0.000014279486,0.00056795764,0.00007910542],"genre_scores_gemma":[0.99897236,0.00012471194,0.0005917414,0.00014224378,0.00008291658,0.000026742246,0.0000036355596,0.00005385346,0.0000018257804],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99710256,0.00007198018,0.00046310268,0.0010039608,0.00041826355,0.0009401476],"domain_scores_gemma":[0.9981457,0.000507075,0.00023662536,0.0005972323,0.0003018725,0.00021148627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060923083,0.0004909826,0.0005826876,0.0009257806,0.0006485882,0.00005995276,0.0005204864,0.00031804736,0.0000014363919],"category_scores_gemma":[0.00095109537,0.00048547986,0.000056451285,0.0020910632,0.0011426583,0.00058935664,0.00026700957,0.00083034724,0.000010795004],"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":[0.000040483043,0.0000353288,0.00014594641,0.00019404046,0.000016100435,0.000044901317,0.00022278236,0.00047584643,0.9591606,0.000058401783,0.00004264985,0.03956288],"study_design_scores_gemma":[0.00068953395,0.00048993406,0.0008313436,0.00027202576,0.000033534892,0.000042920667,0.00012617931,0.066732936,0.929986,0.00031813106,0.000015072889,0.00046235928],"about_ca_topic_score_codex":0.0000076174106,"about_ca_topic_score_gemma":0.00004474607,"teacher_disagreement_score":0.06625709,"about_ca_system_score_codex":0.00016400474,"about_ca_system_score_gemma":0.00014366441,"threshold_uncertainty_score":0.9997597},"labels":[],"label_agreement":null},{"id":"W2795284004","doi":"10.1109/tcsii.2018.2821268","title":"Optimizing Incremental Step Pulse Programming for RRAM Through Device–Circuit Co-Design","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"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":"Institute of Population and Public Health; Ministry of Science and Technology, Taiwan","keywords":"Resistive random-access memory; Overhead (engineering); Computer science; Data retention; Voltage; Electronic engineering; Pulse (music); Circuit design; Electrical engineering; Engineering; Embedded system","score_opus":0.06179542345632436,"score_gpt":0.2864078229932447,"score_spread":0.22461239953692033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795284004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01808928,0.00034357893,0.97454685,0.0000101886935,0.0025839766,0.0021319583,0.00007953608,0.0012307857,0.0009838281],"genre_scores_gemma":[0.9916567,0.000021050453,0.006356974,0.00007226591,0.00068829634,0.00068223896,0.000011429407,0.0001732109,0.0003377997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972456,0.00011981553,0.00072618487,0.0006484516,0.00038851475,0.00087143166],"domain_scores_gemma":[0.9987195,0.00027308796,0.00013903742,0.00053068035,0.0001507871,0.00018686776],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037177926,0.0005136494,0.0005405617,0.00015625157,0.0011692456,0.00017995702,0.00038638964,0.00021922206,0.000035016063],"category_scores_gemma":[0.00001042443,0.00056851184,0.00021081773,0.00033168335,0.000117169075,0.00076194166,0.0000037023135,0.00042457788,0.000041835927],"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":[0.00008100416,0.00022238083,0.0000015660233,0.00077127415,0.00030729253,0.000020560123,0.0048643705,0.60429883,0.31875548,0.0001534774,0.0006747864,0.06984898],"study_design_scores_gemma":[0.002493198,0.0010188125,0.000003295286,0.0011572923,0.0001752867,0.0001779476,0.0013568501,0.07583342,0.874379,0.0000438534,0.041958615,0.0014024565],"about_ca_topic_score_codex":0.000043250948,"about_ca_topic_score_gemma":0.000009117339,"teacher_disagreement_score":0.9735674,"about_ca_system_score_codex":0.00025955215,"about_ca_system_score_gemma":0.00004580617,"threshold_uncertainty_score":0.99967664},"labels":[],"label_agreement":null},{"id":"W2798554798","doi":"10.23919/date.2018.8342235","title":"XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":231,"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":"Resistive random-access memory; XNOR gate; Computer science; MNIST database; Scalability; CMOS; Artificial neural network; Binary number; Convolutional neural network; Artificial intelligence; Computer hardware; Pattern recognition (psychology); Parallel computing; Algorithm; Electronic engineering; Logic gate; Voltage; Mathematics; Electrical engineering; Engineering; Arithmetic","score_opus":0.013259199439575924,"score_gpt":0.23498161420628377,"score_spread":0.22172241476670784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798554798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52729535,0.00053755735,0.4696556,0.00016433989,0.0003086855,0.00030512697,0.000002428783,0.0004284788,0.0013023928],"genre_scores_gemma":[0.98648053,0.000013089724,0.012646375,0.0001720508,0.00034087212,0.000016142587,0.000003545708,0.000026403357,0.00030095835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934757,0.000012069975,0.000116828545,0.0001863582,0.00004045481,0.00029671442],"domain_scores_gemma":[0.9996109,0.00015844131,0.000015280226,0.00011724669,0.000024691913,0.000073458425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005490412,0.00013779162,0.00013954155,0.000035274024,0.00015194852,0.000020816035,0.000071554794,0.000057921283,0.0000148535355],"category_scores_gemma":[0.00002470776,0.00011728158,0.00003280523,0.00009387162,0.000070480695,0.00007227085,0.00004175518,0.00014322264,0.000003024312],"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":[0.00022275165,0.000012008135,0.00011448634,0.000114808245,0.000060411745,0.000014653408,0.0001711235,0.9634387,0.0064397925,0.0005807238,0.002935232,0.025895337],"study_design_scores_gemma":[0.0003503343,0.00022868233,0.00047638422,0.000026602787,0.000014135706,0.000026960612,0.000029458446,0.9952393,0.001158228,0.0010050951,0.0012501626,0.00019465847],"about_ca_topic_score_codex":0.0000015530446,"about_ca_topic_score_gemma":0.000011092903,"teacher_disagreement_score":0.45918518,"about_ca_system_score_codex":0.0000115392895,"about_ca_system_score_gemma":0.0000024713956,"threshold_uncertainty_score":0.4782604},"labels":[],"label_agreement":null},{"id":"W2799818742","doi":"10.1109/iscas.2018.8351110","title":"Hardware Realization of Mixed-Signal Neural Networks with Modular Synapse-Neuron arrays","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Synapse; Artificial neural network; Computer science; Modular design; SIGNAL (programming language); Realization (probability); Neuron; Topology (electrical circuits); Computer hardware; Artificial intelligence; Electrical engineering; Mathematics; Neuroscience; Engineering","score_opus":0.010118893045834965,"score_gpt":0.2040556374286547,"score_spread":0.19393674438281974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799818742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41560283,0.000028790879,0.583351,0.0000043376504,0.00012930771,0.0000615046,8.4497265e-7,0.00019497487,0.0006264081],"genre_scores_gemma":[0.9985808,0.000006636428,0.0010799572,0.00003613155,0.00021327821,0.0000020435389,0.000008993231,0.000028971199,0.000043200835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939775,0.000020364034,0.00015371984,0.0001424625,0.00009438822,0.00019130942],"domain_scores_gemma":[0.99968225,0.000027673263,0.000034064717,0.000153073,0.00005601854,0.00004692007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045043696,0.00012154248,0.00013240144,0.000035878842,0.000060093782,0.000009078621,0.000086770204,0.00004130228,0.000037525846],"category_scores_gemma":[0.000006476389,0.00010084989,0.000025142914,0.00016938464,0.0000460797,0.00013591605,0.000019380892,0.00010493451,0.0000026260584],"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":[0.000021726924,0.000005159782,0.0002044269,0.000025383206,0.000009836601,0.000005503127,0.000035520294,0.9751575,0.02150697,0.00030300432,0.00015206232,0.00257292],"study_design_scores_gemma":[0.00016793994,0.00019029497,0.0006980703,0.000029300178,0.000009884149,0.000016741446,0.000017767397,0.85585487,0.14272597,0.00005810195,0.000099920326,0.00013114378],"about_ca_topic_score_codex":0.0000023949392,"about_ca_topic_score_gemma":0.0000069861117,"teacher_disagreement_score":0.58297795,"about_ca_system_score_codex":0.000010999511,"about_ca_system_score_gemma":0.000002845926,"threshold_uncertainty_score":0.4112539},"labels":[],"label_agreement":null},{"id":"W2802043281","doi":"10.1038/s41565-018-0132-0","title":"A longer-lasting memory in layered semiconductors","year":2018,"lang":"en","type":"letter","venue":"Nature Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Semiconductor; Heterojunction; van der Waals force; Optoelectronics; Materials science; Nanotechnology; Semiconductor memory; Non-volatile memory; Engineering physics; Computer science; Physics; Quantum mechanics; Computer hardware; Molecule","score_opus":0.00874257980274567,"score_gpt":0.2267438996675937,"score_spread":0.21800131986484803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802043281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83117026,0.0105410395,0.0007518208,0.13193738,0.0137687465,0.0011211798,0.00006297621,0.0076614325,0.0029851436],"genre_scores_gemma":[0.7437584,0.00017162906,0.0028211188,0.24140884,0.009919847,0.0000725036,0.00018208203,0.0005093317,0.0011562759],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978599,0.000045236313,0.0004341847,0.00063061644,0.00019648283,0.000833577],"domain_scores_gemma":[0.99897295,0.00023314108,0.000117735355,0.0005928235,0.000052949443,0.000030407908],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00012281843,0.0005347087,0.0006424975,0.00078148645,0.0000673832,0.000016798433,0.0006702385,0.007765064,0.0000507211],"category_scores_gemma":[0.0002725216,0.0005515826,0.00010925564,0.00068862736,0.00014509472,0.000102361264,0.000165907,0.013290308,0.00007326847],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015346037,0.000012913367,0.000023769355,0.0006241209,0.000088804976,0.0029979819,0.00015578687,0.0033285064,0.12585922,0.00002896717,0.85357815,0.013286446],"study_design_scores_gemma":[0.00053108373,0.00007620774,0.0000056639633,0.0004481964,0.000024217044,0.00034857498,0.000030961688,0.0013537145,0.2850225,0.0011869991,0.7099611,0.0010107767],"about_ca_topic_score_codex":0.0000018797951,"about_ca_topic_score_gemma":0.000017435108,"teacher_disagreement_score":0.15916328,"about_ca_system_score_codex":0.00021554835,"about_ca_system_score_gemma":0.000029530493,"threshold_uncertainty_score":0.9996936},"labels":[],"label_agreement":null},{"id":"W2802897963","doi":"10.1109/iscas.2018.8351459","title":"Optimizing an Analog Neuron Circuit Design for Nonlinear Function Approximation","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Office of Naval Research","keywords":"Subthreshold conduction; Computer science; CMOS; Transistor; Offset (computer science); Nonlinear system; Electronic engineering; Digital electronics; Threshold voltage; Electronic circuit; Topology (electrical circuits); Voltage; Electrical engineering; Physics; Engineering","score_opus":0.059912879931069814,"score_gpt":0.26163903795970556,"score_spread":0.20172615802863575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802897963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06862021,0.000009573539,0.929198,0.000004179919,0.00033117438,0.00026569545,9.291562e-7,0.00053543836,0.001034789],"genre_scores_gemma":[0.7979748,0.0000018069892,0.2011738,0.000096629556,0.00062195817,0.00001839752,0.000017091168,0.000030360408,0.00006514332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951947,0.000013985555,0.00011849618,0.00013986356,0.00004908552,0.00015911662],"domain_scores_gemma":[0.9997321,0.000053629625,0.000018357598,0.000114671115,0.000040537743,0.000040716808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011812904,0.0000869644,0.00007371881,0.000044193403,0.00011054572,0.000022143737,0.000053825686,0.000036404155,0.000017713686],"category_scores_gemma":[0.000019882205,0.000085987995,0.000023741195,0.000090864196,0.0000119729475,0.0003037229,0.000007038078,0.000057058307,0.000011785728],"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":[0.000063542786,0.000014508507,0.000004358936,0.000060596885,0.00001194539,7.2923694e-7,0.00023035024,0.6880802,0.2608902,0.00095398776,0.00017495542,0.049514685],"study_design_scores_gemma":[0.00016888238,0.0002809265,0.000028459428,0.0000065290965,0.000008438365,0.0000026939135,0.0000263618,0.90724576,0.09058167,0.0010607349,0.00048229037,0.00010724652],"about_ca_topic_score_codex":3.2044935e-7,"about_ca_topic_score_gemma":0.0000012554704,"teacher_disagreement_score":0.7293546,"about_ca_system_score_codex":0.000026511629,"about_ca_system_score_gemma":0.000005192008,"threshold_uncertainty_score":0.35064885},"labels":[],"label_agreement":null},{"id":"W2803612378","doi":"10.1021/jacs.8b03228","title":"Internal Electric Field Modulation in Molecular Electronic Devices by Atmosphere and Mobile Ions","year":2018,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":44,"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 Alberta","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates - Technology Futures","keywords":"Chemistry; Atmosphere (unit); Electric field; Modulation (music); Ion; Field (mathematics); Chemical physics; Organic chemistry; Meteorology; Physics","score_opus":0.0029583277234662636,"score_gpt":0.22699997612530376,"score_spread":0.2240416484018375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803612378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99289733,0.000487969,0.0063774376,0.00012118443,0.00003850069,0.000032515425,1.8986277e-7,0.000011371529,0.000033505272],"genre_scores_gemma":[0.99863696,0.000110505236,0.00077359565,0.0003727357,0.0000925493,8.9648006e-7,9.192679e-8,0.000008439526,0.000004210394],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995135,0.000014292626,0.00016173921,0.00006162446,0.00008617861,0.00016263759],"domain_scores_gemma":[0.99971414,0.0000630944,0.00010876542,0.000057590296,0.000023164597,0.000033238877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006727299,0.00007012494,0.00012827563,0.000003585575,0.000029056668,0.000009454567,0.00013133093,0.000024723693,0.000004228495],"category_scores_gemma":[0.000020614594,0.000052021704,0.00009045857,0.0002166821,0.00003981131,0.00006283556,0.000030250878,0.00035467854,3.0444707e-7],"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":[0.000010149957,0.000008103204,0.0008097608,0.0000049326363,0.000020692949,4.9579063e-7,0.00009866842,0.0024356823,0.98780817,0.000002841433,0.000545641,0.008254882],"study_design_scores_gemma":[0.00016526683,0.00014530888,0.00060644123,0.00003071451,0.000011341266,0.000036737496,0.00007767271,0.037854202,0.9599718,0.00028825313,0.000728119,0.0000841736],"about_ca_topic_score_codex":0.0000055628684,"about_ca_topic_score_gemma":6.925821e-7,"teacher_disagreement_score":0.035418518,"about_ca_system_score_codex":0.00008959347,"about_ca_system_score_gemma":0.000010052764,"threshold_uncertainty_score":0.21213835},"labels":[],"label_agreement":null},{"id":"W2806498722","doi":"10.1007/s12021-019-09424-z","title":"NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods","year":2019,"lang":"en","type":"preprint","venue":"Neuroinformatics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"Office of Naval Research","keywords":"Computer science; Benchmarking; Neuromorphic engineering; Artificial intelligence; Construct (python library); Deep learning; Convolutional neural network; Key (lock); Machine learning; Artificial neural network; Software; Programming language","score_opus":0.04812619449195356,"score_gpt":0.28854769028864685,"score_spread":0.2404214957966933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806498722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26693112,0.0006600289,0.7259438,0.000013196983,0.0015243397,0.0003340791,0.0000028995044,0.00090381247,0.0036867473],"genre_scores_gemma":[0.8656456,0.0016343356,0.13207485,0.00016954262,0.0001546705,0.000011110076,0.000037450758,0.00017717973,0.000095306685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832433,0.00010195525,0.00065953727,0.00026820012,0.00019928507,0.00044671737],"domain_scores_gemma":[0.99872226,0.0005093255,0.00020986678,0.0003806992,0.00004396241,0.00013391726],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00032558147,0.00046409317,0.00056178943,0.00017150232,0.00015923125,0.00016731003,0.00026561334,0.00023828632,0.0000065859854],"category_scores_gemma":[0.000094537005,0.0005146088,0.000104833336,0.00012295053,0.000037570415,0.00027081254,0.0006698238,0.0026662122,0.000025770787],"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":[0.0000035935534,0.0000028601198,0.000029064217,0.0010538552,0.000017282735,0.000008403198,0.0008180435,0.9812015,0.00049114757,0.0000705799,0.0000100010775,0.016293654],"study_design_scores_gemma":[0.00020872279,0.000045673623,0.000014615287,0.0001881169,0.000039311202,0.00007268065,0.00010153239,0.994505,0.0011585186,0.00055480737,0.0026654908,0.00044552234],"about_ca_topic_score_codex":8.5363513e-7,"about_ca_topic_score_gemma":1.5490174e-7,"teacher_disagreement_score":0.5987144,"about_ca_system_score_codex":0.000030631916,"about_ca_system_score_gemma":0.000016801832,"threshold_uncertainty_score":0.9997305},"labels":[],"label_agreement":null},{"id":"W2806777858","doi":"10.1002/smll.201800756","title":"Charge‐Storage Aromatic Amino Compounds for Nonvolatile Organic Transistor Memory Devices","year":2018,"lang":"en","type":"article","venue":"Small","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Pentacene; Organic field-effect transistor; Materials science; Non-volatile memory; Molecule; Transistor; Flash memory; Layer (electronics); Optoelectronics; Nanotechnology; Field-effect transistor; Thin-film transistor; Chemistry; Organic chemistry; Computer science; Voltage; Physics","score_opus":0.02271813959519156,"score_gpt":0.2239081470265031,"score_spread":0.20119000743131155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806777858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9603209,0.00023911436,0.03603042,0.000022981603,0.00079382025,0.00024437462,0.000006854215,0.00038842595,0.0019530935],"genre_scores_gemma":[0.9937204,0.000003086073,0.004989015,0.000121867786,0.00050821184,0.00001607422,0.000010573553,0.000050129467,0.00058062235],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992675,0.000012302465,0.00019893743,0.00017474392,0.00006455355,0.0002819468],"domain_scores_gemma":[0.9995794,0.00010572401,0.00003445306,0.00018165751,0.000027510589,0.000071240225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087111715,0.00016245547,0.00019330803,0.000048116894,0.00014078202,0.000019076102,0.00015698698,0.000053593812,0.00009953292],"category_scores_gemma":[0.00001644737,0.00016412581,0.000062637315,0.00010869071,0.000037756523,0.00010171233,0.000012777734,0.000100170415,0.00011673071],"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":[0.000057763988,0.00006230687,0.00014086936,0.0011096627,0.00010962662,0.000015486716,0.0047543026,0.0019464218,0.9771202,0.00006969227,0.0021932847,0.012420337],"study_design_scores_gemma":[0.0021272616,0.0006207018,0.0022485133,0.00029138397,0.00014505275,0.00005589128,0.00037996809,0.20060444,0.7560405,0.0007094769,0.035418656,0.0013581335],"about_ca_topic_score_codex":0.0000010415024,"about_ca_topic_score_gemma":0.000044220298,"teacher_disagreement_score":0.22107974,"about_ca_system_score_codex":0.00004600254,"about_ca_system_score_gemma":0.000010384577,"threshold_uncertainty_score":0.66928566},"labels":[],"label_agreement":null},{"id":"W2807050176","doi":"10.1038/s41598-018-23049-3","title":"Author Correction: Exploring the alpha desynchronization hypothesis in resting state networks with intracranial electroencephalography and wiring cost estimates","year":2018,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Toronto Western Hospital; Hospital for Sick Children","funders":"","keywords":"Electroencephalography; Alpha (finance); Computer science; State (computer science); Artificial intelligence; Neuroscience; Statistics; Psychology; Mathematics; Algorithm","score_opus":0.021268004516267282,"score_gpt":0.22800987789679428,"score_spread":0.206741873380527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807050176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67664474,0.0026623537,0.108726256,0.000049325154,0.20821255,0.0015251678,0.0000024534856,0.0011037806,0.001073377],"genre_scores_gemma":[0.98928505,0.0002604115,0.0035003629,0.000014483128,0.0020014134,0.00018901286,0.00008949942,0.00018721282,0.004472561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976448,0.00005161481,0.00056192133,0.00078194553,0.0003354838,0.0006242252],"domain_scores_gemma":[0.9988137,0.00021670673,0.00027915012,0.00047955706,0.00011546146,0.00009541537],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009810125,0.00036367358,0.00033187447,0.00031959757,0.0007303721,0.00048487302,0.00015405584,0.00011364751,0.0000064862998],"category_scores_gemma":[0.00025690344,0.00028657517,0.00004567195,0.0011756725,0.00033180966,0.00045018079,0.000069283094,0.00085983437,0.0000010625546],"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":[0.000034035605,0.000017395381,0.0036327187,0.00021461917,0.0000619739,0.0010470654,0.0008671266,0.85811174,0.00091070484,0.0000013303091,0.0497251,0.08537619],"study_design_scores_gemma":[0.00022794792,0.00014537781,0.006835288,0.0030108248,0.00011453729,0.002561251,0.00019170756,0.95767283,0.0057917386,0.001245636,0.021051966,0.0011509168],"about_ca_topic_score_codex":0.000011490877,"about_ca_topic_score_gemma":0.00027014763,"teacher_disagreement_score":0.3126403,"about_ca_system_score_codex":0.00012418405,"about_ca_system_score_gemma":0.00006963604,"threshold_uncertainty_score":0.99995863},"labels":[],"label_agreement":null},{"id":"W2807232225","doi":"10.1038/s41598-018-20598-5","title":"Resistance Switching and Memristive Hysteresis in Visible-Light-Activated Adsorbed ZnO Thin Films","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"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":"University of Toronto; National Science Foundation","keywords":"Bistability; Memristor; Hysteresis; Optoelectronics; Materials science; Thin film; Semiconductor; Dielectric; Nanotechnology; Ohmic contact; Computer science; Condensed matter physics; Electrical engineering; Physics; Layer (electronics)","score_opus":0.011948709016469258,"score_gpt":0.23837268231853806,"score_spread":0.22642397330206882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807232225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913361,0.00022764459,0.00086590205,0.000030356545,0.002889698,0.00017511279,8.601888e-7,0.00025246356,0.0042218575],"genre_scores_gemma":[0.9976001,0.00000325731,0.0013021107,0.000014935836,0.00006474074,0.000006340798,0.0000041860517,0.00002375077,0.0009806146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984313,0.000033369393,0.00041886937,0.0005637033,0.00021718716,0.00033555797],"domain_scores_gemma":[0.9992401,0.00005852311,0.00011675256,0.00042656832,0.00006943611,0.000088607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061631895,0.00016872716,0.00020716645,0.00018268988,0.00026756155,0.00014896084,0.00009273647,0.00005809019,0.000029207566],"category_scores_gemma":[0.00015179714,0.00016003108,0.000027784055,0.00066632166,0.00008509619,0.0003521731,0.000064636886,0.00018590182,0.000008389433],"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":[0.000020232264,0.000010275378,0.00051337515,0.00006434358,0.000009191032,0.0003234582,0.0011219591,0.0013309276,0.9933735,0.000025593214,0.0020274385,0.001179722],"study_design_scores_gemma":[0.00017625338,0.000018340314,0.0015717878,0.0002866704,0.000005799877,0.000065998036,0.00020639172,0.004918536,0.98220134,0.003278282,0.0069556353,0.0003149883],"about_ca_topic_score_codex":0.0000055956375,"about_ca_topic_score_gemma":0.00009453553,"teacher_disagreement_score":0.011172167,"about_ca_system_score_codex":0.000047664344,"about_ca_system_score_gemma":0.000021437623,"threshold_uncertainty_score":0.6525878},"labels":[],"label_agreement":null},{"id":"W2809764342","doi":"10.1109/icin.2018.8401601","title":"Cloud based content classification with global-connected net (GC-Net)","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cloud computing; MNIST database; Convolutional neural network; Benchmark (surveying); Contextual image classification; Artificial intelligence; Feature extraction; Deep learning; The Internet; Pattern recognition (psychology); Operating system; Image (mathematics)","score_opus":0.043315821166444224,"score_gpt":0.24300389408902318,"score_spread":0.19968807292257895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809764342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73500013,0.000027550415,0.25037828,0.00009962998,0.00031108828,0.00014646564,0.0000027959602,0.0007814674,0.013252605],"genre_scores_gemma":[0.99439913,0.0000014142292,0.004957682,0.0002628275,0.00022107142,0.0000047569797,0.0000074149825,0.000014505098,0.00013119815],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994251,0.00001399027,0.00012969192,0.000154363,0.000083285915,0.00019353446],"domain_scores_gemma":[0.99962825,0.000035994177,0.00002365664,0.00018316216,0.00006455217,0.00006441398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000042760665,0.000116929325,0.00010047763,0.000017337316,0.00006610638,0.00001816642,0.00008039713,0.000038882525,0.000086271495],"category_scores_gemma":[0.000015554388,0.00009252366,0.000020199186,0.0001822012,0.00003979305,0.0000817398,0.000009443468,0.00007016517,0.00006966735],"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":[0.0009409788,0.00018187179,0.007895336,0.0002518688,0.00021034727,0.000058830832,0.00031587147,0.110689625,0.7313702,0.025164412,0.014890723,0.10802995],"study_design_scores_gemma":[0.0013987619,0.00046255626,0.018456768,0.00007480715,0.000024767596,0.000022096785,0.0001536096,0.70613974,0.26349613,0.00021369218,0.009071385,0.00048570966],"about_ca_topic_score_codex":0.000004690209,"about_ca_topic_score_gemma":0.00004396292,"teacher_disagreement_score":0.5954501,"about_ca_system_score_codex":0.000055659242,"about_ca_system_score_gemma":0.000009494678,"threshold_uncertainty_score":0.37730053},"labels":[],"label_agreement":null},{"id":"W2848040679","doi":"10.22214/ijraset.2018.7001","title":"Disruptive Technology for Significant Performance Enhancement","year":2018,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Business","score_opus":0.05782437154284807,"score_gpt":0.38946026838599396,"score_spread":0.33163589684314587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2848040679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8537721,0.00008795259,0.14349613,0.00091325,0.0008723081,0.00040388154,0.0000026230034,0.00015338394,0.0002983763],"genre_scores_gemma":[0.9836941,0.00012018518,0.015800215,0.00001086766,0.00018366841,0.00016455741,5.3789444e-7,0.00001360812,0.000012301656],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986902,0.0000019426777,0.00020601426,0.00022313152,0.00032357176,0.00055515865],"domain_scores_gemma":[0.99925804,0.000097188655,0.000023515922,0.000102901846,0.0004567276,0.00006164239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012355228,0.00009670545,0.000112513924,0.0017212066,0.00025627328,0.00006148579,0.0006523486,0.000079011465,0.000002330889],"category_scores_gemma":[0.00023558001,0.0000918334,0.00001410721,0.0009523987,0.0005353634,0.00013883284,0.00014240347,0.00042278395,0.0000031452826],"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":[0.00005631025,0.000016239304,0.00005862402,0.000030465038,0.000014937552,0.0000054521715,0.000087902,0.003847584,0.8023493,0.027076853,0.00006850293,0.16638781],"study_design_scores_gemma":[0.00061059307,0.00038526615,0.00006480752,0.000108201886,0.0000016050225,0.00008450466,0.00028790807,0.257411,0.71092725,0.016436068,0.013504592,0.00017821384],"about_ca_topic_score_codex":1.735143e-7,"about_ca_topic_score_gemma":3.7155706e-7,"teacher_disagreement_score":0.25356343,"about_ca_system_score_codex":0.00027388587,"about_ca_system_score_gemma":0.00005354553,"threshold_uncertainty_score":0.37448573},"labels":[],"label_agreement":null},{"id":"W2884358675","doi":"10.1016/j.conb.2018.06.011","title":"Beyond STDP — towards diverse and functionally relevant plasticity rules","year":2018,"lang":"en","type":"review","venue":"Current Opinion in Neurobiology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal General Hospital; McGill University Health Centre","funders":"","keywords":"Neuroscience; Plasticity; Synaptic plasticity; Associative learning; Spike-timing-dependent plasticity; Associative property; Association (psychology); Metaplasticity; Neuroplasticity; Computer science; Millisecond; Psychology; Artificial intelligence; Biology; Physics; Mathematics","score_opus":0.09945830507706119,"score_gpt":0.34787853765478594,"score_spread":0.24842023257772475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884358675","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004921409,0.98190254,0.00036789864,0.000004558342,0.016558312,0.0003070478,0.00013080316,0.00015098376,0.0000856903],"genre_scores_gemma":[0.000050399765,0.99877214,0.000070558955,0.0000070250653,0.0008355874,0.000026862792,0.00019243166,0.000037531572,0.0000074492814],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998525,0.00013472445,0.00048763532,0.00046908195,0.000060841725,0.00032267813],"domain_scores_gemma":[0.99931186,0.00032301864,0.00011737812,0.00014937954,0.000024487876,0.00007387438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007640957,0.0003875389,0.0008077623,0.0002005038,0.000065892564,0.0000135725095,0.00018437424,0.00020983952,0.00002643915],"category_scores_gemma":[0.00009226532,0.0003356689,0.000121304016,0.0001434417,0.00012452262,0.00006770011,0.00021162206,0.00067652663,0.00006719385],"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":[0.000009840583,0.000026868285,0.000013322532,0.012495817,0.000028299375,0.0000029880005,0.00001790271,0.00026592225,0.000005921099,0.00044408298,0.0023849597,0.9843041],"study_design_scores_gemma":[0.00016648456,0.00009357781,0.000058022535,0.0038746188,0.000032661887,0.000064688815,0.0000028061597,0.00018065193,0.0000021684864,0.00022429679,0.99497736,0.00032268063],"about_ca_topic_score_codex":4.2411403e-7,"about_ca_topic_score_gemma":7.9910376e-7,"teacher_disagreement_score":0.9925924,"about_ca_system_score_codex":0.0000549685,"about_ca_system_score_gemma":0.000039211627,"threshold_uncertainty_score":0.9999095},"labels":[],"label_agreement":null},{"id":"W2884371298","doi":"10.1007/978-3-319-97676-1_4","title":"Associative Memory: An Spiking Neural Network Robotic Implementation","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Computer science; Bidirectional associative memory; Content-addressable memory; Artificial intelligence; Artificial neural network; Spiking neural network; Spike (software development); Associative property; Coding (social sciences)","score_opus":0.02390516324894185,"score_gpt":0.28097286226465484,"score_spread":0.25706769901571297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884371298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01086107,0.0001998521,0.9845168,0.00003460266,0.0029558188,0.00029976986,0.000002691284,0.0002975862,0.0008318125],"genre_scores_gemma":[0.91705,0.000016946087,0.07811216,0.00068041816,0.003928522,0.000005287615,0.000025404635,0.00009767601,0.00008356355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800783,0.000025579739,0.00034379537,0.0006281178,0.00038103934,0.0006136058],"domain_scores_gemma":[0.999093,0.00021049565,0.00014812722,0.00035333127,0.000090111556,0.000104959996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004012461,0.0003850752,0.0003637421,0.00019524524,0.00026688786,0.00014280657,0.00056585006,0.00015843511,0.00006443147],"category_scores_gemma":[0.00002021729,0.00039048144,0.000067144196,0.0002737417,0.00019942492,0.00044820603,0.00020937684,0.00061254203,0.000014603029],"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":[0.0000018979474,0.0000017542878,0.000030416826,0.000013933739,0.000006495758,0.00001569966,0.00050299946,0.7974963,0.00020854043,0.00008529194,0.000012870196,0.20162377],"study_design_scores_gemma":[0.00019766508,0.00018398261,0.0002444702,0.00020075012,0.000016606495,0.000021598926,0.0000016122973,0.96348524,0.0032956717,0.03161908,0.00012023048,0.00061311724],"about_ca_topic_score_codex":0.0000032444336,"about_ca_topic_score_gemma":0.00011732455,"teacher_disagreement_score":0.9064046,"about_ca_system_score_codex":0.00031880513,"about_ca_system_score_gemma":0.00005089329,"threshold_uncertainty_score":0.9998547},"labels":[],"label_agreement":null},{"id":"W2886884350","doi":"10.1109/smacd.2018.8434895","title":"A Memristive TaOx-Based Median Filter Design for Image Processing Application","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Median filter; Computer science; Salt-and-pepper noise; Noise (video); Peak signal-to-noise ratio; Cadence; Filter (signal processing); Pixel; Electronic engineering; CMOS; Artificial intelligence; Filter design; Image processing; Algorithm; Computer vision; Image (mathematics); Engineering","score_opus":0.025913023498992962,"score_gpt":0.2731947195519925,"score_spread":0.24728169605299952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886884350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066164644,0.000015479236,0.99136555,0.00005996063,0.00006276811,0.00032434566,0.000001976751,0.0003485382,0.001204919],"genre_scores_gemma":[0.79576474,3.0860764e-7,0.20375568,0.00011668474,0.0002168278,0.00007142102,0.0000051033853,0.000019691608,0.0000495133],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955654,0.0000066020666,0.00010027366,0.00013073938,0.000046119465,0.00015974164],"domain_scores_gemma":[0.99970335,0.00008775475,0.000019750247,0.0000852629,0.00006377151,0.00004009571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081229,0.00008634028,0.000074164134,0.000030583033,0.0000932132,0.000017205317,0.00006635497,0.000029934912,0.000019785928],"category_scores_gemma":[0.00002665833,0.00007782143,0.000021133937,0.00008869706,0.000032625132,0.00011478256,0.00000600554,0.000046261026,0.000024463157],"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":[0.00008150745,0.000021739872,0.000009770066,0.00023416971,0.000011805407,0.000002296078,0.0004725012,0.038933557,0.7800129,0.00012457675,0.0018329034,0.1782623],"study_design_scores_gemma":[0.00013324288,0.000030223471,0.000009828345,0.000010747904,0.0000036581243,7.51068e-7,0.000011362271,0.526024,0.47232157,0.0005394131,0.00084074476,0.00007445713],"about_ca_topic_score_codex":4.381217e-7,"about_ca_topic_score_gemma":0.000001766459,"teacher_disagreement_score":0.78914833,"about_ca_system_score_codex":0.000023115403,"about_ca_system_score_gemma":0.000010343878,"threshold_uncertainty_score":0.3173466},"labels":[],"label_agreement":null},{"id":"W2887090061","doi":"10.1109/mnano.2018.2845078","title":"Building Brain-Inspired Computing Systems: Examining the Role of Nanoscale Devices","year":2018,"lang":"en","type":"article","venue":"IEEE Nanotechnology Magazine","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":47,"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; Cisco Systems; National Science Foundation","keywords":"Computer science; Realization (probability); Variety (cybernetics); State (computer science); Memristor; Distributed computing; Process (computing); Parallelism (grammar); Reservoir computing; Computer architecture; Artificial intelligence; Electronic engineering; Artificial neural network; Parallel computing","score_opus":0.013869372748542344,"score_gpt":0.24197004634615826,"score_spread":0.22810067359761593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887090061","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9624905,0.0014381461,0.03293005,0.00011161975,0.0010384786,0.00020291789,0.000003132887,0.0010447268,0.00074041565],"genre_scores_gemma":[0.9967295,0.000010277799,0.002764335,0.00008054473,0.000326611,0.0000063855687,8.331375e-7,0.000044558594,0.000036981444],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986601,0.000045751087,0.00043632876,0.00027761853,0.0001373394,0.00044289633],"domain_scores_gemma":[0.99896336,0.0003409301,0.00014736847,0.00042816426,0.00008394788,0.00003621538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000294099,0.00022679771,0.00033684174,0.00016930589,0.00022762179,0.000018569644,0.0004930468,0.00022955317,0.0000046629084],"category_scores_gemma":[0.00010976387,0.00018670691,0.00004634672,0.0005339299,0.00031319133,0.00011035139,0.00011600898,0.00035920364,0.00003883754],"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":[0.0000074579802,0.000008214255,0.0003134685,0.00006115654,0.000032358166,0.000006232041,0.00013244369,0.014805059,0.9494252,0.0009725961,0.00013874585,0.034097046],"study_design_scores_gemma":[0.0002870075,0.00015632578,0.00031857382,0.00020368978,0.000019930836,0.00008924068,0.00016584106,0.0952485,0.8862369,0.0004438753,0.016584694,0.0002454284],"about_ca_topic_score_codex":0.000003939852,"about_ca_topic_score_gemma":0.000009092025,"teacher_disagreement_score":0.08044344,"about_ca_system_score_codex":0.000042637508,"about_ca_system_score_gemma":0.000011240355,"threshold_uncertainty_score":0.7613687},"labels":[],"label_agreement":null},{"id":"W2891805427","doi":"10.1109/tbcas.2018.2869319","title":"Digital Multiplierless Realization of Coupled Wilson Neuron Model","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Windsor","funders":"","keywords":"Neuromorphic engineering; Realization (probability); Computer science; Field-programmable gate array; Biological neuron model; Mean squared error; Artificial neuron; Artificial neural network; Spiking neural network; Artificial intelligence; Computer engineering; Mathematics; Computer hardware","score_opus":0.0247391426539037,"score_gpt":0.23971444683358498,"score_spread":0.21497530417968128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891805427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24260587,0.00004153293,0.7561967,0.0000042738493,0.0006048126,0.00011641793,0.000033047894,0.00012084982,0.0002765244],"genre_scores_gemma":[0.9997147,0.00003364376,0.000015227684,0.000010566448,0.000095882766,0.000007125695,0.000004819671,0.000020173178,0.000097903896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992091,0.000012705802,0.0002739099,0.00016365753,0.00018243508,0.00015816021],"domain_scores_gemma":[0.99963176,0.000056998524,0.000036233683,0.0001208936,0.00004255064,0.000111540496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000704658,0.000118056225,0.00018400101,0.00009514874,0.00009563896,0.00002659002,0.00006276819,0.00008877202,0.000003427318],"category_scores_gemma":[0.0000042811566,0.00010529001,0.000033885142,0.00017464263,0.000118065705,0.0001321137,6.819019e-7,0.000111927,0.0000039566035],"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":[0.00004189192,0.00018014682,0.000016766617,0.0006308768,0.000069636706,0.000008240342,0.00088754186,0.55027384,0.26856554,0.00034536634,0.00008997176,0.17889017],"study_design_scores_gemma":[0.000356713,0.00013490589,0.000020983443,0.00010180763,0.000010858544,0.00001588789,0.00004932673,0.9926829,0.006274802,0.000024118775,0.00021219204,0.00011554492],"about_ca_topic_score_codex":0.0000034355805,"about_ca_topic_score_gemma":0.0000014302202,"teacher_disagreement_score":0.7571088,"about_ca_system_score_codex":0.00001917607,"about_ca_system_score_gemma":0.000008861563,"threshold_uncertainty_score":0.42936018},"labels":[],"label_agreement":null},{"id":"W2892914188","doi":"10.1587/nolta.9.406","title":"Application of stochastic computing in brainware","year":2018,"lang":"en","type":"article","venue":"Nonlinear Theory and Its Applications IEICE","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Japan Society for the Promotion of Science; University of Tokyo; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Stochastic computing; Computer science; Computation; Probabilistic logic; CMOS; Multiplication (music); Stochastic process; Binary number; Bitstream; Computer hardware; Parallel computing; Computer engineering; Algorithm; Electronic engineering; Artificial intelligence; Decoding methods; Arithmetic; Engineering; Mathematics","score_opus":0.008606198873525355,"score_gpt":0.26471200951337864,"score_spread":0.25610581063985327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892914188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38246343,0.0002666245,0.6163093,0.000009723142,0.000022103459,0.00024735916,0.000007840582,0.000092129376,0.00058154156],"genre_scores_gemma":[0.9973926,0.000008542328,0.0023213488,0.00003933567,0.00017633694,0.000020693813,0.0000090861595,0.000014598718,0.00001749159],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946827,0.000026038559,0.00019257107,0.00014758685,0.00004688062,0.00011868294],"domain_scores_gemma":[0.99946547,0.00026830673,0.000040141633,0.00014531461,0.00004831134,0.00003243315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024290344,0.000084356805,0.000108058855,0.00006291514,0.00007799361,0.0000049369423,0.000099713296,0.000042389915,0.0000055129744],"category_scores_gemma":[0.000034701898,0.00008860875,0.000014221843,0.00026837536,0.000055774308,0.000065774795,0.000031553525,0.00010793618,0.000017441484],"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":[0.00010550003,0.00015533982,0.00012917044,0.0004312034,0.000033893273,8.121847e-7,0.0020399105,0.10483862,0.2161107,0.20477045,0.000013447165,0.47137094],"study_design_scores_gemma":[0.00058404705,0.00007549503,0.0010245566,0.00010099697,0.000022460528,0.000018952132,0.0004049864,0.8996307,0.07588248,0.017956717,0.003913784,0.00038481457],"about_ca_topic_score_codex":5.404756e-7,"about_ca_topic_score_gemma":0.0000020822044,"teacher_disagreement_score":0.7947921,"about_ca_system_score_codex":0.000010738836,"about_ca_system_score_gemma":0.0000050672306,"threshold_uncertainty_score":0.361336},"labels":[],"label_agreement":null},{"id":"W2894696827","doi":"10.1109/jssc.2018.2869150","title":"An Always-On 3.8 &lt;inline-formula&gt; &lt;tex-math notation=\"LaTeX\"&gt;$\\mu$ &lt;/tex-math&gt; &lt;/inline-formula&gt;J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Solid-State Circuits","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":187,"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":"Vlaamse regering; Canadian Institute for Advanced Research","keywords":"Convolutional neural network; Artificial neural network; Computer science; Algorithm; Floating point; Binary number; Static random-access memory; Parallel computing; Computer hardware; Mathematics; Arithmetic; Artificial intelligence","score_opus":0.022860848706270406,"score_gpt":0.2692493579965922,"score_spread":0.2463885092903218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894696827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885347,0.00066228973,0.005974899,0.0001811294,0.0018333493,0.0010221071,0.00014103545,0.0004881757,0.0011622978],"genre_scores_gemma":[0.9946027,0.00045893132,0.001150353,0.0005931733,0.002156826,0.000040757524,0.00007469151,0.00042652898,0.0004960359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99178237,0.00026208113,0.002862197,0.0011662932,0.001867592,0.0020594958],"domain_scores_gemma":[0.99485016,0.00051884796,0.001531258,0.0009692537,0.0011122762,0.0010182005],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0016928654,0.0015567193,0.0017689174,0.001317589,0.0005853883,0.00025650862,0.0013746136,0.0005251806,0.00008377489],"category_scores_gemma":[0.00020250064,0.0013565988,0.0004520173,0.0013571858,0.0003039469,0.0023792281,0.00009931654,0.002015212,0.00032193447],"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":[0.0012346529,0.0009014452,0.00009356982,0.0007100216,0.00040319556,0.0016676597,0.0034817434,0.50927454,0.453994,0.00036340646,0.0009957767,0.026879957],"study_design_scores_gemma":[0.01482406,0.0124988435,0.0054581454,0.00599398,0.00054277683,0.0032439842,0.00040323503,0.4585116,0.47903547,0.0023516354,0.01213688,0.004999399],"about_ca_topic_score_codex":0.0000034698958,"about_ca_topic_score_gemma":0.00008649869,"teacher_disagreement_score":0.05076299,"about_ca_system_score_codex":0.0006974645,"about_ca_system_score_gemma":0.00045610146,"threshold_uncertainty_score":0.9997181},"labels":[],"label_agreement":null},{"id":"W2895677923","doi":"10.1016/j.dib.2018.09.087","title":"Data related to the nanoscale structural and compositional evolution in resistance change memories","year":2018,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"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":"RMIT University; Science and Industry Endowment Fund; Australian Research Council; Ontario Ministry of Natural Resources and Forestry","keywords":"Memristor; Resistive random-access memory; Materials science; Strontium titanate; Nanoscopic scale; Capacitor; Optoelectronics; Biasing; Nanotechnology; Resistor; Non-volatile memory; Transmission electron microscopy; Memory cell; Thin film; Voltage; Electrical engineering; Transistor; Engineering","score_opus":0.04633794436713484,"score_gpt":0.2910263364738991,"score_spread":0.24468839210676424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895677923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959826,0.00077102595,0.0006678341,0.00054576504,0.0004272214,0.00019548382,0.0011634926,0.0000608883,0.00018571717],"genre_scores_gemma":[0.9974909,0.000010337151,0.0014017131,0.00010046068,0.0001321844,0.0000033815484,0.00083658233,0.000007929695,0.000016511463],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994047,0.000022788741,0.00013364927,0.00022470436,0.00007503316,0.00013907038],"domain_scores_gemma":[0.9992942,0.000050100592,0.000014127693,0.00060892076,0.000009707926,0.00002293074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016083248,0.00006923676,0.000075622214,0.0000343591,0.00007826251,0.000018600791,0.00042891075,0.000026830694,0.0000068511363],"category_scores_gemma":[0.00005451782,0.000059791197,0.0000026476382,0.00021322093,0.00005533299,0.0005191414,0.0003385675,0.00012366248,0.0000050597405],"study_design_candidate":"observational","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":[0.0027159615,0.00031374133,0.16084974,0.002284737,0.00035680193,0.00086268777,0.040850196,0.03559618,0.30709144,0.082885854,0.19074388,0.17544879],"study_design_scores_gemma":[0.0009943782,0.000053378204,0.7605637,0.00040940667,0.000011666085,0.00005226116,0.00023250205,0.1595219,0.0028632064,0.0030618007,0.07171357,0.0005222694],"about_ca_topic_score_codex":0.000021708742,"about_ca_topic_score_gemma":0.0015656833,"teacher_disagreement_score":0.5997139,"about_ca_system_score_codex":0.000030224544,"about_ca_system_score_gemma":0.0000040087784,"threshold_uncertainty_score":0.24382143},"labels":[],"label_agreement":null},{"id":"W2896786610","doi":"10.1101/2021.09.01.458493","title":"Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Montreal General Hospital","funders":"National Institute on Aging; Fundação para a Ciência e a Tecnologia; Canadian Institutes of Health Research; Engineering and Physical Sciences Research Council; Biotechnology and Biological Sciences Research Council; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Postsynaptic potential; Nonsynaptic plasticity; Neuroscience; Synaptic plasticity; Metaplasticity; Spike-timing-dependent plasticity; Plasticity; Homosynaptic plasticity; Synaptic scaling; Post-tetanic potentiation; Synapse; Inhibitory postsynaptic potential; Biology; Neuroplasticity; Synaptic augmentation; Excitatory postsynaptic potential; Physics; Genetics; Receptor","score_opus":0.01105873030283301,"score_gpt":0.21136505150357715,"score_spread":0.20030632120074415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896786610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9173598,0.00033837458,0.07960199,0.00005263168,0.0010725824,0.0005269877,0.00008115789,0.00096169306,0.0000047707244],"genre_scores_gemma":[0.99482346,0.00012747628,0.004257739,0.00011882663,0.0004078788,0.000077799166,7.8714413e-7,0.00018053119,0.0000055096243],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969004,0.00014016262,0.0006092192,0.0011105621,0.00042698474,0.0008126863],"domain_scores_gemma":[0.99818385,0.0002741464,0.00015962873,0.00054122217,0.00029036545,0.00055076845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020970633,0.00073787774,0.00079205696,0.0001855161,0.00023421616,0.00041424038,0.00043040988,0.0004142653,0.000030683055],"category_scores_gemma":[0.0005771955,0.00086183177,0.00013112425,0.00022325339,0.000063567604,0.00017748853,0.0010578529,0.0017058979,0.000013537467],"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":[0.000054552715,0.000042834126,0.00076231966,0.00043807222,0.00011884892,0.00015255113,0.000022934011,0.083733365,0.91459703,0.000057639947,0.000010972648,0.000008874606],"study_design_scores_gemma":[0.0007897001,0.00014544833,0.121499844,0.00091636565,0.00018126782,1.8556051e-7,0.0000051615502,0.026405577,0.84820664,0.0000016389514,0.0004084947,0.0014396537],"about_ca_topic_score_codex":0.000005692514,"about_ca_topic_score_gemma":0.0000020679324,"teacher_disagreement_score":0.12073753,"about_ca_system_score_codex":0.00018280896,"about_ca_system_score_gemma":0.00012826241,"threshold_uncertainty_score":0.9993833},"labels":[],"label_agreement":null},{"id":"W2897559245","doi":"10.1109/ijcnn.2018.8489263","title":"Digital Realization of PSTDP and TSTDP Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"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":"Lookup table; Field-programmable gate array; Computer science; Electronic circuit; Realization (probability); Digital electronics; Artificial neural network; Finite-state machine; Table (database); Computer engineering; Computer architecture; Algorithm; Computer hardware; Artificial intelligence; Mathematics; Engineering; Electrical engineering","score_opus":0.006530580120256592,"score_gpt":0.2070431735808806,"score_spread":0.200512593460624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897559245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.832793,0.00002806466,0.12279295,0.0000022536844,0.0000476252,0.00001930097,2.79482e-7,0.00014331947,0.044173203],"genre_scores_gemma":[0.99920267,0.000007739833,0.00028799733,0.000004790283,0.000045093606,1.5957903e-7,0.0000013395673,0.0000056499416,0.0004445556],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982667,0.0000020068824,0.00005714847,0.000040687282,0.000023873401,0.000049628074],"domain_scores_gemma":[0.9999192,0.000016473012,0.000008476273,0.000028735867,0.000013012169,0.000014099398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000016437552,0.00003076307,0.000039216062,0.000015429083,0.000021805463,0.0000069663834,0.000014799324,0.000012179818,0.000012816174],"category_scores_gemma":[0.00001833261,0.000028426562,0.000005800286,0.000046057296,0.000017936078,0.000107740445,0.000011402842,0.000028252276,0.0000035503485],"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":[0.000025150757,0.000015953532,0.017220562,0.00022405553,0.000032004533,0.000004241036,0.0014050865,0.069359034,0.2453897,0.009103266,0.0003998381,0.65682113],"study_design_scores_gemma":[0.0005677172,0.00039872696,0.0064155995,0.000108229535,0.000011017451,0.00003175712,0.00036181527,0.27717146,0.69013846,0.0025075844,0.021876335,0.00041131163],"about_ca_topic_score_codex":2.756211e-7,"about_ca_topic_score_gemma":4.9324467e-7,"teacher_disagreement_score":0.6564098,"about_ca_system_score_codex":0.0000023724995,"about_ca_system_score_gemma":6.928632e-7,"threshold_uncertainty_score":0.11592016},"labels":[],"label_agreement":null},{"id":"W2901250169","doi":"10.3389/fnbot.2018.00075","title":"Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context","year":2018,"lang":"en","type":"article","venue":"Frontiers in Neurorobotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Computer science; Stimulus (psychology); Artificial intelligence; Artificial neural network; Orientation (vector space); Sensory cue; Robot; Spiking neural network; Context (archaeology); Computer vision; Psychology; Cognitive psychology","score_opus":0.01401731235514383,"score_gpt":0.2615378930823366,"score_spread":0.24752058072719277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901250169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70764446,0.00009695806,0.28995436,0.00002446351,0.0018487269,0.00015553691,3.460532e-7,0.00013770064,0.00013743552],"genre_scores_gemma":[0.9913485,0.000033331948,0.008366347,0.00006918391,0.0001224021,0.0000047038193,0.0000017920487,0.000033558325,0.000020154426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898845,0.000080398844,0.00025999142,0.00028247034,0.00009213929,0.00029652865],"domain_scores_gemma":[0.99968433,0.00010130521,0.000043806813,0.00009275769,0.00002722533,0.000050573613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001245885,0.00017257287,0.0002217904,0.00021419874,0.000091380636,0.000034540193,0.00006316221,0.000061129875,0.0000012592496],"category_scores_gemma":[0.00019227825,0.00019772952,0.000020722315,0.00049272634,0.00006891434,0.00028321598,0.000035268233,0.00039383626,0.0000012436689],"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":[0.00006041834,0.00006068355,0.11109228,0.000093342525,0.00001048956,0.000053509593,0.001745675,0.75516886,0.018489668,0.00007216252,0.0002161709,0.11293674],"study_design_scores_gemma":[0.0004588977,0.00016229285,0.036326587,0.00008911905,0.000008886912,0.00001819749,0.00028907508,0.9557702,0.006360549,0.00022921989,0.000074184485,0.00021277559],"about_ca_topic_score_codex":0.000012018228,"about_ca_topic_score_gemma":0.00017934255,"teacher_disagreement_score":0.28370407,"about_ca_system_score_codex":0.00010927239,"about_ca_system_score_gemma":0.00000988778,"threshold_uncertainty_score":0.80631757},"labels":[],"label_agreement":null},{"id":"W2902279125","doi":"10.1109/rtsi.2018.8548396","title":"Forming-Free and Self-Rectifying Resistive Switching Effect in Anodic Titanium Dioxide-Based Memristors","year":2018,"lang":"en","type":"article","venue":"2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Electroforming; Anodizing; Materials science; Resistive touchscreen; Anode; Microscale chemistry; Optoelectronics; Titanium; Memristor; Titanium dioxide; Resistive random-access memory; Photolithography; Nanotechnology; Electrical engineering; Electrode; Chemistry; Composite material; Engineering; Voltage; Metallurgy","score_opus":0.02808908601767282,"score_gpt":0.3217647011645031,"score_spread":0.29367561514683027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902279125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99497765,0.00024859092,0.0017231005,0.0015423662,0.0004189184,0.00034754418,0.000020125119,0.0002105333,0.0005111894],"genre_scores_gemma":[0.9981751,0.00010448189,0.0012477983,0.000097101925,0.00017071606,0.00006323962,0.0000042846164,0.000025373696,0.00011190435],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987555,0.000020103776,0.00019313564,0.00034862198,0.00019924657,0.00048337333],"domain_scores_gemma":[0.9991991,0.00040637644,0.000040293293,0.00016482455,0.0001031417,0.00008628857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006145562,0.00017769201,0.00019003222,0.00028280387,0.00045458516,0.000043014235,0.00022303546,0.0004943656,0.000004855894],"category_scores_gemma":[0.00018379971,0.00016790128,0.0000406759,0.00027532468,0.0003228704,0.00018768496,0.00013246997,0.0011843591,0.0000016515631],"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":[0.0030933481,0.0008991352,0.2464762,0.0025346542,0.0018048412,0.00016494138,0.0027415566,0.0011070917,0.41106194,0.049216524,0.10425126,0.17664851],"study_design_scores_gemma":[0.0066817715,0.0048405007,0.002211975,0.0011029519,0.000035574205,0.00007967775,0.0021996077,0.08419822,0.84149504,0.031266816,0.024908138,0.000979738],"about_ca_topic_score_codex":0.000013184635,"about_ca_topic_score_gemma":0.000034242374,"teacher_disagreement_score":0.4304331,"about_ca_system_score_codex":0.0001563832,"about_ca_system_score_gemma":0.000026569078,"threshold_uncertainty_score":0.68468153},"labels":[],"label_agreement":null},{"id":"W2902709485","doi":"10.1155/2018/4815383","title":"Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universität München; Deutsche Forschungsgemeinschaft","keywords":"Neuromorphic engineering; Computer science; Frame rate; Cluster analysis; Benchmark (surveying); Artificial intelligence; Frame (networking); Computer vision; Low latency (capital markets); Real-time computing; Intelligent transportation system; Wireless sensor network; Latency (audio); Event (particle physics); Tracking (education); Artificial neural network; Engineering; Computer network; Telecommunications","score_opus":0.01889780056279559,"score_gpt":0.2552221026157561,"score_spread":0.23632430205296054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902709485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5841579,0.00007670301,0.41515762,0.000009148264,0.00040114156,0.00013750537,0.000006193033,0.000051686788,0.0000021620513],"genre_scores_gemma":[0.9912816,0.000027802247,0.008454751,0.000014768104,0.00017766074,0.0000051763536,0.000009070551,0.00002796413,0.0000011680679],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910843,0.000015380274,0.00048399973,0.00012154946,0.00013532057,0.00013530719],"domain_scores_gemma":[0.99933386,0.00009833754,0.00020341264,0.000053627136,0.00024519858,0.000065555265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540337,0.00012672502,0.00018176941,0.00012167108,0.00010873459,0.000014564122,0.00004649013,0.00005096368,0.00000163672],"category_scores_gemma":[0.000013054053,0.00012432819,0.00007897919,0.00013932989,0.000028384275,0.0005172785,2.7066739e-7,0.00013627806,4.433929e-7],"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":[0.00036728848,0.0000155222,0.000125758,0.0002595464,0.000012890456,0.000009628335,0.0007336931,0.39988843,0.520153,0.000029413664,9.4239647e-7,0.07840394],"study_design_scores_gemma":[0.001939856,0.0011363483,0.06982152,0.000460879,0.00009643052,0.000018405946,0.0007570106,0.0795207,0.84541917,0.0001253242,0.000477087,0.0002272695],"about_ca_topic_score_codex":5.686239e-7,"about_ca_topic_score_gemma":0.000033876455,"teacher_disagreement_score":0.40712377,"about_ca_system_score_codex":0.000047230114,"about_ca_system_score_gemma":0.000008527355,"threshold_uncertainty_score":0.5069957},"labels":[],"label_agreement":null},{"id":"W2906279845","doi":"10.1109/biocas.2018.8584720","title":"Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Waterloo","funders":"","keywords":"Von Neumann architecture; IBM; Computer science; Neuromorphic engineering; Computation; Crossbar switch; Curse of dimensionality; Artificial neural network; Population; Computer architecture; Models of neural computation; Parallel computing; Computer engineering; Computational science; Computer hardware; Theoretical computer science; Artificial intelligence; Algorithm; Operating system","score_opus":0.010904864030457909,"score_gpt":0.22887784076588802,"score_spread":0.21797297673543012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906279845","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877199,0.000009864161,0.010763032,0.00007542053,0.0007379948,0.00014774961,0.0000012689187,0.00018802391,0.00035677373],"genre_scores_gemma":[0.999533,7.058336e-7,0.0001829934,0.000097393975,0.00015568601,0.00000463155,1.5105e-7,0.00001626387,0.000009166002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995191,0.00001803149,0.0001475168,0.00007935057,0.00009341864,0.00014257632],"domain_scores_gemma":[0.99953294,0.00019114045,0.000028184313,0.00021797113,0.000015495933,0.000014265703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005950507,0.00008850268,0.00007282364,0.000017875336,0.00008719461,0.000010257787,0.00015607501,0.000021213962,0.000025702922],"category_scores_gemma":[0.0000284613,0.00004882503,0.000041072057,0.00015977275,0.000019815563,0.000040848692,0.000030076935,0.000148747,0.0000075235657],"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":[0.000020877611,0.000013450505,0.002608696,0.00032626968,0.0000784768,0.0000055823443,0.0013463654,0.71278006,0.13941486,0.13115078,0.00029797977,0.011956628],"study_design_scores_gemma":[0.00014834956,0.00015890657,0.017196892,0.0001768998,0.000022075115,0.000021116504,0.00077106344,0.4848683,0.49599752,0.00015189781,0.00026771228,0.00021929241],"about_ca_topic_score_codex":0.0000025391746,"about_ca_topic_score_gemma":0.0000038414173,"teacher_disagreement_score":0.35658264,"about_ca_system_score_codex":0.000019488021,"about_ca_system_score_gemma":0.0000025042232,"threshold_uncertainty_score":0.19910268},"labels":[],"label_agreement":null},{"id":"W2907392033","doi":"10.1080/21663831.2018.1561535","title":"<i>Operando</i> diagnostic detection of interfacial oxygen ‘breathing’ of resistive random access memory by bulk-sensitive hard X-ray photoelectron spectroscopy","year":2019,"lang":"en","type":"article","venue":"Materials Research Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"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":"National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Resistive random-access memory; X-ray photoelectron spectroscopy; Materials science; Synchrotron radiation; Oxygen; Synchrotron; Tin; Oxide; Resistive touchscreen; Tin oxide; Photoemission spectroscopy; Nanotechnology; Spectroscopy; Analytical Chemistry (journal); Optoelectronics; Electrode; Optics; Nuclear magnetic resonance; Computer science; Chemistry; Physics; Physical chemistry","score_opus":0.017402265775808903,"score_gpt":0.2840558899907799,"score_spread":0.266653624214971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907392033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99464303,0.00010130342,0.0033677556,0.000084533116,0.0006042097,0.00089763023,0.00006911515,0.00008746569,0.00014495544],"genre_scores_gemma":[0.9994274,0.00008934993,0.000100358644,0.000057087203,0.00016301693,0.000040774947,0.00001798769,0.000050808423,0.000053226308],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977605,0.0004447858,0.0004626485,0.00033851614,0.00044418147,0.0005493766],"domain_scores_gemma":[0.99835813,0.0010611146,0.000101234116,0.000272257,0.00013637383,0.0000708978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010048964,0.00022510112,0.0005596423,0.00022115224,0.00009196218,0.000056278073,0.00032280176,0.00008328478,0.00008907547],"category_scores_gemma":[0.0004256505,0.00021301818,0.00007497963,0.00027562838,0.0001419969,0.00030440072,0.00013438138,0.00034712808,0.00003035838],"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":[0.00177083,0.000020124888,0.00001556182,0.00032120704,0.000063461855,0.000012570349,0.0004779497,0.004047694,0.99264497,0.0000011902509,0.0003893892,0.00023505585],"study_design_scores_gemma":[0.0017704649,0.00021772752,0.00033494798,0.00020901926,0.000013698067,0.000004800477,0.000054730765,0.00020329331,0.99689764,0.000015584304,0.00009433379,0.0001837836],"about_ca_topic_score_codex":0.00015448472,"about_ca_topic_score_gemma":0.000005461002,"teacher_disagreement_score":0.004784358,"about_ca_system_score_codex":0.00014881678,"about_ca_system_score_gemma":0.00001961572,"threshold_uncertainty_score":0.86866295},"labels":[],"label_agreement":null},{"id":"W2909633668","doi":"10.1145/3291054","title":"A System-Level Simulator for RRAM-Based Neuromorphic Computing Chips","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Architecture and Code Optimization","topic":"Advanced Memory and Neural Computing","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":"Neuromorphic engineering; Resistive random-access memory; Computer science; Scalability; Artificial neural network; CMOS; Computer architecture; Electronic engineering; Artificial intelligence; Electrical engineering; Engineering; Voltage","score_opus":0.03445061769588328,"score_gpt":0.24384610407322416,"score_spread":0.20939548637734087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909633668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033315156,0.000025207462,0.9653111,0.0001225578,0.00029113828,0.00033212104,0.00007137196,0.00046950448,0.00006183758],"genre_scores_gemma":[0.89630556,0.00000364525,0.10330624,0.00017438283,0.0001232724,0.000013552889,0.000015846521,0.000041147283,0.000016333339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992675,0.00002773128,0.0001856431,0.00023008148,0.000083054394,0.00020595496],"domain_scores_gemma":[0.9993906,0.0002284961,0.00003516372,0.00022466361,0.00005117748,0.00006987503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006640961,0.00017577429,0.00015237262,0.000118119184,0.0003964061,0.00003366253,0.00010914206,0.00006939979,0.000008217799],"category_scores_gemma":[0.00002229951,0.00017214709,0.000055082903,0.00018602534,0.000043461485,0.000055915938,0.0000032984906,0.0001778714,0.0000021499052],"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":[0.00005940508,0.000012580265,0.0000019497027,0.0001021088,0.000013886336,8.314926e-7,0.000118430835,0.96805465,0.001263592,0.000019984138,0.000004184245,0.030348366],"study_design_scores_gemma":[0.00061381736,0.00018435423,0.000021204258,0.00009198265,0.000032123393,0.000016486028,0.000023214425,0.9860505,0.01241186,0.000069896196,0.00030585832,0.0001786996],"about_ca_topic_score_codex":5.3567607e-7,"about_ca_topic_score_gemma":0.000010069818,"teacher_disagreement_score":0.86299044,"about_ca_system_score_codex":0.000023854616,"about_ca_system_score_gemma":0.000010535485,"threshold_uncertainty_score":0.7019955},"labels":[],"label_agreement":null},{"id":"W2911173308","doi":"10.1145/3287624.3287718","title":"In-memory batch-normalization for resistive memory based binary neural network hardware","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"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":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; Ministry of Trade, Industry and Energy; Canadian Institute for Advanced Research","keywords":"Normalization (sociology); Computer science; Binary number; Resistive random-access memory; Crossbar switch; Computer hardware; Resistive touchscreen; Artificial neural network; Efficient energy use; In-Memory Processing; Parallel computing; Voltage; Arithmetic; Artificial intelligence; Electrical engineering; Mathematics; Search engine; Engineering","score_opus":0.011635765796961161,"score_gpt":0.2299929622600603,"score_spread":0.21835719646309915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911173308","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9428408,0.00031471203,0.045484066,0.00016026261,0.0020705997,0.000983539,0.000008385804,0.0006097611,0.007527878],"genre_scores_gemma":[0.9945295,0.000005482423,0.002919602,0.000551608,0.00029135778,0.000028810453,0.000032797245,0.000046001795,0.0015948329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990835,0.000028142927,0.00022915496,0.00022262469,0.00008853073,0.0003480496],"domain_scores_gemma":[0.9994689,0.00022148229,0.00003234257,0.00018988406,0.00003529631,0.00005206758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013494732,0.00016861732,0.00019456087,0.00007556532,0.00006411774,0.000014423272,0.000113054,0.000070655726,0.00014636868],"category_scores_gemma":[0.000025233972,0.00017004435,0.00006515733,0.0002407563,0.000012051293,0.00030690205,0.000028579936,0.00014562423,0.00003170118],"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":[0.000066297325,0.000007413574,0.00040720118,0.00013128255,0.000004867024,0.000008613508,0.00004192135,0.99092525,0.0045487466,0.00007938825,0.002640291,0.0011387505],"study_design_scores_gemma":[0.0009667079,0.000095469535,0.0024399261,0.00008221905,0.0000082521265,0.0000029960877,0.00009125864,0.9717985,0.02220123,0.00018232845,0.0018142893,0.0003168566],"about_ca_topic_score_codex":0.000002751911,"about_ca_topic_score_gemma":0.000009526444,"teacher_disagreement_score":0.051688712,"about_ca_system_score_codex":0.0000549194,"about_ca_system_score_gemma":0.000012756529,"threshold_uncertainty_score":0.69342077},"labels":[],"label_agreement":null},{"id":"W2912432190","doi":"10.1109/icecs.2018.8617942","title":"Vector Matrix Multiplication Using Crossbar Arrays: A Comparative Analysis","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"","keywords":"Crossbar switch; Matrix multiplication; Computer science; Multiplication (music); Dot product; Memristor; Matrix (chemical analysis); Throughput; Electronic circuit; Microcode; Computer hardware; Parallel computing; Computer architecture; Electronic engineering; Engineering; Electrical engineering; Mathematics; Operating system","score_opus":0.05205806779975424,"score_gpt":0.34260242459336393,"score_spread":0.2905443567936097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912432190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6724202,0.000027335864,0.3263632,0.000003536111,0.000063901905,0.000052437495,0.0000014226135,0.00021220146,0.0008557648],"genre_scores_gemma":[0.97842294,8.544863e-7,0.021311907,0.000013648417,0.00015235324,0.0000021919998,0.0000037251439,0.0000072217417,0.00008513628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951607,0.000010861706,0.00013464136,0.00013457706,0.000058406447,0.00014544121],"domain_scores_gemma":[0.99970335,0.000036797293,0.000023404882,0.00014796817,0.00004757712,0.00004089484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003975415,0.00009173856,0.00014790228,0.00008089361,0.00011280379,0.000022823499,0.000066816094,0.000027527523,0.000081811486],"category_scores_gemma":[0.0000062807976,0.000085810956,0.00005678602,0.00051188044,0.000035626435,0.00012604808,0.000015371288,0.00006209915,0.00006234259],"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":[0.000008249679,0.0000079310275,0.0006155833,0.00000886508,0.00013480468,5.455383e-7,0.00072561536,0.57888466,0.41912466,0.00017638943,0.00003868965,0.0002739998],"study_design_scores_gemma":[0.00008303278,0.000011745422,0.0017553145,0.0000036187228,0.000051192706,0.0000010162333,0.00007840403,0.76592857,0.23171997,0.000027734823,0.00023738955,0.000102017075],"about_ca_topic_score_codex":0.000005945414,"about_ca_topic_score_gemma":0.000016466782,"teacher_disagreement_score":0.30600277,"about_ca_system_score_codex":0.00003733729,"about_ca_system_score_gemma":0.0000031472036,"threshold_uncertainty_score":0.34992692},"labels":[],"label_agreement":null},{"id":"W2912715356","doi":"10.1038/s41598-018-38249-0","title":"Electrochemical Oxidation Induced Multi-Level Memory in Carbon-Based Resistive Switching Devices","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Electrochemistry; Carbon fibers; Resistive touchscreen; Oxygen; Electrical conductor; Anode; Nanotechnology; Optoelectronics; Electrode; Chemical engineering; Computer science; Chemistry; Composite material; Composite number","score_opus":0.024859464097467115,"score_gpt":0.2519343098714339,"score_spread":0.22707484577396678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912715356","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941865,0.00006355784,0.0027943472,0.000008882403,0.0020126852,0.00023096806,1.5880974e-7,0.0001649342,0.00053791684],"genre_scores_gemma":[0.9978618,2.4423906e-7,0.0019332102,0.000015094121,0.000031835763,0.000008340357,0.000014530299,0.000019443687,0.00011550923],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99863875,0.000024404371,0.000352238,0.0004623522,0.00022299588,0.00029926022],"domain_scores_gemma":[0.9993802,0.000068255256,0.00009869797,0.0003458001,0.00004870138,0.0000583476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053785,0.00013753145,0.00016172105,0.0001771706,0.00006303872,0.000058107205,0.00009055862,0.00006831981,0.0000108623535],"category_scores_gemma":[0.00011697177,0.000141049,0.00003876144,0.00042892454,0.000016539094,0.00015652284,0.000025553663,0.00024482972,0.000007928473],"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":[0.000005123714,0.000012390641,0.0028884734,0.000040615778,0.0000031410789,0.000051143328,0.000109747125,0.008755214,0.98707163,0.0000013579833,0.0000070169076,0.0010541407],"study_design_scores_gemma":[0.00016543348,0.000007715809,0.004770021,0.000086368826,0.0000036764359,0.000016655984,0.000054179378,0.05744367,0.93710554,0.00013198485,0.000038536607,0.00017621831],"about_ca_topic_score_codex":0.000007757061,"about_ca_topic_score_gemma":0.000056501834,"teacher_disagreement_score":0.049966097,"about_ca_system_score_codex":0.00009493929,"about_ca_system_score_gemma":0.00005626571,"threshold_uncertainty_score":0.5751811},"labels":[],"label_agreement":null},{"id":"W2912834930","doi":"10.1109/iedm.2018.8614616","title":"Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":19,"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; U.S. Department of Defense","keywords":"Neuromorphic engineering; Computer science; Inference; Exploit; Scalability; Probabilistic logic; Stochastic computing; Unconventional computing; Noise (video); Artificial intelligence; Computer engineering; Theoretical computer science; Distributed computing; Artificial neural network; Image (mathematics)","score_opus":0.008956093663142603,"score_gpt":0.2178505504150373,"score_spread":0.2088944567518947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912834930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6370469,0.00016866613,0.35516834,0.000016104967,0.0002166036,0.00005549801,4.2187105e-7,0.00042785946,0.0068996586],"genre_scores_gemma":[0.9969985,0.0000117740865,0.00074693863,0.000033621298,0.00006899644,0.0000028211157,0.0000010371848,0.000010772848,0.0021255224],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996388,0.0000093558065,0.00007288719,0.00009740686,0.00003615592,0.00014540592],"domain_scores_gemma":[0.9997931,0.000078513855,0.0000072107246,0.000055723016,0.000016863445,0.000048570953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034716213,0.00007362838,0.000063193154,0.00002881479,0.00014144437,0.00001755948,0.000032643584,0.00002291908,0.00015467334],"category_scores_gemma":[0.00005780742,0.00006951144,0.0000081134,0.00008966171,0.00003507719,0.00008201842,0.000023357488,0.00014938752,0.000048124915],"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":[0.000021985099,0.000026259475,0.00037344667,0.00010115853,0.00002730392,0.0000055390087,0.0011639341,0.24603733,0.6201022,0.0010695478,0.0035074558,0.12756383],"study_design_scores_gemma":[0.0008289798,0.0006412578,0.001656107,0.00007954251,0.000032517783,0.000044590546,0.0004361629,0.94771737,0.032462284,0.0011448154,0.014261278,0.00069508783],"about_ca_topic_score_codex":0.0000034528919,"about_ca_topic_score_gemma":0.000005197088,"teacher_disagreement_score":0.70168006,"about_ca_system_score_codex":0.0000073604,"about_ca_system_score_gemma":0.000002373395,"threshold_uncertainty_score":0.28345942},"labels":[],"label_agreement":null},{"id":"W2913881722","doi":"10.1098/rsta.2019.0162","title":"A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing","year":2019,"lang":"en","type":"article","venue":"Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Operand; Cognitive computing; Cognition; Representation (politics); Knowledge representation and reasoning; Artificial intelligence; Cognitive architecture; Multiplication (music); Microprocessor; Associative property; Theoretical computer science; Computer hardware; Mathematics","score_opus":0.01870849569722435,"score_gpt":0.2500882900161636,"score_spread":0.23137979431893926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913881722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6951771,0.00003590385,0.303964,0.00014894635,0.00014050218,0.00023546092,0.000010920493,0.00013360413,0.00015360108],"genre_scores_gemma":[0.9964646,0.0000015819871,0.0034023519,0.000012900236,0.00008490035,0.000013708294,6.0652906e-7,0.000012952701,0.0000064103383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913305,0.000014144404,0.00020285096,0.00021851355,0.00021589546,0.00021553047],"domain_scores_gemma":[0.999004,0.00076974736,0.00003430281,0.00009405031,0.000034789817,0.00006307388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015036605,0.000144709,0.00026270607,0.000020987329,0.00021135152,0.000019572386,0.00014485687,0.000053683623,0.0000028509096],"category_scores_gemma":[0.000032223204,0.00010191778,0.00028257526,0.00029119314,0.0002101814,0.00009496414,0.000021642327,0.00021735753,0.0000021284127],"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":[0.000013062804,0.000082038816,0.000023034434,0.0011000015,0.00009276106,2.620371e-7,0.0004327196,0.9609989,0.015909199,0.020329334,0.000004046834,0.0010146595],"study_design_scores_gemma":[0.00024756574,0.000089452245,0.0001744971,0.00028682774,0.000049968265,0.000007007938,0.00016748521,0.97952783,0.008510755,0.010800404,0.0000026360265,0.00013555985],"about_ca_topic_score_codex":5.662072e-7,"about_ca_topic_score_gemma":1.6338738e-8,"teacher_disagreement_score":0.30128753,"about_ca_system_score_codex":0.000015276864,"about_ca_system_score_gemma":0.000006307614,"threshold_uncertainty_score":0.41560864},"labels":[],"label_agreement":null},{"id":"W2917617771","doi":"10.1504/ijcvr.2019.10019409","title":"Bio-inspired visual attention process using spiking neural networks controlling a camera","year":2019,"lang":"en","type":"article","venue":"International Journal of Computational Vision and Robotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Computer science; Stimulus (psychology); Artificial intelligence; Spiking neural network; Artificial neural network; Implementation; Computer vision","score_opus":0.0122549793155862,"score_gpt":0.3042663193083429,"score_spread":0.2920113399927567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917617771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64174825,0.000180253,0.35660827,0.00008200641,0.0012883277,0.00005230191,0.0000010772054,0.00002327799,0.000016241287],"genre_scores_gemma":[0.993568,0.000031772943,0.005809143,0.00012972632,0.0004302819,2.003518e-7,0.0000074931845,0.00001907857,0.0000043214072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988623,0.000026627391,0.00048121586,0.00009533457,0.00039823996,0.00013626232],"domain_scores_gemma":[0.9990536,0.00018847368,0.0002370937,0.000031705655,0.0004158421,0.000073263895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015347783,0.00013757496,0.0002097456,0.00018605932,0.0000676811,0.0000941505,0.00013718898,0.000051627165,0.000009388262],"category_scores_gemma":[0.000026357182,0.00012206382,0.0000892322,0.0001028552,0.00002500036,0.00039749584,0.00003257294,0.00024165823,0.0000034541472],"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":[0.00006718745,0.000022680317,0.0023715284,0.00001860965,0.00006405547,0.000021863956,0.000064674685,0.988626,0.0022747954,0.00011428081,0.00000474067,0.0063495766],"study_design_scores_gemma":[0.0009987298,0.00009403221,0.003153938,0.00021364671,0.00001894874,0.00030059228,0.000098181874,0.99434346,0.000109932254,0.00052390073,0.000016454327,0.00012815675],"about_ca_topic_score_codex":3.34252e-7,"about_ca_topic_score_gemma":2.243149e-7,"teacher_disagreement_score":0.35181975,"about_ca_system_score_codex":0.000055014345,"about_ca_system_score_gemma":0.000023377388,"threshold_uncertainty_score":0.49776182},"labels":[],"label_agreement":null},{"id":"W2920277430","doi":"10.1021/acsaelm.8b00070","title":"Existence of Resistive Switching Memory and Negative Differential Resistance State in Self-Colored MoS<sub>2</sub>/ZnO Heterojunction Devices","year":2019,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","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":"China Postdoctoral Science Foundation; Institute of Plasma Physics, Chinese Academy of Sciences; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Materials science; Heterojunction; Optoelectronics; Resistive random-access memory; Colored; Non-volatile memory; Layer (electronics); Resistive touchscreen; Sputtering; Switching time; Nanotechnology; Electrical engineering; Thin film; Voltage; Composite material; Engineering","score_opus":0.003661872728710003,"score_gpt":0.18548386979080567,"score_spread":0.18182199706209567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920277430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99834174,0.0001421779,0.00047784776,0.0000052361943,0.00016556961,0.0005541764,0.0000076721435,0.00015436378,0.0001512],"genre_scores_gemma":[0.99955523,0.00017277095,0.00010106963,0.000016988575,0.00004620637,0.000046858917,0.000007887768,0.000037680922,0.000015295527],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99852675,0.00006064828,0.00044097568,0.00035349317,0.00014505736,0.000473067],"domain_scores_gemma":[0.9994236,0.00014400228,0.0001779309,0.00019139206,0.000025254709,0.000037813727],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022307118,0.0002496435,0.0004408455,0.00009382667,0.00005827683,0.0000345623,0.0001294523,0.00007993587,0.00000925997],"category_scores_gemma":[0.000013128257,0.00025200678,0.000020916787,0.00016247253,0.000025782267,0.00017040924,0.00006314801,0.00020119087,0.0000066920497],"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":[0.0003658551,0.0000135514865,0.000019818252,0.00034418557,0.00003728244,0.0000016496414,0.00057498505,0.0012895463,0.9961385,0.00063417736,0.0000029048338,0.0005775527],"study_design_scores_gemma":[0.0006828243,0.000062024876,0.0019694092,0.00010208406,0.000016652546,0.0000019532133,0.000080594466,0.000058566628,0.99250996,0.0042644087,0.000010065859,0.00024147272],"about_ca_topic_score_codex":0.000008530183,"about_ca_topic_score_gemma":0.0000970053,"teacher_disagreement_score":0.0036302314,"about_ca_system_score_codex":0.00015698349,"about_ca_system_score_gemma":0.000026465481,"threshold_uncertainty_score":0.9999932},"labels":[],"label_agreement":null},{"id":"W2929420007","doi":"10.1155/2019/8361369","title":"Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots","year":2019,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Spiking neural network; Robot; Computer science; Artificial neural network; Artificial intelligence; Process (computing); Spatial learning; Operant conditioning; Cognition; Machine learning; Neuroscience; Psychology","score_opus":0.0249665799804781,"score_gpt":0.3013826906422661,"score_spread":0.276416110661788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929420007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87246275,0.00007280116,0.12696368,0.000023799703,0.00027468888,0.00011761805,7.182385e-7,0.00005444628,0.000029516857],"genre_scores_gemma":[0.9995562,0.00002004128,0.00018202877,0.00013869915,0.00007863258,0.00000287882,0.000003078676,0.00000858881,0.000009876987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991842,0.000031354717,0.00016001004,0.00026746962,0.00013602141,0.00022093525],"domain_scores_gemma":[0.99966383,0.0001910081,0.0000329038,0.000042119613,0.000016323947,0.000053787437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000069931244,0.00011302822,0.00011751243,0.00004966524,0.00010045767,0.000051897336,0.000079094294,0.00001821935,0.0000054557995],"category_scores_gemma":[0.000018281318,0.00011693018,0.000015782169,0.00022194545,0.00008581575,0.00028125904,0.00006434897,0.00020006963,0.0000034110215],"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":[0.000004563429,0.0000046902996,0.0043965075,0.000006118014,4.619478e-7,0.0000042573884,0.00038493043,0.9484953,0.00329083,0.00081969466,0.0000019338925,0.04259066],"study_design_scores_gemma":[0.0000900711,0.00015280198,0.019420402,0.000017732275,0.0000014031283,0.00001943759,0.00015135811,0.975023,0.0034829548,0.0014841104,0.00002974291,0.00012698532],"about_ca_topic_score_codex":0.0000044688904,"about_ca_topic_score_gemma":0.0000035122434,"teacher_disagreement_score":0.12709343,"about_ca_system_score_codex":0.000012639845,"about_ca_system_score_gemma":0.0000073857123,"threshold_uncertainty_score":0.47682744},"labels":[],"label_agreement":null},{"id":"W2931400272","doi":"10.18122/td.1782.boisestate","title":"Deep Convolutional Spiking Neural Networks for Image Classification","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":26,"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":"Ministry of Education, India; International Institute of Information Technology, Hyderabad; Canadian Institute for Advanced Research","keywords":"MNIST database; Artificial intelligence; Spiking neural network; Computer science; Stochastic gradient descent; Artificial neural network; Backpropagation; Forgetting; Pattern recognition (psychology); Convolutional neural network; Feature (linguistics); Gradient descent; Deep learning; Machine learning","score_opus":0.03491398023910249,"score_gpt":0.2699561131042656,"score_spread":0.2350421328651631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931400272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036922943,0.0007451821,0.958541,0.000052830623,0.0019133756,0.00030612593,0.0000039331253,0.0005034519,0.0010112064],"genre_scores_gemma":[0.96607584,0.0000435491,0.032471918,0.00008199432,0.00080264785,0.00007098336,0.000331576,0.000048522048,0.00007297815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990068,0.000017633376,0.00028057824,0.0003301122,0.0000893341,0.0002755353],"domain_scores_gemma":[0.9994115,0.00013526689,0.000060489296,0.00024003124,0.00009521832,0.000057458303],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008732618,0.00022329515,0.00022710509,0.0000426407,0.00009673293,0.00008906628,0.00015526659,0.00019046535,0.000043559667],"category_scores_gemma":[0.000034132747,0.00024607664,0.00015350567,0.00006468254,0.000023607507,0.00011645618,0.00015436903,0.00052281393,0.0000018391227],"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":[0.000004696291,0.000005298023,0.000017664386,0.00015535615,0.000021698921,0.0000043745736,0.000022776165,0.9864033,0.0046593575,0.00047579993,0.00012991246,0.008099799],"study_design_scores_gemma":[0.00013156624,0.000005971233,0.00044068167,0.000049593888,0.00001944916,0.0000100954585,0.00005319951,0.9966147,0.0018468372,0.00038155023,0.00018357318,0.00026278663],"about_ca_topic_score_codex":9.274249e-7,"about_ca_topic_score_gemma":0.0000055701607,"teacher_disagreement_score":0.9291529,"about_ca_system_score_codex":0.000088077206,"about_ca_system_score_gemma":0.000014225945,"threshold_uncertainty_score":0.99999917},"labels":[],"label_agreement":null},{"id":"W2941131546","doi":"10.1063/1.5079390","title":"Low-temperature coexistence of memory and threshold switchings in Pt/TiO<i>x</i>/Pt crossbar arrays","year":2019,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"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 Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Crossbar switch; Work (physics); Materials science; Optoelectronics; Condensed matter physics; Physics; Thermodynamics; Computer science; Telecommunications","score_opus":0.006717522272879231,"score_gpt":0.19940663467394498,"score_spread":0.19268911240106576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941131546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99787617,0.000053765558,0.0005152085,0.00004342117,0.0001587568,0.00022757561,0.0000035933203,0.00008978454,0.0010317299],"genre_scores_gemma":[0.9987035,0.000008568598,0.00035489208,0.00076508126,0.000106623476,0.000008782182,0.0000063270054,0.000035334764,0.000010937834],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915653,0.0000059648924,0.00020009276,0.00025101987,0.00012966024,0.00025675102],"domain_scores_gemma":[0.99959946,0.00006184404,0.000049233975,0.00023763921,0.000009576086,0.00004225509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067180554,0.00019818245,0.00026861965,0.000033927747,0.00003990596,0.000021859018,0.00014113766,0.000056386536,0.0000034970458],"category_scores_gemma":[0.000001616735,0.00020506246,0.00004011099,0.0001834188,0.000053949894,0.00015143474,0.00004679609,0.00038391756,0.000011858583],"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":[0.000012916721,0.000009134723,0.00017042377,0.00016311847,0.000008191821,0.0000027609005,0.0006123765,0.1965767,0.8015256,0.00045185833,0.00005898722,0.00040794245],"study_design_scores_gemma":[0.0006389337,0.000013112401,0.0007289418,0.00011849731,0.0000067760348,0.000002256265,0.00011409631,0.0017208207,0.99595064,0.00036018353,0.00004091759,0.00030485273],"about_ca_topic_score_codex":0.0000019220927,"about_ca_topic_score_gemma":7.646432e-7,"teacher_disagreement_score":0.19485588,"about_ca_system_score_codex":0.00002321645,"about_ca_system_score_gemma":0.000004778097,"threshold_uncertainty_score":0.83622044},"labels":[],"label_agreement":null},{"id":"W2942340749","doi":"10.1162/neco_a_01338","title":"Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks","year":2020,"lang":"en","type":"article","venue":"Neural Computation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Nonlinear system; Spiking neural network; Spike (software development); Superposition principle; Postsynaptic potential; Models of neural computation; Function (biology); Simple (philosophy)","score_opus":0.021269089187288367,"score_gpt":0.26899684134211727,"score_spread":0.2477277521548289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942340749","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84521425,0.0000825027,0.15251401,0.00085502875,0.0003894436,0.0002147931,0.0000030963834,0.0004585171,0.00026834436],"genre_scores_gemma":[0.99604785,0.0000019659892,0.0022065677,0.0012045264,0.00040305356,0.000008484608,0.00008338205,0.000039122508,0.000005040902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988141,0.00007333024,0.00037359333,0.00027995597,0.00017055184,0.00028844763],"domain_scores_gemma":[0.9993893,0.00031171148,0.000065480046,0.00006343289,0.000046545443,0.00012355731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003868992,0.0002010011,0.00020329993,0.000102540274,0.00011987994,0.00006317741,0.00011698704,0.0000505565,0.00001451307],"category_scores_gemma":[0.0000621146,0.00023181073,0.000068789115,0.00046213847,0.000026584401,0.00033952313,0.00005118836,0.00056080247,0.000021303878],"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":[0.000028594037,0.000010596649,0.00014536528,0.00002575342,0.000006486711,0.000072511895,0.00034304315,0.97654575,0.0006220066,0.000041858515,0.000100653255,0.022057408],"study_design_scores_gemma":[0.00045048213,0.00007618291,0.0012703295,0.000036636404,0.000007991211,0.00006436698,0.00011221179,0.99694467,0.00034282514,0.00026808766,0.0002150213,0.00021118709],"about_ca_topic_score_codex":0.0000036960316,"about_ca_topic_score_gemma":0.0000073391234,"teacher_disagreement_score":0.15083359,"about_ca_system_score_codex":0.00006805738,"about_ca_system_score_gemma":0.000008306837,"threshold_uncertainty_score":0.94529676},"labels":[],"label_agreement":null},{"id":"W2943151775","doi":"10.1109/iscas.2019.8702555","title":"Logic Circuit and Memory Design for In-Memory Computing Applications using Bipolar RRAMs","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Crossbar switch; Computer science; Resistive random-access memory; XNOR gate; Electronic circuit; Bottleneck; Logic gate; Von Neumann architecture; Sense amplifier; Adder; Semiconductor memory; Parallel computing; Electronic engineering; Computer hardware; CMOS; NAND gate; Embedded system; Electrical engineering; Voltage; Engineering; Algorithm","score_opus":0.05401072240238078,"score_gpt":0.26690201663209295,"score_spread":0.21289129422971217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943151775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31990716,0.0006110881,0.67792434,0.0000056838658,0.0001022156,0.00065270567,8.6780557e-7,0.00018805917,0.0006078928],"genre_scores_gemma":[0.9608428,0.0000138253,0.0388447,0.00009170209,0.00008676878,0.00001371326,0.0000022829615,0.000030817693,0.000073409574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992327,0.000017622915,0.00020357198,0.00022355194,0.000054081476,0.0002684683],"domain_scores_gemma":[0.99946696,0.0002798707,0.000029937833,0.00015473549,0.00001876221,0.000049756036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019929884,0.00013599462,0.00017884141,0.00008492473,0.000084891355,0.000021252941,0.000091934344,0.000060117527,0.000011407467],"category_scores_gemma":[0.000011574317,0.00013971167,0.000030533713,0.00016180897,0.000018514076,0.00012491821,0.000035214463,0.00013707332,0.0000112725265],"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":[0.0000045531924,0.000010682457,0.00027905434,0.00018427365,0.000008959954,0.0000014975576,0.00014426625,0.7946891,0.15986052,0.0008167319,0.000007886037,0.043992452],"study_design_scores_gemma":[0.00065649976,0.00004182149,0.00031701985,0.00006341874,0.0000122396195,0.000031672666,0.0002738282,0.9478478,0.046771783,0.0031208897,0.0004907809,0.00037227277],"about_ca_topic_score_codex":0.0000026926039,"about_ca_topic_score_gemma":0.0000011079334,"teacher_disagreement_score":0.6409356,"about_ca_system_score_codex":0.000045702833,"about_ca_system_score_gemma":0.000009062958,"threshold_uncertainty_score":0.56972766},"labels":[],"label_agreement":null},{"id":"W2943596920","doi":"10.1002/aelm.201900142","title":"Ultrathin TiO<i><sub>x</sub></i> Interface‐Mediated ZnO‐Nanowire Memristive Devices Emulating Synaptic Behaviors","year":2019,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Materials science; Nanowire; Neuromorphic engineering; Nanotechnology; Quantum tunnelling; Electrode; Optoelectronics; Semiconductor; Computer science; Artificial neural network","score_opus":0.004085034461124994,"score_gpt":0.22063199629590105,"score_spread":0.21654696183477606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943596920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99557537,0.00086189446,0.00092185824,0.000014527838,0.0009596465,0.00059913,0.000026645665,0.0008495872,0.00019134727],"genre_scores_gemma":[0.999171,0.00016980071,0.00017548839,0.00007217527,0.00011284364,0.000055028504,0.00007888383,0.00011969468,0.000045060093],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975201,0.00008687176,0.0006277123,0.00051518355,0.00020855843,0.0010416105],"domain_scores_gemma":[0.9990544,0.00021917273,0.00019120531,0.00036239868,0.00006399093,0.00010888669],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023768711,0.00046456995,0.00057154597,0.00010011193,0.00013483955,0.00006681847,0.0002842655,0.00015635283,0.00013004446],"category_scores_gemma":[0.00006375865,0.00047784907,0.00007239206,0.0002598128,0.000038550526,0.000548662,0.00006669685,0.00038747798,0.0003972983],"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":[0.00005645518,0.00001543218,0.000036941296,0.00014040891,0.00005328675,0.0000082682855,0.00013220172,0.05304533,0.9445583,0.00014963395,0.000009615132,0.0017940947],"study_design_scores_gemma":[0.0006054375,0.00019344376,0.00016560756,0.00018366714,0.00003840838,0.000028703394,0.00009069802,0.0008987308,0.99659663,0.00031336432,0.000337114,0.000548187],"about_ca_topic_score_codex":0.0000034233924,"about_ca_topic_score_gemma":0.000017682662,"teacher_disagreement_score":0.052146595,"about_ca_system_score_codex":0.0002738994,"about_ca_system_score_gemma":0.00004349144,"threshold_uncertainty_score":0.9997673},"labels":[],"label_agreement":null},{"id":"W2943669549","doi":"10.1109/iscas.2019.8702206","title":"An MRAM-Based Deep In-Memory Architecture for Deep Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":49,"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":"Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research","keywords":"Magnetoresistive random-access memory; Computer science; CMOS; Artificial neural network; Deep learning; MNIST database; Computer architecture; Artificial intelligence; Electronic engineering; Computer hardware; Engineering; Random access memory","score_opus":0.007178916481206205,"score_gpt":0.22537267591109914,"score_spread":0.21819375942989294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943669549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5024819,0.00009398411,0.49597514,0.000021405569,0.00034270025,0.00027056292,6.164831e-7,0.00027863582,0.0005350935],"genre_scores_gemma":[0.992636,8.509558e-7,0.0066789873,0.0004120294,0.00016889744,0.00001458996,0.000015411628,0.000040280047,0.000032912474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920714,0.000017610424,0.00015974844,0.00020475642,0.000058091155,0.00035264227],"domain_scores_gemma":[0.9995131,0.00015685716,0.000016865883,0.00022669884,0.000012578541,0.00007390351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000071697075,0.0001583101,0.00016451199,0.00006677758,0.00003493206,0.000018484576,0.00014904651,0.00007508506,0.00004891143],"category_scores_gemma":[0.00000854367,0.00014572046,0.000058672365,0.00012666467,0.000010628265,0.000107885404,0.000012533274,0.00024919712,0.000006591181],"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":[0.000028143908,0.000008611024,0.00030611822,0.000040275485,0.0000025777574,0.0000035969972,0.000042695254,0.9403328,0.0053040967,0.000033834236,0.0000057392836,0.05389157],"study_design_scores_gemma":[0.0005666633,0.00008734594,0.00037258246,0.000010020851,0.0000030100991,0.000003904026,0.00003323486,0.9923986,0.005996293,0.00017076363,0.00015830147,0.00019924717],"about_ca_topic_score_codex":0.0000013680727,"about_ca_topic_score_gemma":0.00006248986,"teacher_disagreement_score":0.49015418,"about_ca_system_score_codex":0.00002344195,"about_ca_system_score_gemma":0.000002950815,"threshold_uncertainty_score":0.5942308},"labels":[],"label_agreement":null},{"id":"W2945239841","doi":"10.1145/3299874.3317993","title":"Functionally Complete Boolean Logic and Adder Design Based on 2T2R RRAMs for Post-CMOS In-Memory Computing","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Resistive random-access memory; Logic family; Pass transistor logic; Logic block; Logic gate; In-Memory Processing; Logic synthesis; CMOS; Adder; Logic optimization; Bottleneck; NAND gate; Von Neumann architecture; Computer architecture; Non-volatile memory; Parallel computing; Electronic engineering; Computer hardware; Electronic circuit; Embedded system; Digital electronics; Electrical engineering; Algorithm; Voltage; Field-programmable gate array; Engineering","score_opus":0.03066337012340058,"score_gpt":0.2296381743328542,"score_spread":0.1989748042094536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945239841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38727227,0.00006211758,0.60686994,0.000116610325,0.00042620406,0.00077180995,0.0000062448316,0.0003507822,0.004124054],"genre_scores_gemma":[0.97922254,0.0000010441247,0.019063005,0.0013781249,0.0000787668,0.0000064098326,0.000014634087,0.000031723463,0.00020377616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991465,0.00002748787,0.00019990349,0.00025145165,0.00009509011,0.00027952975],"domain_scores_gemma":[0.9990776,0.0006669445,0.000027981296,0.00013755234,0.00003431388,0.000055569668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018647611,0.00017434629,0.000192478,0.00008710872,0.00007072959,0.00002228412,0.0000776177,0.0000553768,0.000072414674],"category_scores_gemma":[0.000031656535,0.00016156647,0.000043650263,0.000089617155,0.000015964275,0.00008459666,0.000022838827,0.00017125007,0.000034923964],"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":[0.00008960334,0.000015717142,0.00016203271,0.00008432494,0.000007271819,0.0000037871966,0.00003484903,0.9606535,0.030892225,0.00040939948,0.000115376744,0.007531936],"study_design_scores_gemma":[0.0011450658,0.00030376646,0.0027625703,0.00007393387,0.0000047800745,0.0000063488305,0.00008149581,0.9898936,0.004865378,0.00032367132,0.00028438633,0.00025499373],"about_ca_topic_score_codex":0.0000018397901,"about_ca_topic_score_gemma":0.0000033639883,"teacher_disagreement_score":0.59195024,"about_ca_system_score_codex":0.000035214,"about_ca_system_score_gemma":0.000010982479,"threshold_uncertainty_score":0.65884894},"labels":[],"label_agreement":null},{"id":"W2945740268","doi":"10.1002/aelm.201900098","title":"Directed Assembly of Nanoparticle Threshold‐Selector Arrays","year":2019,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Glycomics Centre; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Materials science; Nanoclusters; Electrical conductor; Nanotechnology; Nanoparticle; Resistive random-access memory; Surface modification; Silver nanoparticle; Silicon; Electrode; Optoelectronics; Chemical engineering","score_opus":0.00569454775984327,"score_gpt":0.21978630885171327,"score_spread":0.21409176109187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945740268","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971524,0.00044014218,0.00046919248,0.000010917701,0.00046130925,0.0002975322,0.000006404043,0.0005964828,0.0005656558],"genre_scores_gemma":[0.9992966,0.00008919749,0.00030017635,0.000023921797,0.000056567642,0.000018665574,0.000009709572,0.00005093072,0.000154224],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986363,0.000027863336,0.0003543032,0.00023080285,0.00012948916,0.0006212224],"domain_scores_gemma":[0.99947494,0.00006134367,0.00008113433,0.00029215775,0.00003987656,0.00005054403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001278162,0.00019947243,0.00036803194,0.00004971529,0.000035643614,0.000014215531,0.00016986516,0.000057686084,0.00027287813],"category_scores_gemma":[0.00002179735,0.00019688556,0.000045287932,0.00022009127,0.00001714706,0.00023865135,0.000027659633,0.00011759701,0.000084969484],"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":[0.0000546373,0.000013156403,0.000032876986,0.00008885209,0.00002244892,0.0000011490689,0.000031540203,0.023602828,0.9743524,0.0010128965,0.000020531856,0.0007666738],"study_design_scores_gemma":[0.00048228653,0.00015227389,0.00014281242,0.000040899577,0.000009403461,0.0000066749767,0.000008210099,0.0007875596,0.9964495,0.000859575,0.00084108877,0.00021972772],"about_ca_topic_score_codex":0.0000015541946,"about_ca_topic_score_gemma":0.0000032530013,"teacher_disagreement_score":0.022815268,"about_ca_system_score_codex":0.00007784766,"about_ca_system_score_gemma":0.000025020456,"threshold_uncertainty_score":0.80287606},"labels":[],"label_agreement":null},{"id":"W2945759267","doi":"10.1016/j.mtchem.2019.04.008","title":"A sustainable biomemristive memory device based on natural collagen","year":2019,"lang":"en","type":"article","venue":"Materials Today Chemistry","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Bioelectronics; Materials science; Electrode; Biocompatible material; Nanotechnology; Layer (electronics); Mechanism (biology); Electronics; Solid-state; Work (physics); Optoelectronics; Chemistry; Engineering physics; Biosensor; Electrical engineering; Biomedical engineering; Mechanical engineering; Engineering","score_opus":0.004430024023366163,"score_gpt":0.20428664299180227,"score_spread":0.1998566189684361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945759267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9880897,0.00007055552,0.000036245026,0.000024888783,0.0005193366,0.0002004069,0.000017587503,0.00034549003,0.010695793],"genre_scores_gemma":[0.99389595,0.0000018977922,0.00015141169,0.00011806471,0.00018778097,0.000014640071,0.000044987002,0.000041780673,0.005543462],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990423,0.000016259572,0.0001956509,0.00024525938,0.00012705519,0.00037352368],"domain_scores_gemma":[0.9995001,0.000073569,0.000045302968,0.00027429656,0.000044330045,0.00006239607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012699093,0.0002140462,0.00023705605,0.000027154101,0.000062672676,0.00005160084,0.00016416925,0.00009321765,0.00081853877],"category_scores_gemma":[0.000038020244,0.0002100975,0.000038864822,0.00013513511,0.00001875189,0.000084552616,0.000046801226,0.00008861461,0.00014175985],"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":[0.000056035668,0.000010048531,0.0000062130707,0.000659397,0.000008849475,0.00004567022,0.000033494176,0.013301525,0.98546225,0.000004962582,0.00033651508,0.000075064585],"study_design_scores_gemma":[0.00039554542,0.000016036294,0.000039309078,0.000069851725,0.0000066702346,0.0000061258206,0.00015044706,0.0020692416,0.99493575,0.000026604846,0.0020261111,0.00025833494],"about_ca_topic_score_codex":0.0000034528396,"about_ca_topic_score_gemma":1.02899264e-7,"teacher_disagreement_score":0.011232284,"about_ca_system_score_codex":0.00012487259,"about_ca_system_score_gemma":0.000023751736,"threshold_uncertainty_score":0.89624256},"labels":[],"label_agreement":null},{"id":"W2946271436","doi":"10.1145/3299874.3319458","title":"XNOR-SRAM","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"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":"XNOR gate; MNIST database; Static random-access memory; Computer science; Application-specific integrated circuit; Ternary operation; Convolutional neural network; Binary number; Energy (signal processing); Artificial neural network; Macro; Logic gate; Artificial intelligence; Algorithm; Computer hardware; Arithmetic; Mathematics","score_opus":0.003349846839856219,"score_gpt":0.1705511701240009,"score_spread":0.16720132328414467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946271436","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87650174,0.000031534964,0.0046985936,0.000008444974,0.00027161482,0.000033292778,1.0499434e-7,0.00036883412,0.11808587],"genre_scores_gemma":[0.9966406,0.0000017895512,0.0007381236,0.00005991204,0.00003420698,4.0526047e-7,3.5402002e-7,0.000006675076,0.0025179354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982053,0.0000012843926,0.00003634643,0.000041689782,0.00002308085,0.00007704678],"domain_scores_gemma":[0.9998992,0.000013794636,0.0000021553433,0.00006598381,0.0000027127544,0.00001613067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000012067305,0.000033277785,0.0000357888,0.000009062417,0.0000079124775,0.000003645016,0.000032130974,0.000011118057,0.00028297844],"category_scores_gemma":[0.0000013860963,0.000029204906,0.000011965201,0.000032965974,0.0000016823695,0.00004817544,0.0000082644965,0.00004435924,0.0006958693],"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":[0.0000047243234,0.000008011978,0.0021261298,0.000090531365,0.000015326606,0.000008878531,0.000104973195,0.6227721,0.3020884,0.007585657,0.002561439,0.06263385],"study_design_scores_gemma":[0.00053448393,0.00006471276,0.0028209111,0.000030084022,0.000004233292,0.000023140701,0.00009178615,0.24354826,0.6214374,0.0017027264,0.12923606,0.00050622225],"about_ca_topic_score_codex":1.7832684e-7,"about_ca_topic_score_gemma":2.2135593e-7,"teacher_disagreement_score":0.37922382,"about_ca_system_score_codex":0.000004766482,"about_ca_system_score_gemma":6.356925e-7,"threshold_uncertainty_score":0.8944225},"labels":[],"label_agreement":null},{"id":"W2948073202","doi":"10.3758/s13423-019-01615-8","title":"Offloading memory: Serial position effects","year":2019,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Motivated forgetting; Forgetting; Recall; Psychology; Serial position effect; Cognition; Cognitive psychology; Free recall; Neuroscience","score_opus":0.005545754950533252,"score_gpt":0.22089495052872954,"score_spread":0.2153491955781963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948073202","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7602151,0.18153389,0.0021761772,0.0019165988,0.011307664,0.002932042,0.0000059896106,0.0013545564,0.038557954],"genre_scores_gemma":[0.9316952,0.05500778,0.003964441,0.0058667706,0.0016269723,0.0001264151,0.000037271446,0.00020389906,0.0014712512],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99910855,0.000049072903,0.00027603313,0.0002485495,0.00006501051,0.00025281028],"domain_scores_gemma":[0.999483,0.00008763937,0.00005655011,0.0002919727,0.000011447109,0.000069374175],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019119268,0.00019253673,0.0003538404,0.000030435967,0.00004206465,0.000017233464,0.00015269467,0.00004760961,0.0007736518],"category_scores_gemma":[0.000016506201,0.00019312985,0.00011335914,0.000086636246,0.00000925274,0.000054965807,0.00002440198,0.00019339536,0.004266434],"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":[0.00010809454,0.00008434768,0.00011822247,0.041455336,0.0002119243,0.000038259564,0.00014475227,0.016166614,0.2237179,0.00067335996,0.1850893,0.5321919],"study_design_scores_gemma":[0.001347305,0.00013429094,0.00026229257,0.013002999,0.00010598738,0.00010861712,0.000008045887,0.0010864486,0.021152798,0.00015588333,0.961731,0.00090429786],"about_ca_topic_score_codex":7.682238e-7,"about_ca_topic_score_gemma":1.1559583e-7,"teacher_disagreement_score":0.7766417,"about_ca_system_score_codex":0.00005783271,"about_ca_system_score_gemma":0.000004124394,"threshold_uncertainty_score":0.99650884},"labels":[],"label_agreement":null},{"id":"W2949441595","doi":"10.48550/arxiv.1602.05179","title":"Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Concordia University; Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Backpropagation; Computer science; Artificial neural network; Propagation of uncertainty; Computation; Algorithm; Error function; Hebbian theory; Artificial intelligence","score_opus":0.09259262326894542,"score_gpt":0.18913096511097063,"score_spread":0.09653834184202521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949441595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37243304,0.000078622965,0.625869,0.00007075773,0.00016555126,0.00014642222,0.0000149779835,0.00027603577,0.0009455961],"genre_scores_gemma":[0.9991909,0.00003896788,0.00011395201,0.000034507044,0.0003228773,0.0000012914383,0.000025195586,0.00003805798,0.00023420028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998986,0.00006951728,0.00016473314,0.0004580847,0.00006367407,0.00025800866],"domain_scores_gemma":[0.9992063,0.00013784053,0.000101741614,0.00040855535,0.000059791688,0.00008575406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001291368,0.00026307578,0.00021838542,0.000097038486,0.00012309,0.000046271063,0.00028923672,0.00015184178,0.000006354262],"category_scores_gemma":[0.000009717246,0.00022232905,0.00008252497,0.00016494821,0.000090858506,0.0002818123,0.0002878657,0.00033026235,0.0000055051173],"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":[0.000011724752,0.0000031871368,0.00016022073,0.00011538044,0.000030282548,0.00001895918,0.000027808923,0.989945,0.0015386484,0.006231797,0.000038008788,0.00187898],"study_design_scores_gemma":[0.0002746275,0.000014407334,0.000100925245,0.00020073882,0.00006117616,0.0000017061496,0.00001123239,0.9444346,0.013068082,0.04142835,0.00008417584,0.0003199699],"about_ca_topic_score_codex":0.0000044229405,"about_ca_topic_score_gemma":0.0000020744494,"teacher_disagreement_score":0.6267579,"about_ca_system_score_codex":0.00010810547,"about_ca_system_score_gemma":0.000038809925,"threshold_uncertainty_score":0.9066316},"labels":[],"label_agreement":null},{"id":"W2950211537","doi":"10.3758/s13421-019-00954-0","title":"Visual short-term memory capacity predicts the “bandwidth” of visual long-term memory encoding","year":2019,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Toronto","funders":"National Institute of Mental Health; Office of Naval Research","keywords":"Visual short-term memory; Encoding (memory); Visual memory; Iconic memory; Psychology; Short-term memory; ENCODE; Sensory memory; Long-term memory; Working memory; Cognitive psychology; Computer science; Communication; Cognition; Neuroscience","score_opus":0.024181418244200028,"score_gpt":0.26503623032176193,"score_spread":0.2408548120775619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950211537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9883378,0.00026500394,0.0025885536,0.000016325897,0.0016016879,0.00079190487,0.00002108745,0.00047022084,0.005907404],"genre_scores_gemma":[0.99873215,0.00006150373,0.000073830655,0.00010628719,0.00062608055,0.000034836645,0.000075007156,0.00008041263,0.00020987209],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997718,0.00014347857,0.0006134691,0.00046607715,0.0004921979,0.00056677865],"domain_scores_gemma":[0.998873,0.00033032848,0.00014249372,0.00037431935,0.00013924453,0.0001406197],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047875292,0.0004201436,0.00047492504,0.00017208022,0.0002308691,0.000049306745,0.00030247247,0.00018523255,0.000267151],"category_scores_gemma":[0.00007072026,0.0003704914,0.0002066486,0.0003056709,0.00015722663,0.0005639527,0.00012282033,0.0006138147,0.000115082286],"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":[0.00013045102,0.00014716556,0.0028492224,0.00082911324,0.00013276162,0.000051745348,0.0012080806,0.009479776,0.94335955,0.00001015569,0.00007830456,0.041723687],"study_design_scores_gemma":[0.0011057776,0.00029275438,0.028497577,0.00056193315,0.00017001612,0.00011071926,0.0004098792,0.011710625,0.9564286,0.00009923946,0.000011662479,0.00060122064],"about_ca_topic_score_codex":0.0000033858912,"about_ca_topic_score_gemma":0.000012225919,"teacher_disagreement_score":0.041122466,"about_ca_system_score_codex":0.000094540286,"about_ca_system_score_gemma":0.000030905423,"threshold_uncertainty_score":0.9998747},"labels":[],"label_agreement":null},{"id":"W2951680509","doi":"10.48336/tvfh-3155","title":"Glutamate dynamics determine the magnitude of Hebbian synaptic plasticity","year":2022,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Hebbian theory; Synaptic plasticity; Plasticity; Neuroscience; Dynamics (music); Psychology; Physics; Computer science; Artificial intelligence; Artificial neural network; Biology","score_opus":0.019108495880591794,"score_gpt":0.24855676870761795,"score_spread":0.22944827282702615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951680509","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9406082,0.000080819154,0.00017749364,0.000018533869,0.0128518,0.00087205874,0.00018191125,0.0003482087,0.04486098],"genre_scores_gemma":[0.961507,0.0002332383,0.000105864274,0.0000027251456,0.0023358306,0.0000011673734,0.0003553602,0.00011259537,0.03534621],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959462,0.0008850466,0.00041153387,0.0007014618,0.0012151924,0.00084055343],"domain_scores_gemma":[0.99727273,0.0010051513,0.0002707133,0.0006508619,0.0005262035,0.00027433215],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00054955896,0.00049863773,0.00071127166,0.0010638628,0.001957312,0.00007914156,0.00205769,0.00043121533,0.00013891977],"category_scores_gemma":[0.00021344596,0.00055943656,0.0003750201,0.0017947394,0.00038237902,0.0004395237,0.00060129166,0.0024229786,0.000014909224],"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":[0.05804727,0.0019041928,0.0006635914,0.01106153,0.007744761,0.0499717,0.014416452,0.32108098,0.44851136,0.04234548,0.015702538,0.028550154],"study_design_scores_gemma":[0.023202946,0.006194215,0.0027169758,0.0023665177,0.0047956416,0.00057917833,0.11568234,0.14894548,0.276841,0.0012845093,0.40829566,0.009095504],"about_ca_topic_score_codex":0.00043641485,"about_ca_topic_score_gemma":0.00093135145,"teacher_disagreement_score":0.39259315,"about_ca_system_score_codex":0.0018709839,"about_ca_system_score_gemma":0.00050204503,"threshold_uncertainty_score":0.99987847},"labels":[],"label_agreement":null},{"id":"W2951781132","doi":"10.1016/j.neulet.2017.02.064","title":"Cracking the barcode of fullerene-like cortical microcolumns","year":2017,"lang":"en","type":"article","venue":"Neuroscience Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Neuroscience; Biological neural network; Connectome; Computer science; Artificial neural network; Spiking neural network; Topology (electrical circuits); Nervous system; Nerve net; Biological system; Biology; Artificial intelligence; Mathematics; Functional connectivity; Combinatorics","score_opus":0.02586116444394798,"score_gpt":0.2596998657900507,"score_spread":0.23383870134610274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951781132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9880627,0.000008717605,0.009343449,0.0012731687,0.0009105123,0.00008786562,0.0000013278768,0.00006901307,0.0002432414],"genre_scores_gemma":[0.99728686,0.0000064316573,0.00020220614,0.0024026386,0.00006901141,0.0000031723575,8.9594955e-8,0.000010809811,0.000018773962],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928397,0.00001866795,0.00013347052,0.00016550122,0.0001456093,0.00025277867],"domain_scores_gemma":[0.9994059,0.000074948504,0.000054819204,0.00041282809,0.0000089797895,0.00004252988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008364598,0.00008785765,0.000092977985,0.000021179118,0.0005611263,0.00008116581,0.00063266745,0.00002402429,0.0000036864083],"category_scores_gemma":[0.000101299025,0.000068573296,0.000042750708,0.000059832346,0.00033527112,0.00020647333,0.00010491152,0.00030643368,0.0000043756554],"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":[0.0000017770582,0.0000029076741,0.00078684615,0.0000079279625,5.723944e-7,0.000011014803,0.000048032387,0.02359194,0.97410774,0.00002812948,0.00028060607,0.0011325019],"study_design_scores_gemma":[0.0003140306,0.00005382925,0.118376195,0.000059402493,0.000013101069,0.00006212036,0.00003456466,0.066948265,0.8049459,0.00007043694,0.008789983,0.00033216382],"about_ca_topic_score_codex":0.000002694108,"about_ca_topic_score_gemma":0.0000018449025,"teacher_disagreement_score":0.16916184,"about_ca_system_score_codex":0.000009017445,"about_ca_system_score_gemma":0.0000044876574,"threshold_uncertainty_score":0.43157867},"labels":[],"label_agreement":null},{"id":"W2953295373","doi":"10.1049/iet-cdt.2019.0115","title":"Efficient spiking neural network training and inference with reduced precision memory and computing","year":2019,"lang":"en","type":"article","venue":"IET Computers & Digital Techniques","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Floating point; Computer science; Computer hardware; MNIST database; Fixed-point arithmetic; Spiking neural network; Integer (computer science); Memory footprint; Algorithm; Artificial neural network; Parallel computing; Artificial intelligence","score_opus":0.012287004193813508,"score_gpt":0.23135087901512577,"score_spread":0.21906387482131226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953295373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90937585,0.00019173852,0.08772051,0.0000145842705,0.00018835663,0.00033023453,0.00000144822,0.0011272917,0.0010499911],"genre_scores_gemma":[0.97674096,0.000008577709,0.023011668,0.000058066424,0.00012812154,0.0000029977682,0.0000037171728,0.000038756163,0.000007149277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988505,0.000017266524,0.0002454271,0.00036350483,0.00014711119,0.00037617795],"domain_scores_gemma":[0.9993407,0.0002885607,0.00006579016,0.00017674107,0.000029306055,0.00009887619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012886491,0.00026594923,0.00030066344,0.000077042845,0.00010876083,0.00022345077,0.00013334533,0.000063413936,7.9846296e-7],"category_scores_gemma":[0.000012473699,0.00023914131,0.000031861044,0.00018109426,0.00006692207,0.00026447664,0.00019453133,0.00027304998,0.0000010315681],"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":[0.000024659634,0.000009919303,0.0007381309,0.00008697851,0.000017480248,0.000021587672,0.0007408326,0.38909966,0.004540899,0.000095887845,0.00003616051,0.6045878],"study_design_scores_gemma":[0.00037061938,0.0004442211,0.0017871716,0.0011796545,0.000010645238,0.00026607528,0.0001421427,0.98375,0.0107635865,0.00039266993,0.00025080878,0.0006424334],"about_ca_topic_score_codex":7.268136e-7,"about_ca_topic_score_gemma":2.3893236e-7,"teacher_disagreement_score":0.6039454,"about_ca_system_score_codex":0.000026221373,"about_ca_system_score_gemma":0.000007700571,"threshold_uncertainty_score":0.9751899},"labels":[],"label_agreement":null},{"id":"W2953719737","doi":"10.1016/j.jcis.2019.06.076","title":"Tunneling of photon-generated carrier in the interface barrier induced resistive switching memory behaviour","year":2019,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China","keywords":"Resistive random-access memory; Optoelectronics; Materials science; Indium tin oxide; Quantum tunnelling; Electrode; Voltage; Flash memory; Non-volatile memory; Tin; Layer (electronics); Stack (abstract data type); Schottky barrier; Nanotechnology; Electrical engineering; Chemistry; Diode; Computer science","score_opus":0.012497415634301788,"score_gpt":0.27471965643227225,"score_spread":0.26222224079797046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953719737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99741995,0.0004908937,0.0009316054,0.00002598396,0.0006456984,0.00012697632,0.0000026580783,0.000008836396,0.00034739816],"genre_scores_gemma":[0.99963325,0.000029009234,0.00021477538,0.000033044176,0.00003647804,7.214586e-7,5.628324e-8,0.000009222681,0.00004345428],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883497,0.00004889748,0.00044693408,0.00014383129,0.0003053621,0.00022000734],"domain_scores_gemma":[0.9993347,0.000115171184,0.00017826121,0.00013713186,0.00016258874,0.00007216444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011684836,0.00012757022,0.00025041826,0.00016896563,0.00008701493,0.000057985664,0.00042502387,0.00003952973,0.000011878794],"category_scores_gemma":[0.0001457401,0.00008927471,0.000045365476,0.00052057696,0.00007957403,0.0005417783,0.00006890318,0.00048456382,8.453126e-7],"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":[0.000038748625,0.00000786713,0.0005481159,0.000016833132,0.000005283778,0.0000019138417,0.0026924822,0.034443762,0.9618698,0.000006220585,0.000013380452,0.00035561307],"study_design_scores_gemma":[0.00034225642,0.00019621178,0.0017844301,0.00028276705,0.000008544163,0.000065680724,0.004232357,0.013122775,0.97978115,0.000033167355,0.000040546158,0.000110133886],"about_ca_topic_score_codex":0.00000707932,"about_ca_topic_score_gemma":0.0000049832133,"teacher_disagreement_score":0.02132099,"about_ca_system_score_codex":0.000067561785,"about_ca_system_score_gemma":0.00008039215,"threshold_uncertainty_score":0.36405173},"labels":[],"label_agreement":null},{"id":"W2954143905","doi":"10.1103/physrevresearch.1.033030","title":"Takens-inspired neuromorphic processor: A downsizing tool for random recurrent neural networks via feature extraction","year":2019,"lang":"en","type":"article","venue":"Physical Review Research","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Recurrent neural network; Artificial neural network; Set (abstract data type); Feature (linguistics); Feature extraction; Pattern recognition (psychology)","score_opus":0.0730995200511021,"score_gpt":0.3815461642864464,"score_spread":0.3084466442353443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954143905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8850938,0.07652065,0.02522646,0.0013266505,0.0015218343,0.008858921,0.000014807423,0.0007937776,0.0006430888],"genre_scores_gemma":[0.9939948,0.004597744,0.00013339703,0.00012947386,0.00071140914,0.0002654711,0.000025049438,0.000057443267,0.00008522199],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806786,0.00018356838,0.0002603814,0.00040385066,0.0004259372,0.00065842504],"domain_scores_gemma":[0.9983614,0.00094335916,0.000054080887,0.0003268502,0.00019449071,0.00011978733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066465023,0.00022608283,0.00049090973,0.00005687038,0.00016608782,0.00005180388,0.00024363685,0.000052600502,0.000014467085],"category_scores_gemma":[0.00036476346,0.00018855656,0.00020925887,0.00057091244,0.000034482713,0.00029268884,0.00006653167,0.0011426931,0.000055554545],"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":[0.0005253589,0.00020772984,0.000067186746,0.016562354,0.00006568364,0.000028500965,0.00009919763,0.093859695,0.15276986,0.00028842068,0.0052213683,0.73030466],"study_design_scores_gemma":[0.0009665887,0.00024139494,0.00013591106,0.0017386079,0.00003182838,0.000021726199,0.0000037398381,0.9731785,0.0039186194,0.00037592367,0.019068861,0.00031830036],"about_ca_topic_score_codex":5.87768e-7,"about_ca_topic_score_gemma":5.6958663e-7,"teacher_disagreement_score":0.8793188,"about_ca_system_score_codex":0.0000660279,"about_ca_system_score_gemma":0.000015189645,"threshold_uncertainty_score":0.7689113},"labels":[],"label_agreement":null},{"id":"W2954155227","doi":"10.1109/access.2019.2921003","title":"Building Logistic Spiking Neuron Models Using Analytical Approach","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Regina","funders":"","keywords":"Spiking neural network; Computer science; Biological neuron model; Artificial neural network; Integrator; Artificial intelligence; Oscillation (cell signaling); Chaotic; Biological system","score_opus":0.1383638562127704,"score_gpt":0.34438787091146694,"score_spread":0.20602401469869655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954155227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5462868,0.00004163842,0.45136285,0.0000016738239,0.00040665222,0.00007922208,5.530817e-7,0.00016677959,0.0016538069],"genre_scores_gemma":[0.9946895,0.000004083258,0.00498911,0.00005514173,0.00020183434,0.0000017629056,9.0224415e-7,0.000040347346,0.000017274502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990583,0.000017263015,0.00019288958,0.00025961862,0.00013632113,0.00033559607],"domain_scores_gemma":[0.99958163,0.00007018484,0.00003228726,0.00023085294,0.000018845823,0.000066194276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000085421074,0.00016184097,0.00020101666,0.00008630661,0.000064205735,0.00009238123,0.0003141372,0.000057551202,0.000008772755],"category_scores_gemma":[0.0000121470575,0.0001630116,0.00005326252,0.00024071209,0.000018232664,0.00064690696,0.00007417021,0.0002617283,0.000007377202],"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":[0.0000042578413,0.0000062824533,0.00017022285,0.00008618023,0.000007804831,0.000009115553,0.000015237164,0.9736837,0.023984343,0.0007951599,0.000007802752,0.0012298619],"study_design_scores_gemma":[0.00013291542,0.000008505492,0.00008584153,0.000037041635,0.000014886036,0.000020027719,0.000005563058,0.9892225,0.008784043,0.001461741,0.000028786268,0.00019812342],"about_ca_topic_score_codex":0.00000460269,"about_ca_topic_score_gemma":1.9259761e-7,"teacher_disagreement_score":0.44840273,"about_ca_system_score_codex":0.000053268614,"about_ca_system_score_gemma":0.0000070649103,"threshold_uncertainty_score":0.664742},"labels":[],"label_agreement":null},{"id":"W2954594665","doi":"10.1002/aisy.201900034","title":"CMOS Compatible Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub> Ferroelectric Tunnel Junctions for Neuromorphic Devices","year":2019,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Bayerische Forschungsallianz; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Memristor; Artificial neural network; Materials science; CMOS; Von Neumann architecture; Nucleation; Spiking neural network; Optoelectronics; Computer science; Ferroelectricity; Voltage; Electronic engineering; Artificial intelligence; Electrical engineering; Physics; Engineering","score_opus":0.025578353456723008,"score_gpt":0.2315105384768563,"score_spread":0.20593218502013327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954594665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86704177,0.0024817856,0.11702422,0.00003938594,0.0087872,0.0029807377,0.0000680777,0.0013276936,0.00024911514],"genre_scores_gemma":[0.9968474,0.0010621208,0.00023380466,0.00018075672,0.000667229,0.0004302159,0.00016571814,0.0003446239,0.00006813841],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9944938,0.00019403093,0.0016301234,0.0013416642,0.00065892306,0.0016814831],"domain_scores_gemma":[0.9966925,0.0008014556,0.00054486404,0.0011074304,0.00038098864,0.00047277365],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004472223,0.0011158959,0.0012955919,0.00059933064,0.00058186334,0.00018839704,0.00069316797,0.00033194342,0.000009145393],"category_scores_gemma":[0.00016533687,0.0011874742,0.00050399825,0.0014269389,0.00008648113,0.0009704789,0.00014927132,0.0009804738,0.00089421665],"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":[0.00008628413,0.0000813079,0.00017732667,0.0007943746,0.00011467,0.000016497735,0.00012222391,0.3413831,0.64413255,0.00019386415,0.00038175654,0.012516047],"study_design_scores_gemma":[0.00092396466,0.00048948545,0.0005054466,0.00070098136,0.000119553915,0.00014565553,0.00031326222,0.12577602,0.86463434,0.00020508216,0.0049602706,0.0012259602],"about_ca_topic_score_codex":0.000006420785,"about_ca_topic_score_gemma":0.00005481962,"teacher_disagreement_score":0.22050178,"about_ca_system_score_codex":0.0004971607,"about_ca_system_score_gemma":0.00007456985,"threshold_uncertainty_score":0.9998837},"labels":[],"label_agreement":null},{"id":"W2956926510","doi":"10.1088/1361-6528/ab3260","title":"Synaptic learning behavior of a TiO <sub>2</sub> nanowire memristor","year":2019,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Waterloo","funders":"","keywords":"Materials science; Memristor; Oxygen; Metastability; Voltage; Chemical physics; Forgetting; Nanotechnology; Electronic engineering; Electrical engineering; Physics","score_opus":0.005602220729514007,"score_gpt":0.197935471471527,"score_spread":0.19233325074201302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956926510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99683034,0.0004016111,0.0011974192,0.000022052112,0.000402876,0.0001727561,0.0000010191369,0.00082115264,0.00015077434],"genre_scores_gemma":[0.99952394,0.000042204283,0.00030269474,0.00000993379,0.000021138942,0.000015784117,0.0000021875865,0.000035117715,0.000046980193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922246,0.000015508049,0.0002177723,0.00019233034,0.00008274933,0.0002691831],"domain_scores_gemma":[0.99958926,0.000055811313,0.000059928407,0.00024115216,0.000027224418,0.000026632011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006443588,0.00013990923,0.00025372466,0.00015698391,0.000042667994,0.0000030431831,0.00017437899,0.0002659212,0.000016759212],"category_scores_gemma":[0.000048297086,0.00015037924,0.000059718986,0.00023483843,0.000055685432,0.00006780514,0.00006348275,0.00045997754,0.00013624904],"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":[0.0000055240325,0.000011101752,0.00045513755,0.00005514724,0.000018315077,0.000014136558,0.00003501199,0.008945005,0.9623146,0.00028163745,0.00000831662,0.027856067],"study_design_scores_gemma":[0.00024156364,0.00013517024,0.00011007819,0.000039521816,0.000018942805,0.000044679036,0.00004500494,0.002225387,0.9959644,0.00010394172,0.00091079663,0.00016048198],"about_ca_topic_score_codex":5.747856e-7,"about_ca_topic_score_gemma":0.0000018477742,"teacher_disagreement_score":0.033649832,"about_ca_system_score_codex":0.00005306922,"about_ca_system_score_gemma":0.000009947109,"threshold_uncertainty_score":0.6132288},"labels":[],"label_agreement":null},{"id":"W2959171759","doi":"10.1038/s41534-021-00381-7","title":"An artificial spiking quantum neuron","year":2021,"lang":"en","type":"preprint","venue":"npj Quantum Information","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; Creative Destruction Lab; Perimeter Institute; University of Toronto","funders":"Office of Naval Research; Office of Science; Army Research Office; Danmarks Frie Forskningsfond; Advanced Scientific Computing Research; Carlsbergfondet; Natur og Univers, Det Frie Forskningsråd; U.S. Department of Energy","keywords":"Neuromorphic engineering; Computer science; Spiking neural network; Quantum; Biological neuron model; Artificial neural network; Quantum computer; Artificial intelligence; Implementation; Graph; Theoretical computer science; Physics","score_opus":0.025993720979573334,"score_gpt":0.260381481664875,"score_spread":0.23438776068530168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959171759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8711839,0.00009347497,0.12356559,0.000027077285,0.003321678,0.00022754366,0.000012272451,0.00089391717,0.00067458337],"genre_scores_gemma":[0.9981747,0.00008410677,0.00053725817,0.00013300551,0.0004234173,0.00001861547,0.0005898669,0.000037079866,0.0000019359873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998436,0.000045187102,0.00069084985,0.00020525653,0.00027643808,0.0003463152],"domain_scores_gemma":[0.99908197,0.000039479244,0.00018927439,0.0004849524,0.00010294967,0.00010134912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019979457,0.0003281618,0.00030478567,0.00018900924,0.0001486143,0.00035230053,0.00026732174,0.00027827625,0.00003138535],"category_scores_gemma":[0.00005618286,0.00037714632,0.00011332942,0.00016279481,0.000019083993,0.0018344952,0.00015335873,0.0009237897,0.00006770273],"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":[0.000021871683,0.000015989579,0.000019339888,0.00053431664,0.000016204765,0.000016607806,0.0018911879,0.9423585,0.013920493,0.0022629392,0.00007926527,0.03886332],"study_design_scores_gemma":[0.0000941428,0.000043771863,0.00048575338,0.00018578449,0.000019039271,0.00002228259,0.00049327704,0.9809671,0.014106647,0.0015725619,0.0015548656,0.0004547942],"about_ca_topic_score_codex":0.000008796882,"about_ca_topic_score_gemma":0.0000060123316,"teacher_disagreement_score":0.12699085,"about_ca_system_score_codex":0.00009741222,"about_ca_system_score_gemma":0.000047490652,"threshold_uncertainty_score":0.99986804},"labels":[],"label_agreement":null},{"id":"W2963421552","doi":"","title":"Architectural Complexity Measures of Recurrent Neural Networks","year":2016,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Université de Montréal","funders":"","keywords":"Recurrent neural network; Feed forward; Computer science; Computability; Feedforward neural network; Graph; Nonlinear system; Computational complexity theory; Measure (data warehouse); Dependency (UML); Artificial intelligence; Algorithm; Theoretical computer science; Artificial neural network; Data mining; Engineering; Control engineering","score_opus":0.05000399624100138,"score_gpt":0.2526875896740306,"score_spread":0.20268359343302922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963421552","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7353548,0.00070196344,0.26067954,0.00007135877,0.0016252603,0.00032109092,0.000013643664,0.00066647324,0.0005658716],"genre_scores_gemma":[0.99964654,0.00000707616,0.00011245923,0.00003125284,0.00015610084,0.000012214533,0.000008561436,0.000013307578,0.000012486369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987259,0.000041734696,0.0006209655,0.00009345794,0.00023946923,0.00027849348],"domain_scores_gemma":[0.9993753,0.00006328915,0.00021260914,0.00014148501,0.00013412024,0.000073199386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015093751,0.00018212399,0.00023885394,0.000104297156,0.000113362235,0.000067966925,0.00016960621,0.000057357134,0.0000035698674],"category_scores_gemma":[0.000044141692,0.00012261981,0.000057535453,0.00019727212,0.000074237316,0.0012971467,0.00003038696,0.00015935379,0.000005362964],"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":[0.000025006813,0.0000036368417,0.00019172941,0.00039978724,0.0000072017483,5.575256e-7,0.0002960855,0.5630034,0.0031302248,0.00010107648,0.00008587203,0.43275538],"study_design_scores_gemma":[0.00034448731,0.000047347126,0.00068127795,0.00030119505,0.0000066095618,0.00007058434,0.00006826278,0.9934579,0.004081719,0.000028720688,0.00070570374,0.0002061531],"about_ca_topic_score_codex":0.000003306504,"about_ca_topic_score_gemma":0.0000011240901,"teacher_disagreement_score":0.4325492,"about_ca_system_score_codex":0.000046572877,"about_ca_system_score_gemma":0.000008423659,"threshold_uncertainty_score":0.500029},"labels":[],"label_agreement":null},{"id":"W2965766329","doi":"10.1109/aicas.2019.8771594","title":"Memristor Emulators for an Adaptive DPE Algorithm: Comparative Study","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Institut de Valorisation des Données","keywords":"Memristor; Computer science; Matrix multiplication; Resistive random-access memory; Process (computing); Dot product; Algorithm; Resistive touchscreen; Multiplication (music); Electronic engineering; Voltage; Engineering; Electrical engineering; Mathematics","score_opus":0.056423978746561645,"score_gpt":0.30751175283494125,"score_spread":0.2510877740883796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965766329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8940375,0.000029597897,0.10161286,0.0000015924905,0.00043405904,0.00081865024,0.0000051220773,0.00034004627,0.0027205676],"genre_scores_gemma":[0.9877203,3.8871008e-7,0.011675128,0.000017965152,0.00008964045,0.00002087423,0.0000035058888,0.000020576408,0.0004516155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940515,0.000017468526,0.00013525634,0.00018738263,0.00007445839,0.00018029826],"domain_scores_gemma":[0.99964416,0.00008524703,0.000018786235,0.00015431411,0.000035384375,0.00006212437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006526249,0.00013362012,0.00020045006,0.000036172965,0.000056458135,0.000011037688,0.00009575938,0.000025908925,0.00003570548],"category_scores_gemma":[0.0000022016543,0.00012010971,0.000036691326,0.00007881999,0.000007972676,0.00020960109,0.000017737324,0.00009404968,0.00004810568],"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":[0.0003334097,0.0005526305,0.000985923,0.00007549428,0.00035164092,0.000010079092,0.014461064,0.90592575,0.023189312,0.0018799626,0.0015706714,0.050664064],"study_design_scores_gemma":[0.0012421027,0.0023147368,0.000624501,0.0000098150385,0.000019661527,0.0000022687136,0.007765214,0.9609303,0.024915192,0.00027465794,0.0015015056,0.000400056],"about_ca_topic_score_codex":0.0000028062316,"about_ca_topic_score_gemma":0.000010334373,"teacher_disagreement_score":0.0936828,"about_ca_system_score_codex":0.000037701553,"about_ca_system_score_gemma":0.00000432366,"threshold_uncertainty_score":0.48979318},"labels":[],"label_agreement":null},{"id":"W2968287765","doi":"10.1109/jetcas.2019.2933774","title":"Input-Aware Flow-Based Computing on Memristor Crossbars With Applications to Edge Detection","year":2019,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"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":"Royal Bank of Canada; University of Central Florida; National Science Foundation","keywords":"Crossbar switch; Memristor; Computer science; Von Neumann architecture; Bottleneck; Computation; Parallel computing; Enhanced Data Rates for GSM Evolution; Edge computing; Neuromorphic engineering; In-Memory Processing; Computer engineering; Computer hardware; Algorithm; Artificial intelligence; Electronic engineering; Artificial neural network; Embedded system; Engineering; Telecommunications","score_opus":0.015932090203200588,"score_gpt":0.24768696130105652,"score_spread":0.23175487109785595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968287765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9332054,0.00015636766,0.06532657,0.000034140972,0.00071436056,0.00026016438,0.0000017638017,0.00008223957,0.00021895888],"genre_scores_gemma":[0.99935585,0.000017870165,0.000066014494,0.0000710425,0.00039347867,0.0000075217345,0.0000012544565,0.000022253269,0.00006472216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991234,0.00004288849,0.00026225782,0.00019660426,0.0001432727,0.00023158643],"domain_scores_gemma":[0.99957097,0.00008698425,0.00006110285,0.00010252174,0.00007043045,0.000108021915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001690471,0.00015333996,0.00021594137,0.00019548557,0.00022443122,0.000089833426,0.00006121965,0.000065675194,0.0000010623479],"category_scores_gemma":[0.000010130695,0.00013221314,0.000018089178,0.0003117994,0.0000094217885,0.000059012014,0.0000045299794,0.00045296527,0.0000027840924],"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":[0.00001889234,0.000016614358,0.0012693229,0.00015004347,0.000017892191,0.000011629898,0.0003852727,0.9447657,0.010179532,0.000054291904,0.000018827888,0.043111987],"study_design_scores_gemma":[0.0019742192,0.0009545523,0.0072030956,0.0014636236,0.00002189021,0.00031309575,0.0003077768,0.9646963,0.010189874,0.00003619124,0.012112508,0.0007268826],"about_ca_topic_score_codex":0.0000037712884,"about_ca_topic_score_gemma":0.000010001256,"teacher_disagreement_score":0.066150405,"about_ca_system_score_codex":0.000093503564,"about_ca_system_score_gemma":0.000013893434,"threshold_uncertainty_score":0.5391495},"labels":[],"label_agreement":null},{"id":"W2969231383","doi":"10.1016/j.neunet.2019.09.004","title":"Spiking Neural Networks and online learning: An overview and perspectives","year":2019,"lang":"en","type":"preprint","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"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":"Brain Research New Zealand; Electronic Components and Systems for European Leadership; University of Toronto; University of New South Wales; Auckland University of Technology, New Zealand; European Commission; Eusko Jaurlaritza","keywords":"Computer science; Retraining; Merge (version control); Exploit; Artificial intelligence; Artificial neural network; Machine learning; Data stream mining; Data science","score_opus":0.04739321630824926,"score_gpt":0.2858375905889676,"score_spread":0.23844437428071835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969231383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91012937,0.07807167,0.008679397,0.00008121156,0.0016949077,0.0004572232,0.000006014774,0.0007772904,0.000102926526],"genre_scores_gemma":[0.9869647,0.010384565,0.00029757482,0.00016215413,0.0018814201,0.000009408453,0.000091321795,0.00014533153,0.00006350357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767923,0.0001626402,0.00042022712,0.0008912333,0.00016900468,0.00067766814],"domain_scores_gemma":[0.9988897,0.0002201638,0.00015916233,0.0004359266,0.000052831554,0.00024220589],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00016164253,0.0006806799,0.0007348073,0.000098577926,0.00021507664,0.00021353253,0.00027892,0.00047358836,0.00001333976],"category_scores_gemma":[0.0000294785,0.00068850786,0.00012546038,0.00015454594,0.00010489261,0.00030695915,0.00074860966,0.0033995751,6.4710525e-7],"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":[0.000025158812,0.000014003519,0.001514251,0.00019671532,0.00003382429,0.000031922846,0.00025755214,0.9321096,0.000042949403,0.000042575244,0.000025006037,0.06570644],"study_design_scores_gemma":[0.0003108253,0.00013673393,0.006941664,0.000262272,0.00005990024,0.00008480287,0.00021834645,0.9909605,0.0000075104876,0.00010445724,0.00023739481,0.00067559595],"about_ca_topic_score_codex":0.000008547736,"about_ca_topic_score_gemma":0.000017097584,"teacher_disagreement_score":0.076835364,"about_ca_system_score_codex":0.000053502743,"about_ca_system_score_gemma":0.0000059285226,"threshold_uncertainty_score":0.9995566},"labels":[],"label_agreement":null},{"id":"W2969255209","doi":"10.1021/acs.nanolett.9b01554","title":"Fast Spiking of a Mott VO<sub>2</sub>–Carbon Nanotube Composite Device","year":2019,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":101,"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":"Natural Sciences and Engineering Research Council of Canada; Stanford SystemX Alliance; Stanford University; National Science Foundation","keywords":"Neuromorphic engineering; Carbon nanotube; Materials science; Optoelectronics; Nanotechnology; Nanotube; Thermal conduction; Transient (computer programming); Electronics; Nanoscopic scale; Voltage; Electrical engineering; Computer science","score_opus":0.0064997292435694885,"score_gpt":0.19310170519933406,"score_spread":0.18660197595576458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969255209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971749,0.00010426648,0.0010276819,0.00010197179,0.00055362476,0.00015493053,0.0000021646786,0.00021579313,0.00066463585],"genre_scores_gemma":[0.9986266,0.000007923296,0.0005791897,0.0006499131,0.00008000267,0.0000027972085,0.0000034571456,0.00004202999,0.0000081040835],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906695,0.000021612725,0.00024227455,0.00020313359,0.000161532,0.00030447304],"domain_scores_gemma":[0.9995697,0.00007056301,0.000061005474,0.00023406459,0.000015945685,0.000048710786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007079408,0.00018228458,0.00024265774,0.00009241236,0.000034875768,0.000012872983,0.00016067628,0.000056982903,0.0000041502108],"category_scores_gemma":[0.000004756674,0.0001935388,0.00007752841,0.00021238839,0.000022627155,0.00012183822,0.000049004364,0.00016691524,0.000033517797],"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":[0.000009612878,0.00000570345,0.0007949359,0.000121721314,0.000018460081,0.000009528093,0.0001398654,0.05513775,0.93948144,0.000012599655,0.00002878897,0.004239574],"study_design_scores_gemma":[0.00032724495,0.000023611816,0.0007408882,0.00015512039,0.000012509344,0.000011852211,0.000016919194,0.006929793,0.9913499,0.0000072072335,0.00020967549,0.00021531263],"about_ca_topic_score_codex":0.0000027887686,"about_ca_topic_score_gemma":0.0000012518478,"teacher_disagreement_score":0.051868405,"about_ca_system_score_codex":0.00004894843,"about_ca_system_score_gemma":0.0000043652085,"threshold_uncertainty_score":0.7892283},"labels":[],"label_agreement":null},{"id":"W2969594826","doi":"10.1021/acs.nanolett.9b02683","title":"A Unified Capacitive-Coupled Memristive Model for the Nonpinched Current–Voltage Hysteresis Loop","year":2019,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Memristor; Hysteresis; Capacitive sensing; Capacitor; Resistor; Nonlinear system; Voltage; Physics; Control theory (sociology); Topology (electrical circuits); Materials science; Condensed matter physics; Computer science; Electrical engineering; Engineering; Quantum mechanics","score_opus":0.0233471628114526,"score_gpt":0.24253745667160478,"score_spread":0.21919029386015218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969594826","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6233699,0.00021140743,0.3749572,0.00017748043,0.0005482952,0.00048119915,0.000020808744,0.00015589969,0.00007780936],"genre_scores_gemma":[0.9984512,0.000013232636,0.00051803637,0.000551527,0.0001206194,0.00004756033,0.000009662641,0.000041662195,0.00024655255],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914,0.00001302934,0.00017688282,0.00022198945,0.00011832007,0.00032974145],"domain_scores_gemma":[0.99928826,0.00033828785,0.000045545334,0.00025292122,0.000031523756,0.000043438366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010838991,0.00019261308,0.00019367247,0.00004813975,0.0001109656,0.000025008969,0.00020925807,0.0000421958,0.000012402365],"category_scores_gemma":[0.00002724464,0.00015145492,0.00010779348,0.000119653116,0.00003492307,0.0001328531,0.000030294043,0.00017559328,0.00004043507],"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":[0.000035192912,0.0000057667185,0.00002301837,0.000095666146,0.000034266086,0.0000012866363,0.0004981721,0.29420227,0.7020704,0.00011964312,0.00057816866,0.0023361524],"study_design_scores_gemma":[0.0007863996,0.000017329634,0.00008480881,0.000065194035,0.00003298091,0.0000016100056,0.000068728914,0.94375014,0.05385224,0.00007651119,0.0009987983,0.00026523453],"about_ca_topic_score_codex":0.0000021294645,"about_ca_topic_score_gemma":0.0000020714729,"teacher_disagreement_score":0.6495479,"about_ca_system_score_codex":0.000062766645,"about_ca_system_score_gemma":0.000008576652,"threshold_uncertainty_score":0.6176153},"labels":[],"label_agreement":null},{"id":"W2969766346","doi":"10.1109/vlsi-tsa.2019.8804707","title":"Stochastic Filament Formation on the Cycling Endurance of Backfilled Contact Resistive Random Access Memory Cells","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"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":"Institute of Population and Public Health","keywords":"Reset (finance); Resistive random-access memory; Protein filament; Computer science; Random access; Resistive touchscreen; Key (lock); Non-volatile memory; Electrical conductor; Window (computing); Random access memory; Materials science; Computer hardware; Electronic engineering; Electrical engineering; Engineering; Composite material; Computer network; Voltage","score_opus":0.017643348705558788,"score_gpt":0.23592337387739606,"score_spread":0.21828002517183728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969766346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9366082,0.00003412097,0.054490775,0.00003136171,0.00035467133,0.00052571215,0.000003936168,0.000090326896,0.007860915],"genre_scores_gemma":[0.9995137,0.000010097755,0.00010182296,0.00008330604,0.00003339374,0.000010243605,0.0000022600968,0.000012386747,0.00023279396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935526,0.00003190473,0.00023633454,0.00010660902,0.00012455566,0.00014533973],"domain_scores_gemma":[0.9989666,0.00073354354,0.00007117733,0.00017875976,0.000027036443,0.000022926653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015380123,0.00011387554,0.00018215044,0.000035637546,0.00005107584,0.000016280153,0.0001556068,0.000027700296,0.00014315077],"category_scores_gemma":[0.00003538794,0.00007585216,0.00005450201,0.0000814455,0.000009608899,0.00021002872,0.00002928997,0.00013682911,0.000045632914],"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":[0.00015099043,0.0000068291165,0.0000025143381,0.000067624664,0.000014892562,5.9974496e-7,0.00020684546,0.7221261,0.2762803,0.00024163918,0.00010878879,0.0007928583],"study_design_scores_gemma":[0.0011662596,0.000040373867,0.00028430865,0.00017034648,0.000008625305,9.981954e-7,0.00024317343,0.15637721,0.8414715,0.00009459906,0.00002186737,0.00012074419],"about_ca_topic_score_codex":0.0000024983055,"about_ca_topic_score_gemma":0.0000019004709,"teacher_disagreement_score":0.56574893,"about_ca_system_score_codex":0.000034163713,"about_ca_system_score_gemma":0.000004022894,"threshold_uncertainty_score":0.30931613},"labels":[],"label_agreement":null},{"id":"W2969934711","doi":"10.1063/1.5100019","title":"Mechanism analysis of a flexible organic memristive memory with capacitance effect and negative differential resistance state","year":2019,"lang":"en","type":"article","venue":"APL Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Materials science; Capacitance; Quantum tunnelling; Optoelectronics; Electronics; Voltage; Nanotechnology; Differential capacitance; Thermal conduction; Electrode; Memristor; Engineering physics; Capacitor; Electrical engineering; Composite material; Physics; Engineering","score_opus":0.003977475180047122,"score_gpt":0.19427309894732622,"score_spread":0.1902956237672791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969934711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931181,0.00006883872,0.005942284,0.0000027234855,0.00023485276,0.00028385935,0.00005984519,0.000111022106,0.00017843822],"genre_scores_gemma":[0.9991872,0.000015890284,0.00035483963,0.00000707615,0.000021313414,0.000012805013,0.000007491037,0.00002770663,0.00036567872],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916714,0.000068903384,0.00021755749,0.00022913657,0.000116562085,0.00020067868],"domain_scores_gemma":[0.99945116,0.00017210146,0.00010193875,0.0001975598,0.000035648154,0.00004158404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011304434,0.00018507225,0.0005327808,0.00010397991,0.00003800878,0.000022539043,0.00007990902,0.000036581914,0.00023706951],"category_scores_gemma":[0.000016040578,0.00014915146,0.00003247595,0.00031769119,0.00003815913,0.00012344882,0.000023128381,0.00006208503,0.000008225662],"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":[0.0002271834,0.000004698383,0.00005363649,0.00019263993,0.0004440209,0.0000059130007,0.0005948231,0.0008149931,0.9972132,0.00037750546,0.0000032695393,0.000068126916],"study_design_scores_gemma":[0.00043924444,0.00011430249,0.0023878533,0.00008391812,0.00024342712,0.0000014746048,0.00006606037,0.0001671581,0.9956652,0.0006541612,0.0000017070257,0.00017552006],"about_ca_topic_score_codex":0.000007853231,"about_ca_topic_score_gemma":0.000019006051,"teacher_disagreement_score":0.0060690627,"about_ca_system_score_codex":0.000027228436,"about_ca_system_score_gemma":0.000005170528,"threshold_uncertainty_score":0.60822195},"labels":[],"label_agreement":null},{"id":"W2970674713","doi":"","title":"Deep Learning without Weight Transport","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University; University of Toronto","funders":"","keywords":"Computer science; Path (computing); Task (project management); Artificial intelligence; Deep learning; Artificial neural network; Sign (mathematics); Synaptic weight; Deep neural networks; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.02232819926881737,"score_gpt":0.1501353192350316,"score_spread":0.12780711996621422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970674713","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91044766,0.000035269677,0.0781814,0.0000020648636,0.00019564742,0.00006919636,2.760472e-7,0.00038911228,0.010679364],"genre_scores_gemma":[0.997136,0.000046615332,0.000103105835,0.000015084513,0.000039713603,7.154731e-8,0.0000027647122,0.000021914555,0.0026347504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994563,0.000014494823,0.000071626404,0.00021589898,0.000027714574,0.00021396969],"domain_scores_gemma":[0.9997179,0.000029057295,0.00001989794,0.00014966493,0.000015163072,0.0000683028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004117753,0.0001182158,0.00012694711,0.00005605297,0.00006478745,0.0000043705036,0.00013232745,0.0000504085,0.00013225476],"category_scores_gemma":[0.0000025939175,0.00013635056,0.000059320715,0.0002191629,0.00001742632,0.00021818597,0.000016195996,0.00023848968,0.00025858742],"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":[0.000014843046,0.0000049148225,0.041636094,0.000025405174,0.000014979146,0.000060620725,0.000071172966,0.9520553,0.0036454464,0.0019846514,0.00000250292,0.00048409324],"study_design_scores_gemma":[0.0007881502,0.0000597743,0.008109049,0.000039853527,0.000033861983,0.000010382908,0.0001607943,0.97796285,0.0066831103,0.0006743359,0.005051472,0.0004263916],"about_ca_topic_score_codex":0.0000015628622,"about_ca_topic_score_gemma":0.0000034957072,"teacher_disagreement_score":0.08668832,"about_ca_system_score_codex":0.000037428967,"about_ca_system_score_gemma":0.000004064849,"threshold_uncertainty_score":0.55602145},"labels":[],"label_agreement":null},{"id":"W2973268939","doi":"10.1039/c9nr06403f","title":"Programmable, electroforming-free TiO<sub>x</sub>/TaO<sub>x</sub> heterojunction-based non-volatile memory devices","year":2019,"lang":"en","type":"article","venue":"Nanoscale","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electroforming; Materials science; Heterojunction; Optoelectronics; Nanotechnology","score_opus":0.005796315319097759,"score_gpt":0.19752855073776338,"score_spread":0.1917322354186656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973268939","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909694,0.00025843503,0.004197602,0.00004193756,0.0011076938,0.00073769497,0.000008284802,0.0009851089,0.0016938355],"genre_scores_gemma":[0.99851036,0.000026055914,0.0004938325,0.00023214871,0.0003410381,0.00008900993,0.000044978726,0.0001287069,0.00013386078],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997589,0.00004141822,0.0004891451,0.0005595961,0.00039980302,0.0009210669],"domain_scores_gemma":[0.99867624,0.00016872473,0.00013540611,0.00072304544,0.00009603602,0.00020052498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020451784,0.00047520312,0.0004517706,0.00019054845,0.00023810177,0.00008049574,0.0004214455,0.00024027436,0.000024073992],"category_scores_gemma":[0.000041450905,0.0004887881,0.00021555663,0.00053756457,0.00005139884,0.00050490146,0.000098030636,0.0005584486,0.00039847597],"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":[0.00005926849,0.00005282231,0.0017998705,0.00028338484,0.00003700168,0.000012800943,0.000051978634,0.022134682,0.95647895,0.0000065598506,0.0007920881,0.018290604],"study_design_scores_gemma":[0.0011044289,0.0002000859,0.0019512407,0.00014002169,0.000035760386,0.000015663325,0.000022330689,0.044753693,0.9496128,0.00011217426,0.0014862141,0.0005655844],"about_ca_topic_score_codex":0.0000042265942,"about_ca_topic_score_gemma":0.000051572755,"teacher_disagreement_score":0.022619013,"about_ca_system_score_codex":0.00017494905,"about_ca_system_score_gemma":0.00006524543,"threshold_uncertainty_score":0.9997564},"labels":[],"label_agreement":null},{"id":"W2973854525","doi":"10.1016/j.ssc.2019.113718","title":"Electric field-controlled crossover effect in oxygen-deficient titanium-oxide memory bits","year":2019,"lang":"en","type":"article","venue":"Solid State Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Voltage; Oxide; Crossover study; Materials science; Electric field; Oxygen; Titanium oxide; Biasing; Condensed matter physics; Chemistry; Physics; Computer science; Metallurgy; Quantum mechanics","score_opus":0.009266105046601249,"score_gpt":0.2665842515788734,"score_spread":0.25731814653227214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973854525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98226297,0.0015710645,0.0009603238,0.000113524424,0.00018441112,0.00063899386,0.000003443704,0.00023176766,0.014033496],"genre_scores_gemma":[0.99849194,0.00038597436,0.00040255458,0.00014293652,0.000011822771,0.00005829017,0.0000074181326,0.000029887407,0.00046915264],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894977,0.00011792441,0.0003285123,0.00015999291,0.000104430495,0.00033937008],"domain_scores_gemma":[0.9980179,0.0008057533,0.000051249233,0.001038437,0.000033936227,0.000052691477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026385326,0.00016648186,0.0003149936,0.00014613096,0.000119541466,0.000033762804,0.0005238908,0.000054553988,0.000028890983],"category_scores_gemma":[0.00008682341,0.00016147633,0.00008881073,0.00041244007,0.000014086304,0.00013986955,0.00015285597,0.00044601256,0.00023319632],"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":[0.0003212897,0.00013296546,0.0017899963,0.00015128819,0.00009945249,0.000010868769,0.0010786157,0.41858166,0.5667767,0.0007617346,0.00056034577,0.009735077],"study_design_scores_gemma":[0.01481758,0.00052400166,0.013525629,0.0003288893,0.0000713851,0.000024836369,0.00020828594,0.5704147,0.38898146,0.0027360015,0.0069010267,0.0014661811],"about_ca_topic_score_codex":0.000009534109,"about_ca_topic_score_gemma":0.00004779953,"teacher_disagreement_score":0.17779525,"about_ca_system_score_codex":0.000098296405,"about_ca_system_score_gemma":0.000019010016,"threshold_uncertainty_score":0.65848136},"labels":[],"label_agreement":null},{"id":"W2973952117","doi":"10.1039/c9tc04880d","title":"Interface-engineered reliable HfO<sub>2</sub>-based RRAM for synaptic simulation","year":2019,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Burnaby Hospital; Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Resistive random-access memory; Materials science; Interface (matter); Tin; Germanium compounds; Optoelectronics; Computational science; Computer science; Composite material; Electrical engineering; Germanium; Metallurgy; Silicon; Engineering; Voltage","score_opus":0.009847472905606384,"score_gpt":0.23210314613986283,"score_spread":0.22225567323425643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973952117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792833,0.00009507857,0.019499922,0.00001668934,0.00086522877,0.000115462535,0.000009042054,0.00006730751,0.000047993923],"genre_scores_gemma":[0.99857444,0.0000079634765,0.0010120809,0.000013753589,0.00032330255,0.0000024262845,0.000005679594,0.000035552133,0.000024833686],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916786,0.000007336668,0.00043930652,0.00009254004,0.00010685221,0.00018609983],"domain_scores_gemma":[0.9994133,0.00013287878,0.00017496389,0.00012696018,0.00009233946,0.00005953907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020268303,0.00014430165,0.00028449178,0.000028428465,0.000024531364,0.00003660792,0.00012239022,0.000089868096,0.000058369973],"category_scores_gemma":[0.00008109158,0.00013814439,0.00007619116,0.000048521542,0.000007727413,0.00014648393,0.000012409781,0.00012289267,0.000011980792],"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":[0.000058532096,0.0000054614466,0.0000014225506,0.0003155562,0.000015705398,0.0000019330296,0.000006437822,0.4793617,0.52011245,3.419452e-7,0.000046786907,0.00007363042],"study_design_scores_gemma":[0.0006647732,0.000052044335,0.000007079801,0.00021752302,0.000021424905,0.000020259173,0.000011498901,0.060212057,0.93822193,0.000051549905,0.00038296735,0.0001368813],"about_ca_topic_score_codex":5.3438317e-8,"about_ca_topic_score_gemma":1.0372836e-8,"teacher_disagreement_score":0.41914967,"about_ca_system_score_codex":0.00007149078,"about_ca_system_score_gemma":0.00001850051,"threshold_uncertainty_score":0.5633365},"labels":[],"label_agreement":null},{"id":"W2980140840","doi":"10.1016/b978-0-12-815924-8.00016-5","title":"Tin dioxide ion-gated transistors","year":2019,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Transistor; Tin dioxide; Materials science; Optoelectronics; Ion; Gating; Tin; Silicon dioxide; Oxide; Thin-film transistor; Nanotechnology; Tin oxide; Conductivity; Voltage; Electrical engineering; Chemistry; Layer (electronics); Composite material; Engineering; Metallurgy; Doping; Biophysics","score_opus":0.013103298528004207,"score_gpt":0.20840225797734957,"score_spread":0.19529895944934536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980140840","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007706922,0.001254341,0.00011899287,0.000007397886,0.0012411914,0.00035242937,0.000016934762,0.0005812978,0.9956567],"genre_scores_gemma":[0.008959753,0.00008344106,0.00022605162,0.00010183488,0.00038347777,0.000003921766,0.000022139904,0.00020402238,0.9900153],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987694,0.00001037303,0.00037934096,0.00033894836,0.0001857508,0.00031620407],"domain_scores_gemma":[0.9993107,0.000066058674,0.00007515852,0.0004127822,0.000030465848,0.000104866755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007527509,0.00046477476,0.0005108625,0.000114783674,0.00006276651,0.000014943675,0.00021677399,0.00029275392,0.00014488334],"category_scores_gemma":[0.0000050088897,0.00047738967,0.00023619269,0.000012228608,0.000043386757,0.000042197044,0.000034243898,0.0007005542,0.0004906501],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009107439,0.0000014556701,6.6226244e-7,0.00026569425,0.000080323014,0.00004835463,0.00012037621,0.0017937816,0.0029860265,0.00080536143,0.00011017181,0.9937787],"study_design_scores_gemma":[0.00019476053,0.00002667936,0.0000025537606,0.0005610186,0.000053528598,0.000015714468,0.0000024018984,0.00014682302,0.0064842333,0.0009996117,0.99093866,0.0005740053],"about_ca_topic_score_codex":5.1725145e-8,"about_ca_topic_score_gemma":0.0000032713006,"teacher_disagreement_score":0.99320465,"about_ca_system_score_codex":0.0001011511,"about_ca_system_score_gemma":0.00002776724,"threshold_uncertainty_score":0.9997678},"labels":[],"label_agreement":null},{"id":"W2980650864","doi":"10.1039/c9tc90222h","title":"Correction: Interface-engineered reliable HfO<sub>2</sub>-based RRAM for synaptic simulation","year":2019,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Burnaby Hospital; Simon Fraser University","funders":"","keywords":"Resistive random-access memory; Materials science; Interface (matter); Nanotechnology; Composite material; Engineering; Electrical engineering","score_opus":0.009311160849404752,"score_gpt":0.23046852196721068,"score_spread":0.22115736111780593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980650864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9691421,0.00007893816,0.027360726,0.000014428457,0.0031317128,0.00010959699,0.000005512519,0.00007789265,0.00007912815],"genre_scores_gemma":[0.99886274,0.000008012131,0.00055079395,0.00001238197,0.00045716652,0.0000027308506,0.0000055575947,0.000032556003,0.000068051566],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992034,0.0000078486755,0.00042352415,0.00009371955,0.000103725695,0.00016777818],"domain_scores_gemma":[0.99938095,0.00015986922,0.00017967873,0.00011699952,0.000106751475,0.000055754386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018602934,0.00013995658,0.00026814645,0.000029213526,0.00003073791,0.00003823837,0.00010145135,0.000091159425,0.00007107688],"category_scores_gemma":[0.00009691306,0.00013687236,0.00007454176,0.00005752642,0.000007687885,0.00015113203,0.00001048144,0.0001386468,0.000011891419],"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":[0.00006629796,0.000005582599,0.0000016800828,0.00020362176,0.000014711315,0.0000015789458,0.0000065421136,0.48558116,0.5138132,1.4319819e-7,0.00019315683,0.00011236574],"study_design_scores_gemma":[0.0005470711,0.00005743237,0.000008505321,0.00023231332,0.000020524609,0.000036427482,0.000015102857,0.11931785,0.87907904,0.000022705699,0.0005379036,0.00012513257],"about_ca_topic_score_codex":7.439655e-8,"about_ca_topic_score_gemma":1.7644018e-8,"teacher_disagreement_score":0.3662633,"about_ca_system_score_codex":0.00008722491,"about_ca_system_score_gemma":0.000020243217,"threshold_uncertainty_score":0.5581493},"labels":[],"label_agreement":null},{"id":"W2981139958","doi":"10.1162/neco_a_01242","title":"Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits","year":2019,"lang":"en","type":"article","venue":"Neural Computation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"University of Pittsburgh; Purdue University","keywords":"Randomness; Spike (software development); Computer science; Set (abstract data type); Minimax; Robustness (evolution); Algorithm; Mathematics; Discrete mathematics; Mathematical economics; Statistics","score_opus":0.025055562073573796,"score_gpt":0.254319749484462,"score_spread":0.22926418741088822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981139958","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93980545,0.0001284711,0.057960782,0.000096530435,0.00071548915,0.00032731547,0.000004806324,0.00050339295,0.0004577478],"genre_scores_gemma":[0.9957856,0.0000042148295,0.0035598516,0.0003644367,0.000121177814,0.000005105923,0.000084057705,0.000043917902,0.00003163437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883807,0.000043746863,0.00028313504,0.0003236286,0.00020897818,0.00030245326],"domain_scores_gemma":[0.9994901,0.00017244127,0.000071064285,0.00009623307,0.00006739859,0.000102769685],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007186534,0.00024316667,0.0002213983,0.000087213215,0.00010509598,0.00008057531,0.00008324361,0.000072517156,0.000019630945],"category_scores_gemma":[0.000011943586,0.00026511188,0.00004722982,0.00025379902,0.000031917134,0.00033251307,0.000025311565,0.00024581162,0.00006484277],"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":[0.00001475954,0.000015396705,0.00074606354,0.00009121848,0.000011126486,0.000010965134,0.00012901473,0.938891,0.021326782,0.000029232304,0.00007025644,0.038664192],"study_design_scores_gemma":[0.0010391313,0.00022084084,0.0062267277,0.000039518287,0.0000145344875,0.000039028095,0.000047992922,0.98600924,0.0056842947,0.000089112866,0.0002796189,0.00030995824],"about_ca_topic_score_codex":0.0000017272491,"about_ca_topic_score_gemma":0.0000015750655,"teacher_disagreement_score":0.05598014,"about_ca_system_score_codex":0.000059094884,"about_ca_system_score_gemma":0.0000131077,"threshold_uncertainty_score":0.9999801},"labels":[],"label_agreement":null},{"id":"W2981391656","doi":"10.1016/j.jcis.2019.10.087","title":"Non-zero-crossing current-voltage hysteresis behavior induced by capacitive effects in bio-memristor","year":2019,"lang":"en","type":"article","venue":"Journal of Colloid and Interface Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hysteresis; Capacitive sensing; Zero crossing; Materials science; Memristor; Optoelectronics; Voltage; Transistor; Electrical engineering; Doping; Nanotechnology; Condensed matter physics; Physics; Engineering","score_opus":0.009348141769528893,"score_gpt":0.2693460662881773,"score_spread":0.2599979245186484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981391656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948533,0.0007183657,0.002762441,0.0000069462403,0.0013752079,0.00015049257,0.0000018167302,0.000012400241,0.000119004304],"genre_scores_gemma":[0.99973494,0.000022655144,0.00014254247,0.000014351029,0.000031589007,0.0000014075146,6.87713e-8,0.000009429842,0.000043029257],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990723,0.000013467139,0.00028896943,0.00015243328,0.00021495247,0.00025787292],"domain_scores_gemma":[0.9995396,0.00006760743,0.00012739391,0.000085372434,0.00006919431,0.0001108076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033156344,0.00012947709,0.00023681553,0.00019986961,0.00010948803,0.0001281951,0.00022513766,0.00003365351,0.0000037888499],"category_scores_gemma":[0.000054465716,0.000111686764,0.000037742717,0.00037687895,0.0001414313,0.00079929945,0.00005014116,0.0003688837,0.000004774156],"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":[0.000014792508,0.000017349836,0.0015421394,0.00005074829,0.0000020549924,0.0000019914964,0.0006185345,0.00050308835,0.99334115,0.0000021144817,0.000037139245,0.0038688844],"study_design_scores_gemma":[0.0005525344,0.00024557943,0.00556241,0.0005579385,0.0000071870622,0.000030217016,0.000127717,0.0020028288,0.9904698,0.000019646623,0.00027131336,0.00015283505],"about_ca_topic_score_codex":0.0000016178024,"about_ca_topic_score_gemma":0.0000010006262,"teacher_disagreement_score":0.004881602,"about_ca_system_score_codex":0.00013926267,"about_ca_system_score_gemma":0.000050992854,"threshold_uncertainty_score":0.4554454},"labels":[],"label_agreement":null},{"id":"W2981562349","doi":"10.1007/s00521-019-04522-0","title":"Revisiting the XOR problem: a neurorobotic implementation","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Computer science; Task (project management); Embedding; Artificial neural network; Associative property; XOR gate; Artificial intelligence; Robot; Perceptron; Action (physics); Binary number; Theoretical computer science; Arithmetic; Algorithm; Logic gate","score_opus":0.012553649665021245,"score_gpt":0.2751605017608246,"score_spread":0.26260685209580337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981562349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97369266,0.00026771514,0.023102883,0.0005143217,0.000091262504,0.00066848355,0.0000018898459,0.00042461412,0.001236189],"genre_scores_gemma":[0.998619,0.000013230982,0.0008354576,0.00017462372,0.00027740636,0.000021052689,0.0000063095827,0.0000178177,0.00003508138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935883,0.00002338072,0.00018326884,0.0001769204,0.000068468435,0.00018915755],"domain_scores_gemma":[0.99958897,0.00015238524,0.000043832166,0.0001600057,0.000018911893,0.000035889047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000093634255,0.0001057106,0.000093848066,0.000023458475,0.0002634388,0.000053091895,0.000107827516,0.00001788238,0.000007713945],"category_scores_gemma":[0.0000026904738,0.00008425478,0.000027594962,0.00017659004,0.000018309327,0.00007141885,0.000052535805,0.0001834784,0.000023073331],"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":[0.0000022082422,0.0000047115423,0.00275265,0.00018538072,0.000012967324,9.333256e-7,0.00025554057,0.27838364,0.034438178,0.0050038905,0.00008748394,0.6788724],"study_design_scores_gemma":[0.0008248293,0.00007399501,0.02107667,0.00010986647,0.00005715367,0.00016795588,0.00087018637,0.93929434,0.0071829604,0.002030668,0.027618997,0.00069236464],"about_ca_topic_score_codex":0.0000021456424,"about_ca_topic_score_gemma":5.186196e-7,"teacher_disagreement_score":0.67818004,"about_ca_system_score_codex":0.000010480497,"about_ca_system_score_gemma":0.0000031172663,"threshold_uncertainty_score":0.34358105},"labels":[],"label_agreement":null},{"id":"W2981662070","doi":"10.1038/s41598-019-51700-0","title":"Time and rate dependent synaptic learning in neuro-mimicking resistive memories","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":26,"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":"RMIT University; Australian Research Council; Ontario Ministry of Natural Resources and Forestry","keywords":"Neuromorphic engineering; Memristor; Computer science; Electroforming; Scalability; CMOS; Artificial neural network; Synaptic plasticity; Resistive random-access memory; Artificial intelligence; Neuroscience; Computer architecture; Nanotechnology; Electronic engineering; Materials science; Electrical engineering; Biology; Engineering; Voltage","score_opus":0.007767701981680691,"score_gpt":0.20961917398623453,"score_spread":0.20185147200455383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981662070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959533,0.00017600472,0.000098741395,0.000012774993,0.0023191476,0.00015014337,1.8820201e-7,0.00018609015,0.0011035624],"genre_scores_gemma":[0.9978101,0.000004101924,0.000106530635,0.000008746249,0.000023311834,0.000002409108,0.000004036556,0.000017656832,0.0020230904],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988914,0.00004068214,0.0002615073,0.00041714232,0.00013823187,0.00025099356],"domain_scores_gemma":[0.99951,0.00012043968,0.00006940621,0.00022494559,0.000025559726,0.00004966212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007214621,0.000114403316,0.00015693784,0.00012476677,0.00012832982,0.00010555391,0.000051939795,0.000032479868,0.000028853518],"category_scores_gemma":[0.0001850363,0.00011536185,0.00002202557,0.00023262048,0.00006422209,0.00021928537,0.00006835022,0.00021935128,0.000038046815],"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":[0.000008713564,0.0000060654115,0.0064608697,0.00007033883,0.000010552885,0.00089939806,0.0005985091,0.45361403,0.53639215,0.000011330183,0.00007544051,0.0018525796],"study_design_scores_gemma":[0.0007644355,0.00013198919,0.024425961,0.00068716216,0.00004314121,0.0015980321,0.00084065733,0.37963298,0.57744575,0.004772878,0.008211427,0.00144555],"about_ca_topic_score_codex":0.0000011884821,"about_ca_topic_score_gemma":0.00000497429,"teacher_disagreement_score":0.07398104,"about_ca_system_score_codex":0.00003347646,"about_ca_system_score_gemma":0.000014482962,"threshold_uncertainty_score":0.47043198},"labels":[],"label_agreement":null},{"id":"W2982134097","doi":"10.1002/adma.201905433","title":"Magneto‐Memristive Switching in a 2D Layer Antiferromagnet","year":2019,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Waterloo","funders":"Army Research Office; Fundamental Research Funds for the Central Universities; Ministry of Science, ICT and Future Planning; Canada First Research Excellence Fund; National Research Foundation of Korea; Air Force Office of Scientific Research; National Key Research and Development Program of China; Renmin University of China; Office of Naval Research; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Materials science; Condensed matter physics; Hysteresis; Antiferromagnetism; Quantum tunnelling; Memristor; Magnetization; Electric field; Magneto; Magnetoresistive random-access memory; Spin (aerodynamics); Coupling (piping); Field (mathematics); Ferromagnetism; Resistive touchscreen; Optoelectronics; Magnetic field; Nanotechnology; Voltage; Electronic engineering; Random access memory; Electrical engineering; Physics; Computer science","score_opus":0.008898927288042022,"score_gpt":0.23166374577243604,"score_spread":0.222764818484394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982134097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99493724,0.00018617259,0.00059601164,0.000013390215,0.0012407183,0.00031574306,0.000011116876,0.00032181112,0.002377818],"genre_scores_gemma":[0.99758697,0.000049340004,0.0019075918,0.0000740074,0.00008923356,0.000017914068,0.000008248724,0.000048640268,0.0002180566],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887544,0.000030065614,0.00032688968,0.00026444643,0.0001241296,0.000379042],"domain_scores_gemma":[0.9995743,0.00007506567,0.000048755417,0.00023092923,0.000022851262,0.000048092897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016869283,0.00020929266,0.0003296353,0.00008628985,0.000031002037,0.00003016869,0.00013822052,0.00006349626,0.00033383653],"category_scores_gemma":[0.000036977457,0.000212051,0.000028295663,0.00014807812,0.0000091854245,0.00037399138,0.000055168704,0.0001344843,0.00027681538],"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":[0.00003765752,0.0000075791236,0.00010629941,0.00009298897,0.000003933942,0.000025588557,0.000121060795,0.034416225,0.9619691,0.0001790643,0.000019013687,0.0030214847],"study_design_scores_gemma":[0.00075429975,0.00007254778,0.0028418847,0.00013739994,0.000004340762,0.0000147366345,0.000078751145,0.00067623437,0.9923396,0.0009505407,0.0017866001,0.00034307322],"about_ca_topic_score_codex":0.0000053143526,"about_ca_topic_score_gemma":0.000006273091,"teacher_disagreement_score":0.03373999,"about_ca_system_score_codex":0.00006499383,"about_ca_system_score_gemma":0.000007714756,"threshold_uncertainty_score":0.8647189},"labels":[],"label_agreement":null},{"id":"W2984607076","doi":"10.7554/elife.22901.027","title":"Author response: Towards deep learning with segregated dendrites","year":2017,"lang":"en","type":"peer-review","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Neocortex; Categorization; Neuroscience; Machine learning; Cognitive science; Biology; Psychology","score_opus":0.04302602550598017,"score_gpt":0.32215333086403386,"score_spread":0.2791273053580537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984607076","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014909042,0.5431679,0.115964025,0.06549177,0.02174438,0.005470644,0.00025432245,0.021088883,0.21190906],"genre_scores_gemma":[0.0077538765,0.0019964373,0.004794855,0.00033740103,0.0005343243,0.000040543917,0.00018971619,0.00023212194,0.9841207],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982868,0.00012613216,0.00033571047,0.00042905073,0.00033398904,0.00048835704],"domain_scores_gemma":[0.99871624,0.00022750729,0.00016308264,0.00059800857,0.000145564,0.00014960144],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004887002,0.0005306703,0.00075151725,0.000120962686,0.00031110592,0.000092991824,0.0004772912,0.00026255,0.00038898474],"category_scores_gemma":[0.00060161826,0.0004178032,0.00013159633,0.0001481618,0.000054103784,0.0001644623,0.00011567864,0.0014124509,0.000114481474],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024510192,0.000015859041,0.000013459692,0.008895979,0.00031321688,0.00064411206,0.00013641367,0.039526775,0.0011547892,0.000019645257,0.6433707,0.30566394],"study_design_scores_gemma":[0.00019490492,0.00010521345,0.00005450417,0.005240292,0.00010880597,0.00012917543,0.000014598057,0.004468721,0.0022269594,0.000010079167,0.9867936,0.0006531803],"about_ca_topic_score_codex":0.000007971909,"about_ca_topic_score_gemma":0.000033894245,"teacher_disagreement_score":0.7722117,"about_ca_system_score_codex":0.000086302556,"about_ca_system_score_gemma":0.00006418872,"threshold_uncertainty_score":0.9998274},"labels":[],"label_agreement":null},{"id":"W2985570251","doi":"10.1002/aelm.201900595","title":"Threshold Switching in Single Metal‐Oxide Nanobelt Devices Emulating an Artificial Nociceptor","year":2019,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Materials science; Nociceptor; Nanotechnology; Optoelectronics; Chemistry","score_opus":0.012084652810447263,"score_gpt":0.24537531816105698,"score_spread":0.23329066535060972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985570251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970259,0.0003896275,0.00082146627,0.000011024008,0.0005826242,0.00037964337,0.000003520031,0.00045093612,0.0003352737],"genre_scores_gemma":[0.99870247,0.000019566793,0.0008792122,0.000076543496,0.00015904645,0.000022746273,0.000019676825,0.000084265965,0.000036464524],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787223,0.000056467692,0.00056939153,0.00042423082,0.00016427085,0.0009134148],"domain_scores_gemma":[0.9993876,0.000080896745,0.00011733348,0.00032217315,0.000022557608,0.00006940827],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031627645,0.00031464585,0.00046302093,0.00011464298,0.00008902034,0.000072491,0.00023706979,0.00009723622,0.00016710645],"category_scores_gemma":[0.000027181171,0.00032816618,0.00005003962,0.00023243728,0.000010963617,0.00090842607,0.000050872753,0.00026682243,0.00007271588],"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":[0.000043773183,0.000021516093,0.00007804928,0.00008254206,0.000012165826,0.000004166568,0.00012444932,0.12422083,0.8705576,0.0012737726,3.9863667e-7,0.0035807393],"study_design_scores_gemma":[0.00036415234,0.00013598132,0.0001600004,0.00010753233,0.000009816072,0.000009545137,0.00010934953,0.0022759594,0.993071,0.003016956,0.00033048057,0.0004092179],"about_ca_topic_score_codex":0.000010452588,"about_ca_topic_score_gemma":0.00012842937,"teacher_disagreement_score":0.12251341,"about_ca_system_score_codex":0.00021161926,"about_ca_system_score_gemma":0.000023040528,"threshold_uncertainty_score":0.99991703},"labels":[],"label_agreement":null},{"id":"W2992514841","doi":"10.1016/j.nanoen.2019.104386","title":"Capacitive effect: An original of the resistive switching memory","year":2019,"lang":"en","type":"article","venue":"Nano Energy","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Program for Innovation Team Building at Institutions of Higher Education in Chongqing; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Materials science; Capacitance; Capacitive sensing; Resistive random-access memory; Memristor; Ion; Optoelectronics; Nanotechnology; Resistive touchscreen; Electron; Computer science; Electronic engineering; Electrode; Physics; Artificial neural network; Chemistry; Physical chemistry","score_opus":0.004041218147599496,"score_gpt":0.19716931595638837,"score_spread":0.19312809780878887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992514841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99154395,0.000100514124,0.0011974514,0.0000052513706,0.00061775884,0.000057522288,0.0000030900621,0.000074227886,0.0064002615],"genre_scores_gemma":[0.9993737,0.0000017818589,0.000097664124,0.00003858556,0.000083178864,0.00000508842,0.0000012931966,0.000018437306,0.00038028072],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942726,0.00007129693,0.00010668992,0.00012356143,0.00012214707,0.0001490735],"domain_scores_gemma":[0.9996174,0.0000999981,0.000038228616,0.00019739459,0.00001901505,0.000027967119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009082242,0.00010079688,0.00013791831,0.000032345153,0.00007767087,0.000004338052,0.00015673769,0.00003100726,0.000012835831],"category_scores_gemma":[0.000010993105,0.00007532571,0.000050416762,0.00013377251,0.00001556032,0.00008133322,0.000042191754,0.0001376345,0.0000039731203],"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":[0.000032923308,0.0000067257374,0.000133344,0.000033336968,0.00001564324,0.00000305294,0.00022419306,0.2134537,0.77455103,0.0024476293,0.000027196242,0.009071232],"study_design_scores_gemma":[0.00025567022,0.00011189479,0.00040536158,0.000051044033,0.000008779794,0.000010897695,0.00007307186,0.006960849,0.99046856,0.00026960543,0.0012627327,0.000121529774],"about_ca_topic_score_codex":0.000025718538,"about_ca_topic_score_gemma":0.000009666069,"teacher_disagreement_score":0.21591754,"about_ca_system_score_codex":0.000042746517,"about_ca_system_score_gemma":0.000010425698,"threshold_uncertainty_score":0.30716935},"labels":[],"label_agreement":null},{"id":"W2993364807","doi":"10.1088/1361-6528/ab5ead","title":"Heterogeneous stimuli induced nonassociative learning behavior in ZnO nanowire memristor","year":2019,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"","keywords":"Memristor; Neuromorphic engineering; Habituation; Materials science; Nanowire; Nanotechnology; Computer science; Electronic engineering; Biological system; Artificial neural network; Neuroscience; Artificial intelligence; Engineering","score_opus":0.013561657477407757,"score_gpt":0.24490412013215837,"score_spread":0.2313424626547506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993364807","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99775976,0.00018498085,0.00026642863,0.000038489925,0.00047369653,0.00024994143,0.0000014815231,0.0007902679,0.00023495905],"genre_scores_gemma":[0.99942505,0.000017324866,0.00026525892,0.000028419736,0.000025480096,0.000030314333,0.0000032654004,0.00004006869,0.0001647875],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901646,0.000031105927,0.00021853091,0.0002577671,0.000091240516,0.00038487674],"domain_scores_gemma":[0.99962753,0.00008466438,0.000052844764,0.00018399232,0.000020977577,0.000030012992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007583768,0.00017479643,0.00027712982,0.0001979347,0.000052427247,0.000006355818,0.00018835498,0.00037734272,0.00004013088],"category_scores_gemma":[0.00006798047,0.0001948492,0.000050620987,0.00029732892,0.000023468248,0.000078685254,0.00007531154,0.0007869549,0.00015041915],"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":[0.000016325394,0.00003242125,0.005946421,0.00002642929,0.000016726168,0.00012401788,0.00023773429,0.07744407,0.88173556,0.00010108351,0.0000072858306,0.03431191],"study_design_scores_gemma":[0.0012887338,0.0004834862,0.001880017,0.00006506419,0.000017376846,0.00007102797,0.00020904966,0.019223085,0.9722104,0.00021613693,0.0036850679,0.00065052445],"about_ca_topic_score_codex":0.0000047317117,"about_ca_topic_score_gemma":0.000021171238,"teacher_disagreement_score":0.09047485,"about_ca_system_score_codex":0.00021525745,"about_ca_system_score_gemma":0.000011700346,"threshold_uncertainty_score":0.794572},"labels":[],"label_agreement":null},{"id":"W2999003874","doi":"10.1109/syscon47679.2020.9275895","title":"Machine Learning-Based Self-Compensating Approximate Computing","year":2020,"lang":"en","type":"preprint","venue":"2020 IEEE International Systems Conference (SysCon)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Overhead (engineering); Compensation (psychology); Process (computing); Energy (signal processing); Efficient energy use; Multiplication (music); Power (physics); Range (aeronautics); Approximation error; Computer engineering; Computer hardware; Algorithm","score_opus":0.04035836263325595,"score_gpt":0.2684552409130203,"score_spread":0.22809687827976435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999003874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1773718,0.001071322,0.7778019,0.00071184343,0.022378594,0.0018369491,0.00028357585,0.006281274,0.012262759],"genre_scores_gemma":[0.9946706,0.000054513417,0.0027481187,0.00011082745,0.0017363352,0.00005316539,0.0003543816,0.00013599546,0.00013600496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655265,0.00021105868,0.0011193684,0.00091940403,0.00066439563,0.00053312216],"domain_scores_gemma":[0.99815166,0.00033480485,0.0005739636,0.00037925452,0.00032399193,0.00023630861],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003782558,0.0007481609,0.00090861076,0.00016734748,0.00019642699,0.0005101185,0.0010959296,0.00032839307,0.000082875296],"category_scores_gemma":[0.00013634819,0.0008257953,0.00025610926,0.00017680971,0.000046198053,0.00017648586,0.00043094924,0.0022364152,0.0001616764],"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":[0.00002076701,0.000023588595,0.00051540585,0.0012899509,0.00027932707,0.00011536478,0.00043584447,0.9898837,0.0053345747,0.00078443706,0.00015614342,0.0011608854],"study_design_scores_gemma":[0.00050313905,0.000039788858,0.0000577859,0.0013057052,0.000043674852,0.000038185834,0.00010001585,0.9899248,0.0037683016,0.00017704573,0.0032803973,0.0007611763],"about_ca_topic_score_codex":0.000074100695,"about_ca_topic_score_gemma":0.000008711289,"teacher_disagreement_score":0.8172989,"about_ca_system_score_codex":0.00033169476,"about_ca_system_score_gemma":0.00014671774,"threshold_uncertainty_score":0.9994193},"labels":[],"label_agreement":null},{"id":"W2999084662","doi":"10.1002/adma.202070010","title":"Memristive Switching: Magneto‐Memristive Switching in a 2D Layer Antiferromagnet (Adv. Mater. 2/2020)","year":2020,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spintronics; Neuromorphic engineering; Materials science; Magnetism; Memristor; Antiferromagnetism; Nanotechnology; Nanoscopic scale; Tunnel magnetoresistance; Condensed matter physics; Layer (electronics); Ferromagnetism; Electrical engineering; Computer science; Physics; Engineering; Artificial neural network","score_opus":0.017225684449818635,"score_gpt":0.24494416885898115,"score_spread":0.2277184844091625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999084662","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98987496,0.00037422974,0.005963918,0.00024250991,0.001077479,0.0005612677,0.00006572759,0.0008649331,0.0009749499],"genre_scores_gemma":[0.9943041,0.00015019588,0.0043620705,0.0004935182,0.00042344493,0.00005321934,0.00003068543,0.00013335655,0.000049415],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997361,0.00010332199,0.00081303343,0.0006739724,0.0002908531,0.00075784215],"domain_scores_gemma":[0.9990891,0.00015093414,0.00016467957,0.00030670856,0.000062499355,0.0002260671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002609301,0.0005482664,0.0007811527,0.00009867449,0.00013423801,0.000104847284,0.00035697836,0.00014241239,0.00028631612],"category_scores_gemma":[0.0002189265,0.0005640839,0.00007939284,0.0003874333,0.000028426926,0.0006887217,0.00015056711,0.00038756596,0.00017919992],"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":[0.00020669917,0.000018342156,0.000051241816,0.0002618697,0.000020392365,0.0002933633,0.0011147996,0.03700769,0.9575873,0.00011164763,0.00012603475,0.0032006437],"study_design_scores_gemma":[0.001263612,0.00019894718,0.0012652553,0.00021936897,0.000026981383,0.000040032115,0.0003875544,0.0030177974,0.9899046,0.000902068,0.0019845318,0.0007892682],"about_ca_topic_score_codex":0.0000147583,"about_ca_topic_score_gemma":0.000010143393,"teacher_disagreement_score":0.03398989,"about_ca_system_score_codex":0.00013085442,"about_ca_system_score_gemma":0.000023835832,"threshold_uncertainty_score":0.99968106},"labels":[],"label_agreement":null},{"id":"W3001855497","doi":"10.1109/tcsi.2020.2965935","title":"A Memristive Multiplier Using Semi-Serial IMPLY-Based Adder","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adder; Computer science; Multiplier (economics); Arithmetic; Computation; Figure of merit; Carry-save adder; Serial binary adder; Very-large-scale integration; Memristor; Computer engineering; Computer architecture; Parallel computing; Electronic engineering; Algorithm; Mathematics; Engineering; Embedded system; Telecommunications","score_opus":0.03389157101584122,"score_gpt":0.22888940891850723,"score_spread":0.194997837902666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001855497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3768135,0.00029776618,0.62000847,0.000052290223,0.0013235549,0.00047452125,0.00006133933,0.00045679844,0.0005117283],"genre_scores_gemma":[0.99942064,0.000013394414,0.00010692861,0.00015015763,0.00020040797,0.000017122577,0.0000021090761,0.00005117888,0.000038039903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989482,0.000053316824,0.00026145994,0.00030426195,0.00016204143,0.00027071484],"domain_scores_gemma":[0.9994602,0.00007852499,0.00003978935,0.00014669486,0.000028378203,0.00024641008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007158317,0.00023531883,0.00028573722,0.00005980852,0.00023745438,0.00005033918,0.00007567006,0.000111458074,0.000023295344],"category_scores_gemma":[0.000006452229,0.00023053789,0.000097401295,0.00018211822,0.000043859207,0.00010211488,7.423666e-7,0.00024025603,0.0000068724016],"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":[0.000014898006,0.000007649815,0.0000021681894,0.00010793156,0.00004388131,0.000012545909,0.00029395847,0.67483073,0.31983423,0.0000062774357,0.000016279566,0.0048294207],"study_design_scores_gemma":[0.0014395493,0.00013667058,0.000019648582,0.0002009705,0.00009167217,0.000051621464,0.000738355,0.8469479,0.14710866,0.0000028516188,0.0027126803,0.0005494286],"about_ca_topic_score_codex":0.000007443875,"about_ca_topic_score_gemma":0.0000023107132,"teacher_disagreement_score":0.6226072,"about_ca_system_score_codex":0.00006134804,"about_ca_system_score_gemma":0.000027545027,"threshold_uncertainty_score":0.9401062},"labels":[],"label_agreement":null},{"id":"W3001881666","doi":"10.1002/cta.2745","title":"Digital FPGA implementation of spontaneous astrocyte signalling","year":2020,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"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":"Field-programmable gate array; ModelSim; MATLAB; Realization (probability); Computer science; Overhead (engineering); Software; Computer hardware; Astrocyte; Embedded system; Neuroscience; VHDL; Mathematics; Central nervous system; Biology","score_opus":0.013466006219666166,"score_gpt":0.2609152473249942,"score_spread":0.24744924110532804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001881666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28797585,0.00016864265,0.71101356,0.00003468668,0.000057101355,0.000059424503,0.00002135872,0.000019452737,0.0006499369],"genre_scores_gemma":[0.99950725,0.000044511697,0.00007308387,0.00006324197,0.00029020864,0.000002355548,0.000004794541,0.000007687316,0.0000068401305],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947363,0.000011465567,0.00028216824,0.000053867472,0.0001231068,0.000055770506],"domain_scores_gemma":[0.9995508,0.00013737028,0.00012507125,0.000028718243,0.0001029446,0.00005506672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000094090414,0.00005779462,0.00009150847,0.00004244267,0.000024367031,0.000024509767,0.00014729878,0.000014990424,0.000031470314],"category_scores_gemma":[0.000016518585,0.000057427817,0.00004505776,0.000052316416,0.000023085217,0.00019496986,0.000014982648,0.00009686055,0.0000022012164],"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":[0.00009223422,0.000024194895,0.00014931016,0.00003655187,0.00019203557,0.000052740736,0.00077755307,0.030047555,0.1661459,0.14162454,0.000019418714,0.66083795],"study_design_scores_gemma":[0.0031858375,0.00055744586,0.0018673191,0.00023068393,0.00018836296,0.003768379,0.0063522942,0.001647414,0.6642073,0.2833922,0.03386695,0.00073582877],"about_ca_topic_score_codex":6.344937e-8,"about_ca_topic_score_gemma":1.0613891e-7,"teacher_disagreement_score":0.7115314,"about_ca_system_score_codex":0.000014830801,"about_ca_system_score_gemma":0.0000111352265,"threshold_uncertainty_score":0.23418385},"labels":[],"label_agreement":null},{"id":"W3003725703","doi":"10.1063/1.5140994","title":"Investigation of resistive switching and transport mechanisms of Al2O3/TiO2−<i>x</i> memristors under cryogenic conditions (1.5 K)","year":2020,"lang":"en","type":"article","venue":"AIP Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Institut quantique; Université de Sherbrooke","funders":"H2020 Excellent Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Memristor; Thermal conduction; Quantum tunnelling; Diode; Resistive touchscreen; Electrical conductor; Conductance; Commutation; Negative resistance","score_opus":0.019856381828670177,"score_gpt":0.22553475959666444,"score_spread":0.20567837776799427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003725703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8102441,0.00092477794,0.18821464,0.00010713699,0.00008697094,0.00009264109,0.00001691111,0.00008628401,0.000226545],"genre_scores_gemma":[0.99503493,0.00012527537,0.0046561593,0.00012441713,0.00002783333,0.0000039987326,0.000009120834,0.000014859883,0.0000033872805],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994054,0.000015667893,0.00024337179,0.00013923633,0.000095339725,0.00010097128],"domain_scores_gemma":[0.99967295,0.000085193395,0.0000813543,0.00006607741,0.000028986815,0.000065424356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048666156,0.000103526014,0.00018781425,0.000036217596,0.000052782627,0.0000019057436,0.00006118703,0.000031395553,0.00000730263],"category_scores_gemma":[0.000016006885,0.00010771596,0.000037494137,0.00016227913,0.000054828066,0.00025190812,0.000009668925,0.000097637865,6.8093493e-7],"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":[0.000016565793,0.0000027955327,0.00048759443,0.00022319633,0.000025923002,0.0000021745582,0.00086252147,0.03898095,0.9541695,0.0047991998,0.000009798328,0.00041975616],"study_design_scores_gemma":[0.00034737837,0.00012828673,0.003621516,0.00012329817,0.000047146288,0.000004183728,0.00093718444,0.0023837816,0.9329063,0.059024997,0.0002630606,0.00021285475],"about_ca_topic_score_codex":0.0000019045533,"about_ca_topic_score_gemma":0.00000722397,"teacher_disagreement_score":0.18479085,"about_ca_system_score_codex":0.000012633439,"about_ca_system_score_gemma":0.000010583792,"threshold_uncertainty_score":0.43925294},"labels":[],"label_agreement":null},{"id":"W3004441219","doi":"10.1038/s41598-020-58820-y","title":"Microtubules as Sub-Cellular Memristors","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":59,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Allard Foundation","keywords":"Memristor; Resistor; Capacitor; Microtubule; Electrical element; Physics; Nanotechnology; Voltage; Materials science; Biology","score_opus":0.014208260476116138,"score_gpt":0.2057761710755831,"score_spread":0.19156791059946696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004441219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98914987,0.00051828433,0.002441644,0.00008464134,0.0048661646,0.000113572416,4.2450816e-7,0.0004836183,0.0023417913],"genre_scores_gemma":[0.9990461,0.0000032654898,0.00033526018,0.00006356237,0.0001388255,0.0000027378157,0.000009866254,0.000021146265,0.0003792244],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989554,0.0000098764285,0.00025520232,0.00037170175,0.00018487328,0.00022289055],"domain_scores_gemma":[0.999475,0.000013313833,0.00004949502,0.00028055257,0.000028994085,0.00015263745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018806658,0.000113026166,0.00012161639,0.00004049755,0.00016372846,0.00008186691,0.00007359407,0.00003338226,0.000061436935],"category_scores_gemma":[0.00007217322,0.0001129582,0.000063009815,0.00029154873,0.00006434517,0.0001260376,0.000041214913,0.00012429268,0.00014450759],"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":[0.0000013680738,0.0000038716944,0.00006266169,0.000032429725,0.0000051555357,0.00091969804,0.00025525593,0.011158525,0.97758293,0.000017382323,0.008763911,0.0011968296],"study_design_scores_gemma":[0.00003216517,0.000008843173,0.000007432273,0.000010428874,0.0000047307517,0.00010374889,0.000028014376,0.0017949777,0.88798445,0.0008868922,0.109007545,0.00013074646],"about_ca_topic_score_codex":7.5153974e-7,"about_ca_topic_score_gemma":4.4622797e-7,"teacher_disagreement_score":0.100243635,"about_ca_system_score_codex":0.000022257283,"about_ca_system_score_gemma":0.000017433704,"threshold_uncertainty_score":0.46063018},"labels":[],"label_agreement":null},{"id":"W3004686506","doi":"10.1016/j.cap.2020.02.002","title":"Memristive effect with non-zero-crossing current-voltage hysteresis behavior based on Ag doped Lophatherum gracile Brongn","year":2020,"lang":"en","type":"article","venue":"Current Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Materials science; Doping; Memristor; Capacitive sensing; Nanotechnology; Hysteresis; Optoelectronics; Tin oxide; Voltage; Electronic engineering; Electrical engineering; Condensed matter physics; Physics","score_opus":0.022868249412810283,"score_gpt":0.26058558966041895,"score_spread":0.23771734024760866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004686506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8179976,0.00025378243,0.17898647,0.000004976072,0.00062600034,0.00094440515,0.00005155776,0.0005883983,0.0005468271],"genre_scores_gemma":[0.9989701,0.0000058945475,0.00018599088,0.00003534769,0.00048036806,0.00013538318,0.000079729456,0.00010465669,0.0000025388556],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984312,0.000025454532,0.0002527042,0.00050541305,0.00030841678,0.0004767853],"domain_scores_gemma":[0.9992051,0.00015220465,0.00010775922,0.0003040651,0.00003065322,0.00020018067],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006163138,0.0004938472,0.0004440303,0.000035436664,0.00026686344,0.0001153279,0.00023633539,0.000050692233,0.000008914963],"category_scores_gemma":[0.000007557956,0.00044118546,0.00012071646,0.0003858513,0.00009041189,0.00013979965,0.00005678694,0.0006373847,0.00009835949],"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":[0.0004885105,0.00023589398,0.0008274744,0.0011829054,0.000040458774,0.000018007753,0.00079202995,0.33601907,0.07878207,0.00011950905,0.00030672687,0.58118737],"study_design_scores_gemma":[0.0049859667,0.000748216,0.0014583592,0.0009113202,0.00035842235,0.0000016721772,0.00004918108,0.2417844,0.7438751,0.00013943641,0.0037638575,0.0019241091],"about_ca_topic_score_codex":3.1085008e-7,"about_ca_topic_score_gemma":1.8517136e-7,"teacher_disagreement_score":0.665093,"about_ca_system_score_codex":0.000077606375,"about_ca_system_score_gemma":0.000027858203,"threshold_uncertainty_score":0.999804},"labels":[],"label_agreement":null},{"id":"W3005426360","doi":"10.1088/1361-6528/ab72b6","title":"Reliable resistive switching of epitaxial single crystalline cubic Y-HfO <sub>2</sub> RRAMs with Si as bottom electrodes","year":2020,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Materials science; Resistive random-access memory; Amorphous solid; Raman spectroscopy; Electrode; Doping; Transmission electron microscopy; Heterojunction; Tin; Crystallite; Analytical Chemistry (journal); Optoelectronics; Nanotechnology; Crystallography; Optics","score_opus":0.009144730299940328,"score_gpt":0.192939709033294,"score_spread":0.18379497873335365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005426360","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9317095,0.0004340807,0.06595576,0.0005237698,0.00006771999,0.00017603066,0.0000023760097,0.0009523804,0.00017833307],"genre_scores_gemma":[0.99436855,0.00006572778,0.005257349,0.000131053,0.00009677923,0.000009230654,0.0000044357125,0.000059590846,0.000007275171],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987246,0.000018856168,0.00031485836,0.00035028,0.00014481157,0.00044661295],"domain_scores_gemma":[0.9994273,0.0000914272,0.00010690419,0.0002442202,0.00005972336,0.00007039752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007138406,0.00023951387,0.00039001042,0.00011183201,0.00009550823,0.000010400602,0.00024806455,0.0002645252,0.000005914791],"category_scores_gemma":[0.00018053905,0.0002304346,0.000055302353,0.0005454107,0.00006957221,0.00012809299,0.00007770996,0.0006929551,0.0000166264],"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":[0.00013270206,0.000021854763,0.000023688062,0.00007868291,0.00003377812,0.00005910902,0.000097499185,0.022902215,0.96839017,0.00072581606,0.0000385119,0.0074959556],"study_design_scores_gemma":[0.00045153897,0.00078178337,0.00000923497,0.00007721586,0.00002404826,0.000043674383,0.00007466617,0.0057734093,0.9902599,0.0013231958,0.0009358157,0.00024552539],"about_ca_topic_score_codex":0.0000048545658,"about_ca_topic_score_gemma":0.000011235419,"teacher_disagreement_score":0.062659,"about_ca_system_score_codex":0.000063070496,"about_ca_system_score_gemma":0.00002988148,"threshold_uncertainty_score":0.93968505},"labels":[],"label_agreement":null},{"id":"W3005956544","doi":"10.36505/exling-2019/10/0039/000401","title":"Simulating alphabet recitation under thalamic lesions","year":2019,"lang":"en","type":"article","venue":"ExLing Conferences","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Ontario Brain Institute; Hospital for Sick Children","funders":"","keywords":"Basal ganglia; Computer science; Thalamus; Pointer (user interface); Neuroscience; Neurocognitive; Working memory; Associative property; Artificial intelligence; Alphabet; Speech recognition; Artificial neural network; Psychology; Cognition; Mathematics; Central nervous system","score_opus":0.03360659112161282,"score_gpt":0.27085633686392563,"score_spread":0.2372497457423128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005956544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9743018,0.00013477178,0.017045453,0.00002879516,0.00060654763,0.00008912944,9.721996e-7,0.00030847843,0.007484034],"genre_scores_gemma":[0.9986416,0.000017342465,0.0010137147,0.000036557874,0.000084768326,0.0000018874558,0.0000071394174,0.000014182326,0.00018280972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943054,0.000018370436,0.00015012857,0.00013921346,0.0000896314,0.00017209427],"domain_scores_gemma":[0.99954903,0.00026335206,0.00003033126,0.00010424942,0.000023114191,0.000029906161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008340352,0.00010121369,0.00011558467,0.000046304023,0.00007616054,0.000033507073,0.000088662586,0.00004179541,0.00009302829],"category_scores_gemma":[0.000026569905,0.00009566782,0.00002931822,0.000096340664,0.000012049909,0.00015680333,0.000017624807,0.00014592549,0.000106786894],"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":[0.0000024547867,0.0000041024464,0.0022318151,0.000037307815,0.000012682816,0.0000013803558,0.00035934517,0.92367643,0.046070088,0.0032712312,0.000012960319,0.024320189],"study_design_scores_gemma":[0.00043997285,0.00006616731,0.009656571,0.0003158725,0.000013684587,0.0000048499883,0.0015975768,0.92709494,0.048302513,0.011168064,0.0008548779,0.00048491923],"about_ca_topic_score_codex":0.000002725531,"about_ca_topic_score_gemma":0.0000037649877,"teacher_disagreement_score":0.024339778,"about_ca_system_score_codex":0.000015581212,"about_ca_system_score_gemma":0.000014662879,"threshold_uncertainty_score":0.39012206},"labels":[],"label_agreement":null},{"id":"W3006423901","doi":"10.1021/acsami.9b21530","title":"Toward a Reliable Synaptic Simulation Using Al-Doped HfO<sub>2</sub> RRAM","year":2020,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":129,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Key Laboratory of Electronic Thin Films and Integrated Devices; State Administration of Foreign Experts Affairs; Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Resistive random-access memory; Materials science; Neuromorphic engineering; Doping; Optoelectronics; Performance enhancement; Nanotechnology; Computer science; Artificial neural network; Voltage; Electrical engineering; Artificial intelligence","score_opus":0.04085030071308631,"score_gpt":0.25473764891175554,"score_spread":0.21388734819866922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006423901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99386173,0.00013716743,0.004262517,0.000069833644,0.0004957417,0.00032740989,0.0000119280485,0.0006604057,0.00017324658],"genre_scores_gemma":[0.99855906,0.00004444075,0.00072578975,0.0003585736,0.00021068516,0.000016258246,0.000009987033,0.00007303161,0.0000021618216],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987498,0.000019035031,0.0004176756,0.0003304238,0.00013447912,0.0003486015],"domain_scores_gemma":[0.9995593,0.000071537856,0.000094961,0.00017005064,0.000026178086,0.00007801811],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010840964,0.00027567142,0.00035865963,0.000040994597,0.000093121314,0.00011785292,0.00019154683,0.00007907533,0.000044309065],"category_scores_gemma":[0.00003318492,0.00028019404,0.000016846088,0.0001415829,0.000029517558,0.00021205681,0.00012678652,0.00014539225,0.0001415832],"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":[0.000050827213,0.0000038841777,4.6224213e-7,0.000099040466,0.00002253549,0.000004529691,0.00021612423,0.41926417,0.5796704,0.000020726198,0.0000436718,0.0006036062],"study_design_scores_gemma":[0.000269873,0.000041071733,0.0000027282895,0.00006308661,0.000029362289,0.0000049035416,0.000099664314,0.015741996,0.98313504,0.00015358617,0.00015815569,0.00030051483],"about_ca_topic_score_codex":0.0000017589878,"about_ca_topic_score_gemma":3.2163592e-7,"teacher_disagreement_score":0.40352216,"about_ca_system_score_codex":0.00005015185,"about_ca_system_score_gemma":0.000008853885,"threshold_uncertainty_score":0.999965},"labels":[],"label_agreement":null},{"id":"W3007832176","doi":"10.1007/978-3-030-35441-1_10","title":"Hybrid Memristor-CMOS Based FIR Filter Design","year":2020,"lang":"en","type":"book-chapter","venue":"Springer proceedings in complexity","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Memristor; CMOS; Electronic engineering; Infinite impulse response; Cadence; Verilog; Finite impulse response; Computer science; MATLAB; Filter (signal processing); Electrical engineering; Engineering; Digital filter; Computer hardware; Field-programmable gate array","score_opus":0.0876795155463629,"score_gpt":0.23969172073831155,"score_spread":0.15201220519194864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007832176","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007561031,0.0014349764,0.089796126,0.00061017624,0.0028160627,0.0030780837,0.00012363833,0.0052505843,0.8893293],"genre_scores_gemma":[0.8694367,0.00014331045,0.10514088,0.0017403843,0.0026999475,0.0001288605,0.00007440371,0.0010408983,0.019594645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977721,0.000006735985,0.00060651806,0.0007402975,0.0003543172,0.00052004715],"domain_scores_gemma":[0.9991433,0.000117275995,0.00016539395,0.00023379932,0.0000890599,0.00025115823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023480448,0.0007107068,0.00076117634,0.00022179936,0.00011768728,0.00007115431,0.0005355947,0.00022851344,0.00040665647],"category_scores_gemma":[0.000089004556,0.00082288834,0.00019983077,0.00007962673,0.00013470996,0.00019881022,0.00019449547,0.001439928,0.00015096342],"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":[0.0022663833,0.00040863012,0.00072305405,0.026264938,0.0013605226,0.0032879878,0.0030043707,0.18102421,0.08017155,0.46742338,0.14846122,0.08560375],"study_design_scores_gemma":[0.002102306,0.00036834987,0.00033583477,0.002738479,0.00014872888,0.000086633074,0.000015582818,0.2120018,0.082879886,0.16938928,0.52521586,0.004717239],"about_ca_topic_score_codex":0.000002050318,"about_ca_topic_score_gemma":0.0000018621787,"teacher_disagreement_score":0.86973464,"about_ca_system_score_codex":0.00034361778,"about_ca_system_score_gemma":0.000046206074,"threshold_uncertainty_score":0.9994222},"labels":[],"label_agreement":null},{"id":"W3009615583","doi":"10.1002/aisy.201900189","title":"Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing","year":2020,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":143,"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":"Institute for Information and Communications Technology Promotion; Engineering and Physical Sciences Research Council; Universität Zürich; Eidgenössische Technische Hochschule Zürich; Leverhulme Trust","keywords":"Neuromorphic engineering; Memristor; CMOS; Computer science; Computer architecture; Spike (software development); Cognitive computing; Noise (video); Electronic engineering; Artificial neural network; Artificial intelligence; Engineering; Cognition; Neuroscience","score_opus":0.07016496878597489,"score_gpt":0.26790065241082667,"score_spread":0.19773568362485178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009615583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6403307,0.0033532968,0.35294843,0.00012337495,0.0011443963,0.0011949154,0.00011516427,0.0006687784,0.0001209587],"genre_scores_gemma":[0.99615526,0.00009444281,0.00300353,0.00030843957,0.00025580756,0.000024555051,0.000054627482,0.00007073901,0.000032601693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984873,0.000041022307,0.00049939216,0.0004146773,0.00013368748,0.0004239265],"domain_scores_gemma":[0.99915606,0.00029687525,0.000103217826,0.00015732669,0.00006808021,0.00021841718],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011072292,0.0003008463,0.0004426933,0.000046544897,0.00016074098,0.000038511414,0.00017577378,0.00004446844,0.000009127043],"category_scores_gemma":[0.0000794746,0.00030800505,0.00008372743,0.00014834633,0.000039304625,0.00021648184,0.000079301666,0.00022057368,0.000014559653],"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":[0.00008228124,0.000016427899,0.0003192482,0.0012940011,0.00018370083,0.000032397897,0.0011383404,0.5933253,0.39318052,0.0011134851,0.00078556855,0.008528689],"study_design_scores_gemma":[0.0010648043,0.00037213127,0.00010941386,0.0003670275,0.000096202566,0.00008820394,0.002626044,0.54746157,0.37449983,0.00018006856,0.07216201,0.00097270566],"about_ca_topic_score_codex":0.000004341619,"about_ca_topic_score_gemma":0.0000012117979,"teacher_disagreement_score":0.35582456,"about_ca_system_score_codex":0.00005765646,"about_ca_system_score_gemma":0.0000075452435,"threshold_uncertainty_score":0.9999372},"labels":[],"label_agreement":null},{"id":"W3010895336","doi":"10.1109/eurosoi-ulis45800.2019.9041876","title":"Thin film BIMOS transistor for low-power spiking neuron design in 28nm FD-SOI CMOS technology","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"NMOS logic; CMOS; Transistor; Robustness (evolution); Neuromorphic engineering; Computer science; Electronic engineering; Silicon on insulator; Electrical engineering; Materials science; Optoelectronics; Silicon; Engineering; Artificial neural network; Voltage; Artificial intelligence","score_opus":0.012398243637042344,"score_gpt":0.21833593531875267,"score_spread":0.20593769168171033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010895336","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7087363,0.00013060088,0.28809407,0.000105454084,0.00047007084,0.00065293966,0.0000015250439,0.0006425906,0.0011664156],"genre_scores_gemma":[0.9861207,0.000006628429,0.013308429,0.00013406435,0.000030222705,0.000028797152,0.0000012940345,0.000046491128,0.0003233668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908036,0.000015348496,0.00023232476,0.00025207372,0.000065448905,0.00035442755],"domain_scores_gemma":[0.99955815,0.00016296147,0.0000231833,0.00020543089,0.000015940957,0.000034313845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011987966,0.00017220908,0.00021878805,0.00017116973,0.00003820992,0.000010880542,0.00017037688,0.00012641537,0.000071761126],"category_scores_gemma":[0.000027952934,0.00016786685,0.00005477021,0.0002593533,0.000015341939,0.00014386546,0.000019633035,0.00025812106,0.00004280899],"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":[0.000047402056,0.000021503018,0.00022490264,0.0001317854,0.0000089912655,0.00001557055,0.00018027872,0.7406813,0.2517392,0.00075987895,0.00020789476,0.00598131],"study_design_scores_gemma":[0.0012572769,0.000263837,0.00026160735,0.0001294886,0.000008767196,0.000016550766,0.00016049,0.5824623,0.40750605,0.0011446984,0.006287311,0.00050163065],"about_ca_topic_score_codex":0.0000010651279,"about_ca_topic_score_gemma":0.00000422678,"teacher_disagreement_score":0.27738437,"about_ca_system_score_codex":0.000046077734,"about_ca_system_score_gemma":0.000009703747,"threshold_uncertainty_score":0.68454117},"labels":[],"label_agreement":null},{"id":"W3011267950","doi":"10.1021/acsaelm.0c00099","title":"Nonvolatile Memristive Switching in Self-assembled Nanoparticle Dimers","year":2020,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Deutsche Forschungsgemeinschaft","keywords":"Memristor; Agglomerate; Materials science; Nanoparticle; Nanotechnology; Crystallite; Nanoscopic scale; Scanning electron microscope; Electrical conductor; Non-volatile memory; Protein filament; Self-assembly; Silicon; Nanostructure; Optoelectronics; Chemical engineering; Composite material; Electronic engineering","score_opus":0.006991804162888059,"score_gpt":0.19901594362671837,"score_spread":0.19202413946383032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011267950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978159,0.00009216963,0.000757538,0.00006595568,0.00008291338,0.00029391976,0.0000014269383,0.00054034346,0.00034985217],"genre_scores_gemma":[0.9994756,0.00003555859,0.000087288136,0.0001849297,0.00011985946,0.000044257733,0.000003761627,0.000046417274,0.0000023164914],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998701,0.000025545562,0.00032058277,0.00025769672,0.00010123672,0.00059393374],"domain_scores_gemma":[0.9996861,0.000054352637,0.000048908896,0.000125251,0.000008082501,0.00007728379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001582237,0.00019783709,0.00029708698,0.000033229284,0.000060833823,0.000037686623,0.0001553599,0.00006296003,0.000057361925],"category_scores_gemma":[0.000011671327,0.00020725699,0.000018400853,0.00022746198,0.0000036571937,0.00010884184,0.00005147608,0.0002033733,0.000073693234],"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":[0.000047935147,0.000009775988,0.000010468067,0.00005442283,0.000019926434,0.0000048853585,0.0005841132,0.007590792,0.98966736,0.0015121769,0.000028038196,0.0004701094],"study_design_scores_gemma":[0.0004862462,0.000041971743,0.0000654504,0.0000099216,0.000011581112,0.0000020162158,0.00005618823,0.0007730959,0.99733585,0.00064519356,0.00034542652,0.00022706905],"about_ca_topic_score_codex":0.0000035534053,"about_ca_topic_score_gemma":0.0000037935038,"teacher_disagreement_score":0.007668483,"about_ca_system_score_codex":0.000120622375,"about_ca_system_score_gemma":0.000026074169,"threshold_uncertainty_score":0.8451695},"labels":[],"label_agreement":null},{"id":"W3013060976","doi":"10.1038/s41467-020-15378-7","title":"Spiking neurons from tunable Gaussian heterojunction transistors","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":110,"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":"Center for Hierarchical Materials Design; National Institute of Standards and Technology; Division of Materials Research; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense; Division of Electrical, Communications and Cyber Systems; Materials Research Science and Engineering Center, Harvard University; Office of Naval Research; Northwestern University; U.S. Department of Commerce; Division of Emerging Frontiers in Research and Innovation; National Science Foundation","keywords":"Neuromorphic engineering; Computer science; Transistor; Electronic circuit; Spiking neural network; Bandwidth (computing); Materials science; Nanotechnology; Optoelectronics; Artificial neural network; Artificial intelligence; Electrical engineering; Voltage; Telecommunications; Engineering","score_opus":0.030866673927372974,"score_gpt":0.2551533303239693,"score_spread":0.22428665639659634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013060976","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7996437,0.027470395,0.10059178,0.025914177,0.0029887708,0.0008623632,0.00012248568,0.005624228,0.036782123],"genre_scores_gemma":[0.99432594,0.0001818742,0.0044072755,0.00085981924,0.00012290356,0.000007158909,0.000056447097,0.000026832247,0.00001173242],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948406,0.000038485832,0.00014374447,0.00013054478,0.00007099942,0.0001321521],"domain_scores_gemma":[0.9991921,0.00010592669,0.000023379946,0.0005789173,0.000018057497,0.00008160033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025643363,0.00010853419,0.00010817242,0.000028735825,0.00021544886,0.000019782312,0.0004518515,0.00011194615,0.000019603487],"category_scores_gemma":[0.000039215236,0.000117438874,0.000055891698,0.00023166278,0.00003585014,0.00015610224,0.00006616455,0.0009812146,0.000024988685],"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":[0.0000701178,0.0001536436,0.0024650344,0.00015280349,0.00023950482,0.000024678866,0.00844358,0.25938183,0.654614,0.005920833,0.018209381,0.050324623],"study_design_scores_gemma":[0.00070773216,0.00007352332,0.006919754,0.00008557945,0.0000993601,0.0000090382255,0.00043609348,0.22227201,0.04298328,0.00060744345,0.72505754,0.0007486803],"about_ca_topic_score_codex":0.0000037375585,"about_ca_topic_score_gemma":0.000039021368,"teacher_disagreement_score":0.70684814,"about_ca_system_score_codex":0.000029671293,"about_ca_system_score_gemma":0.0000059158224,"threshold_uncertainty_score":0.47890183},"labels":[],"label_agreement":null},{"id":"W3014221162","doi":"10.1101/2020.03.30.015511","title":"Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto; Mila - Quebec Artificial Intelligence Institute; University of Ottawa; Canadian Institute for Advanced Research; The Scarborough Hospital","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Synaptic plasticity; Neuroscience; Metaplasticity; Plasticity; Postsynaptic potential; Computer science; Nonsynaptic plasticity; Synaptic scaling; Biology; Physics","score_opus":0.0164781942289971,"score_gpt":0.21295798576287908,"score_spread":0.19647979153388198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014221162","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9794043,0.00044326342,0.016487014,0.00015453686,0.0011661507,0.0005171841,0.00006636861,0.0017424839,0.000018740438],"genre_scores_gemma":[0.99855965,0.00010639857,0.00051635737,0.000092608,0.0004366867,0.000077943456,4.1138145e-7,0.00020773923,0.000002203278],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996926,0.00016344716,0.0006636269,0.0010067462,0.00036736805,0.00087279826],"domain_scores_gemma":[0.9986145,0.00022351838,0.00017338229,0.00046278923,0.000103628736,0.00042221905],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00029024883,0.0007049901,0.00080717914,0.00029743885,0.00015215606,0.0001347606,0.0005844115,0.0004918076,0.000017676708],"category_scores_gemma":[0.00048314608,0.00085872784,0.0001346801,0.0005319271,0.000082594175,0.000121581834,0.0005548329,0.003604692,0.000046169214],"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":[0.000023070057,0.000049053844,0.002398428,0.0010436921,0.00015299713,0.00083583273,0.000037517835,0.3144236,0.68068856,0.0002994697,0.000019968375,0.000027827842],"study_design_scores_gemma":[0.0023361084,0.0002666538,0.10039839,0.0027089133,0.00025372359,4.215662e-7,0.000017544875,0.30760407,0.58056486,0.00005134043,0.0013352513,0.0044627087],"about_ca_topic_score_codex":0.000016215732,"about_ca_topic_score_gemma":0.0000056908098,"teacher_disagreement_score":0.1001237,"about_ca_system_score_codex":0.00049676985,"about_ca_system_score_gemma":0.00020162127,"threshold_uncertainty_score":0.99938637},"labels":[],"label_agreement":null},{"id":"W3014827905","doi":"10.1109/tcsii.2020.2984932","title":"An Efficient Spiking Neuron Hardware System Based on the Hardware-Oriented Modified Izhikevich Neuron (HOMIN) Model","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Biological neuron model; Computer science; Neuron; Realization (probability); Computer hardware; Artificial neural network; Artificial intelligence; Neuroscience; Mathematics; Psychology","score_opus":0.032804964801666676,"score_gpt":0.22741780698570824,"score_spread":0.19461284218404157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014827905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20869267,0.000076048455,0.78569174,0.00010091643,0.0018997204,0.0011419462,0.00015724752,0.0017192045,0.00052049244],"genre_scores_gemma":[0.9986371,0.000003934513,0.000064563996,0.0004751998,0.00029786638,0.0002452412,0.000011534557,0.0002006542,0.00006395131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964282,0.00033493806,0.0007837899,0.00095614407,0.00075821514,0.00073871634],"domain_scores_gemma":[0.99795,0.00029930362,0.00016769937,0.0010504157,0.00012617686,0.00040641223],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026395317,0.0006675509,0.0006240526,0.00017951452,0.0010377651,0.0001779748,0.0006508045,0.00018987303,0.000008660305],"category_scores_gemma":[0.000018888479,0.0005979558,0.0002617036,0.00055377115,0.00005546637,0.0002827022,0.000006192185,0.0009964767,0.000033700864],"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":[0.00006502659,0.00009710649,9.063954e-7,0.00047156206,0.000028437938,0.00004610699,0.0009642226,0.9315781,0.065467715,0.0001994465,0.00007242338,0.0010089582],"study_design_scores_gemma":[0.00076754077,0.0002819512,0.0000102936265,0.00060568785,0.00006533624,0.00002668588,0.00033198306,0.95511806,0.041568145,7.5375027e-7,0.000688028,0.0005355077],"about_ca_topic_score_codex":0.000019275838,"about_ca_topic_score_gemma":0.000001961654,"teacher_disagreement_score":0.7899444,"about_ca_system_score_codex":0.00022288758,"about_ca_system_score_gemma":0.000057064964,"threshold_uncertainty_score":0.9996472},"labels":[],"label_agreement":null},{"id":"W3014927864","doi":"10.1109/jstsp.2020.2983607","title":"Impact of Synaptic Strength on Propagation of Asynchronous Spikes in Biologically Realistic Feed-Forward Neural Network","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; Krembil Foundation","funders":"","keywords":"Computer science; Asynchronous communication; Artificial neural network; Backpropagation; Artificial intelligence; Computer network","score_opus":0.022360573999809768,"score_gpt":0.26855153659123077,"score_spread":0.246190962591421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014927864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98542863,0.0003557214,0.013964146,0.000028061031,0.00006199525,0.00008721025,0.0000012964458,0.000021016906,0.00005191613],"genre_scores_gemma":[0.9984801,0.000027674447,0.0011473943,0.000014829926,0.00031445283,6.00701e-7,0.0000012582833,0.000013266362,4.276557e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987233,0.00006425427,0.0007241686,0.000105936255,0.0001606295,0.00022168622],"domain_scores_gemma":[0.99921006,0.0001589513,0.00036487618,0.00004188302,0.00016374062,0.000060489143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001649203,0.00014444102,0.00039497792,0.0001224361,0.000023872366,0.000011883995,0.00014591003,0.00008085926,0.0000044792805],"category_scores_gemma":[0.00019256379,0.000115284995,0.00006521753,0.00069046376,0.00003378157,0.00015539982,0.00000970634,0.0005361154,1.3348856e-7],"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":[0.00018555018,0.000038469585,0.005273268,0.00019353667,0.000014703448,0.000036053163,0.00020757047,0.8668799,0.10241022,0.000007468376,0.000003967817,0.024749339],"study_design_scores_gemma":[0.0013995875,0.0042365035,0.04918108,0.0018323928,0.00004691092,0.00009224662,0.00006936415,0.85502684,0.086895466,0.00085848855,0.0000022224572,0.0003588862],"about_ca_topic_score_codex":0.0000024294704,"about_ca_topic_score_gemma":0.0000029625937,"teacher_disagreement_score":0.04390781,"about_ca_system_score_codex":0.00010361221,"about_ca_system_score_gemma":0.000088964065,"threshold_uncertainty_score":0.47011858},"labels":[],"label_agreement":null},{"id":"W3017135028","doi":"10.1109/icit45562.2020.9067234","title":"Coherency overhead of Processing-in-Memory in the presence of shared data","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Interleaving; Overhead (engineering); Latency (audio); Data access; Uniform memory access; Embedded system; Parallel computing; Computer architecture; Distributed computing; Memory management; Computer hardware; Semiconductor memory; Operating system; Database","score_opus":0.08057474048743878,"score_gpt":0.2879996296397095,"score_spread":0.2074248891522707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017135028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98655874,0.00071111805,0.004629734,0.00019073539,0.000042101798,0.00024512954,0.000015827687,0.000065013075,0.007541611],"genre_scores_gemma":[0.9990927,0.000008915994,0.0008094742,0.000051428284,0.000016490212,0.0000016331395,0.0000044866238,0.0000052885184,0.000009609892],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950635,0.0000152539415,0.00019169864,0.0001074464,0.000084818785,0.00009442185],"domain_scores_gemma":[0.9996762,0.000071623814,0.000026594538,0.00020250338,0.000008989545,0.000014037391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009360644,0.000054570195,0.00011175537,0.000017541237,0.000006532133,0.0000035482315,0.0004008167,0.000017669909,0.0000244008],"category_scores_gemma":[0.00009198512,0.00004044006,0.000008953811,0.00023712989,0.000016526943,0.00018513059,0.00008016294,0.000112647554,0.0000010782339],"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":[0.00013301834,0.00018864042,0.013012385,0.004459168,0.000014783925,0.00007735457,0.01726868,0.34004793,0.5131795,0.0003640999,0.0024980525,0.10875644],"study_design_scores_gemma":[0.0008700682,0.000107593536,0.018656375,0.0004534695,0.000007864208,0.0000048714355,0.0017986296,0.8486107,0.1285002,0.00035340368,0.00037518083,0.0002616334],"about_ca_topic_score_codex":0.0000074416566,"about_ca_topic_score_gemma":0.000019275038,"teacher_disagreement_score":0.5085628,"about_ca_system_score_codex":0.00000326689,"about_ca_system_score_gemma":0.000010302314,"threshold_uncertainty_score":0.16490978},"labels":[],"label_agreement":null},{"id":"W3017201749","doi":"10.1101/2020.04.20.051342","title":"Dendrites decrease the synaptic weight resolution necessary to implement linearly separable computations","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Computation; Soma; Perceptron; Neuromorphic engineering; Computer science; Constraint (computer-aided design); Models of neural computation; Artificial neural network; Complement (music); Synaptic weight; Algorithm; Artificial intelligence; Mathematics; Neuroscience","score_opus":0.019833754259517868,"score_gpt":0.24133451824437846,"score_spread":0.2215007639848606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017201749","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8619554,0.0026229925,0.12844789,0.0013697027,0.0019043391,0.0014647353,0.00024430672,0.00196888,0.000021714925],"genre_scores_gemma":[0.98320764,0.0001290413,0.015055739,0.00055464654,0.0007294724,0.00018072415,0.0000013808683,0.00013948357,0.0000018862769],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976749,0.000119972065,0.0005703255,0.0007031497,0.00031723682,0.0006144537],"domain_scores_gemma":[0.9981267,0.00021921551,0.00014640349,0.0008107857,0.0002044901,0.00049239845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031218893,0.00052833103,0.00044510566,0.00016396547,0.00037987865,0.00021565413,0.0006216114,0.00020632557,0.00003351791],"category_scores_gemma":[0.00023828141,0.0005050364,0.00014319847,0.00065730483,0.00004174937,0.00016486559,0.0005361348,0.0009471255,0.00012902576],"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":[0.000046386,0.00007155783,0.0005023164,0.00077780994,0.00040860465,0.00015429457,0.0000753096,0.44459775,0.54806024,0.0012670433,0.0040237373,0.000014934911],"study_design_scores_gemma":[0.0010317999,0.00020245567,0.023893677,0.001368236,0.0005247248,2.6601202e-7,0.000025841258,0.49552426,0.44048503,0.000074187046,0.03403929,0.0028302302],"about_ca_topic_score_codex":0.000017168017,"about_ca_topic_score_gemma":0.0000035614512,"teacher_disagreement_score":0.121252194,"about_ca_system_score_codex":0.0002585182,"about_ca_system_score_gemma":0.00019312366,"threshold_uncertainty_score":0.9997401},"labels":[],"label_agreement":null},{"id":"W3018235471","doi":"10.1109/aicas48895.2020.9073791","title":"Fault-Tolerant-Driven Clustering for Large Scale Neuromorphic Computing Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Crossbar switch; Neuromorphic engineering; Computer science; Cluster analysis; Fault tolerance; Memristor; Artificial neural network; Metric (unit); Set (abstract data type); Computation; Parallel computing; Distributed computing; Computer engineering; Algorithm; Artificial intelligence; Engineering","score_opus":0.04121339554166623,"score_gpt":0.24355036963643156,"score_spread":0.20233697409476534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018235471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27788037,0.00010544924,0.71944326,0.00011944205,0.00055827847,0.00031591373,0.00001128506,0.0009387889,0.00062720734],"genre_scores_gemma":[0.9948007,0.000004163697,0.0041914587,0.0004227651,0.00046695082,0.000006886074,0.000008430268,0.00005288526,0.000045776007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990027,0.00001732133,0.0002677497,0.00023883715,0.00009053939,0.00038287288],"domain_scores_gemma":[0.999582,0.00010651183,0.000033084078,0.00011588588,0.000027936589,0.00013459753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066347624,0.00017230069,0.0002468757,0.000026377054,0.00015071148,0.000048694183,0.00015658794,0.00004880276,0.000008067642],"category_scores_gemma":[0.000019269253,0.00017382261,0.00007198017,0.00012761133,0.000007293746,0.00012138466,0.00007876498,0.0001719675,0.000024885518],"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":[0.000010198776,0.000005025134,0.000065686356,0.00034593383,0.00001087155,0.000009819369,0.00042150347,0.9696683,0.028217046,0.00010028631,0.00038929709,0.00075597543],"study_design_scores_gemma":[0.0005002094,0.00004803557,0.000033126136,0.000052508134,0.0000080554555,0.000016674065,0.00018190169,0.9891743,0.0045675873,0.0000042526644,0.0052082874,0.00020503592],"about_ca_topic_score_codex":8.678237e-7,"about_ca_topic_score_gemma":0.0000023644257,"teacher_disagreement_score":0.7169203,"about_ca_system_score_codex":0.000019053936,"about_ca_system_score_gemma":0.0000039088013,"threshold_uncertainty_score":0.7088281},"labels":[],"label_agreement":null},{"id":"W3018431305","doi":"10.1039/d0na00195c","title":"Nonlinear ion drift-diffusion memristance description of TiO<sub>2</sub>RRAM devices","year":2020,"lang":"en","type":"article","venue":"Nanoscale Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"Advanced Materials and Bioengineering Research; Science Foundation Ireland; University of Calgary","keywords":"Materials science; Hysteresis; Dopant; Nonlinear system; Memristor; Ohmic contact; Ion; Diffusion; Quantum tunnelling; Anode; Optoelectronics; Condensed matter physics; Nanotechnology; Electronic engineering; Chemistry; Electrode; Physics; Doping; Thermodynamics","score_opus":0.01583100493733944,"score_gpt":0.22448581888861857,"score_spread":0.20865481395127913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018431305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97175694,0.006133321,0.020646416,0.00008096336,0.0005087407,0.00016203402,0.0000104813125,0.0003652724,0.00033584578],"genre_scores_gemma":[0.99421346,0.001315607,0.0039809914,0.00013588901,0.00028980966,0.000007050584,0.000013703055,0.00003247266,0.000011000552],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988995,0.000025712821,0.00034582606,0.00026928468,0.00021201943,0.00024761033],"domain_scores_gemma":[0.99951726,0.00006724274,0.00010800035,0.00014437632,0.000057255846,0.00010583825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056891975,0.00019772263,0.0002674502,0.0000437089,0.000092681345,0.000015538188,0.00016528649,0.00007818921,0.000006078469],"category_scores_gemma":[0.000050274924,0.00019557525,0.00007489168,0.00032937666,0.00005059842,0.000582483,0.00004056184,0.00019815867,0.000020414669],"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":[0.000040492487,0.000020152007,0.0004732397,0.0002599385,0.0000065734425,0.0000049673317,0.00016969535,0.01621753,0.9527483,0.000024758678,0.00004463069,0.029989714],"study_design_scores_gemma":[0.00029387,0.00011150286,0.00062130316,0.00012858647,0.000013330133,0.0000029382338,0.00010325968,0.013000523,0.9731334,0.00016845835,0.012203158,0.00021967753],"about_ca_topic_score_codex":5.493677e-7,"about_ca_topic_score_gemma":0.000015364632,"teacher_disagreement_score":0.029770035,"about_ca_system_score_codex":0.000028127393,"about_ca_system_score_gemma":0.000008519099,"threshold_uncertainty_score":0.79753274},"labels":[],"label_agreement":null},{"id":"W3020565402","doi":"10.1109/ijcnn48605.2020.9206631","title":"Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"MNIST database; Computer science; Dot product; Matrix multiplication; Computer engineering; Range (aeronautics); Deep learning; Resistive random-access memory; Flash memory; Floating point; Software; Artificial neural network; Crossbar switch; Algorithm; Artificial intelligence; Computational science; Voltage; Computer hardware; Electrical engineering; Engineering; Mathematics; Physics","score_opus":0.056609861270743494,"score_gpt":0.28732405515100673,"score_spread":0.23071419388026324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020565402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12333023,0.0002279675,0.87255836,0.000052768046,0.00088234455,0.000562773,0.000038050064,0.0021431418,0.00020438989],"genre_scores_gemma":[0.74974275,0.0000052421956,0.24911158,0.00018153225,0.00061082136,0.000008831097,0.00007185648,0.00020162358,0.00006579736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974636,0.00004727755,0.00059935555,0.0008178978,0.00037714487,0.00069476693],"domain_scores_gemma":[0.9983629,0.0005117317,0.0002221784,0.00053579366,0.00009964194,0.0002677314],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013639744,0.0007848957,0.00075634033,0.00017994491,0.00021937217,0.00013228769,0.00052231044,0.00027754993,0.000111140725],"category_scores_gemma":[0.0001327461,0.0007303719,0.00019102731,0.00030625233,0.000051477957,0.00016686332,0.00047509553,0.0015852263,0.00007315982],"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":[0.000035732024,0.000015142497,0.000046576453,0.0008074881,0.00008634427,0.00006800469,0.00010067937,0.9904611,0.0046552233,0.000051341933,0.000102011116,0.003570372],"study_design_scores_gemma":[0.0006019922,0.000039346127,0.00009401712,0.0009085056,0.00010104352,0.00003641675,0.000013446854,0.98372245,0.013088637,0.00025020275,0.0001432777,0.0010006655],"about_ca_topic_score_codex":0.000007921363,"about_ca_topic_score_gemma":0.000002900223,"teacher_disagreement_score":0.6264125,"about_ca_system_score_codex":0.00017370953,"about_ca_system_score_gemma":0.00014855758,"threshold_uncertainty_score":0.99951476},"labels":[],"label_agreement":null},{"id":"W3021000812","doi":"10.1021/acsanm.0c00173","title":"Passive Filters for Nonvolatile Storage Based on Capacitive-Coupled Memristive Effects in Nanolayered Organic–Inorganic Heterojunction Devices","year":2020,"lang":"en","type":"article","venue":"ACS Applied Nano Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Capacitive sensing; Materials science; Optoelectronics; Nanodevice; Memristor; Voltage; Filter (signal processing); Power (physics); Nanotechnology; Electrical engineering; Engineering; Physics","score_opus":0.01130528711138972,"score_gpt":0.20502414573010416,"score_spread":0.19371885861871443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021000812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9760847,0.000015113096,0.02160932,0.000053750307,0.0005813487,0.0012197095,0.000057494188,0.00033192892,0.00004660548],"genre_scores_gemma":[0.99830955,0.0000030401807,0.0005131681,0.00056412723,0.00021775946,0.00021899251,0.000077321565,0.000090779664,0.000005251648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998667,0.000046269623,0.00036314703,0.00041138736,0.0001379793,0.00037420157],"domain_scores_gemma":[0.99921805,0.00036286624,0.00012325251,0.00017473006,0.000031730982,0.00008940221],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011073056,0.0003350612,0.00047848854,0.00009151105,0.0001053306,0.0000540457,0.00016071065,0.00013296194,0.00007867316],"category_scores_gemma":[0.00008876454,0.00033414684,0.00004052733,0.0002606502,0.000021254007,0.00012092944,0.000033957593,0.00012569211,0.00004571019],"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":[0.0003576877,0.000018050552,0.000005453663,0.00037949765,0.000022534285,0.0000102858285,0.00032905492,0.016281657,0.9822068,0.000048060876,0.00007626173,0.00026461223],"study_design_scores_gemma":[0.00168631,0.00021387784,0.00026464649,0.000089998524,0.00002464363,7.865873e-7,0.00007019587,0.0075617093,0.9895975,0.00007392896,0.00006912315,0.0003472513],"about_ca_topic_score_codex":0.0000024601086,"about_ca_topic_score_gemma":0.00000420827,"teacher_disagreement_score":0.02222483,"about_ca_system_score_codex":0.00013028739,"about_ca_system_score_gemma":0.000017589686,"threshold_uncertainty_score":0.99991107},"labels":[],"label_agreement":null},{"id":"W3021928213","doi":"10.1007/978-1-4020-2075-9_10","title":"Three-Dimensional Feedforward Neural Networks and Their Realization by Nano-Devices","year":2004,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hypercube; Feedforward neural network; Realization (probability); Feed forward; Binary decision diagram; Embedding; Binary number; Computer science; Artificial neural network; Topology (electrical circuits); Boolean function; Binary tree; Theoretical computer science; Algorithm; Mathematics; Parallel computing; Artificial intelligence; Engineering; Combinatorics; Arithmetic","score_opus":0.011544235372694327,"score_gpt":0.19815796848155307,"score_spread":0.18661373310885873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021928213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009876251,0.033756852,0.5559865,0.0002318777,0.0031039102,0.0015319062,0.00011718135,0.0040456746,0.3913498],"genre_scores_gemma":[0.94977635,0.00075207604,0.0008858197,0.0009479144,0.0011505684,0.000009411308,0.0006344745,0.0004090054,0.04543439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991717,0.0000030111382,0.00023948922,0.00027337557,0.000096949545,0.00021549505],"domain_scores_gemma":[0.99962234,0.000059217506,0.00006097958,0.00014905527,0.000027711198,0.000080690224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004106744,0.0003634431,0.00028491486,0.000048857233,0.00010225725,0.000030263269,0.00008677525,0.00023831897,0.0001159733],"category_scores_gemma":[0.00000202816,0.00029935653,0.00006445441,0.000023529792,0.000039297138,0.00012335146,0.000060877937,0.0003056652,0.0000058123915],"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":[0.000014244313,0.0000034004618,0.0000089383,0.00011082668,0.000070094466,0.000010097597,0.000021934122,0.95409995,0.0007157505,0.020198055,0.002480146,0.022266535],"study_design_scores_gemma":[0.00069026294,0.00013358647,0.000033620498,0.000620171,0.00006982221,0.00010283975,0.000007593612,0.9235641,0.0023091147,0.0390305,0.031855125,0.0015832973],"about_ca_topic_score_codex":0.0000053871568,"about_ca_topic_score_gemma":0.000039916267,"teacher_disagreement_score":0.9399001,"about_ca_system_score_codex":0.000040748448,"about_ca_system_score_gemma":0.0000060227176,"threshold_uncertainty_score":0.9999459},"labels":[],"label_agreement":null},{"id":"W3023749416","doi":"10.1039/d0nr01671c","title":"Conductive-bridging random-access memories for emerging neuromorphic computing","year":2020,"lang":"en","type":"article","venue":"Nanoscale","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Centre","funders":"National Research Foundation of Korea","keywords":"Neuromorphic engineering; Memristor; Bridging (networking); Von Neumann architecture; Scalability; Computer science; Resistive random-access memory; Nanotechnology; Artificial neural network; Computer architecture; Nanoclusters; Materials science; Artificial intelligence; Electronic engineering; Electrical engineering; Engineering; Voltage; Computer network","score_opus":0.060481767132668465,"score_gpt":0.28231348458872935,"score_spread":0.2218317174560609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023749416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70964056,0.00029780582,0.28717226,0.00046068957,0.000880073,0.00034928622,0.000009810084,0.00073953916,0.00044995666],"genre_scores_gemma":[0.997072,0.000011611218,0.0017941294,0.0004672689,0.0005526788,0.000011279518,0.00000885056,0.000055846118,0.000026362959],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894905,0.000024458843,0.00026638148,0.00028417885,0.000114257185,0.00036164868],"domain_scores_gemma":[0.9994614,0.00020394975,0.000052449162,0.00011506532,0.00004614831,0.0001209531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000919015,0.0001985512,0.00029977193,0.000039352944,0.00023802242,0.00006626239,0.00023284917,0.00004603751,0.000016122514],"category_scores_gemma":[0.00013494097,0.0002106953,0.00009678169,0.00024843792,0.000036957397,0.00032458623,0.00009063223,0.00021212081,0.000008046919],"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":[0.00017552855,0.00001582518,0.00047653855,0.00057839515,0.000057626552,0.00003679207,0.0015192823,0.4162482,0.5545888,0.00019788892,0.0021975827,0.023907565],"study_design_scores_gemma":[0.0017107202,0.000060535996,0.00013969325,0.000053483374,0.000024843423,0.000013936036,0.00011627096,0.44562814,0.5448581,0.00015593127,0.0068792244,0.00035909112],"about_ca_topic_score_codex":8.688659e-7,"about_ca_topic_score_gemma":5.826076e-7,"teacher_disagreement_score":0.2874314,"about_ca_system_score_codex":0.00001920839,"about_ca_system_score_gemma":0.00000938771,"threshold_uncertainty_score":0.8591905},"labels":[],"label_agreement":null},{"id":"W3024455584","doi":"10.1109/vrw50115.2020.0-271","title":"NIDIT: Workshop on Novel Input Devices and Interaction Techniques","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science","score_opus":0.07267849627518419,"score_gpt":0.3029409391475789,"score_spread":0.23026244287239472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024455584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.989579,0.00020422766,0.005236784,0.0018310703,0.0003391629,0.00024204155,0.000031208758,0.00033428572,0.0022021933],"genre_scores_gemma":[0.996624,0.0018093684,0.00023200452,0.0009559933,0.00024344442,0.000010580924,0.000009591313,0.00003080508,0.000084231724],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998558,0.00003653104,0.00040865055,0.0005332734,0.00016682521,0.00029673468],"domain_scores_gemma":[0.9990416,0.00035785232,0.000113601476,0.00016472263,0.00004709337,0.000275108],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015231043,0.0003757361,0.0003722347,0.000054533924,0.00016044498,0.00027553207,0.00014406581,0.00020222613,0.000029081244],"category_scores_gemma":[0.000079837744,0.0003335746,0.0000340425,0.00012596209,0.000116161355,0.0004571208,0.00006926791,0.000830154,0.0000093140925],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013102205,0.00019710037,0.000154888,0.0005760707,0.00019912099,0.00006821554,0.0062956098,0.04004605,0.106148526,0.0017480167,0.002908372,0.8403478],"study_design_scores_gemma":[0.0035020409,0.0042841956,0.01572567,0.009275163,0.0002856606,0.00016920993,0.01244994,0.22563002,0.64693046,0.0010086945,0.07594584,0.0047931382],"about_ca_topic_score_codex":0.00001252269,"about_ca_topic_score_gemma":0.000056747424,"teacher_disagreement_score":0.83555466,"about_ca_system_score_codex":0.00002343805,"about_ca_system_score_gemma":0.0000115491,"threshold_uncertainty_score":0.9999116},"labels":[],"label_agreement":null},{"id":"W3025319221","doi":"10.1149/ma2020-01522890mtgabs","title":"Charge Carrier Transport in a Polymeric Semiconductor: Poly(3-hexylthiophene-2,5-diyl) (P3HT)","year":2020,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pentacene; Organic semiconductor; Materials science; Charge carrier; Semiconductor; Polaron; Polymer; Organic electronics; Electron mobility; Perylene; Activation energy; Transistor; Chemical physics; Condensed matter physics; Optoelectronics; Nanotechnology; Electron; Chemistry; Physical chemistry; Thin-film transistor; Molecule; Voltage; Organic chemistry; Physics","score_opus":0.020654788383865644,"score_gpt":0.22785175436410451,"score_spread":0.20719696598023887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025319221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9796286,0.00089777604,0.000027631619,0.00019963188,0.00046435418,0.000186177,0.000016978407,0.0006154392,0.017963363],"genre_scores_gemma":[0.998507,0.00002147176,0.00049863546,0.00037964602,0.0004372273,0.000009835246,0.000013870403,0.00007719671,0.000055065142],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99830586,0.000025555646,0.00052340963,0.00036945633,0.00019792488,0.0005778046],"domain_scores_gemma":[0.9993517,0.000095330644,0.00008110875,0.00018233778,0.00001679958,0.00027275996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016988898,0.00029895845,0.00033662296,0.000077754856,0.000084602696,0.000017621811,0.0002151955,0.00012139115,0.000045745477],"category_scores_gemma":[0.00009552702,0.0003329777,0.00008779565,0.00036790446,0.000034279343,0.00022009114,0.00002330382,0.00060755806,0.00004848171],"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":[0.000019123721,0.00001333887,0.0011624559,0.00014373274,0.000013270859,0.00012866902,0.002030733,0.08857927,0.9072962,0.0000029959922,0.000052485713,0.0005577714],"study_design_scores_gemma":[0.000584815,0.000045505505,0.00418985,0.00013204533,0.000015975,0.00002632473,0.00026568517,0.004300974,0.98529065,0.00003180423,0.004549144,0.00056725927],"about_ca_topic_score_codex":0.00003727363,"about_ca_topic_score_gemma":0.0000075676176,"teacher_disagreement_score":0.08427829,"about_ca_system_score_codex":0.000055436172,"about_ca_system_score_gemma":0.000030376656,"threshold_uncertainty_score":0.9999122},"labels":[],"label_agreement":null},{"id":"W3026675290","doi":"10.1016/j.robot.2020.103566","title":"A spiking network classifies human sEMG signals and triggers finger reflexes on a robotic hand","year":2020,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"Horizon 2020; Horizon 2020 Framework Programme; European Commission","keywords":"Computer science; Artificial intelligence; Spiking neural network; Robot; Artificial neural network; Kinematics; Robotics; Computer vision; Pattern recognition (psychology)","score_opus":0.04033566378203116,"score_gpt":0.24422092330349518,"score_spread":0.20388525952146402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026675290","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8818344,0.0153197395,0.093672276,0.00083680387,0.0018076585,0.0012206604,0.000009176923,0.0013116677,0.0039876066],"genre_scores_gemma":[0.9986009,0.000048785772,0.00052274234,0.00013729639,0.0005186807,0.0000064411947,0.000003017953,0.000042418633,0.0001197336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989284,0.0000377917,0.00031629115,0.00028309698,0.00009774892,0.00033663903],"domain_scores_gemma":[0.9995147,0.00012645886,0.00007182516,0.000113860275,0.000016063701,0.00015707602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001201226,0.00023558013,0.00039775766,0.000038949285,0.0003373575,0.00021216877,0.000073986885,0.00008413436,0.000002061275],"category_scores_gemma":[0.000015212924,0.00021946471,0.000040653595,0.0001147526,0.000051578583,0.00008654783,0.000045089488,0.0002270253,0.000004217501],"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":[0.0000065376867,0.0000045014262,0.00009452581,0.00027386763,0.000030814525,0.000023787003,0.00038627532,0.9826144,0.014594077,0.0010362101,0.00015298772,0.00078204664],"study_design_scores_gemma":[0.0006273971,0.0003894335,0.0002326324,0.00071832136,0.00006333132,0.00004194359,0.00023582463,0.9913644,0.003575987,0.0002446442,0.0018822508,0.0006237859],"about_ca_topic_score_codex":0.00000394835,"about_ca_topic_score_gemma":0.0000020089192,"teacher_disagreement_score":0.116766475,"about_ca_system_score_codex":0.00002926149,"about_ca_system_score_gemma":0.00000823594,"threshold_uncertainty_score":0.89495116},"labels":[],"label_agreement":null},{"id":"W3027048508","doi":"10.3791/61026","title":"In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased &lt;em&gt;a&lt;/em&gt;-VO&lt;sub&gt;&lt;em&gt;x&lt;/em&gt;&lt;/sub&gt;","year":2020,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Neuromorphic engineering; Materials science; Crossbar switch; Fabrication; Resistive random-access memory; Vanadium oxide; Amorphous solid; Biasing; Optoelectronics; Nanotechnology; Transmission electron microscopy; Resistive touchscreen; Dielectric; Computer science; Electrical engineering; Oxide; Voltage; Engineering; Telecommunications; Chemistry","score_opus":0.019529430394479706,"score_gpt":0.33247344167697046,"score_spread":0.3129440112824908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027048508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9427286,0.0062983087,0.04872774,0.00013526667,0.000656111,0.0009507289,0.000026380183,0.00022619368,0.0002507031],"genre_scores_gemma":[0.98770255,0.0007416024,0.01024483,0.0004559071,0.0004338148,0.00003412472,0.00005258675,0.00029181046,0.00004278194],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99255383,0.0005449772,0.00252538,0.00111938,0.0018174828,0.0014389725],"domain_scores_gemma":[0.9960587,0.00052257854,0.0014498456,0.00061060634,0.00047385617,0.0008843866],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010860198,0.0012592146,0.0019132034,0.0015198061,0.0004642686,0.00033318388,0.00075953547,0.0004875949,0.000060200928],"category_scores_gemma":[0.00037473408,0.0012004466,0.0005098742,0.0026062292,0.00014269502,0.001269965,0.000104644096,0.0011729636,0.000017866245],"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":[0.0038292897,0.0007506081,0.00008587457,0.00037706795,0.00021163448,0.0001802718,0.0029134394,0.012799479,0.96885794,0.00003275601,0.00057833124,0.009383314],"study_design_scores_gemma":[0.012095558,0.0026688688,0.0010070753,0.0013785023,0.00018975984,0.000108909146,0.0002692466,0.022946997,0.95516497,0.000045355668,0.002979717,0.001145019],"about_ca_topic_score_codex":0.0000032729956,"about_ca_topic_score_gemma":0.000010689321,"teacher_disagreement_score":0.044973973,"about_ca_system_score_codex":0.0007431691,"about_ca_system_score_gemma":0.00024500093,"threshold_uncertainty_score":0.99904454},"labels":[],"label_agreement":null},{"id":"W3029653213","doi":"10.1016/j.nanoen.2020.104938","title":"Biomemristors as the next generation bioelectronics","year":2020,"lang":"en","type":"article","venue":"Nano Energy","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":163,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bioelectronics; Nanotechnology; Memristor; Flexibility (engineering); Biocompatible material; Biomaterial; Materials science; Biomedicine; Computer science; Biochemical engineering; Engineering; Biosensor; Bioinformatics; Electrical engineering; Biomedical engineering","score_opus":0.04609219125266358,"score_gpt":0.22547875574461612,"score_spread":0.17938656449195253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029653213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94260734,0.005079267,0.046086885,0.0016815417,0.0010307217,0.00007681247,0.0000026936113,0.0006753006,0.0027594606],"genre_scores_gemma":[0.997945,0.00007106858,0.00009773176,0.00131109,0.0004249809,0.00000596653,0.000007352934,0.000017519447,0.00011930896],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999583,0.000018028824,0.00008448129,0.00009719905,0.00007586284,0.00014141558],"domain_scores_gemma":[0.9998388,0.000015838083,0.000014675269,0.00008329831,0.000008940296,0.00003839154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000024597457,0.00007437235,0.000058941652,0.000016333646,0.00013252627,0.00001653027,0.00010989765,0.000023593844,0.000013569039],"category_scores_gemma":[0.000008359462,0.000059922604,0.000029720773,0.00020112886,0.000010317495,0.000055096225,0.000027081867,0.000085621,0.000015503056],"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":[0.0000024433766,0.0000016591595,3.8119592e-7,0.0000024490819,0.000005818736,0.0000023438286,0.00009120376,0.24840234,0.7262173,0.0021616546,0.0016732928,0.021439087],"study_design_scores_gemma":[0.00006558735,0.00005402052,3.933862e-7,0.0000010157038,0.0000034777436,0.000006492038,0.000017681326,0.2154935,0.5085694,0.00011099765,0.27559385,0.000083547224],"about_ca_topic_score_codex":0.000005882741,"about_ca_topic_score_gemma":0.0000044102762,"teacher_disagreement_score":0.27392057,"about_ca_system_score_codex":0.00003687005,"about_ca_system_score_gemma":0.000011234092,"threshold_uncertainty_score":0.24435729},"labels":[],"label_agreement":null},{"id":"W3034257362","doi":"10.48550/arxiv.2006.05011","title":"RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer vision; Artificial intelligence; Computer science; RGB color model; Tracking (education); Calibration; Video tracking; Event (particle physics); Object (grammar); Computer graphics (images); Camera resectioning; Mathematics; Physics; Psychology","score_opus":0.08417172110600368,"score_gpt":0.20236976273726293,"score_spread":0.11819804163125924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034257362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3122108,0.000071209906,0.68567705,0.000033815417,0.00063389284,0.000402086,0.00003861883,0.000501238,0.00043127468],"genre_scores_gemma":[0.9987167,0.000073180076,0.0005467807,0.000057847483,0.00028638582,0.0000020798657,0.000071759525,0.000056391083,0.00018883831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886763,0.000034980578,0.00020140706,0.0005574818,0.00004865282,0.00028984464],"domain_scores_gemma":[0.9993298,0.00010293722,0.000100937854,0.00029408254,0.000049516,0.00012274383],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006918485,0.00029933182,0.0003116875,0.00009499825,0.00012933688,0.00004135189,0.00030553466,0.00018747285,0.000016001177],"category_scores_gemma":[0.000032051703,0.0003818713,0.00022339712,0.00021239978,0.000027059956,0.00021584764,0.00020979774,0.000537736,0.000010478498],"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":[0.000035235895,0.000010640717,0.00007328889,0.00030675196,0.00005799873,0.00006215227,0.00018046,0.99254405,0.003858357,0.0015955195,0.00012341465,0.0011521444],"study_design_scores_gemma":[0.000406048,0.00004576963,0.00011125299,0.0001372668,0.00008997324,0.0000030023962,0.00013551352,0.9749685,0.017867591,0.0051958296,0.0005624677,0.00047681804],"about_ca_topic_score_codex":0.0000066757343,"about_ca_topic_score_gemma":0.0000114673485,"teacher_disagreement_score":0.6865059,"about_ca_system_score_codex":0.00016601586,"about_ca_system_score_gemma":0.00004085885,"threshold_uncertainty_score":0.9998633},"labels":[],"label_agreement":null},{"id":"W3034495101","doi":"10.1016/j.cnsns.2020.105399","title":"Dynamics of spiking map-based neural networks in problems of supervised learning","year":2020,"lang":"en","type":"article","venue":"Communications in Nonlinear Science and Numerical Simulation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"Russian Science Foundation","keywords":"Computer science; Artificial neural network; Artificial intelligence; Spiking neural network; Perspective (graphical); Dynamics (music); Coupling (piping); Biological neuron model; Range (aeronautics); Key (lock); Supervised learning; Machine learning; Pattern recognition (psychology); Biological system; Physics; Biology","score_opus":0.039823182373032964,"score_gpt":0.2976528637170585,"score_spread":0.25782968134402556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034495101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82195693,0.00024592847,0.17722875,0.0003141625,0.000022286997,0.00014080317,9.871494e-7,0.00003881907,0.000051328283],"genre_scores_gemma":[0.9914312,0.000023555396,0.008492836,0.000027889346,0.000007585062,0.0000019579008,0.000009039594,0.0000057813004,1.7706957e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936104,0.00003968483,0.00027427182,0.00010259912,0.00010805072,0.000114367955],"domain_scores_gemma":[0.9994222,0.00025348258,0.00005211958,0.00017860369,0.000058821428,0.000034753055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023822529,0.000058605212,0.00012735404,0.00008974887,0.00006231129,0.000008611686,0.00027567399,0.000027483442,0.0000011570671],"category_scores_gemma":[0.0001888003,0.00006152158,0.000014189479,0.0010726632,0.00016166357,0.00017414923,0.0001015331,0.00023183964,1.3914173e-7],"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":[0.0000054623997,0.000014703085,0.013674745,0.000029785506,4.0055411e-7,1.2593641e-7,0.00019714228,0.96590906,0.0017324302,0.00003490976,3.063972e-8,0.0184012],"study_design_scores_gemma":[0.00018876183,0.0000373835,0.0034640261,0.000047159036,0.0000013872078,1.0209282e-7,0.00006325914,0.9957125,0.00038466437,0.000028364793,0.000014909866,0.000057488654],"about_ca_topic_score_codex":0.0000075520024,"about_ca_topic_score_gemma":0.000006844842,"teacher_disagreement_score":0.16947424,"about_ca_system_score_codex":0.000033761884,"about_ca_system_score_gemma":0.000012935201,"threshold_uncertainty_score":0.2508777},"labels":[],"label_agreement":null},{"id":"W3035155389","doi":"10.1063/5.0009984","title":"Ion-gated transistors based on porous and compact TiO2 films: Effect of Li ions in the gating medium","year":2020,"lang":"en","type":"article","venue":"AIP Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Fonds de recherche du Québec – Nature et technologies; Università di Bologna; CMC Microsystems","keywords":"Materials science; Gating; Transistor; Ion; Optoelectronics; Nanotechnology; Bioelectronics; Voltage; Chemistry; Electrical engineering; Organic chemistry; Biosensor","score_opus":0.011949458900336133,"score_gpt":0.24181548091309157,"score_spread":0.22986602201275544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035155389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99555,0.00088627526,0.0018035067,0.0006235426,0.00012547299,0.00019051228,0.0000063641182,0.000114401824,0.0006999271],"genre_scores_gemma":[0.9994665,0.0000330637,0.00018890253,0.00025742158,0.00003393518,0.0000028264599,0.000004623987,0.000011807895,9.1730533e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941087,0.00007134641,0.0001598613,0.000121774516,0.00010340999,0.00013274154],"domain_scores_gemma":[0.99908555,0.0007564103,0.000033601755,0.00007890884,0.000006811236,0.00003871558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012204011,0.000117692354,0.00018209252,0.00003258541,0.000052911553,0.000006017564,0.00008979813,0.00002398574,0.000006626312],"category_scores_gemma":[0.00009949692,0.00008328602,0.000030037305,0.00021354009,0.000036519054,0.000095374104,0.0000041873723,0.0001822097,0.0000010455406],"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":[0.000087661094,0.000014433851,0.0025609175,0.00032010255,0.000008486223,0.000020940253,0.0012766271,0.95382816,0.0337925,0.000019522633,0.00009544232,0.0079752235],"study_design_scores_gemma":[0.0024949424,0.001914384,0.008799142,0.00071298715,0.00004828227,0.0000131710385,0.00095086906,0.6023754,0.37652114,0.000105132814,0.005498851,0.00056574354],"about_ca_topic_score_codex":0.000002467519,"about_ca_topic_score_gemma":0.0000069446373,"teacher_disagreement_score":0.35145277,"about_ca_system_score_codex":0.000009242567,"about_ca_system_score_gemma":0.000004496184,"threshold_uncertainty_score":0.3396305},"labels":[],"label_agreement":null},{"id":"W3037758067","doi":"10.1002/pssr.202000252","title":"Fully Printed Inverters using Metal‐Oxide Semiconductor and Graphene Passives on Flexible Substrates","year":2020,"lang":"en","type":"article","venue":"physica status solidi (RRL) - Rapid Research Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"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":"University of Waterloo; Deutsche Forschungsgemeinschaft; Karlsruhe Institute of Technology","keywords":"Materials science; Transistor; Graphene; Printed electronics; Optoelectronics; Polyimide; Flexible electronics; Printed circuit board; Inverter; Substrate (aquarium); Oxide; Resistor; Inkwell; Electrical engineering; Nanotechnology; Layer (electronics); Composite material; Voltage; Engineering","score_opus":0.10994183645431573,"score_gpt":0.3220611220787364,"score_spread":0.21211928562442067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037758067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959346,0.00038506748,0.0009965679,0.0016525182,0.00012546373,0.00038127488,0.000025741994,0.00037758777,0.0001212021],"genre_scores_gemma":[0.99762505,0.00023920133,0.00074901135,0.00097679,0.00027307373,0.000022443777,0.000016934922,0.000090293994,0.000007215579],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99713975,0.00022275554,0.00030834915,0.00060460024,0.0005189364,0.0012056179],"domain_scores_gemma":[0.9986447,0.0004082261,0.000059455513,0.00030966612,0.000080198224,0.0004977318],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019632517,0.00037289003,0.00041624423,0.00022802643,0.00033892135,0.00015092167,0.0002818814,0.00005924123,0.000016366437],"category_scores_gemma":[0.00011935277,0.0003564525,0.00012604699,0.00078037794,0.00032933833,0.0005542922,0.00015392211,0.0010101242,0.00002442214],"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":[0.00009387577,0.000027570486,0.00007982492,0.00018833631,0.00012681275,0.000047658606,0.00081620965,0.020451594,0.97515523,0.00010756628,0.0009273301,0.0019779895],"study_design_scores_gemma":[0.0006136908,0.00017670488,0.0004377232,0.00010802509,0.000022043698,0.0000057217353,0.00056182465,0.044514697,0.9517933,0.00023034491,0.0010863293,0.00044957528],"about_ca_topic_score_codex":0.000024245464,"about_ca_topic_score_gemma":0.000001402297,"teacher_disagreement_score":0.024063103,"about_ca_system_score_codex":0.00010789205,"about_ca_system_score_gemma":0.000041350053,"threshold_uncertainty_score":0.9998887},"labels":[],"label_agreement":null},{"id":"W3037830872","doi":"10.1088/1361-6528/aba6b4","title":"Conductive filament evolution dynamics revealed by cryogenic (1.5 K) multilevel switching of CMOS-compatible Al <sub>2</sub> O <sub>3</sub> /TiO <sub>2</sub> resistive memories","year":2020,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut interdisciplinaire d'innovation technologique; Institut quantique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Université de Sherbrooke","keywords":"Resistive random-access memory; Electrical conductor; Protein filament; Thermal conduction; Quantum tunnelling; Conductance; Resistive touchscreen; Conductivity","score_opus":0.011628941575735354,"score_gpt":0.21390734918750245,"score_spread":0.2022784076117671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037830872","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8968867,0.00080630335,0.098812185,0.0005995128,0.0006521854,0.0007837787,0.00015059367,0.001278177,0.000030530897],"genre_scores_gemma":[0.99741447,0.00028312,0.0016280877,0.00020961078,0.0001239811,0.000089145775,0.00009714339,0.00015096324,0.0000034710968],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961656,0.00015021667,0.001116567,0.0010644606,0.0004597353,0.0010434339],"domain_scores_gemma":[0.99798614,0.00036652948,0.0005174948,0.0006464557,0.0002748935,0.00020848673],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002884128,0.00076468807,0.0011034253,0.00038328217,0.00038810578,0.000031654632,0.000600548,0.0007789691,0.000003637959],"category_scores_gemma":[0.0006434602,0.00087791274,0.000259945,0.0010359188,0.00029968994,0.00045796382,0.00038330318,0.0015536299,0.00004955428],"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":[0.00019266458,0.00007323499,0.00009681479,0.00021902611,0.0001778553,0.000040289517,0.0004660348,0.019741913,0.9580472,0.000697283,0.000448218,0.01979946],"study_design_scores_gemma":[0.0012519866,0.00035278485,0.00030423273,0.00019126968,0.00010614145,0.000043783166,0.0005530337,0.07756305,0.91719586,0.0016396763,0.00007111479,0.00072706625],"about_ca_topic_score_codex":0.000009447384,"about_ca_topic_score_gemma":0.00007116859,"teacher_disagreement_score":0.10052775,"about_ca_system_score_codex":0.00088836206,"about_ca_system_score_gemma":0.00011350598,"threshold_uncertainty_score":0.9993672},"labels":[],"label_agreement":null},{"id":"W3037950881","doi":"10.1016/j.ymssp.2020.106961","title":"Pre-classified reservoir computing for the fault diagnosis of 3D printers","year":2020,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Dongguan University of Technology; National Natural Science Foundation of China; ASCRS Research Foundation","keywords":"Computer science; Spare part; Fault (geology); Encoder; Data mining; Domain (mathematical analysis); Artificial intelligence; Pattern recognition (psychology); Engineering","score_opus":0.047904352167386685,"score_gpt":0.2674828463253875,"score_spread":0.2195784941580008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037950881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.344925,0.0017999065,0.65245914,0.00015273906,0.00011343301,0.00037378064,0.0000037996142,0.00012783942,0.000044380642],"genre_scores_gemma":[0.9987083,0.000016408443,0.0009377127,0.0000622705,0.00021915855,0.000025451714,9.771518e-7,0.000022482944,0.000007229217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991013,0.000029078408,0.0003456543,0.000193183,0.00012610877,0.00020471202],"domain_scores_gemma":[0.9992842,0.00043310938,0.00008720611,0.000066421104,0.00004718553,0.00008188997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023111595,0.0001307272,0.0002505154,0.000014292397,0.00017998605,0.00005901588,0.0001485976,0.000058481575,0.000002256235],"category_scores_gemma":[0.00006450214,0.000091393435,0.000046977926,0.00011751778,0.000024302419,0.000090634065,0.00006482458,0.00017213418,3.0408893e-7],"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":[0.00018430888,0.00002788114,0.00035921836,0.0057094567,0.000103362356,0.000006773293,0.0019047392,0.67902714,0.12526041,0.0010697608,0.00018914527,0.18615778],"study_design_scores_gemma":[0.00025826538,0.000095127725,0.000039848437,0.00035317088,0.000021074842,0.0000043406976,0.00028095546,0.9775447,0.020301774,0.000062690095,0.0009196632,0.00011833702],"about_ca_topic_score_codex":0.000003418697,"about_ca_topic_score_gemma":6.7188796e-7,"teacher_disagreement_score":0.6537833,"about_ca_system_score_codex":0.00001278263,"about_ca_system_score_gemma":0.000007973852,"threshold_uncertainty_score":0.37269163},"labels":[],"label_agreement":null},{"id":"W3039629617","doi":"10.1038/s41467-020-17031-9","title":"A framework for on-implant spike sorting based on salient feature selection","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"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":"Computer science; Pattern recognition (psychology); Salient; Artificial intelligence; Sorting; Spike (software development); Spike sorting; Feature selection; Feature extraction; Algorithm","score_opus":0.03833850476742638,"score_gpt":0.31696141310949344,"score_spread":0.27862290834206704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039629617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050730504,0.0039001976,0.8447655,0.08277912,0.0021466136,0.002805667,0.00021380345,0.004648812,0.008009811],"genre_scores_gemma":[0.929382,0.000021316931,0.0652743,0.005011173,0.00017481294,0.000041793177,0.000058103313,0.00002992899,0.000006535145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942994,0.000030931893,0.00012812288,0.00014945953,0.0000934593,0.00016809047],"domain_scores_gemma":[0.9986892,0.0006818956,0.00004687137,0.0004701284,0.00004185807,0.00007008511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006471805,0.0001298885,0.00011806942,0.000042866035,0.0003009367,0.000022485841,0.0003671161,0.00021296887,0.0000030758472],"category_scores_gemma":[0.00036693236,0.0001266401,0.00006956866,0.00027917014,0.0000137413235,0.00003900308,0.00003956781,0.0014785568,0.000008079212],"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":[0.00015633802,0.00011293077,0.00019404566,0.00010740584,0.000054538952,0.0000024357994,0.00055154937,0.87273556,0.016006634,0.07010587,0.022505473,0.017467232],"study_design_scores_gemma":[0.000388379,0.00024877448,0.00033157563,0.00020220003,0.000032844455,0.000003533429,0.000056720593,0.878457,0.02681684,0.0019824463,0.0911487,0.0003309633],"about_ca_topic_score_codex":1.523358e-7,"about_ca_topic_score_gemma":0.000004854007,"teacher_disagreement_score":0.87865156,"about_ca_system_score_codex":0.000043465312,"about_ca_system_score_gemma":0.000011195668,"threshold_uncertainty_score":0.64236754},"labels":[],"label_agreement":null},{"id":"W3040510583","doi":"10.1109/etfa46521.2020.9211968","title":"Towards a Programming Paradigm for Reconfigurable Computing: Asynchronous Graph Programming","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Carleton University","funders":"","keywords":"Computer science; Control reconfiguration; Asynchronous communication; Programming paradigm; Distributed computing; Parallel computing; Graph; Event-driven programming; Programming language; Parallel programming model; Execution model; Computer architecture; Software; Reactive programming; Inductive programming; Theoretical computer science; Embedded system","score_opus":0.032614992773997424,"score_gpt":0.2575800573631385,"score_spread":0.22496506458914106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040510583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056254044,0.0004888215,0.9345116,0.00056976936,0.00039882035,0.0012440741,0.0000036095978,0.002927978,0.003601253],"genre_scores_gemma":[0.9174185,0.000010205496,0.081789486,0.00028349576,0.00034557062,0.000050907616,0.000013839078,0.00006543797,0.0000225796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859864,0.000013757599,0.0003334097,0.000341352,0.00010998179,0.00060285605],"domain_scores_gemma":[0.99946505,0.00009015601,0.000048619408,0.00014229259,0.00003169449,0.00022216354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012251077,0.00025346008,0.00029885222,0.000045465935,0.00018526115,0.0000919634,0.00019883073,0.00007421199,0.000022695385],"category_scores_gemma":[0.00005360225,0.00024953907,0.00014574545,0.00031513325,0.000029906905,0.00017129446,0.000025276035,0.00022137398,0.000015160403],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029043207,0.000024212331,0.00003966359,0.00046040822,0.00005909155,0.000014242556,0.0007077776,0.0658718,0.004077957,0.0018538279,0.00035537287,0.9265066],"study_design_scores_gemma":[0.0021389152,0.0010255573,0.000077100856,0.00018612921,0.00008271355,0.000076040254,0.0007062452,0.54958034,0.1697579,0.002123127,0.27262276,0.0016231603],"about_ca_topic_score_codex":0.0000026743824,"about_ca_topic_score_gemma":0.0000022732268,"teacher_disagreement_score":0.9248834,"about_ca_system_score_codex":0.00003562219,"about_ca_system_score_gemma":0.000019673907,"threshold_uncertainty_score":0.9999957},"labels":[],"label_agreement":null},{"id":"W3041843271","doi":"10.1109/isqed48828.2020.9137023","title":"Reducing Impact of CNFET Process Imperfections on Shape of Activation Function by Using Connection Pruning and Approximate Neuron Circuit","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pruning; Computer science; Artificial neural network; Leverage (statistics); Process (computing); Carbon nanotube field-effect transistor; Artificial intelligence; Electronic engineering; Transistor; Voltage; Electrical engineering; Engineering; Field-effect transistor","score_opus":0.03710457112131676,"score_gpt":0.26837923558860827,"score_spread":0.2312746644672915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041843271","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9119968,0.000014755861,0.08737516,0.00000806925,0.00005103347,0.00013384392,0.0000030841147,0.00013868262,0.00027861167],"genre_scores_gemma":[0.9998327,0.000005734756,0.00007159085,0.000012938184,0.00004593566,0.0000019797149,0.000006317582,0.000020472244,0.0000023580149],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999457,0.000016482303,0.00019130761,0.00015279319,0.000072981326,0.00010940791],"domain_scores_gemma":[0.99972194,0.000056240406,0.000084676656,0.00005592374,0.000038066086,0.000043168726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049292288,0.000108926084,0.00014386419,0.00006080332,0.00007282566,0.000009095401,0.000026086731,0.00003992694,0.000015141793],"category_scores_gemma":[0.000055152163,0.000105472864,0.000034952074,0.0002499024,0.000013908406,0.00028967654,0.000009464939,0.00013331474,2.3352503e-7],"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":[0.000026913785,0.000006350918,0.00017002736,0.00010939602,0.000010455926,6.041296e-8,0.00018554974,0.42656106,0.5703912,0.000011307462,0.000006576062,0.00252113],"study_design_scores_gemma":[0.00015648604,0.0002110929,0.0005757977,0.000054628137,0.000011591633,0.0000034256816,0.00013092809,0.600832,0.39792916,0.000023752751,0.0000014172062,0.00006972055],"about_ca_topic_score_codex":0.000008484049,"about_ca_topic_score_gemma":1.2760371e-7,"teacher_disagreement_score":0.17427096,"about_ca_system_score_codex":0.000038486072,"about_ca_system_score_gemma":0.000009325742,"threshold_uncertainty_score":0.43010587},"labels":[],"label_agreement":null},{"id":"W3043159233","doi":"10.1088/1361-6528/aba70f","title":"Roadmap on emerging hardware and technology for machine learning","year":2020,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":189,"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":"Engineering and Physical Sciences Research Council","keywords":"Von Neumann architecture; Neuromorphic engineering; Computer architecture; Computer science; Efficient energy use; Deep learning; Architecture; Field (mathematics); Artificial neural network; Embedded system; Artificial intelligence; Electrical engineering; Engineering; Operating system","score_opus":0.014034373446953982,"score_gpt":0.23097726500460572,"score_spread":0.21694289155765173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043159233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91694045,0.0018742485,0.066815674,0.009430315,0.00021907952,0.0003172103,0.0000074329696,0.004227254,0.00016831988],"genre_scores_gemma":[0.99706405,0.0000893649,0.0024898509,0.00020313963,0.00005567845,0.000021938164,0.0000041832122,0.000036314235,0.000035483063],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931586,0.00000625938,0.00013587491,0.00024014278,0.000039174898,0.00026271783],"domain_scores_gemma":[0.99975264,0.00005811923,0.000027000211,0.00010483533,0.00001822221,0.00003919519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003554268,0.00014109803,0.00019447281,0.00015138325,0.00012046997,0.0000061367346,0.00013221247,0.00022496961,0.0000067797946],"category_scores_gemma":[0.00023362593,0.0001459755,0.000024276187,0.00030328444,0.00004867724,0.000039258215,0.0000694655,0.00049595075,0.000011653462],"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":[0.00005250168,0.000009624134,0.0004776745,0.00017924547,0.00003977905,0.00003646656,0.00017398904,0.042007722,0.5041588,0.00868823,0.00021462285,0.44396138],"study_design_scores_gemma":[0.0007993251,0.00059107126,0.0000068584436,0.000038207567,0.000013482633,0.00003791065,0.00014317124,0.1802779,0.6565167,0.0024672712,0.15877534,0.00033275876],"about_ca_topic_score_codex":2.3019665e-7,"about_ca_topic_score_gemma":9.568238e-7,"teacher_disagreement_score":0.4436286,"about_ca_system_score_codex":0.000015279173,"about_ca_system_score_gemma":0.000003292765,"threshold_uncertainty_score":0.5952708},"labels":[],"label_agreement":null},{"id":"W3043827589","doi":"10.1093/cercor/bhaa192","title":"Age-Related Compensatory Reconfiguration of PFC Connections during Episodic Memory Retrieval","year":2020,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"National Institute of Mental Health; National Institute on Aging; National Institutes of Health","keywords":"Episodic memory; Control reconfiguration; Psychology; Cognitive psychology; Autobiographical memory; Neuroscience; Developmental psychology; Cognition; Computer science","score_opus":0.018784816870619465,"score_gpt":0.22089521975210177,"score_spread":0.2021104028814823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043827589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99404234,0.00017207432,0.00075309444,0.000094139665,0.00050710834,0.00017864091,0.0000069734306,0.00050510356,0.0037405523],"genre_scores_gemma":[0.99949265,0.000014423198,0.00011824308,0.0000695423,0.00013189012,0.0000016362042,0.000018642831,0.000032647953,0.000120308265],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904937,0.000036494206,0.00038606438,0.0002095259,0.00011354475,0.00020501822],"domain_scores_gemma":[0.9995473,0.000061020404,0.00008245162,0.00014881992,0.00003914753,0.00012123718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055006494,0.00015511409,0.00025197436,0.000055409608,0.00011360573,0.000012151597,0.00011358282,0.00008522308,0.00023929776],"category_scores_gemma":[0.00006954539,0.00017404577,0.00008752679,0.0002975415,0.00005895137,0.0001875829,0.000028168042,0.00031926093,0.000057527017],"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":[0.00006216374,0.000010878592,0.00019281189,0.00020773795,0.00005133383,0.000041915268,0.0006689418,0.031986803,0.9648345,0.00036236455,0.00014518532,0.0014353347],"study_design_scores_gemma":[0.0015439007,0.00018446603,0.027385345,0.000115823175,0.000056836172,0.00006280737,0.0004003824,0.047439247,0.9214677,0.0004445688,0.00034966134,0.00054926606],"about_ca_topic_score_codex":0.0000017036416,"about_ca_topic_score_gemma":0.0000024853923,"teacher_disagreement_score":0.043366835,"about_ca_system_score_codex":0.000041218726,"about_ca_system_score_gemma":0.000013167437,"threshold_uncertainty_score":0.709738},"labels":[],"label_agreement":null},{"id":"W3044692559","doi":"10.31234/osf.io/9umav_v1","title":"Holographic Declarative Memory and the Fan Effect: A Test Case for A New Memory Module for ACT-R","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Declarative memory; Memory test; Computer science; Test (biology); Cognitive psychology; Long-term memory; Cognitive science; Programming language; Psychology; Artificial intelligence; Geology; Cognition; Neuroscience; Paleontology","score_opus":0.01925115527073459,"score_gpt":0.27672453074872605,"score_spread":0.2574733754779915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044692559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34648913,0.004376067,0.63073015,0.0005870618,0.0018270306,0.010073856,0.00023842794,0.0011253715,0.0045529087],"genre_scores_gemma":[0.9819619,0.00011100267,0.013071553,0.00029552664,0.000469945,0.0011826403,0.000035671128,0.000066326094,0.002805444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869126,0.000058162546,0.00031222275,0.00050938566,0.0000704588,0.00035852307],"domain_scores_gemma":[0.99514043,0.0041832593,0.000080397345,0.0004361375,0.00005583198,0.000103968036],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004199138,0.00044360798,0.00062018144,0.00014003573,0.00025658865,0.00006753347,0.00024007763,0.00026777343,0.000004553531],"category_scores_gemma":[0.0004352607,0.00031105013,0.00029878345,0.00013441368,0.000107589585,0.00007201149,0.00030104516,0.00058520696,6.792854e-7],"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":[0.0025499926,0.00013876228,0.00015975029,0.016476123,0.002089852,0.00057851966,0.006158486,0.5525143,0.008616038,0.004153519,0.016767928,0.38979673],"study_design_scores_gemma":[0.01776018,0.0008144884,0.00010101684,0.001079133,0.0012783895,0.0010559524,0.00066809275,0.7238237,0.20354702,0.045141112,0.0024266257,0.0023042934],"about_ca_topic_score_codex":0.00002663279,"about_ca_topic_score_gemma":0.00009381906,"teacher_disagreement_score":0.6354728,"about_ca_system_score_codex":0.000033351338,"about_ca_system_score_gemma":0.00004798681,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W3045034882","doi":"10.3389/fnbot.2020.568359","title":"Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics","year":2020,"lang":"en","type":"preprint","venue":"Frontiers in Neurorobotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Neuromorphic engineering; Computer science; Spiking neural network; Python (programming language); Interfacing; Workflow; Computer architecture; Artificial neural network; Embedded system; Compiler; Hardware acceleration; Computer hardware; Convolutional neural network; Artificial intelligence; Field-programmable gate array; Operating system","score_opus":0.02455480800232665,"score_gpt":0.2430092921898781,"score_spread":0.21845448418755145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045034882","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015170948,0.0012812301,0.96867967,0.00083270774,0.0116446875,0.001327821,0.000096989424,0.0007951291,0.00017080139],"genre_scores_gemma":[0.39885813,0.0017931568,0.5921713,0.003954622,0.0014473962,0.0001587402,0.0002437222,0.0010062368,0.0003666896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973645,0.000072667455,0.0006954828,0.000922973,0.00022886683,0.00071545923],"domain_scores_gemma":[0.99873257,0.00016348745,0.00013882341,0.00061811035,0.00007878642,0.0002682298],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010707613,0.00072689354,0.00095267384,0.00022098614,0.00010930309,0.00013193082,0.00053349114,0.0004335231,0.0000033101235],"category_scores_gemma":[0.00026256053,0.0008708876,0.00020394832,0.00023531522,0.000098085606,0.00015939218,0.00059394556,0.0020270704,0.0000027950778],"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":[0.000045732784,0.00002353351,0.00038342227,0.0011856575,0.000044705248,0.000120318764,0.00029459407,0.978257,0.0004601905,0.00004714165,0.017677614,0.001460118],"study_design_scores_gemma":[0.00079062564,0.0001094456,0.00042849762,0.00030196752,0.00009011908,0.000017663398,0.000087088716,0.9909626,0.00121997,0.002868758,0.0022237964,0.00089942897],"about_ca_topic_score_codex":5.608687e-7,"about_ca_topic_score_gemma":0.000001790579,"teacher_disagreement_score":0.38368717,"about_ca_system_score_codex":0.00010177144,"about_ca_system_score_gemma":0.000053244912,"threshold_uncertainty_score":0.9993742},"labels":[],"label_agreement":null},{"id":"W3045801979","doi":"","title":"Processing in Storage Class Memory.","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Class (philosophy); Computer science; Artificial intelligence","score_opus":0.02189769290242655,"score_gpt":0.2247622787195146,"score_spread":0.20286458581708805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045801979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92993903,0.0002874613,0.037196223,0.00032224794,0.000095404714,0.000083520186,3.0015684e-7,0.00073386653,0.031341918],"genre_scores_gemma":[0.9984811,0.000002825798,0.0009863992,0.0003760456,0.000075039265,0.0000013900974,4.299243e-7,0.000012434507,0.000064333624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999661,0.0000046998807,0.00008769969,0.00008562096,0.000041432544,0.000119506476],"domain_scores_gemma":[0.99989855,0.000008742365,0.0000062499603,0.000037043625,0.0000042658658,0.00004516785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000022496875,0.000060435563,0.000073292635,0.00001670271,0.000016996959,0.000009237742,0.000053941993,0.00001956408,0.000035899142],"category_scores_gemma":[0.000011819793,0.000058388097,0.000011811397,0.000148475,0.000005189058,0.00011661806,0.000015388208,0.00012692087,0.000023440754],"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":[0.000009885455,0.000006969348,0.0001562773,0.00022173893,0.0000020322107,0.00008276346,0.0011830378,0.7722922,0.12845808,0.00011502179,0.00051624514,0.09695574],"study_design_scores_gemma":[0.00029876476,0.00002008728,0.00037530623,0.000029493893,0.0000017222842,0.0000040596574,0.00025087918,0.9098503,0.08493216,0.000074742544,0.003959333,0.00020313395],"about_ca_topic_score_codex":3.6635103e-7,"about_ca_topic_score_gemma":0.0000020177938,"teacher_disagreement_score":0.1375581,"about_ca_system_score_codex":0.000014133871,"about_ca_system_score_gemma":0.0000035741166,"threshold_uncertainty_score":0.23809975},"labels":[],"label_agreement":null},{"id":"W3046528139","doi":"10.21468/scipostphys.12.1.039","title":"Spiking neuromorphic chip learns entangled quantum states","year":2022,"lang":"en","type":"article","venue":"SciPost Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"European Research Council; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Neuromorphic engineering; Qubit; Realization (probability); Computer science; Quantum; Quantum computer; Artificial neural network; Block (permutation group theory); Computer architecture; Physics; Artificial intelligence; Quantum mechanics; Mathematics","score_opus":0.026620170221147637,"score_gpt":0.22057590802982552,"score_spread":0.1939557378086779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046528139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955067,0.00013384131,0.0023624222,0.0000501176,0.0008235546,0.00010350232,0.00001679344,0.00046844358,0.000534616],"genre_scores_gemma":[0.9994291,0.000011324135,0.0000742364,0.0001288127,0.00021084647,0.000011040702,0.000030096346,0.00004616358,0.00005835981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917555,0.000038742426,0.00013373129,0.00017814565,0.00018398502,0.00028984176],"domain_scores_gemma":[0.9996585,0.00006208699,0.000033214907,0.00018137769,0.000012056591,0.000052748732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007623598,0.0001371419,0.00013286607,0.000029077324,0.0003427514,0.000024228168,0.00017924931,0.000011581677,0.0000611211],"category_scores_gemma":[0.000006653198,0.0001570626,0.00005752726,0.00029277394,0.00002218621,0.00012902063,0.00014666896,0.00039103808,0.000028741659],"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":[0.000012386691,0.00003016419,0.0002211771,0.000036129728,0.000017085842,0.00004761557,0.0006446043,0.907111,0.082992055,0.0017953926,0.00023460442,0.006857817],"study_design_scores_gemma":[0.0010843618,0.000456619,0.0014277595,0.000051487765,0.000053930227,0.00008938974,0.0011842232,0.78367925,0.16103563,0.030610882,0.019133212,0.0011932413],"about_ca_topic_score_codex":0.0000018926974,"about_ca_topic_score_gemma":6.638355e-7,"teacher_disagreement_score":0.12343171,"about_ca_system_score_codex":0.000049043272,"about_ca_system_score_gemma":0.000008974612,"threshold_uncertainty_score":0.6404827},"labels":[],"label_agreement":null},{"id":"W3081173589","doi":"10.1037/rev0000250","title":"CUE: A unified spiking neuron model of short-term and long-term memory.","year":2020,"lang":"en","type":"article","venue":"Psychological Review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Term (time); Short-term memory; Long-term memory; Neuroscience; Psychology; Cognitive psychology; Computer science; Cognition; Working memory; Physics","score_opus":0.12620694625795584,"score_gpt":0.34906116726294695,"score_spread":0.2228542210049911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081173589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9360318,0.048189066,0.01337759,0.0002975962,0.000087269924,0.00034292473,0.0000025843378,0.0002178115,0.0014533584],"genre_scores_gemma":[0.95808756,0.039848447,0.000581387,0.0013984746,0.000049798036,0.0000071035524,0.0000024042465,0.00001686655,0.000007971584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990845,0.000034174132,0.00032924867,0.0002701686,0.00009557486,0.00018635728],"domain_scores_gemma":[0.9995919,0.000049091672,0.00004013241,0.00017500529,0.000014662324,0.00012920373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009705215,0.00016384607,0.00038662762,0.000013537529,0.000027162005,0.00000752515,0.00016033427,0.000049465405,0.00003366164],"category_scores_gemma":[0.00007710695,0.00013010368,0.00007809973,0.00014724782,0.000037382404,0.0000683858,0.00005874622,0.00024928612,0.000007174042],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064637425,0.00010405261,0.0018480187,0.01535999,0.00004243304,0.00018130123,0.00016558595,0.011942183,0.23044465,0.0005093421,0.00050327304,0.73883456],"study_design_scores_gemma":[0.0091543775,0.006731217,0.25282457,0.06532091,0.0021959154,0.0015316559,0.00006801809,0.55970025,0.0783021,0.00574452,0.006898161,0.011528335],"about_ca_topic_score_codex":2.6469985e-8,"about_ca_topic_score_gemma":6.2237845e-8,"teacher_disagreement_score":0.7273062,"about_ca_system_score_codex":0.0000053215776,"about_ca_system_score_gemma":0.0000014903908,"threshold_uncertainty_score":0.53054744},"labels":[],"label_agreement":null},{"id":"W3081302630","doi":"10.1002/aisy.202000115","title":"In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives","year":2020,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":211,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke; University of Toronto","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; National Tsing Hua University; Centre National de la Recherche Scientifique; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Von Neumann architecture; Bottleneck; Resistive random-access memory; Matrix multiplication; In-Memory Processing; Computer architecture; Crossbar switch; Key (lock); Memristor; Integrated circuit; Supercomputer; Massively parallel; CMOS; Context (archaeology); Memory bandwidth; Parallel computing; Embedded system; Electronic engineering; Electrical engineering; Engineering; Telecommunications; Search engine","score_opus":0.09113227362423608,"score_gpt":0.2852373357719019,"score_spread":0.19410506214766582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081302630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73731273,0.19240573,0.0650924,0.00033866666,0.0010073335,0.0027488116,0.000028921158,0.00075177307,0.00031359468],"genre_scores_gemma":[0.9929159,0.0057622097,0.0009039514,0.000056434004,0.00012857141,0.00011911524,0.000018753692,0.0000673466,0.000027669957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780154,0.00019599152,0.0007494118,0.0006371396,0.00017887885,0.00043702166],"domain_scores_gemma":[0.99905884,0.00030005543,0.00013736669,0.00027225606,0.000064679596,0.00016677116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028323333,0.0003906542,0.0005678787,0.00021859648,0.00005651609,0.00002523885,0.00025206074,0.000111210335,0.0000118840135],"category_scores_gemma":[0.0001326674,0.00041123276,0.000055547356,0.00038232323,0.00005306822,0.00061173405,0.00006978821,0.00053913635,0.000021796279],"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":[0.000095106014,0.00009433,0.00018621478,0.0009098929,0.00009745514,0.000057821697,0.015758494,0.49830323,0.4633138,0.0011372218,0.00004630521,0.020000128],"study_design_scores_gemma":[0.0042144647,0.00065683393,0.0026019446,0.0019192079,0.00008518406,0.0001323601,0.07478254,0.5834382,0.31468955,0.0007674816,0.013817963,0.00289428],"about_ca_topic_score_codex":0.00007334038,"about_ca_topic_score_gemma":0.00004695692,"teacher_disagreement_score":0.2556032,"about_ca_system_score_codex":0.0004744959,"about_ca_system_score_gemma":0.000021851029,"threshold_uncertainty_score":0.99983394},"labels":[],"label_agreement":null},{"id":"W3084270633","doi":"10.1002/adfm.202003683","title":"Dual‐Gated MoS<sub>2</sub> Memtransistor Crossbar Array","year":2020,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":134,"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":"Natural Sciences and Engineering Research Council of Canada; National Institute of Standards and Technology; Materials Research Science and Engineering Center, Harvard University; National Research Foundation of Korea; Office of Naval Research; Northwestern University; National Science Foundation","keywords":"Neuromorphic engineering; Crossbar switch; Memristor; Synaptic weight; Materials science; Transistor; Hebbian theory; Computer science; Gating; Electronic circuit; Artificial neural network; Electronic engineering; Optoelectronics; Artificial intelligence; Voltage; Electrical engineering","score_opus":0.017275122199355373,"score_gpt":0.20164184555582815,"score_spread":0.18436672335647278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084270633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9639843,0.00015853102,0.03203059,0.0002031724,0.0021119905,0.00021049449,0.00008351492,0.0009745549,0.0002428471],"genre_scores_gemma":[0.99725884,0.000049955423,0.0010666801,0.00063642225,0.0007709599,0.000024703624,0.00009303838,0.00007337774,0.000026021393],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986513,0.00003398226,0.00039966952,0.00035314303,0.00020198306,0.00035989613],"domain_scores_gemma":[0.9994616,0.00007962626,0.000062839776,0.00015862285,0.00006324766,0.00017409478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000076172895,0.0002854092,0.0003287427,0.000039355444,0.00016477016,0.000044160446,0.00009537046,0.00008835228,0.00025085313],"category_scores_gemma":[0.000071583934,0.00029706515,0.00007308906,0.00021332176,0.00004560142,0.0003886434,0.000023459641,0.0001604727,0.000320424],"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":[0.00015410777,0.000007916869,0.00000288043,0.00008147669,0.000026674263,0.000014488002,0.00006791647,0.14113809,0.8569857,0.00008645541,0.00027618592,0.001158135],"study_design_scores_gemma":[0.00056107464,0.00006940577,0.00025944886,0.00002928709,0.000016574424,0.000018145796,0.000024441712,0.00016283782,0.9930318,0.0002908353,0.005206523,0.00032962818],"about_ca_topic_score_codex":3.581779e-7,"about_ca_topic_score_gemma":5.224192e-7,"teacher_disagreement_score":0.14097525,"about_ca_system_score_codex":0.000054258788,"about_ca_system_score_gemma":0.000015810829,"threshold_uncertainty_score":0.99994814},"labels":[],"label_agreement":null},{"id":"W3085822547","doi":"10.1364/cleo_si.2020.sth3r.2","title":"VO2 electro-optic memory and oscillator for neuromorphic computing","year":2020,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Neuromorphic engineering; Computer science; Artificial neural network; Artificial intelligence","score_opus":0.03885725418092064,"score_gpt":0.23202175954345122,"score_spread":0.19316450536253058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085822547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9787812,0.00031609816,0.018596604,0.0008152563,0.0001388839,0.00031852743,0.000009323781,0.00030389204,0.0007202025],"genre_scores_gemma":[0.9971691,0.00034166506,0.0013681309,0.00080598844,0.0002209305,0.0000061504134,0.0000085737865,0.00004601581,0.000033442437],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987427,0.000021300326,0.00023299678,0.00037252935,0.00010994581,0.00052052474],"domain_scores_gemma":[0.9993398,0.00018180633,0.000052156287,0.00012463464,0.000053229567,0.00024834226],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008130786,0.00028106026,0.00032325072,0.00004954527,0.00020653963,0.000087043816,0.00011559387,0.00008645324,0.0000046316763],"category_scores_gemma":[0.0000662356,0.00028659336,0.000046884794,0.00014304825,0.00006564861,0.00010038166,0.00004065719,0.0003629733,0.0000043563637],"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":[0.00042314903,0.00005494899,0.00018211649,0.00093162165,0.00016249689,0.000092448696,0.0011211834,0.027112544,0.8681426,0.041048083,0.0015292519,0.05919954],"study_design_scores_gemma":[0.0009017258,0.0011493753,0.00007622203,0.00006036953,0.000051529503,0.000039900584,0.00010745619,0.896277,0.098457456,0.001380994,0.0009853569,0.0005125784],"about_ca_topic_score_codex":4.9597884e-7,"about_ca_topic_score_gemma":6.564583e-7,"teacher_disagreement_score":0.86916447,"about_ca_system_score_codex":0.000020035282,"about_ca_system_score_gemma":0.000028975626,"threshold_uncertainty_score":0.99995863},"labels":[],"label_agreement":null},{"id":"W3087769036","doi":"10.3389/fnbot.2020.00056","title":"Editorial: Tactile Intelligence in Robots","year":2020,"lang":"en","type":"editorial","venue":"Frontiers in Neurorobotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Ulsan National Institute of Science and Technology","keywords":"Computer science; Robot; Artificial intelligence; Volume (thermodynamics); Front (military); Human–computer interaction; Computer vision","score_opus":0.0088979696130071,"score_gpt":0.23508559394830672,"score_spread":0.22618762433529963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087769036","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015099131,0.00051752664,0.052855242,0.000027520768,0.94572526,0.00025557616,0.000021620777,0.00031394797,0.00026821395],"genre_scores_gemma":[0.0003860692,0.0009978228,0.009355393,0.000028638726,0.98889595,0.000009536339,0.000064886175,0.00017718956,0.000084501145],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99751395,0.000080360034,0.0006797139,0.0005838902,0.0005398591,0.00060221035],"domain_scores_gemma":[0.99888796,0.00044555977,0.00010600893,0.00036887763,0.000051988798,0.00013961097],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00013995261,0.00051392586,0.0007761773,0.00029894875,0.000036252364,0.00005409879,0.00064581225,0.0008034409,0.000004062828],"category_scores_gemma":[0.0009678977,0.00062671554,0.000100549914,0.0006357301,0.000050406437,0.000202155,0.00014694098,0.0033606098,0.000019668334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017196586,0.0000101191945,0.00002351208,0.00014097133,0.0000058828164,0.00008772464,0.00005957515,0.42657992,0.00003226461,0.0000014593886,0.57172453,0.0013168677],"study_design_scores_gemma":[0.00025216438,0.000066717694,0.0000070293654,0.00024288488,0.00001715075,0.000001077214,0.00004117059,0.06359933,0.00046596437,0.00043281133,0.93424684,0.0006268843],"about_ca_topic_score_codex":0.000004754364,"about_ca_topic_score_gemma":0.0000137182415,"teacher_disagreement_score":0.36298057,"about_ca_system_score_codex":0.00029604213,"about_ca_system_score_gemma":0.00008427314,"threshold_uncertainty_score":0.9996184},"labels":[],"label_agreement":null},{"id":"W3088883037","doi":"10.1088/1361-6528/abb301","title":"Erratum: Conductive filament evolution dynamics revealed by cryogenic (1.5 K) multilevel switching of CMOS-compatible Al <sub>2</sub> O <sub>3</sub> /TiO <sub>2</sub> resistive memories (2020 <i>Nanotechnology</i> 31 445205)","year":2020,"lang":"en","type":"erratum","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Institut quantique; Université de Sherbrooke","funders":"","keywords":"Materials science; Protein filament; Resistive touchscreen; Electrical conductor; Nanotechnology; Resistive random-access memory; Dynamics (music); Optoelectronics; CMOS; Engineering physics; Condensed matter physics; Composite material; Electrical engineering; Physics; Engineering; Voltage; Acoustics","score_opus":0.008898235281948445,"score_gpt":0.21658273040439652,"score_spread":0.20768449512244808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088883037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9149519,0.0068869954,0.051141307,0.0014706354,0.016764715,0.0026369793,0.0010113161,0.004819894,0.00031621457],"genre_scores_gemma":[0.99010205,0.004049426,0.0019199306,0.00037138234,0.0007197061,0.00043763162,0.0015128483,0.000671183,0.00021582567],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99018973,0.00032638706,0.002809499,0.003013325,0.0011127222,0.0025483358],"domain_scores_gemma":[0.99430376,0.0005553594,0.001980154,0.0020718449,0.0006984675,0.00039039587],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0005844412,0.0025321932,0.0036215328,0.0014020654,0.0008555942,0.00009098319,0.0020835467,0.00596029,0.0000059567114],"category_scores_gemma":[0.0013746689,0.0029817135,0.0008088708,0.002692647,0.0010542062,0.00067143544,0.0013511723,0.009513772,0.000109798784],"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":[0.00028193698,0.00015843083,0.000015317895,0.00086832605,0.00065549806,0.00021563467,0.00025235253,0.0047336686,0.85988593,0.00054370885,0.11904003,0.013349165],"study_design_scores_gemma":[0.0019123051,0.00077328924,0.000060442682,0.0012393487,0.0005244637,0.00023210219,0.0005037377,0.027505768,0.9559505,0.0046981853,0.004137097,0.0024627892],"about_ca_topic_score_codex":0.000026611531,"about_ca_topic_score_gemma":0.00033004294,"teacher_disagreement_score":0.11490293,"about_ca_system_score_codex":0.0028957035,"about_ca_system_score_gemma":0.0007235411,"threshold_uncertainty_score":0.9987414},"labels":[],"label_agreement":null},{"id":"W3089057106","doi":"10.1109/tcsii.2020.3026642","title":"AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Memristor; Convolutional neural network; Artificial neural network; Artificial intelligence; Scheme (mathematics); Pattern recognition (psychology); Algorithm; Electronic engineering; Engineering; Mathematics","score_opus":0.0377382900993719,"score_gpt":0.2473695638933039,"score_spread":0.20963127379393198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089057106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3716401,0.00012378705,0.625575,0.000050485367,0.00081373926,0.0006927848,0.00012654976,0.00056059053,0.00041695958],"genre_scores_gemma":[0.9990766,0.000023302357,0.0001822724,0.00021710292,0.00012373658,0.00019070467,0.0000026280734,0.00008299932,0.000100625606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978267,0.00010326676,0.0005966538,0.00058674806,0.00032073614,0.00056591776],"domain_scores_gemma":[0.9989276,0.00020365759,0.00007373866,0.0003014012,0.000093890405,0.0003996893],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000105909756,0.00041593728,0.0005176249,0.000192026,0.00023919308,0.00007503433,0.0003264893,0.00012777743,0.000024152409],"category_scores_gemma":[0.00002040384,0.00046960416,0.00010028732,0.000720878,0.000042183867,0.00046750062,0.000006335931,0.00071063667,0.000085311134],"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":[0.000038406546,0.000038642775,0.000016689113,0.000126291,0.0000485564,0.000049456667,0.004468278,0.8732133,0.11709961,0.00004654949,0.000097471784,0.0047567356],"study_design_scores_gemma":[0.0021592013,0.0008070619,0.0003253162,0.0016136598,0.00004707761,0.000030783012,0.0018639338,0.15908776,0.82522935,0.000043638913,0.006833037,0.0019591672],"about_ca_topic_score_codex":0.00007797777,"about_ca_topic_score_gemma":0.000038950217,"teacher_disagreement_score":0.7141256,"about_ca_system_score_codex":0.00015777854,"about_ca_system_score_gemma":0.000056185774,"threshold_uncertainty_score":0.9997756},"labels":[],"label_agreement":null},{"id":"W3090300952","doi":"10.1109/iscas45731.2020.9181024","title":"An 8-Channel 0.45mm<sup>2</sup>/Channel EEG Recording IC with ADC-Free Mixed-Signal In-Channel Motion Artifact Detection and Removal","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University","funders":"","keywords":"Channel (broadcasting); Artifact (error); CMOS; Offset (computer science); Computer science; SIGNAL (programming language); Signal processing; Electronic engineering; Bandwidth (computing); Computer hardware; Artificial intelligence; Digital signal processing; Engineering; Telecommunications","score_opus":0.01864864350911111,"score_gpt":0.21062893229333224,"score_spread":0.19198028878422113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090300952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6871234,0.0000806314,0.3115407,0.00009624194,0.00013418029,0.00022052626,0.0000045487413,0.00052387826,0.0002759006],"genre_scores_gemma":[0.9983267,0.000029710014,0.0011565742,0.00012513391,0.00025901146,0.000014068152,0.000010155605,0.00006168606,0.000016941873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983864,0.00006785091,0.00034918307,0.0005256153,0.0001958409,0.00047514073],"domain_scores_gemma":[0.99933064,0.00006390061,0.000058136124,0.00024407543,0.000042696098,0.00026053822],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001934632,0.00034197952,0.0003176518,0.00016427718,0.0001703947,0.00006322993,0.00016741292,0.00013121983,0.000027226619],"category_scores_gemma":[0.000051365565,0.00032758102,0.00005212344,0.0004293141,0.00003329622,0.00074899796,0.00007000622,0.0004590797,0.000016260627],"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":[0.00017519975,0.000023401308,0.000044659784,0.00011693229,0.000022273594,0.0000901794,0.0009990882,0.92871046,0.046929114,0.000019869527,0.00003525966,0.022833548],"study_design_scores_gemma":[0.0009579039,0.00043803573,0.00037516045,0.00008386892,0.00001968702,0.00016419332,0.0013263951,0.92098516,0.07420453,0.0009654773,0.00002743244,0.0004521766],"about_ca_topic_score_codex":0.00002340603,"about_ca_topic_score_gemma":0.00006870044,"teacher_disagreement_score":0.31120333,"about_ca_system_score_codex":0.00007183953,"about_ca_system_score_gemma":0.000008867305,"threshold_uncertainty_score":0.9999176},"labels":[],"label_agreement":null},{"id":"W3090335708","doi":"10.1109/iscas45731.2020.9181154","title":"An Efficient Spiking Neuron Hardware System Based on the Hardware-Oriented Modified Izhikevich Neuron (HOMIN) Model","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biological neuron model; Computer science; Neuron; Computer hardware; Replicate; Artificial intelligence; Artificial neural network; Neuroscience; Mathematics; Psychology","score_opus":0.03125822550364553,"score_gpt":0.22915279453416082,"score_spread":0.19789456903051528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090335708","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5108774,0.000021034062,0.48393962,0.00032413058,0.00034092148,0.0004127703,0.000010785599,0.0014862098,0.0025871324],"genre_scores_gemma":[0.9972631,9.323746e-7,0.00085568766,0.0015568066,0.00017235515,0.000022111983,0.000012008467,0.000087752924,0.000029269437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982538,0.000097654265,0.0003444617,0.0005085537,0.00034242592,0.00045307903],"domain_scores_gemma":[0.9989872,0.00015298286,0.000057794397,0.0005264034,0.000049778497,0.00022586313],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014275889,0.00035114196,0.00027953397,0.000060326736,0.0002715025,0.000073576826,0.0003788759,0.000068558686,0.000014091319],"category_scores_gemma":[0.00006039345,0.00026542658,0.00011179967,0.00032497552,0.000022035743,0.00011949035,0.0000590835,0.00046576923,0.000037016245],"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":[0.000057191584,0.000020573072,0.000009610628,0.00012973146,0.000005017957,0.00003274902,0.0001768356,0.97845125,0.019086987,0.00137493,0.0000976196,0.00055751053],"study_design_scores_gemma":[0.00037247557,0.00013657143,0.00006666238,0.0000779106,0.000016709677,0.0000040467125,0.00014791022,0.9762036,0.022443255,0.00000302327,0.0002499571,0.00027785104],"about_ca_topic_score_codex":0.000001931122,"about_ca_topic_score_gemma":7.7573236e-7,"teacher_disagreement_score":0.4863857,"about_ca_system_score_codex":0.00007453314,"about_ca_system_score_gemma":0.000017888186,"threshold_uncertainty_score":0.9999798},"labels":[],"label_agreement":null},{"id":"W3090668887","doi":"10.1109/iscas45731.2020.9180632","title":"Time Step Impact on Performance and Accuracy of Izhikevich Neuron: Software Simulation and Hardware Implementation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Field-programmable gate array; Computer science; Software; Biological neuron model; Function (biology); Euler method; Power consumption; Ordinary differential equation; Euler's formula; Artificial neural network; Computer hardware; Power (physics); Differential equation; Artificial intelligence; Mathematics","score_opus":0.02486895972419836,"score_gpt":0.30238401040923907,"score_spread":0.27751505068504073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090668887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939584,0.000039318606,0.0056636673,0.000026507352,0.000016440385,0.000116898715,0.0000060001,0.000114560295,0.000058190577],"genre_scores_gemma":[0.9992991,0.000019669742,0.00053831504,0.00008468282,0.00003097063,8.557837e-7,0.000008566447,0.000011151563,0.0000067057313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996332,0.000008086401,0.0001175082,0.00009855333,0.00005692276,0.00008571695],"domain_scores_gemma":[0.9997374,0.00012527838,0.000027668222,0.00004738877,0.000014792486,0.00004748304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002319188,0.00008596238,0.000095355674,0.000019139969,0.0000356429,0.000009888623,0.000024761453,0.000016205291,0.000050081624],"category_scores_gemma":[0.000024814532,0.0000730695,0.000013669237,0.00006324658,0.000008453761,0.00022636275,0.000020418971,0.000061331484,0.0000041717685],"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":[0.000037747628,0.0000023434409,0.0032550727,0.00014989809,0.000008566045,0.0000011545021,0.00027268496,0.87192184,0.007191229,0.0000031803854,0.00006917754,0.11708713],"study_design_scores_gemma":[0.00033878133,0.00024638028,0.032090046,0.000019595336,0.0000072912067,0.0000023424043,0.00004440843,0.95374304,0.013256465,0.00000382675,0.00014817057,0.0000996749],"about_ca_topic_score_codex":6.7337066e-7,"about_ca_topic_score_gemma":2.1849057e-7,"teacher_disagreement_score":0.11698746,"about_ca_system_score_codex":0.000007707113,"about_ca_system_score_gemma":0.0000028553425,"threshold_uncertainty_score":0.29796878},"labels":[],"label_agreement":null},{"id":"W3091257769","doi":"10.1109/iscas45731.2020.9180556","title":"RF-Rate Hybrid CNN Accelerator Based on Analog-CMOS and Xilinx RFSoC","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; CMOS; Hardware acceleration; Analogue electronics; Application-specific integrated circuit; Computer hardware; Electronic engineering; Field-programmable gate array; Electronic circuit; Electrical engineering; Engineering","score_opus":0.025409331681582714,"score_gpt":0.21792553315374755,"score_spread":0.19251620147216483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091257769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97404295,0.00006601598,0.019696651,0.00040655644,0.00012738167,0.00010562323,0.0000052505347,0.00056590035,0.004983657],"genre_scores_gemma":[0.9966511,0.000009417704,0.0006692612,0.0024545356,0.00013342853,0.000002423569,0.0000040439704,0.000020772191,0.00005501423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994622,0.00001021446,0.00011881567,0.00018111563,0.000058545498,0.00016909327],"domain_scores_gemma":[0.9996723,0.000082089035,0.000012645208,0.0000942335,0.000011096824,0.00012762587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004148711,0.00012766344,0.00012901382,0.000023280727,0.000056097673,0.000024788924,0.000066043496,0.000022906243,0.00011121485],"category_scores_gemma":[0.000030395611,0.00011586582,0.000027913196,0.00010056896,0.000011828283,0.00008374279,0.000021070866,0.00015819396,0.000046386744],"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":[0.0001243001,0.00003200643,0.00073294074,0.00028608297,0.000042428313,0.00022981301,0.00019699399,0.56819826,0.38792065,0.000276926,0.006589705,0.03536991],"study_design_scores_gemma":[0.0004208855,0.000106377986,0.0003956082,0.000017010247,0.0000068391305,0.0000024523636,0.000022357352,0.5657029,0.42950407,0.0000557009,0.003541156,0.00022461766],"about_ca_topic_score_codex":3.2841967e-7,"about_ca_topic_score_gemma":6.3308704e-7,"teacher_disagreement_score":0.041583423,"about_ca_system_score_codex":0.000009955918,"about_ca_system_score_gemma":0.0000055652504,"threshold_uncertainty_score":0.4724871},"labels":[],"label_agreement":null},{"id":"W3092451347","doi":"10.3389/fnbot.2020.568359","title":"Nengo and Low-Power AI Hardware for Robust, Embedded Neurorobotics.","year":2020,"lang":"en","type":"article","venue":"PubMed","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":"University of Waterloo","funders":"","keywords":"Computer science; Neuromorphic engineering; Spiking neural network; Python (programming language); Interfacing; Workflow; Computer architecture; Artificial neural network; Convolutional neural network; Embedded system; Compiler; Computer hardware; Deep learning; Hardware acceleration; Artificial intelligence; Field-programmable gate array; Operating system","score_opus":0.03137819273900902,"score_gpt":0.20721187612274825,"score_spread":0.17583368338373923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092451347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62354463,0.00091368845,0.3640811,0.0035178207,0.0015737074,0.0024359776,0.00003515457,0.0019613446,0.0019365654],"genre_scores_gemma":[0.99649006,0.000011002871,0.0015620801,0.0014728012,0.00020540039,0.00015284479,0.0000034797781,0.0000393084,0.00006302133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936265,0.000007228733,0.0001191294,0.00017453212,0.000051301246,0.00028515558],"domain_scores_gemma":[0.9996622,0.00005970472,0.000014657999,0.00008595964,0.000017872298,0.00015959593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037858652,0.00011439934,0.00013329585,0.000017300727,0.000050799652,0.000028012517,0.000075195385,0.00003531922,0.0000040839354],"category_scores_gemma":[0.0000991576,0.000117253534,0.00003720421,0.000077792545,0.000013372355,0.00012296783,0.00003476171,0.00013477978,0.0000028957077],"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":[0.00006712316,0.00001405601,0.00016122154,0.00043362778,0.000041444004,0.000028896482,0.0005407292,0.8835524,0.0029713851,0.00019534148,0.007064271,0.104929514],"study_design_scores_gemma":[0.0032056472,0.00013600998,0.022313835,0.00004106753,0.00008226328,0.000034047032,0.00016543533,0.8592736,0.064651094,0.0005640566,0.04822919,0.0013037575],"about_ca_topic_score_codex":7.3702935e-8,"about_ca_topic_score_gemma":2.1781987e-7,"teacher_disagreement_score":0.37294546,"about_ca_system_score_codex":0.000009031938,"about_ca_system_score_gemma":0.000002183053,"threshold_uncertainty_score":0.47814605},"labels":[],"label_agreement":null},{"id":"W3094022166","doi":"10.1039/d0mh01240h","title":"Bending good beats breaking bad: phase separation patterns in individual cathode particles upon lithiation and delithiation","year":2020,"lang":"en","type":"article","venue":"Materials Horizons","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"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":"National Research Council Canada; Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Saskatchewan; Deutsche Forschungsgemeinschaft; National Aeronautics and Space Administration","keywords":"Spinodal decomposition; Materials science; Coupling (piping); Phase (matter); Separation (statistics); Cathode; Bending; Chemical physics; Work (physics); Spinodal; Decomposition; Composite material; Chemistry; Thermodynamics; Physical chemistry; Physics; Organic chemistry; Computer science","score_opus":0.038072056640139115,"score_gpt":0.2995001336927642,"score_spread":0.26142807705262505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094022166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98345244,0.000057156518,0.015640283,0.00012816829,0.00024817043,0.00018415526,0.00006384386,0.00020525037,0.000020523452],"genre_scores_gemma":[0.9986239,0.000023867162,0.00091505615,0.000050940638,0.0002505503,0.000017334389,0.00009418548,0.000023409271,7.241602e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911875,0.00007117625,0.00030564918,0.00019491442,0.00010203927,0.00020746804],"domain_scores_gemma":[0.99974084,0.000050519146,0.00006217807,0.00006958081,0.0000121791245,0.0000647182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019602184,0.00013568101,0.0001688786,0.000058185753,0.00008498527,0.00010282334,0.000055487482,0.00006327671,0.00001534199],"category_scores_gemma":[0.000046032943,0.00014945847,0.000012844915,0.00012363434,0.000006063906,0.0003681924,0.000037116206,0.000085821965,0.0000058184187],"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":[0.000030291065,0.000016886745,0.0016450128,0.000095131145,0.000009402662,0.00000796627,0.0026537904,0.011174043,0.9713958,0.00028572316,0.0000059600698,0.012679998],"study_design_scores_gemma":[0.00072592293,0.00014948884,0.009290787,0.00008583641,0.000026379124,0.000007939821,0.0001878945,0.020898266,0.96808577,0.0002170061,0.00007933073,0.00024538714],"about_ca_topic_score_codex":0.000012240025,"about_ca_topic_score_gemma":0.000024704348,"teacher_disagreement_score":0.015171483,"about_ca_system_score_codex":0.000034703804,"about_ca_system_score_gemma":0.0000060218795,"threshold_uncertainty_score":0.60947394},"labels":[],"label_agreement":null},{"id":"W3094313818","doi":"10.1109/tcsi.2020.3026076","title":"High Speed and Low Digital Resources Implementation of Hodgkin-Huxley Neuronal Model Using Base-2 Functions","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":43,"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":"Neuromorphic engineering; Field-programmable gate array; Computer science; Realization (probability); Hodgkin–Huxley model; Set (abstract data type); Biological neuron model; Implementation; Base (topology); Artificial neural network; Computer hardware; Artificial intelligence; Mathematics; Neuroscience; Programming language","score_opus":0.02787221500666228,"score_gpt":0.22575721748927813,"score_spread":0.19788500248261584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094313818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8209152,0.00009790323,0.17833382,0.000028078628,0.00019921677,0.00016900203,0.0000985302,0.00009212272,0.00006612009],"genre_scores_gemma":[0.9997692,0.000027164082,0.00004511248,0.00003526945,0.00005791397,0.0000032613002,0.0000039187066,0.00002841453,0.00002973969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991806,0.00002495526,0.00026570723,0.00022190117,0.00014838325,0.00015845592],"domain_scores_gemma":[0.9996437,0.000047204787,0.00004993098,0.000094787574,0.000023363194,0.00014105256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048051756,0.00015686313,0.00020923698,0.00006782464,0.00016313359,0.000055768378,0.0000427458,0.000048982827,0.000007552668],"category_scores_gemma":[0.0000025456443,0.00015917531,0.000052574353,0.0001331812,0.000044516717,0.00020692947,0.0000012596718,0.00012929783,8.0388696e-7],"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":[0.000006686263,0.000006394152,0.000014517768,0.00013478177,0.00003313276,0.0000020111204,0.0003657716,0.7638705,0.22518027,0.000014423744,0.00000607718,0.010365444],"study_design_scores_gemma":[0.001043616,0.0002095418,0.00017563219,0.00014147995,0.000099662,0.000059592498,0.002431865,0.939143,0.056144297,0.000016534274,0.00015642498,0.0003783605],"about_ca_topic_score_codex":0.0000107586575,"about_ca_topic_score_gemma":0.000002764039,"teacher_disagreement_score":0.17885399,"about_ca_system_score_codex":0.000021334175,"about_ca_system_score_gemma":0.000013199869,"threshold_uncertainty_score":0.64909804},"labels":[],"label_agreement":null},{"id":"W3099834229","doi":"","title":"Associative Memories Based on Multiple-Valued Sparse Clustered Networks","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Associative property; Content-addressable memory; Binary number; Content-addressable storage; Implementation; Algorithm; Theoretical computer science; Artificial intelligence; Artificial neural network; Arithmetic; Mathematics","score_opus":0.026515081016561793,"score_gpt":0.25081030675413213,"score_spread":0.22429522573757033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099834229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031889282,0.00014888354,0.9300691,0.00017214002,0.0049720183,0.00081067084,0.00006589846,0.0022353744,0.029636635],"genre_scores_gemma":[0.9948203,0.000017410053,0.0031028108,0.0002909103,0.0007973123,0.000039708506,0.000042823987,0.000092696224,0.0007960454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983853,0.00009372023,0.00036071864,0.00046394713,0.00022401428,0.00047229248],"domain_scores_gemma":[0.99823684,0.00096549565,0.00012672544,0.00049256656,0.000065932094,0.00011244318],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018939977,0.0004803066,0.0005019909,0.00009230995,0.00010510148,0.000047484096,0.00028787472,0.00038841795,0.00008097514],"category_scores_gemma":[0.00026714316,0.0003868339,0.00019601686,0.00008189754,0.000036754285,0.00007032951,0.00025371794,0.000884292,0.000035531233],"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":[0.000049461894,0.000014128453,0.0001581457,0.000070325244,0.00005924036,0.000014358668,0.0000647972,0.9939948,0.00018630318,0.0000588169,0.0010481888,0.0042814896],"study_design_scores_gemma":[0.00069882267,0.000034289194,0.00045918263,0.00041078607,0.00002454002,5.850377e-7,0.000019003666,0.98395365,0.012831991,0.00071270164,0.00031151695,0.00054293236],"about_ca_topic_score_codex":0.0000020479295,"about_ca_topic_score_gemma":0.000012886912,"teacher_disagreement_score":0.962931,"about_ca_system_score_codex":0.00026744726,"about_ca_system_score_gemma":0.000025160378,"threshold_uncertainty_score":0.9998584},"labels":[],"label_agreement":null},{"id":"W3100504110","doi":"10.1021/acsmaterialslett.0c00364","title":"Self-Powered Memory Systems","year":2020,"lang":"en","type":"article","venue":"ACS Materials Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"","keywords":"Neuromorphic engineering; Computer science; Artificial neural network; Artificial intelligence; In-Memory Processing; Applications of artificial intelligence; Power consumption; Computer architecture; Power (physics)","score_opus":0.013300068525078722,"score_gpt":0.1928934101613778,"score_spread":0.17959334163629906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100504110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99442273,0.00012489238,0.0015943857,0.0006441161,0.001670274,0.00016153228,0.000008762926,0.0011277593,0.00024551543],"genre_scores_gemma":[0.9966962,0.000016800608,0.00044097894,0.0021172517,0.00066755246,0.000008876108,0.0000071422473,0.00004012794,0.0000051009583],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992836,0.000033703265,0.00020965806,0.0001561479,0.00008829382,0.00022858629],"domain_scores_gemma":[0.9997249,0.000028491973,0.000032554362,0.00012898381,0.0000074515733,0.0000775747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060182083,0.00014492159,0.00019953887,0.000019219227,0.00004449527,0.00006021001,0.00014342037,0.000037338006,0.00003063534],"category_scores_gemma":[0.000013223569,0.00014506394,0.000022194728,0.00007122984,0.000010481342,0.00013743255,0.000036913163,0.000072335424,0.00017965306],"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":[0.0000069247835,0.0000017341429,0.0000026331338,0.00022699754,0.000018144361,0.000037464448,0.00019641666,0.014249599,0.9834239,0.00003594608,0.0017345467,0.00006572841],"study_design_scores_gemma":[0.00021989364,0.000016456022,0.00002687501,0.000024904066,0.0000117666195,0.000013379173,0.000038759696,0.00081028783,0.99238175,0.0000051550414,0.006225004,0.00022579236],"about_ca_topic_score_codex":0.0000012386024,"about_ca_topic_score_gemma":3.528693e-8,"teacher_disagreement_score":0.013439311,"about_ca_system_score_codex":0.000022054017,"about_ca_system_score_gemma":0.0000023960326,"threshold_uncertainty_score":0.59155357},"labels":[],"label_agreement":null},{"id":"W3100966888","doi":"10.48550/arxiv.2006.13394","title":"Conductive filament evolution dynamics revealed by cryogenic (1.5 K) multilevel switching of CMOS-compatible Al2O3/TiO2 resistive memories","year":2020,"lang":"en","type":"article","venue":"LillOA (Université de Lille (University Of Lille))","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"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":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Resistive random-access memory; Materials science; Quantum tunnelling; Protein filament; Nanotechnology; Thermal conduction; Condensed matter physics; Conductance; Electrical conductor; Optoelectronics; Memristor; Joule heating; Voltage; Electrical engineering; Physics; Composite material","score_opus":0.007418947483822862,"score_gpt":0.15279321860512965,"score_spread":0.1453742711213068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100966888","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9185835,0.00037901785,0.076195374,0.0003841589,0.00014432539,0.00030038675,0.00019781207,0.00023946009,0.0035759392],"genre_scores_gemma":[0.9930039,0.00013399782,0.0058773216,0.00003972316,0.000029001223,2.7688344e-7,0.000052784893,0.000033821663,0.00082919264],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857616,0.000083028695,0.00023916503,0.00041619182,0.00028501687,0.0004004199],"domain_scores_gemma":[0.99890584,0.00015672373,0.00028036346,0.00026680352,0.00015516319,0.00023508132],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010979126,0.00029551255,0.0005572272,0.00018112466,0.00044187892,0.000007047765,0.0004533328,0.00017602889,0.00013686321],"category_scores_gemma":[0.000045044475,0.0004122915,0.000221562,0.00054876204,0.00014007246,0.0005333701,0.00032616407,0.00034055058,0.000013319294],"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":[0.001492375,0.00017580611,0.0033160527,0.0009853363,0.00092339085,0.0001873186,0.034473673,0.08183669,0.85974514,0.0039885133,0.0074540954,0.005421618],"study_design_scores_gemma":[0.007901335,0.0011240392,0.01758063,0.000602535,0.00073216716,0.000053533044,0.10403655,0.7600445,0.099041454,0.0008320433,0.005863993,0.0021871892],"about_ca_topic_score_codex":0.00019049995,"about_ca_topic_score_gemma":0.00014660416,"teacher_disagreement_score":0.7607037,"about_ca_system_score_codex":0.00087517424,"about_ca_system_score_gemma":0.000054306915,"threshold_uncertainty_score":0.99983287},"labels":[],"label_agreement":null},{"id":"W3103440960","doi":"","title":"TiO2 and Biomaterials based Memristor Devices and its In-Memory Computing Applications","year":2020,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"University of Waterloo","keywords":"Memristor; Computer science; Nanotechnology; Materials science; Computer architecture; Engineering; Electrical engineering","score_opus":0.01082154630096655,"score_gpt":0.20320487687051092,"score_spread":0.19238333056954438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103440960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985168,0.00059992686,0.00014962262,0.00007328741,0.00011355741,0.00025690766,0.000017564178,0.000120182274,0.00015214576],"genre_scores_gemma":[0.99602205,0.00009062205,0.0014222324,0.0000118386815,0.000041396255,5.4355195e-7,0.00015606901,0.000024894149,0.0022303783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994267,0.000021733475,0.000111297464,0.0002323731,0.000071144066,0.00013675634],"domain_scores_gemma":[0.99967515,0.000059832884,0.00008944282,0.00007986699,0.000029325365,0.00006639979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052715204,0.00016420348,0.0002922609,0.00014584146,0.000107924076,0.000012999188,0.00010742026,0.000119369004,0.000009197069],"category_scores_gemma":[0.000005775925,0.00021119756,0.000029270603,0.00012747668,0.000023537868,0.0000953094,0.000033012864,0.00009475387,0.0000042746883],"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":[0.0002002463,0.000047315778,0.0004902189,0.010259176,0.00013014553,0.00009960291,0.07417278,0.010163723,0.8795631,0.00017297335,0.000102417616,0.024598291],"study_design_scores_gemma":[0.0046156086,0.0003060179,0.034082215,0.002627003,0.0005392029,0.000021621034,0.15022628,0.24442618,0.5552765,0.00021699585,0.0046470095,0.0030153384],"about_ca_topic_score_codex":0.00018803266,"about_ca_topic_score_gemma":0.0014129885,"teacher_disagreement_score":0.32428658,"about_ca_system_score_codex":0.000026685899,"about_ca_system_score_gemma":0.000012228863,"threshold_uncertainty_score":0.8612387},"labels":[],"label_agreement":null},{"id":"W3107346677","doi":"10.1021/acsami.0c10796","title":"From Memristive Materials to Neural Networks","year":2020,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Memristor; Neuromorphic engineering; Computer science; Artificial neural network; Resistive random-access memory; Process (computing); Computer architecture; Artificial intelligence; Electronic engineering; Electrical engineering; Engineering; Voltage","score_opus":0.016403235489806652,"score_gpt":0.22624357311595417,"score_spread":0.2098403376261475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107346677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99393076,0.00008103203,0.002947953,0.00019597288,0.0014220747,0.00034118356,0.00012884612,0.0006654789,0.0002866996],"genre_scores_gemma":[0.99711823,0.000014207118,0.00054415956,0.0008493396,0.0012996882,0.000047584468,0.00004523477,0.000071837094,0.0000097099955],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986849,0.000029999803,0.00042019205,0.0003794052,0.00010918499,0.00037633892],"domain_scores_gemma":[0.99951255,0.000076079334,0.00006702268,0.00019309351,0.000018343111,0.00013288227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007793088,0.0003134152,0.00045396312,0.0000274358,0.00006925276,0.00016303777,0.0003320027,0.000095419615,0.00049466814],"category_scores_gemma":[0.000020669488,0.000300628,0.000011557795,0.00010850886,0.000026113283,0.00011780625,0.00022185266,0.00011950706,0.00026021403],"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":[0.00016309033,0.0000036985002,5.770596e-7,0.00002915195,0.00003054311,0.0000073936767,0.0006280193,0.07347338,0.9235258,0.000058024012,0.000880308,0.0012000053],"study_design_scores_gemma":[0.0001977094,0.000050181803,0.000020408197,0.000026003672,0.000018433288,0.0000013695355,0.0001645644,0.00020635857,0.99857336,0.00017710146,0.0002400036,0.00032451193],"about_ca_topic_score_codex":0.000013538295,"about_ca_topic_score_gemma":0.0000012860166,"teacher_disagreement_score":0.075047545,"about_ca_system_score_codex":0.000025122004,"about_ca_system_score_gemma":0.0000027825436,"threshold_uncertainty_score":0.99994457},"labels":[],"label_agreement":null},{"id":"W3107548225","doi":"10.1109/tit.2020.3040255","title":"Information Density in Multi-Layer Resistive Memories","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Layer (electronics); Resistive touchscreen; Computer science; Materials science; Nanotechnology; Operating system","score_opus":0.022912714619729005,"score_gpt":0.23359460668806378,"score_spread":0.21068189206833476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107548225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07558698,0.0000058811042,0.9222966,0.00006700248,0.00032757537,0.00017538585,0.000019520321,0.00036019395,0.0011608733],"genre_scores_gemma":[0.9981839,0.0000150367005,0.0009813064,0.0007595151,0.000015733121,0.000016698577,0.000008927455,0.000006791092,0.000012077414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932325,0.000033268057,0.000339027,0.000045627057,0.000113866205,0.00014497082],"domain_scores_gemma":[0.9996546,0.00009225324,0.000047531605,0.00009342771,0.000048043363,0.00006414318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013288154,0.00012605364,0.000116123236,0.0001477385,0.000113399416,0.00003579024,0.00007946252,0.00006481434,0.00004207153],"category_scores_gemma":[0.000025216177,0.00013115178,0.00004409701,0.00027226057,0.000025498939,0.002742846,0.0000010717661,0.00029464843,0.00030103687],"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":[0.00018524236,0.000010075897,0.0000038245016,0.00010055184,0.000014928849,8.4038504e-7,0.009959673,0.93562746,0.0008506523,0.0006242143,0.000063294545,0.052559223],"study_design_scores_gemma":[0.0022378697,0.00013480254,0.0011096429,0.00010037692,0.000028621216,0.000011739778,0.006607089,0.37604457,0.6082371,0.0009778723,0.0038412607,0.00066906895],"about_ca_topic_score_codex":0.0000012076499,"about_ca_topic_score_gemma":0.00000338481,"teacher_disagreement_score":0.92259693,"about_ca_system_score_codex":0.00006511906,"about_ca_system_score_gemma":0.000011930416,"threshold_uncertainty_score":0.53482145},"labels":[],"label_agreement":null},{"id":"W3108707191","doi":"10.1039/d0tc04655h","title":"Resistive switching behaviour in a polymannose film for multistate non-volatile memory application","year":2020,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Innovation Cluster (Canada); Vancouver Biotech (Canada)","funders":"Ministry of Higher Education, Malaysia","keywords":"Materials science; Resistive touchscreen; Biodegradation; Resistive random-access memory; Non-volatile memory; Optoelectronics; Electrical engineering; Organic chemistry; Engineering","score_opus":0.009757642664815937,"score_gpt":0.23896627415739444,"score_spread":0.2292086314925785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108707191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9857636,0.00008196648,0.013733599,0.00007353773,0.00012637067,0.00011921887,0.000021796257,0.00002920839,0.00005072068],"genre_scores_gemma":[0.9976326,0.000009890822,0.0018352111,0.000033837674,0.0004351665,0.000008552806,0.000006249769,0.000023434814,0.000015051538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920815,0.000009048143,0.0004573654,0.00010138886,0.000080465325,0.00014357296],"domain_scores_gemma":[0.99956596,0.000045436238,0.00020253431,0.000067496134,0.000042198928,0.00007636472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015259566,0.00011525452,0.0002458118,0.000018291525,0.000031424945,0.000023018663,0.000125542,0.00005423294,0.000014096058],"category_scores_gemma":[0.0000704487,0.00011604612,0.00004729985,0.000057726666,0.000006782933,0.00012799505,0.000021095571,0.00013732801,0.0000015630173],"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":[0.00015129402,0.00000992263,0.00002962617,0.00027779286,0.000008309229,0.000021688487,0.00026129966,0.014969,0.9836004,3.236242e-7,0.00011523018,0.0005551633],"study_design_scores_gemma":[0.0006604571,0.000023247827,0.00041361523,0.000107555745,0.000013197227,0.00002281868,0.00010760254,0.0060609784,0.99235547,0.000036949194,0.000084716805,0.00011342075],"about_ca_topic_score_codex":0.0000014940953,"about_ca_topic_score_gemma":2.8153576e-7,"teacher_disagreement_score":0.011898389,"about_ca_system_score_codex":0.00004568199,"about_ca_system_score_gemma":0.00001640977,"threshold_uncertainty_score":0.47322232},"labels":[],"label_agreement":null},{"id":"W3111596116","doi":"10.48550/arxiv.2012.09299","title":"Oxygen vacancy engineering of TaO x -based resistive memories by Zr doping for improved variability and synaptic behavior","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Doping; Materials science; Oxygen; Resistive touchscreen; Resistive random-access memory; Vacancy defect; Engineering physics; Optoelectronics; Nanotechnology; Condensed matter physics; Electrical engineering; Physics; Engineering; Voltage","score_opus":0.03968827655124012,"score_gpt":0.18035398182551948,"score_spread":0.14066570527427935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111596116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6378617,0.00007312126,0.36086303,0.000013637986,0.00023929245,0.0005281745,0.000155286,0.00023389852,0.000031823947],"genre_scores_gemma":[0.9967085,0.00003734404,0.0030710502,0.000011289448,0.00005538344,0.000007245772,0.000043645563,0.000046013694,0.000019524228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998805,0.000031940326,0.0002503921,0.0006021518,0.000035705132,0.00027480104],"domain_scores_gemma":[0.99898165,0.00035052665,0.00012496447,0.00033131673,0.000086072294,0.00012548844],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013347357,0.00032468673,0.0004714645,0.00008520544,0.00007814424,0.000016724005,0.0002609176,0.00018360835,0.0000039373376],"category_scores_gemma":[0.0001503717,0.00041503436,0.00013621913,0.00018651247,0.00006112046,0.00010879864,0.00024369171,0.0004242482,4.2495776e-7],"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":[0.00014256372,0.000033727978,0.0005435119,0.0024298704,0.00018405863,0.00002605746,0.00009853142,0.9349373,0.060301814,0.0009369793,0.000029638484,0.00033591953],"study_design_scores_gemma":[0.00073533563,0.00009537648,0.0014340691,0.00025486614,0.0003492351,0.0000010849161,0.00004318709,0.95569056,0.039492544,0.0012208519,0.00008210828,0.0006007878],"about_ca_topic_score_codex":0.000008909807,"about_ca_topic_score_gemma":0.0000018832728,"teacher_disagreement_score":0.35884675,"about_ca_system_score_codex":0.00014595261,"about_ca_system_score_gemma":0.000046868514,"threshold_uncertainty_score":0.9998301},"labels":[],"label_agreement":null},{"id":"W3113721125","doi":"10.1016/j.mtadv.2020.100125","title":"Multistate resistive switching behaviors for neuromorphic computing in memristor","year":2020,"lang":"en","type":"article","venue":"Materials Today Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Memristor; Von Neumann architecture; Unconventional computing; Computer science; Resistive random-access memory; DNA computing; Resistive touchscreen; In-Memory Processing; Computer architecture; Computation; Artificial neural network; Electronic engineering; Distributed computing; Artificial intelligence; Algorithm; Electrical engineering; Voltage; Engineering; Search engine","score_opus":0.029372553626955542,"score_gpt":0.25850095454429295,"score_spread":0.2291284009173374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113721125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97859424,0.00041135287,0.019156221,0.000097462194,0.00089482754,0.0003818373,0.000045626133,0.000373376,0.000045050612],"genre_scores_gemma":[0.9930303,0.000050224065,0.0064028506,0.00013835866,0.00028077487,0.000023040664,0.00001678891,0.000051795756,0.000005861761],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987763,0.0000453532,0.00042751437,0.00031416296,0.00009091232,0.0003457315],"domain_scores_gemma":[0.9995295,0.00017457048,0.00008942518,0.00010065462,0.00002301165,0.00008285386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015257225,0.00020876585,0.0003531849,0.000048463648,0.00011155951,0.000041221934,0.00015899034,0.000043612712,0.000014774791],"category_scores_gemma":[0.0001231828,0.00022733337,0.0000399885,0.00013231722,0.000020286323,0.00027846644,0.000048000336,0.00012793033,0.0000086226355],"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":[0.00010237259,0.000009120325,0.00011243875,0.00028074064,0.0000046430873,0.0000447556,0.00061950355,0.073271014,0.9191865,0.00004243271,0.000022892125,0.0063036378],"study_design_scores_gemma":[0.00091100263,0.00012354678,0.0013536622,0.00015928406,0.000016101763,0.0000072725775,0.00015678172,0.011795266,0.98108494,0.00027401422,0.0036320447,0.000486078],"about_ca_topic_score_codex":0.000004385193,"about_ca_topic_score_gemma":0.000008486574,"teacher_disagreement_score":0.061898496,"about_ca_system_score_codex":0.000038613674,"about_ca_system_score_gemma":0.0000063681837,"threshold_uncertainty_score":0.92703855},"labels":[],"label_agreement":null},{"id":"W3114847385","doi":"10.1109/icecs49266.2020.9294907","title":"Low Power Memristor-Based Shift Register Design","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; CMOS; Shift register; Memistor; Computer science; Electronic engineering; Transistor; Resistive random-access memory; Electrical engineering; Engineering; Telecommunications; Voltage; Chip","score_opus":0.036158058131725186,"score_gpt":0.21962980808494234,"score_spread":0.18347174995321716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114847385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025081228,0.000052975254,0.9652597,0.00048200504,0.00016733239,0.00009347101,5.5756317e-7,0.00077293155,0.008089806],"genre_scores_gemma":[0.9894548,7.4741666e-7,0.00861599,0.0017348794,0.000067782064,0.000002254899,8.3939926e-7,0.00002303187,0.00009963919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995409,0.00001346071,0.00010480587,0.00012432103,0.00006603436,0.0001504452],"domain_scores_gemma":[0.9997258,0.00005866919,0.000010723372,0.000107529704,0.000006956258,0.000090274036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035278517,0.00009829942,0.00009545646,0.000014224914,0.00003071714,0.000012167486,0.00008871851,0.000030442568,0.00020879562],"category_scores_gemma":[0.000020410578,0.000090343616,0.0000373697,0.0000841319,0.0000106443285,0.000071909846,0.0000118368935,0.00011171467,0.00012792018],"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":[0.000053021056,0.000012886683,0.000029532433,0.0000964597,0.0000143408415,0.000075250886,0.00043233877,0.92269415,0.06313393,0.00031602904,0.011113354,0.002028679],"study_design_scores_gemma":[0.0005835807,0.00015213738,0.00009580785,0.000031729298,0.000008458266,0.0000025987895,0.000038882976,0.20621403,0.7699183,0.00016894551,0.022340411,0.000445123],"about_ca_topic_score_codex":1.4310413e-7,"about_ca_topic_score_gemma":1.85578e-7,"teacher_disagreement_score":0.9643736,"about_ca_system_score_codex":0.000015969224,"about_ca_system_score_gemma":0.0000056799468,"threshold_uncertainty_score":0.3684106},"labels":[],"label_agreement":null},{"id":"W3117291443","doi":"10.1109/icecs49266.2020.9294786","title":"Hybrid Memristor-CMOS Based Up-Down Counter Design","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Windsor","funders":"","keywords":"Memristor; CMOS; Computer science; Electronic engineering; Electrical engineering; Engineering","score_opus":0.037410053699729225,"score_gpt":0.21822906056066402,"score_spread":0.1808190068609348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117291443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02339215,0.00007833014,0.97109944,0.00026135566,0.00032237466,0.000120984056,0.0000022435754,0.00085991825,0.0038631929],"genre_scores_gemma":[0.989612,0.0000029654439,0.007877306,0.002143739,0.00014500968,0.000004197157,0.0000026024013,0.000026811447,0.00018541106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994678,0.000014923278,0.00012188766,0.00013900913,0.00008233806,0.00017403024],"domain_scores_gemma":[0.9996745,0.00007469782,0.0000113090355,0.00009874207,0.000014233524,0.0001264949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044273474,0.00011560074,0.000113478825,0.000015636824,0.000041938772,0.000014575856,0.000100501464,0.000018901814,0.0004186305],"category_scores_gemma":[0.00003257892,0.000107858184,0.000038495677,0.00006192246,0.000010267136,0.00009241927,0.000014675459,0.00012728691,0.00015756779],"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":[0.00004760229,0.000007197701,0.000018750769,0.00008711662,0.000018405783,0.00006413754,0.00013492783,0.84616095,0.10739216,0.00010069649,0.040348236,0.0056198486],"study_design_scores_gemma":[0.00025614756,0.00003693006,0.000006754528,0.000008239274,0.0000052517235,0.0000036954868,0.00001052337,0.55500513,0.42349687,0.000042946333,0.020966804,0.00016069731],"about_ca_topic_score_codex":4.4479268e-7,"about_ca_topic_score_gemma":1.4634533e-7,"teacher_disagreement_score":0.9662198,"about_ca_system_score_codex":0.000024989891,"about_ca_system_score_gemma":0.000009799378,"threshold_uncertainty_score":0.458371},"labels":[],"label_agreement":null},{"id":"W3117570688","doi":"10.1109/ojcas.2020.3047418","title":"Hardware-Aware Design for Edge Intelligence","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"McGill University","funders":"","keywords":"Computer science; Software deployment; Enhanced Data Rates for GSM Evolution; Inference; Edge device; Cloud computing; Latency (audio); Edge computing; Deep neural networks; Server; Bandwidth (computing); Distributed computing; Computer architecture; Computer network; Artificial intelligence; Artificial neural network; Telecommunications; Software engineering; Operating system","score_opus":0.15559306119440827,"score_gpt":0.3026185713682348,"score_spread":0.1470255101738265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117570688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021310415,0.0016791887,0.97509325,0.00005656516,0.0011861637,0.0003807694,0.0000053697972,0.000020644531,0.0002676319],"genre_scores_gemma":[0.9990044,0.00007931623,0.00029486427,0.00006410484,0.0004943934,0.0000042546617,2.3503212e-7,0.000019020892,0.000039361767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992627,0.000035554254,0.00038073584,0.00009143891,0.00008981984,0.0001397539],"domain_scores_gemma":[0.99942726,0.00014359425,0.000120995996,0.000058280708,0.000096952244,0.00015294716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003321207,0.000101303274,0.00029437963,0.000026848087,0.00007113119,0.00014752816,0.00028416413,0.000034189514,0.0000033079864],"category_scores_gemma":[0.000040208517,0.00008398295,0.000045003002,0.000073323376,0.000010969893,0.0003150839,0.000016609865,0.00013601956,0.0000026535772],"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":[0.00006661138,0.00001335784,0.00004614406,0.0007480781,0.000119262346,0.00010456514,0.0022262735,0.86923444,0.05350806,0.00032436772,0.005292733,0.06831608],"study_design_scores_gemma":[0.0024278704,0.0023330776,0.000073774216,0.0022837948,0.00015077411,0.0017779694,0.0035447497,0.67568856,0.24851339,0.00091044157,0.06106317,0.0012324282],"about_ca_topic_score_codex":5.388608e-7,"about_ca_topic_score_gemma":1.0810423e-7,"teacher_disagreement_score":0.97769403,"about_ca_system_score_codex":0.0000145044905,"about_ca_system_score_gemma":0.000019961195,"threshold_uncertainty_score":0.34247252},"labels":[],"label_agreement":null},{"id":"W3118199233","doi":"10.1049/cds2.12003","title":"Pinched hysteresis loops in non‐linear resonators","year":2020,"lang":"en","type":"article","venue":"IET Circuits Devices & Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Linearity; Hysteresis; Electronic circuit; Resonator; Diode; Loop (graph theory); Control theory (sociology); Nonlinear system; Physics; Topology (electrical circuits); Mathematics; Optoelectronics; Condensed matter physics; Computer science; Quantum mechanics","score_opus":0.027318921415836927,"score_gpt":0.23841534513230514,"score_spread":0.21109642371646822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118199233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99057794,0.0022873913,0.0030728795,0.000079652,0.00084862683,0.0004162078,0.000011352282,0.0005409491,0.0021650032],"genre_scores_gemma":[0.9991606,0.00001835803,0.000032836706,0.00019653483,0.0004661099,0.000020203102,0.000005472754,0.00005778132,0.000042101583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851996,0.0000611838,0.0004825646,0.00033317783,0.00020921088,0.00039393522],"domain_scores_gemma":[0.9993897,0.00010932265,0.00007379873,0.00022430278,0.000031738604,0.00017111415],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001571632,0.00024305432,0.0003978347,0.00008210855,0.000053807988,0.000046970756,0.00029462372,0.000107040476,0.00001018157],"category_scores_gemma":[0.0000362679,0.00024939718,0.00006541136,0.0005090128,0.000015387073,0.00024163797,0.000041933778,0.00029283777,0.00017110727],"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":[0.00003842077,0.00006490262,0.024630312,0.0077928924,0.00019572974,0.00071237166,0.009312893,0.8400741,0.09022031,0.00042814403,0.0014856906,0.025044233],"study_design_scores_gemma":[0.0033081188,0.00028726325,0.014124707,0.0024252846,0.00008309888,0.00011502715,0.0040273075,0.8592918,0.039161723,0.00006878895,0.07448679,0.0026200903],"about_ca_topic_score_codex":0.000019552495,"about_ca_topic_score_gemma":0.000023030345,"teacher_disagreement_score":0.0730011,"about_ca_system_score_codex":0.00006625986,"about_ca_system_score_gemma":0.000012114139,"threshold_uncertainty_score":0.9999958},"labels":[],"label_agreement":null},{"id":"W3120110670","doi":"10.18280/ts.370607","title":"Design and Realization of a Hyperchaotic Memristive System for Communication System on FPGA","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Memristor; Attractor; Field-programmable gate array; Chaotic; Lyapunov exponent; Nonlinear system; Computer science; Electronic circuit; Communications system; Secure communication; Realization (probability); CHAOS (operating system); Topology (electrical circuits); Electronic engineering; Control theory (sociology); Computer hardware; Mathematics; Engineering; Telecommunications; Physics; Encryption; Electrical engineering; Artificial intelligence","score_opus":0.046257169159708574,"score_gpt":0.23479734569227023,"score_spread":0.18854017653256167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120110670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0727534,0.00011605081,0.92619604,0.000027232305,0.00003162399,0.0005415988,0.000008414024,0.00018703846,0.00013860018],"genre_scores_gemma":[0.99647665,0.000007744891,0.0033905555,0.000021879416,0.00004038129,0.000032590953,0.000012847347,0.000016115351,0.0000012341203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994649,0.000057041816,0.00020877442,0.00010233612,0.00007864215,0.00008829103],"domain_scores_gemma":[0.9995997,0.00018526927,0.00006744092,0.00007052732,0.00003501558,0.000042060637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000144919,0.00008879624,0.0001474557,0.00002598531,0.00006326608,0.000008106296,0.00006696318,0.000026139898,0.0000014322623],"category_scores_gemma":[0.000011740172,0.00008687585,0.000021454374,0.00006938414,0.000013268181,0.000052214426,0.000011381161,0.000047881636,0.0000010754862],"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":[0.00021304275,0.0000147045375,0.000009684403,0.0021551214,0.00004705104,0.000002063632,0.0017140253,0.9035205,0.077730894,0.009111262,0.000086712775,0.005394894],"study_design_scores_gemma":[0.00068985537,0.0003112988,0.000058815916,0.00039033676,0.000040099236,0.000004163237,0.00075539574,0.8743536,0.123190634,0.000032688782,0.000059545928,0.00011356215],"about_ca_topic_score_codex":7.332136e-7,"about_ca_topic_score_gemma":1.2456304e-7,"teacher_disagreement_score":0.9237232,"about_ca_system_score_codex":0.000050145612,"about_ca_system_score_gemma":0.0000042070815,"threshold_uncertainty_score":0.35426942},"labels":[],"label_agreement":null},{"id":"W3120552573","doi":"10.1088/2632-2153/ac34db","title":"Miniaturizing neural networks for charge state autotuning in quantum dots","year":2021,"lang":"en","type":"preprint","venue":"Machine Learning Science and Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke; Perimeter Institute; University of Waterloo","funders":"","keywords":"Computer science; Qubit; Artificial neural network; Quantum computer; Quantum dot; Quantum; Electronic engineering; Topology (electrical circuits); Electrical engineering; Artificial intelligence; Physics; Engineering; Quantum mechanics","score_opus":0.011405911392885482,"score_gpt":0.2516826439136773,"score_spread":0.2402767325207918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120552573","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98337966,0.0032822324,0.011209246,0.00032073192,0.00073452824,0.00025465112,0.0000024754054,0.00077146804,0.00004499968],"genre_scores_gemma":[0.99775404,0.0002180108,0.0018089601,0.000042333406,0.000055086257,0.00003720062,0.000014298865,0.000037278325,0.000032776537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981648,0.000025711977,0.00030821358,0.0006501786,0.00015317953,0.0006979224],"domain_scores_gemma":[0.99938685,0.00010251892,0.0001034521,0.00025257468,0.00009027435,0.00006431442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006633508,0.00028503747,0.000403609,0.0006899299,0.0003341761,0.00013091086,0.00042598878,0.00025129484,0.0000027652748],"category_scores_gemma":[0.00043907747,0.00029845804,0.000039624683,0.0010098829,0.00026008586,0.00016989475,0.0007349233,0.0022112208,4.8305725e-7],"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":[0.000006338546,0.000008171223,0.0034623179,0.00016499542,0.000008220968,0.000053586995,0.00030715676,0.8957644,0.020000888,0.00024470812,0.0000027518915,0.07997648],"study_design_scores_gemma":[0.00021363943,0.000051765677,0.00033451032,0.00016796033,0.000005961864,0.000033467,0.00008522817,0.99422413,0.003708929,0.000542387,0.00032699425,0.00030499825],"about_ca_topic_score_codex":0.000010535126,"about_ca_topic_score_gemma":0.000018015444,"teacher_disagreement_score":0.09845977,"about_ca_system_score_codex":0.00008223758,"about_ca_system_score_gemma":0.000051476054,"threshold_uncertainty_score":0.9999468},"labels":[],"label_agreement":null},{"id":"W3121087233","doi":"10.1109/ssci47803.2020.9308196","title":"Exploring the Relationship Between Topology and Function in Evolved Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Network topology; Artificial neural network; Computer science; Selection (genetic algorithm); Topology (electrical circuits); Function (biology); Neuroevolution; Evolutionary algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.17277320757153472,"score_gpt":0.26101297083385555,"score_spread":0.08823976326232083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121087233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94788855,0.00009863948,0.05099786,0.00046580026,0.00014140503,0.000052991367,8.977047e-8,0.0001417458,0.00021292805],"genre_scores_gemma":[0.99953485,0.0000062302192,0.0000798327,0.00013870366,0.00022052796,0.0000046964915,0.0000012284813,0.0000073710744,0.0000065734143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996818,0.000021142338,0.000093287264,0.00007700963,0.000022799764,0.00010400599],"domain_scores_gemma":[0.99956375,0.0003532361,0.000007125125,0.00004341751,0.000002758392,0.000029692996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044871475,0.00005276848,0.00006186228,0.000014714853,0.000054047414,0.0000075489343,0.000033335382,0.000018923432,0.000004112615],"category_scores_gemma":[0.000044277906,0.000040919556,0.00001025189,0.00012878275,0.0000121091625,0.0001536457,0.000021496151,0.00022068094,0.0000018053472],"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":[0.0000100656825,6.580635e-7,0.2809439,0.000012121681,0.0000036404072,0.0000020378045,0.00029884084,0.7014846,0.00015991212,0.0009627554,0.000024447598,0.016097011],"study_design_scores_gemma":[0.0001257555,0.000025330088,0.5872254,0.0000040562445,0.000004419388,0.0000011391598,0.00013222703,0.41182643,0.00016248597,0.0003257831,0.00010152633,0.00006546005],"about_ca_topic_score_codex":0.0000016185982,"about_ca_topic_score_gemma":0.0000051951915,"teacher_disagreement_score":0.30628148,"about_ca_system_score_codex":0.0000066658376,"about_ca_system_score_gemma":6.522609e-7,"threshold_uncertainty_score":0.16686511},"labels":[],"label_agreement":null},{"id":"W3122393936","doi":"10.1109/icast51195.2020.9319475","title":"Improved Spiking Neural Networks with multiple neurons for digit recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"MNIST database; Spiking neural network; Neuromorphic engineering; Computer science; Artificial intelligence; Artificial neural network; Convolutional neural network; Deep learning; Machine learning; Computational neuroscience","score_opus":0.03059847672615579,"score_gpt":0.21433130986601648,"score_spread":0.1837328331398607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122393936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38686517,0.000018463092,0.61161524,0.00011558295,0.00014066073,0.00027021536,0.0000039747924,0.0006636794,0.00030704582],"genre_scores_gemma":[0.9946357,0.0000026495745,0.004470767,0.0005282265,0.00027910405,0.000017042688,0.000017469378,0.0000361709,0.00001284469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994822,0.0000046232167,0.0001114551,0.00015535111,0.000032946267,0.00021343735],"domain_scores_gemma":[0.9997147,0.00010750106,0.00001830272,0.00006041007,0.000018955323,0.00008013934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000013723669,0.00011396543,0.00010230935,0.0000119098095,0.000066777124,0.000025095867,0.00005601925,0.000026783273,0.000006700996],"category_scores_gemma":[0.000032884196,0.0000982517,0.000035529683,0.00009499045,0.000008535242,0.00017388773,0.00001615796,0.00013136806,0.0000017305492],"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":[0.00008557554,0.0000044993744,0.00012543335,0.000049381782,0.000010605114,0.000004924036,0.00005091767,0.87094927,0.023876429,0.0000042998686,0.00008280611,0.10475584],"study_design_scores_gemma":[0.00045366437,0.00014168293,0.000050297345,0.00000866874,0.000008229833,0.000004834224,0.00002133425,0.9651859,0.033502538,0.000011739712,0.00046362917,0.000147481],"about_ca_topic_score_codex":6.110243e-7,"about_ca_topic_score_gemma":0.000004875986,"teacher_disagreement_score":0.60777056,"about_ca_system_score_codex":0.00000723243,"about_ca_system_score_gemma":0.0000017660695,"threshold_uncertainty_score":0.40065882},"labels":[],"label_agreement":null},{"id":"W3127440096","doi":"10.1038/s41467-021-27274-9","title":"Analog programing of conducting-polymer dendritic interconnections and control of their morphology","year":2021,"lang":"en","type":"preprint","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Biochip; Massively parallel; Neuromorphic engineering; Computer science; Materials science; Duty cycle; Memristor; Nanotechnology; Branching (polymer chemistry); Bridging (networking); Biological system; Artificial intelligence; Electronic engineering; Voltage; Artificial neural network; Electrical engineering; Parallel computing; Engineering","score_opus":0.0390523434808068,"score_gpt":0.3013048039895927,"score_spread":0.2622524605087859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127440096","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9201052,0.071309775,0.006999378,0.00023996957,0.00051127985,0.0002849268,0.000072597526,0.00013497676,0.00034187536],"genre_scores_gemma":[0.9950013,0.00090035715,0.003885379,0.000035852925,0.00002957038,0.000033736767,0.00008328059,0.000024606563,0.000005926764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913,0.00013233739,0.0003764154,0.00017341971,0.000053661803,0.00013413483],"domain_scores_gemma":[0.9978723,0.0006509261,0.00015533117,0.0011037546,0.00018100573,0.000036693236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012587073,0.00016924572,0.0004100459,0.00012844744,0.0000898461,0.000014008294,0.0005036728,0.00037956506,0.000005972622],"category_scores_gemma":[0.00015520437,0.00017572686,0.00010515413,0.00017278823,0.00022351506,0.000056576046,0.0005668421,0.0019061483,1.4446286e-7],"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":[0.000015412677,0.00024574524,0.002516766,0.0012661678,0.0009340161,0.000009892267,0.0032654977,0.012054813,0.95249754,0.0069635157,0.00003929257,0.020191344],"study_design_scores_gemma":[0.0013982538,0.00013595204,0.002963265,0.0022551024,0.0006392266,0.00044092067,0.0058875466,0.056390725,0.926033,0.00182815,0.0009217486,0.0011061057],"about_ca_topic_score_codex":0.0000096973845,"about_ca_topic_score_gemma":0.00008963482,"teacher_disagreement_score":0.074896075,"about_ca_system_score_codex":0.00002257882,"about_ca_system_score_gemma":0.00002935845,"threshold_uncertainty_score":0.82813716},"labels":[],"label_agreement":null},{"id":"W3128147185","doi":"10.1021/acsabm.1c00015","title":"A Battery-Like Self-Selecting Biomemristor from Earth-Abundant Natural Biomaterials","year":2021,"lang":"en","type":"article","venue":"ACS Applied Bio Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Memristor; Battery (electricity); Neuromorphic engineering; Graphene; Nanotechnology; Density functional theory; Materials science; Ionic liquid; Electronics; Computer science; Electrical engineering; Physics; Artificial neural network; Engineering; Chemistry; Artificial intelligence","score_opus":0.007558621173623398,"score_gpt":0.20015113324360756,"score_spread":0.19259251206998415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128147185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99352264,0.0003438656,0.0002605089,0.000030514402,0.004087874,0.00023715869,0.00010707366,0.0012091016,0.00020125655],"genre_scores_gemma":[0.99484277,0.000057669455,0.0035199784,0.00024567868,0.0009322303,0.000033724595,0.00021960004,0.00009210153,0.000056246405],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99807924,0.000060777234,0.0005857806,0.00051406975,0.00019172735,0.0005683966],"domain_scores_gemma":[0.9992165,0.00011960111,0.00011866398,0.00039838805,0.00004711243,0.00009972492],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000196998,0.00038947802,0.00055058545,0.00008806713,0.0001960809,0.0002615591,0.0002309449,0.00016116205,0.00034331577],"category_scores_gemma":[0.000021853744,0.0003811725,0.000054890625,0.000298023,0.000033030927,0.00015008701,0.00016853244,0.000097738724,0.00027370095],"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":[0.000029526567,0.000020639314,0.0000054599523,0.00009726676,0.00008470091,0.000056873832,0.00017305631,0.000075972486,0.99823624,0.00005718193,0.00030125267,0.0008618131],"study_design_scores_gemma":[0.00042786732,0.000013112241,0.00023647367,0.00004714001,0.000035687226,0.0000347119,0.000056686258,0.000025947409,0.9927572,0.00020196613,0.0056904876,0.000472715],"about_ca_topic_score_codex":0.000011267833,"about_ca_topic_score_gemma":0.000005071746,"teacher_disagreement_score":0.005479051,"about_ca_system_score_codex":0.000067580724,"about_ca_system_score_gemma":0.000027043803,"threshold_uncertainty_score":0.99986404},"labels":[],"label_agreement":null},{"id":"W3128880130","doi":"10.1002/pssa.202000655","title":"Electrocatalytic Hydrolysis‐Modulated Multistate Resistive Switching Behaviors in Memristors","year":2021,"lang":"en","type":"article","venue":"physica status solidi (a)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Memristor; Resistive random-access memory; Neuromorphic engineering; Resistive touchscreen; Computer data storage; Computer science; Materials science; Nanotechnology; Internet of Things; Graphene; Mechanism (biology); Optoelectronics; Electrical engineering; Engineering; Physics; Artificial neural network; Computer hardware; Artificial intelligence; Voltage; Embedded system","score_opus":0.009065963931018434,"score_gpt":0.24280205472488456,"score_spread":0.23373609079386612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128880130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99640685,0.00036128834,0.0020315284,0.000024498588,0.0001750525,0.000114046146,0.0000109944995,0.00027672976,0.00059902004],"genre_scores_gemma":[0.9991122,0.00007977216,0.00047862524,0.000025482832,0.000079037614,0.00001567386,0.000058011494,0.00006228628,0.000088896355],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99841154,0.000048954895,0.00029959105,0.00037837372,0.00017470993,0.0006868472],"domain_scores_gemma":[0.9993794,0.00010760477,0.000053836673,0.0002875004,0.000044837685,0.0001268194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005920457,0.00025455447,0.00034230592,0.00009756738,0.000114073686,0.00002989383,0.00011163171,0.000049955277,0.000008491907],"category_scores_gemma":[0.000046588582,0.00029661643,0.0000994996,0.00060762936,0.000021581252,0.00022903079,0.00005570568,0.00044725483,0.000026912472],"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":[0.00002342899,0.00007397185,0.00023245238,0.000050862014,0.000039429426,0.00022970127,0.0010775223,0.117921785,0.87547505,0.000057817764,0.00004723244,0.0047707753],"study_design_scores_gemma":[0.0008560165,0.00004424313,0.007706146,0.000110854264,0.00006364518,0.000014825191,0.00024256189,0.11056088,0.878098,0.00097049837,0.00066553813,0.0006668014],"about_ca_topic_score_codex":0.00003770549,"about_ca_topic_score_gemma":0.00008166892,"teacher_disagreement_score":0.0074736937,"about_ca_system_score_codex":0.00025190695,"about_ca_system_score_gemma":0.000041115232,"threshold_uncertainty_score":0.9999486},"labels":[],"label_agreement":null},{"id":"W3130607852","doi":"10.1016/j.neuron.2021.01.009","title":"Visualizing a joint future of neuroscience and neuromorphic engineering","year":2021,"lang":"en","type":"article","venue":"Neuron","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Google","keywords":"Neuromorphic engineering; Computational neuroscience; Neuroscience; Focus (optics); Systems neuroscience; Computer science; Joint (building); Cognitive science; Human–computer interaction; Artificial intelligence; Artificial neural network; Psychology; Engineering; Physics","score_opus":0.026153655258327555,"score_gpt":0.22560869349831866,"score_spread":0.1994550382399911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130607852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964336,0.0006430127,0.0018579703,0.000054299144,0.0007213301,0.000036835143,0.0000013973457,0.00015826659,0.00009324797],"genre_scores_gemma":[0.9992406,0.00017127644,0.00033610212,0.00011299756,0.00010344882,0.0000010715785,5.651773e-7,0.0000214779,0.000012457574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994221,0.000014345152,0.0001395954,0.00017346075,0.00008937621,0.00016115633],"domain_scores_gemma":[0.99974954,0.000030728894,0.000020150444,0.00012791809,0.00001727701,0.00005440478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000342042,0.000101113255,0.00012867467,0.00004029569,0.00003458552,0.00001484047,0.000052526564,0.00002448761,0.000002670163],"category_scores_gemma":[0.000044126085,0.000110111796,0.00002699993,0.00020163953,0.000015964804,0.00010384757,0.000056376626,0.00017146817,7.5235687e-7],"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":[0.0000010338429,0.0000042500474,0.00004156009,0.00008989097,7.794551e-7,0.00007586432,0.000058281308,0.027560877,0.96925706,0.0001776621,0.00001100264,0.0027217418],"study_design_scores_gemma":[0.00024918155,0.00008274935,0.022516174,0.0000738342,0.000010119854,0.00036249863,0.00003136369,0.15836373,0.8102904,0.000040048883,0.007733612,0.00024630636],"about_ca_topic_score_codex":2.0130025e-7,"about_ca_topic_score_gemma":1.8924297e-7,"teacher_disagreement_score":0.15896668,"about_ca_system_score_codex":0.0000048325232,"about_ca_system_score_gemma":0.0000057342863,"threshold_uncertainty_score":0.44902286},"labels":[],"label_agreement":null},{"id":"W3134585653","doi":"10.1109/icjece.2020.2978403","title":"Investigating the Impact of Imprecise Computation in Memristive Memory Arrays on the Classification Performance of Deep Fully Connected Neural Networks","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Von Neumann architecture; Computation; Artificial neural network; Dram; Computer hardware; Computer engineering; Transfer (computing); Parallel computing; Computer architecture; Artificial intelligence; Algorithm","score_opus":0.012611868951045928,"score_gpt":0.20460010116015087,"score_spread":0.19198823220910494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134585653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90554947,0.00056686305,0.093645215,0.00003847348,0.00012119944,0.000057149024,7.4752415e-7,0.000007042943,0.000013834177],"genre_scores_gemma":[0.9992976,0.000019967127,0.00054068444,0.000021062468,0.00010752817,9.683745e-7,0.0000012978737,0.000010482506,3.6407454e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925846,0.000046137327,0.00034862172,0.000074175434,0.00007808811,0.00019451643],"domain_scores_gemma":[0.99907404,0.00051682536,0.000110378685,0.00006903104,0.000112164475,0.00011755157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001627182,0.000111962596,0.00020200547,0.0001207875,0.000052753945,0.000019244637,0.000111182206,0.00004011912,0.0000019288711],"category_scores_gemma":[0.000097611264,0.000075777265,0.00006333254,0.00047523275,0.00003150413,0.00007914932,0.000008371695,0.00043377504,5.325116e-8],"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":[0.0000054910124,0.0000033893298,0.00078738004,0.000015954143,0.000023565326,0.000008671374,0.00021174824,0.97482705,0.0041585066,0.0000663762,0.000010298805,0.019881569],"study_design_scores_gemma":[0.00015546566,0.00014786304,0.05408742,0.00008992159,0.000007270204,0.000072528106,0.000021082007,0.94219303,0.0031259183,0.000028257327,0.0000011500146,0.00007010158],"about_ca_topic_score_codex":0.000025999872,"about_ca_topic_score_gemma":0.000034360542,"teacher_disagreement_score":0.09374817,"about_ca_system_score_codex":0.000082111794,"about_ca_system_score_gemma":0.000067879024,"threshold_uncertainty_score":0.30901074},"labels":[],"label_agreement":null},{"id":"W3134948768","doi":"10.1021/acs.nanolett.1c00539","title":"Bipolar Resistive Switching in Junctions of Gallium Oxide and p-type Silicon","year":2021,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":44,"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 Alberta","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft; University of Alberta; Alberta Innovates - Technology Futures","keywords":"Materials science; Gallium; Silicon; Optoelectronics; Doping; Hysteresis; Dissolution; Layer (electronics); Oxide; Resistive touchscreen; Nanotechnology; Condensed matter physics; Chemistry; Electrical engineering","score_opus":0.009767150881959426,"score_gpt":0.21335669622839878,"score_spread":0.20358954534643936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134948768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973458,0.0007151229,0.001385622,0.0001793511,0.00020260028,0.000030383697,6.7727274e-7,0.000043382242,0.00009704504],"genre_scores_gemma":[0.9989004,0.000053595064,0.00074255554,0.00023869646,0.00002697238,8.5084247e-7,0.0000013329421,0.000010104691,0.000025500434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996096,0.000017152053,0.000115454655,0.00010227511,0.00004548364,0.00011003944],"domain_scores_gemma":[0.9998101,0.000060492697,0.000015689337,0.000080038895,0.000012956982,0.0000207164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045036766,0.00006312578,0.00009853642,0.00005198295,0.000028470658,0.000005426629,0.000027964168,0.000024571178,0.000004321566],"category_scores_gemma":[0.00004360475,0.000069645466,0.0000172305,0.00018262917,0.000011120175,0.00007242754,0.000019184014,0.00010646493,0.0000018221364],"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":[0.0000047800236,0.000003404237,0.0016776535,0.000032988453,0.000005950201,0.000021483564,0.00010664529,0.011581432,0.98534733,0.000026181677,0.000077039615,0.0011150945],"study_design_scores_gemma":[0.00023943625,0.000011345752,0.023354352,0.00009838293,0.000008776533,0.000013330548,0.00008780719,0.0008573265,0.9727776,0.000044527158,0.002373558,0.0001335318],"about_ca_topic_score_codex":0.00000730516,"about_ca_topic_score_gemma":0.000017608725,"teacher_disagreement_score":0.021676697,"about_ca_system_score_codex":0.000021796357,"about_ca_system_score_gemma":0.000005283121,"threshold_uncertainty_score":0.28400597},"labels":[],"label_agreement":null},{"id":"W3136002183","doi":"10.21203/rs.3.rs-87924/v1","title":"Electric-field control of field-free spin-orbit torque switching via laterally modulated Rashba effect in Pt/Co/AlOx structures","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Electric field; Rashba effect; Condensed matter physics; Torque; Materials science; Field (mathematics); Spin (aerodynamics); Spin–orbit interaction; Physics; Spintronics; Ferromagnetism; Quantum mechanics; Mathematics","score_opus":0.02075097003338031,"score_gpt":0.34569253805513295,"score_spread":0.3249415680217526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136002183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643865,0.0011142395,0.0315202,0.0003665847,0.00033045938,0.0014305941,0.000015429547,0.0003055363,0.0005304358],"genre_scores_gemma":[0.9989895,0.00012048725,0.0002890813,0.000084935884,0.00034255785,0.000059193815,0.000021231968,0.00008280033,0.000010180636],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99661547,0.00050968956,0.0006368038,0.0006357553,0.0007215045,0.0008807636],"domain_scores_gemma":[0.9969946,0.0017523223,0.0001013463,0.0008017669,0.00014943827,0.00020053296],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008276284,0.0004550461,0.00085683435,0.00050333794,0.00010755592,0.0000813548,0.00095004524,0.00054357835,0.00006213104],"category_scores_gemma":[0.0013684845,0.0004357233,0.00022611572,0.00053848285,0.000014946874,0.00012235176,0.00055889017,0.0043017305,0.000008946301],"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":[0.0005200551,0.000022376662,0.0018471156,0.0046442347,0.00014283523,0.00036394113,0.00043101306,0.25114787,0.72212285,0.000106206084,0.00032053364,0.018330945],"study_design_scores_gemma":[0.0017035989,0.00093907095,0.0046854727,0.0012845714,0.000020099864,0.000009624205,0.00001926057,0.21706389,0.7587262,0.014892071,0.00007308422,0.00058309385],"about_ca_topic_score_codex":0.00023146707,"about_ca_topic_score_gemma":0.000039585226,"teacher_disagreement_score":0.036603287,"about_ca_system_score_codex":0.00019998163,"about_ca_system_score_gemma":0.000087128836,"threshold_uncertainty_score":0.99980944},"labels":[],"label_agreement":null},{"id":"W3137364963","doi":"10.1002/aelm.202001241","title":"Phase Change Random Access Memory for Neuro‐Inspired Computing","year":2021,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; National Key Laboratory of Electronic Thin Films and Integrated Devices; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Memristor; Computer science; Realization (probability); Maturity (psychological); Phase change; Random access memory; Neuromorphic engineering; Phase-change memory; Mainstream; Reservoir computing; Big data; Artificial intelligence; Data science; Artificial neural network; Engineering; Electrical engineering; Engineering physics; Computer hardware; Psychology","score_opus":0.032135537411867234,"score_gpt":0.3172969552206068,"score_spread":0.28516141780873955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137364963","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94402987,0.0017821436,0.0508189,0.0000973292,0.001516538,0.00083308533,0.00004040992,0.00073638925,0.0001453233],"genre_scores_gemma":[0.99735993,0.00030720542,0.00075583655,0.0004911668,0.0006636513,0.00014380763,0.00010632907,0.00009483116,0.00007723751],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99809307,0.00006502231,0.0004349189,0.0004132301,0.00011394829,0.0008798022],"domain_scores_gemma":[0.99914736,0.00028110016,0.00009960838,0.00031098523,0.000075778284,0.00008515264],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020496176,0.00029799863,0.0004976313,0.000057495345,0.000187552,0.00009429207,0.0002570195,0.000072327966,0.000091475405],"category_scores_gemma":[0.0001329982,0.00032381123,0.00009486775,0.00021301604,0.000021028938,0.0005042139,0.000090077214,0.0001525711,0.000009427621],"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":[0.00021688122,0.000044770626,6.3556746e-7,0.00021938994,0.000032173808,0.000022039061,0.00007100215,0.01102649,0.9430752,0.00032786006,0.000057512552,0.044906076],"study_design_scores_gemma":[0.006368799,0.00014892395,0.0000079977335,0.00006377316,0.000028398077,0.000041042334,0.000016042712,0.0029098347,0.9807208,0.0010789058,0.008259357,0.00035609794],"about_ca_topic_score_codex":0.000001132181,"about_ca_topic_score_gemma":0.000004653523,"teacher_disagreement_score":0.053330053,"about_ca_system_score_codex":0.000094852454,"about_ca_system_score_gemma":0.00003987279,"threshold_uncertainty_score":0.9999214},"labels":[],"label_agreement":null},{"id":"W3137431486","doi":"10.1016/j.mtphys.2021.100393","title":"Synaptic devices based neuromorphic computing applications in artificial intelligence","year":2021,"lang":"en","type":"article","venue":"Materials Today Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":267,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Memristor; Von Neumann architecture; Materials science; Computer science; Quantum computer; Unconventional computing; Computer architecture; Cognitive computing; Artificial neural network; Transistor; Nanotechnology; Spiking neural network; Artificial intelligence; Electronic engineering; Quantum; Neuroscience; Distributed computing; Electrical engineering; Engineering; Physics; Voltage; Cognition","score_opus":0.04305840218911718,"score_gpt":0.259886337811878,"score_spread":0.2168279356227608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137431486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7903809,0.000048828897,0.20890395,0.000023662347,0.0002848096,0.000107559055,0.0000084197645,0.00017569157,0.0000661731],"genre_scores_gemma":[0.99790645,0.000003903139,0.0016993228,0.00007697198,0.00023826366,0.000011275288,0.000035803656,0.000026379395,0.0000016503197],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917704,0.000046230034,0.00028669724,0.00019828156,0.00007698493,0.000214762],"domain_scores_gemma":[0.9995718,0.00013614309,0.00004111771,0.0001875227,0.000029831132,0.000033600078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008560222,0.00013657963,0.00019629452,0.000024424135,0.0000714883,0.0000574969,0.00010976665,0.000036130245,0.000031117772],"category_scores_gemma":[0.000015638967,0.00015223645,0.00002712644,0.00028234322,0.000020860625,0.00008337864,0.00004272405,0.000103621394,0.00003464054],"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":[0.0000027346748,0.00003164469,0.000035839217,0.00013341592,0.0000069577623,0.000020894377,0.00005843109,0.3067305,0.6828843,0.0026048003,0.0000014655202,0.0074890577],"study_design_scores_gemma":[0.000029760213,0.000007084207,0.00017287259,0.000054181473,0.000007116032,0.0000028832626,0.000045417593,0.05924177,0.93586683,0.0043501887,0.000062093364,0.00015980873],"about_ca_topic_score_codex":0.0000018093091,"about_ca_topic_score_gemma":0.000004275735,"teacher_disagreement_score":0.2529826,"about_ca_system_score_codex":0.000023597508,"about_ca_system_score_gemma":0.000016459426,"threshold_uncertainty_score":0.6208022},"labels":[],"label_agreement":null},{"id":"W3138783654","doi":"10.1088/2634-4386/abf150","title":"Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control","year":2021,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Council Canada; Seventh Framework Programme; Horizon 2020 Framework Programme; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada; European Commission; Intel Corporation","keywords":"Neuromorphic engineering; Keyword spotting; Computer science; Benchmark (surveying); Latency (audio); Spiking neural network; Artificial neural network; Spotting; Multiplication (music); Computer architecture; Embedded system; Artificial intelligence; Telecommunications","score_opus":0.017763652390324132,"score_gpt":0.19435009650640206,"score_spread":0.17658644411607793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138783654","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94295925,0.00045544843,0.055182915,0.000040509094,0.0002743643,0.00027110733,0.000001055175,0.0005974478,0.00021788046],"genre_scores_gemma":[0.9971515,0.000027251133,0.0024737406,0.00007974576,0.00016810538,0.000007735746,0.0000024550727,0.0000781177,0.000011361921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985658,0.00003194622,0.0002899995,0.00045542713,0.00016228645,0.00049455307],"domain_scores_gemma":[0.9992807,0.00025216537,0.00004962498,0.00019743269,0.000052991225,0.00016709387],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012457608,0.00036457067,0.00044137426,0.00009911955,0.00018270325,0.00008815756,0.00008023171,0.000067045854,0.0000029361004],"category_scores_gemma":[0.00005649584,0.00036060732,0.000038346534,0.0002835045,0.00003674272,0.00011222217,0.00007245618,0.0006192303,0.0000034438485],"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":[0.00004706893,0.000016662234,0.0010592614,0.00027236974,0.00004497697,0.00036826433,0.0001864217,0.9809033,0.013129953,0.00024670013,0.0000034446364,0.0037215347],"study_design_scores_gemma":[0.0010497797,0.00023996443,0.01287587,0.0008989071,0.000032980257,0.0005405211,0.000041507374,0.98045206,0.003347475,0.000018822746,0.000058382484,0.0004437021],"about_ca_topic_score_codex":0.0000020977902,"about_ca_topic_score_gemma":9.731526e-7,"teacher_disagreement_score":0.05419221,"about_ca_system_score_codex":0.000029551136,"about_ca_system_score_gemma":0.000014045583,"threshold_uncertainty_score":0.9998846},"labels":[],"label_agreement":null},{"id":"W3141535301","doi":"10.1016/j.matt.2021.03.002","title":"A molecular computing approach to solving optimization problems via programmable microdroplet arrays","year":2021,"lang":"en","type":"article","venue":"Matter","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Toronto","funders":"H2020 Marie Skłodowska-Curie Actions; H2020 European Research Council; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Scalability; Von Neumann architecture; Computer science; Binary number; Molecular machine; Computational science; Parallel computing; Theoretical computer science; Nanotechnology; Materials science; Mathematics","score_opus":0.009037065173914656,"score_gpt":0.20041821442754104,"score_spread":0.1913811492536264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141535301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07373459,0.000107777465,0.92193717,0.00010055432,0.00012859222,0.00019964349,6.472604e-7,0.00026112617,0.003529875],"genre_scores_gemma":[0.7323062,0.0000010375113,0.2667463,0.00064336887,0.00010208514,0.000015019,0.000024491063,0.00005554925,0.00010595696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915427,0.000021333612,0.00017634017,0.00024315929,0.00008656843,0.0003183371],"domain_scores_gemma":[0.9996805,0.000012200828,0.000020840987,0.0001816771,0.000039414033,0.0000653578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065073116,0.00014084202,0.00014162062,0.00004058602,0.0000812247,0.00007728389,0.00008675672,0.000039305585,0.000029339199],"category_scores_gemma":[0.000006259567,0.00015454285,0.000041551717,0.0002474447,0.0000075568355,0.00008681848,0.000075094256,0.00014116535,0.00006032413],"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":[7.0029364e-7,0.000011547835,0.00007858684,0.000123901,0.000009379378,0.000011167444,0.00020460016,0.85315984,0.14447404,0.000007245646,0.00016696652,0.0017520037],"study_design_scores_gemma":[0.00015874351,0.000009675643,0.000032060794,0.00007361037,0.000009230701,0.00007631076,0.000035001194,0.85159904,0.14602506,0.000041406292,0.001670252,0.0002695891],"about_ca_topic_score_codex":0.0000010381541,"about_ca_topic_score_gemma":2.2811557e-7,"teacher_disagreement_score":0.6585716,"about_ca_system_score_codex":0.000032029602,"about_ca_system_score_gemma":0.0000050284893,"threshold_uncertainty_score":0.6302075},"labels":[],"label_agreement":null},{"id":"W3159146981","doi":"10.1021/acsaelm.1c00271","title":"A True Random Number Generator Based on Ionic Liquid Modulated Memristors","year":2021,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Department of Science and Technology of Sichuan Province; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Memristor; Ionic liquid; Neuromorphic engineering; Materials science; Nanotechnology; Computer science; Ultrashort pulse; Topology (electrical circuits); Artificial neural network; Electronic engineering; Electrical engineering; Artificial intelligence; Chemistry; Physics; Engineering","score_opus":0.005742239491315662,"score_gpt":0.20766593358095217,"score_spread":0.2019236940896365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159146981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954434,0.00014051714,0.0023132525,0.00004048586,0.00039187836,0.00025637317,0.000009912214,0.0004955971,0.00090859196],"genre_scores_gemma":[0.9988747,0.00006426222,0.00008064318,0.00034223218,0.0002881081,0.00008283913,0.00008057118,0.00008409722,0.000102511454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984012,0.000061827915,0.00035930867,0.00036116975,0.0001656293,0.0006508198],"domain_scores_gemma":[0.99937457,0.00007913269,0.000057619374,0.00038157857,0.000029963432,0.000077107725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020752811,0.00029225426,0.00041200343,0.00004165666,0.00012212174,0.000052647654,0.00014421054,0.0001327383,0.0007558568],"category_scores_gemma":[0.000021113776,0.00029574713,0.000052962467,0.00022199933,0.000017182501,0.000048828148,0.00003017072,0.00020987286,0.00015666404],"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":[0.00029622664,0.000020154015,2.7350765e-7,0.000043353644,0.000032350377,0.000013703807,0.000018726927,0.091457024,0.906234,0.0014778792,0.00020391436,0.00020236858],"study_design_scores_gemma":[0.0015427666,0.00005143998,0.000003880877,0.000020378699,0.000023487833,0.000009698055,0.0000045424636,0.0007633903,0.9940007,0.0004178861,0.0028431206,0.00031873406],"about_ca_topic_score_codex":0.0000013441677,"about_ca_topic_score_gemma":0.0000023287143,"teacher_disagreement_score":0.09069364,"about_ca_system_score_codex":0.0002122154,"about_ca_system_score_gemma":0.00009145948,"threshold_uncertainty_score":0.99994946},"labels":[],"label_agreement":null},{"id":"W3160315930","doi":"10.1049/cds2.12076","title":"Characterizing a standard cell library for large scale design of memristive based signal processing","year":2021,"lang":"en","type":"article","venue":"IET Circuits Devices & Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Memristor; Computer science; Adder; CMOS; Electronic engineering; Computer architecture; Subtractor; Resistive random-access memory; Standard cell; Computer engineering; Computer hardware; Integrated circuit; Electrical engineering; Engineering; Voltage","score_opus":0.021407092818179083,"score_gpt":0.2325132050692894,"score_spread":0.2111061122511103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160315930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09954033,0.0046920436,0.8930312,0.000016287218,0.00046063654,0.00067959755,0.00026948235,0.00046024198,0.00085023226],"genre_scores_gemma":[0.99723464,0.000010643192,0.0021793086,0.00006738338,0.00024417613,0.000055125627,0.000055451725,0.00007720305,0.00007607132],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985796,0.00008878382,0.0004491789,0.00029716606,0.00019870563,0.00038651796],"domain_scores_gemma":[0.999178,0.00027011213,0.00017430555,0.00016232456,0.00012534184,0.000089921785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024203195,0.00021869401,0.0004346671,0.0000695404,0.00013348085,0.000104707506,0.00015821122,0.00008950038,0.000016976599],"category_scores_gemma":[0.000010145125,0.00022753574,0.000089739566,0.00029087233,0.000013796508,0.0004908469,0.00002582845,0.00014550274,0.0000032716953],"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":[0.00008073096,0.0000939482,0.0008802814,0.013151982,0.0000722781,0.000080178885,0.0022385542,0.17173441,0.7967695,0.000050855935,0.00028999662,0.014557299],"study_design_scores_gemma":[0.00081804185,0.00009499623,0.00010001348,0.0013456818,0.00004706053,0.00001620356,0.000975399,0.32822877,0.66060346,0.000018518173,0.0073822085,0.0003696645],"about_ca_topic_score_codex":3.7273762e-7,"about_ca_topic_score_gemma":6.745241e-7,"teacher_disagreement_score":0.8976943,"about_ca_system_score_codex":0.000038152044,"about_ca_system_score_gemma":0.00012630968,"threshold_uncertainty_score":0.92786384},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W3160893238","doi":"10.1007/s00422-021-00876-8","title":"A model of feedforward, global, and lateral inhibition in the locust visual system predicts responses to looming stimuli","year":2021,"lang":"en","type":"article","venue":"Biological Cybernetics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Looming; Lateral inhibition; Feed forward; Neuroscience; Locust; Inhibitory postsynaptic potential; Stimulus (psychology); Tonic (physiology); Computer science; Artificial intelligence; Control theory (sociology); Biological system; Biology; Psychology; Physics; Engineering; Optics","score_opus":0.0494206535462519,"score_gpt":0.27929161181907247,"score_spread":0.22987095827282056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160893238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932992,0.00025237916,0.0059814095,0.000035884208,0.00004201092,0.00011097425,0.000019509132,0.00005872421,0.00019992644],"genre_scores_gemma":[0.9985672,0.00002883464,0.001268814,0.00007960889,0.000034474655,0.0000043911164,0.0000046836244,0.0000045616703,0.0000074335094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935865,0.00007275018,0.0001944052,0.00014092214,0.00007555639,0.00015772994],"domain_scores_gemma":[0.9997076,0.00012822723,0.00001942697,0.00007848746,0.000024949359,0.000041340547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013468429,0.00009565493,0.00014549245,0.000017479688,0.00002830983,0.000015212567,0.00004617439,0.000076000826,0.0000010319441],"category_scores_gemma":[0.000100328325,0.00006598449,0.000023197044,0.0001538267,0.000032632033,0.000025764806,0.00007113669,0.00009725952,0.0000014862087],"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":[0.00027511807,0.000086961896,0.011743779,0.0002830828,0.00001936729,0.00023824975,0.0008363929,0.75239825,0.22520234,0.0035215572,0.0000670815,0.005327803],"study_design_scores_gemma":[0.0009861173,0.00057180354,0.04601739,0.00054535543,0.00002506008,0.0001942817,0.0009303342,0.8893252,0.059689965,0.0011353755,0.00013886164,0.00044027242],"about_ca_topic_score_codex":0.0000016978992,"about_ca_topic_score_gemma":0.0000045883876,"teacher_disagreement_score":0.16551237,"about_ca_system_score_codex":0.000033641816,"about_ca_system_score_gemma":0.0000073940732,"threshold_uncertainty_score":0.2690769},"labels":[],"label_agreement":null},{"id":"W3161070551","doi":"10.1088/1361-6463/abfef7","title":"Electronic phase separation induced non-volatile bi-polar resistive switching in spatially confined manganite microbridges","year":2021,"lang":"en","type":"article","venue":"Journal of Physics D Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Manganite; Polar; Materials science; Phase (matter); Condensed matter physics; Resistive touchscreen; Electric field; Electrical resistivity and conductivity; Chemical physics; Chemistry; Ferromagnetism; Electrical engineering; Physics","score_opus":0.013192268699021738,"score_gpt":0.266937836049097,"score_spread":0.25374556735007525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161070551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.849005,0.00007399983,0.1501706,0.000012635886,0.0001640027,0.000111586094,0.0000056905133,0.000031358104,0.0004251626],"genre_scores_gemma":[0.99804544,0.000030205138,0.0009299176,0.000053067724,0.00086083525,0.0000032537123,0.000020789457,0.000048682192,0.000007823047],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987638,0.000032811746,0.00048612268,0.00017488161,0.0002057861,0.00033658068],"domain_scores_gemma":[0.99924284,0.000112033194,0.0002885397,0.00015287058,0.00013659599,0.0000671462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001430052,0.00023919962,0.00041575285,0.000053898555,0.00008884057,0.00005100219,0.00014453288,0.00006362175,0.000003910639],"category_scores_gemma":[0.000010685711,0.00026131468,0.000117327865,0.00044463627,0.000012438671,0.00029859264,0.00003534135,0.00083000324,0.0000065946356],"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":[0.00007094448,0.00011307777,0.000010373151,0.000043603817,0.00006037095,0.000029175635,0.0005076527,0.11846293,0.8606509,0.0017009943,0.000026776308,0.018323198],"study_design_scores_gemma":[0.0016708594,0.00009492229,0.00012389026,0.000097490985,0.000043407486,0.000012971889,0.000099752884,0.028209021,0.9607997,0.008484693,0.00009934247,0.0002639667],"about_ca_topic_score_codex":0.0000022088143,"about_ca_topic_score_gemma":0.000007720394,"teacher_disagreement_score":0.14924067,"about_ca_system_score_codex":0.0001563034,"about_ca_system_score_gemma":0.00016088401,"threshold_uncertainty_score":0.9999839},"labels":[],"label_agreement":null},{"id":"W3161204726","doi":"10.1109/icfpt51103.2020.00017","title":"From TensorFlow Graphs to LUTs and Wires: Automated Sparse and Physically Aware CNN Hardware Generation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Field-programmable gate array; Computer hardware; Convolutional neural network; Throughput; Parallel computing; Embedded system; Computer engineering; Computer architecture; Artificial intelligence","score_opus":0.02715652103885282,"score_gpt":0.23195144834808776,"score_spread":0.20479492730923493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161204726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9839656,0.000082047154,0.01426291,0.00033430054,0.00007297158,0.00011132761,0.0000128535185,0.0010837846,0.00007423177],"genre_scores_gemma":[0.9943331,0.000019989471,0.004774503,0.00061267323,0.00020071746,0.0000034601335,0.00001668413,0.000018482997,0.000020423298],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950343,0.000009926136,0.000101981415,0.00020357384,0.000059412723,0.00012166052],"domain_scores_gemma":[0.9997375,0.000025727819,0.000009627174,0.00007068715,0.000017214434,0.00013926456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001172805,0.00011676107,0.00012743573,0.000018434544,0.000060602804,0.00003674434,0.000038199796,0.00003111361,0.000012218829],"category_scores_gemma":[0.000017509265,0.00010805655,0.000015565845,0.00009981957,0.000010071676,0.00012221656,0.00003570613,0.00007220756,0.000012483144],"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":[0.000017100072,0.000007928103,0.000257192,0.00005616722,0.000032882308,0.000028564318,0.0014291253,0.07579321,0.89690316,0.000071771916,0.0045142854,0.020888643],"study_design_scores_gemma":[0.00022312696,0.000050288705,0.0019508679,0.000022482514,0.0000101897285,0.00000194845,0.00008244021,0.9115216,0.08525594,0.00007469705,0.00061547686,0.00019092429],"about_ca_topic_score_codex":0.00000557168,"about_ca_topic_score_gemma":0.0000067131714,"teacher_disagreement_score":0.8357284,"about_ca_system_score_codex":0.0000054741736,"about_ca_system_score_gemma":0.0000024029378,"threshold_uncertainty_score":0.44064182},"labels":[],"label_agreement":null},{"id":"W3161552295","doi":"10.1002/advs.202003765","title":"Negative Photoconductance Effect: An Extension Function of the TiO<i><sub>x</sub></i>‐Based Memristor","year":2021,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":149,"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":"Natural Science Foundation of Chongqing; Natural Science Foundation of Guizhou Province; National Natural Science Foundation of China","keywords":"Memristor; Photoelectric effect; Materials science; Optoelectronics; Resistive random-access memory; Quantum dot; Nanotechnology; Graphene; Voltage; Physics; Quantum mechanics","score_opus":0.01082946518382604,"score_gpt":0.23709322541654063,"score_spread":0.22626376023271458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161552295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98695946,0.00013881423,0.011154365,0.00001612427,0.00094044773,0.000284751,0.00000207984,0.00013073097,0.0003732205],"genre_scores_gemma":[0.9981456,0.000007296064,0.001630127,0.00013233614,0.00003567261,0.000023696848,0.0000010869212,0.00001531239,0.000008914819],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988336,0.000053150063,0.00018146937,0.0003614726,0.00030429612,0.0002660149],"domain_scores_gemma":[0.9990889,0.00015773662,0.00007702238,0.0004620392,0.00013671028,0.00007760514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023400741,0.00014300509,0.00015892225,0.000046350557,0.00028823665,0.000016368047,0.00024575205,0.000030729712,0.0000034344073],"category_scores_gemma":[0.0003003606,0.00011368547,0.000054689153,0.0010845958,0.00026889204,0.0005484274,0.00005158086,0.00019563366,0.000003942457],"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":[0.000018528659,0.000009335909,0.000024201916,0.00002110309,0.0000012813102,0.0000029455673,0.000043843567,0.11599332,0.8676398,0.000049021663,0.0000024416686,0.016194174],"study_design_scores_gemma":[0.00030413741,0.00008950003,0.0031095527,0.00008530332,0.000007204745,0.0000059643025,0.000037895654,0.01621257,0.97947043,0.0004736387,0.000069654656,0.00013417019],"about_ca_topic_score_codex":3.9761147e-7,"about_ca_topic_score_gemma":0.000008009848,"teacher_disagreement_score":0.11183061,"about_ca_system_score_codex":0.000079483645,"about_ca_system_score_gemma":0.000071996,"threshold_uncertainty_score":0.4635959},"labels":[],"label_agreement":null},{"id":"W3162006056","doi":"10.1038/s41593-021-00857-x","title":"Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits","year":2021,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":264,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Ottawa; Canadian Institute for Advanced Research; Mila - Quebec Artificial Intelligence Institute; University of Toronto; General Dynamics (Canada); Université de Montréal; The Scarborough Hospital","funders":"CIHR Skin Research Training Centre; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Government of Canada; Novartis Foundation","keywords":"Synaptic plasticity; Neuroscience; Bursting; Postsynaptic potential; Metaplasticity; Nonsynaptic plasticity; Plasticity; Computer science; Synaptic scaling; Biology; Physics","score_opus":0.011222128235289335,"score_gpt":0.24192386120933784,"score_spread":0.2307017329740485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162006056","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99292696,0.00021122776,0.0046880776,0.00016282314,0.001089435,0.00006596431,0.0000029931873,0.00026095688,0.00059154304],"genre_scores_gemma":[0.99932563,0.000026856005,0.000053377673,0.00039575284,0.000058207286,0.0000028420116,0.00000105399,0.000016882908,0.00011938253],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987446,0.000060885865,0.00015685848,0.00038241726,0.00024833452,0.00040686986],"domain_scores_gemma":[0.9995257,0.00020208082,0.00002198806,0.000114518065,0.000028942894,0.000106772735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009845766,0.00014138952,0.00015346991,0.00008294918,0.00013081984,0.00004471043,0.00021424609,0.00012194936,0.0000053575336],"category_scores_gemma":[0.00087984616,0.00014743676,0.000030920764,0.0006303151,0.000060590883,0.00012712971,0.000093194176,0.0019525454,0.0000033895433],"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":[0.000003017293,0.00001788747,0.000956263,0.000034161036,0.0000011645851,0.000855583,0.00007046225,0.4345732,0.5572831,0.00037295485,0.000009461347,0.0058227153],"study_design_scores_gemma":[0.0009436979,0.00017243091,0.07800043,0.00021549962,0.000014152839,0.0008884292,0.000087780485,0.47398955,0.44111928,0.00095726823,0.00270317,0.0009083064],"about_ca_topic_score_codex":9.456251e-7,"about_ca_topic_score_gemma":0.000016320875,"teacher_disagreement_score":0.11616382,"about_ca_system_score_codex":0.000060423048,"about_ca_system_score_gemma":0.000040998373,"threshold_uncertainty_score":0.8482947},"labels":[],"label_agreement":null},{"id":"W3164118928","doi":"10.1021/acs.jpclett.1c01420","title":"Refining the Negative Differential Resistance Effect in a TiO<sub><i>x</i></sub>-Based Memristor","year":2021,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"Natural Science Foundation of Chongqing; Natural Science Foundation of Guizhou Province; National Natural Science Foundation of China","keywords":"Memristor; Materials science; Voltage; Thermal conduction; Nanoporous; Optoelectronics; Refining (metallurgy); Condensed matter physics; Nanotechnology; Electrical engineering; Physics; Composite material; Engineering; Metallurgy","score_opus":0.005131884125369343,"score_gpt":0.20214817505331617,"score_spread":0.1970162909279468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164118928","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99723464,0.00009859109,0.0014640568,0.0009566429,0.00008689128,0.000026611686,0.0000013900698,0.000020703388,0.0001104796],"genre_scores_gemma":[0.999184,0.0000039466577,0.00003172594,0.00024475504,0.0005079545,0.0000019553618,8.2896923e-7,0.000018336556,0.0000064636165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991683,0.00011733012,0.00022051187,0.000086779146,0.00021156197,0.00019555575],"domain_scores_gemma":[0.9986831,0.00095695787,0.00011570178,0.00017032636,0.00003015306,0.00004374458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017198652,0.00014885844,0.00023067766,0.0000110695055,0.00008304254,0.000018052788,0.00021963417,0.000025251953,0.0000027169904],"category_scores_gemma":[0.00011415445,0.00009233188,0.00013684806,0.00017766024,0.00007361822,0.000053869735,0.000027533059,0.0007337848,0.0000014623109],"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":[0.000094940886,0.000013713792,0.000006364426,0.00007325764,0.000023393144,0.00005833928,0.00018960676,0.06765838,0.93130726,0.0000012258317,0.00013711708,0.0004363757],"study_design_scores_gemma":[0.00048693173,0.000012640815,0.00020142751,0.00017943325,0.000038294624,0.000014497556,0.000028471859,0.0029017415,0.9959366,0.000056818866,0.000040777955,0.00010241208],"about_ca_topic_score_codex":2.5459835e-7,"about_ca_topic_score_gemma":0.0000018649766,"teacher_disagreement_score":0.06475664,"about_ca_system_score_codex":0.000093014394,"about_ca_system_score_gemma":0.000017097012,"threshold_uncertainty_score":0.3765185},"labels":[],"label_agreement":null},{"id":"W3166004829","doi":"10.1017/psa.2021.15","title":"Energy Requirements Undermine Substrate Independence and Mind-Body Functionalism","year":2022,"lang":"en","type":"article","venue":"Philosophy of Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":43,"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":"Functionalism (philosophy of mind); Autonomy; Independence (probability theory); Neuromorphic engineering; Computer science; Cognitive science; Mind–body problem; Artificial intelligence; Robot; Psychology; Human–computer interaction; Epistemology; Philosophy; Political science; Law; Artificial neural network; Mathematics","score_opus":0.03958796106142953,"score_gpt":0.254974634923143,"score_spread":0.21538667386171345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166004829","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99221426,0.00028310073,0.0017113939,0.000116509545,0.00048480692,0.000042651696,0.00000617063,0.000045300458,0.00509579],"genre_scores_gemma":[0.99956685,0.000011835844,0.00025686788,0.000060187882,0.000049531165,0.000004811193,0.0000012090609,0.000005811647,0.00004291387],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906594,0.000009693455,0.00013985738,0.00020172328,0.0004160462,0.00016672908],"domain_scores_gemma":[0.99972814,0.000024856368,0.000040722687,0.00011454729,0.000031808846,0.00005993892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021330194,0.00007741744,0.00008304933,0.0001122555,0.00030764335,0.000009827181,0.0002184314,0.0000108062795,0.000037397465],"category_scores_gemma":[0.000009397988,0.000083951825,0.000015713184,0.00043786087,0.00033668155,0.00030298892,0.00013822902,0.00010925078,8.2080015e-7],"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":[0.000023431157,0.000042838456,0.0005346139,0.00004906473,0.0000106913185,0.000023809738,0.00025708321,0.05917918,0.89303106,0.03921558,0.000020178135,0.007612468],"study_design_scores_gemma":[0.0009406136,0.0004602383,0.0076543638,0.000067156245,0.000021884036,0.00022078642,0.00029076144,0.097484276,0.6574678,0.23296544,0.001673265,0.0007534389],"about_ca_topic_score_codex":0.0000028252819,"about_ca_topic_score_gemma":3.7271795e-7,"teacher_disagreement_score":0.2355633,"about_ca_system_score_codex":0.00003920368,"about_ca_system_score_gemma":0.000025098283,"threshold_uncertainty_score":0.3423456},"labels":[],"label_agreement":null},{"id":"W3166099765","doi":"10.1007/s00339-021-04658-8","title":"Switching characteristics of NiOx crossbar arrays driven by low-temperature electroforming","year":2021,"lang":"en","type":"article","venue":"Applied Physics A","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Electroforming; Crossbar switch; Non-blocking I/O; Electrical conductor; Materials science; Optoelectronics; Resistive touchscreen; Work (physics); Voltage; Condensed matter physics; Resistive random-access memory; Commutation; Nanotechnology; Electrical engineering; Chemistry; Physics; Composite material; Thermodynamics; Engineering; Layer (electronics)","score_opus":0.0043877947750477694,"score_gpt":0.19825363961084472,"score_spread":0.19386584483579694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166099765","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9876967,0.00006326269,0.010962658,0.000005529648,0.00010554964,0.00006757618,0.000011508286,0.00012723583,0.00095998484],"genre_scores_gemma":[0.9989925,0.000021395272,0.0005396028,0.00008069775,0.00023197103,0.000006894257,0.000062597916,0.00004235526,0.00002199133],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923223,0.0000057470593,0.00020605931,0.00017686668,0.00011208121,0.00026702194],"domain_scores_gemma":[0.9996186,0.000045339035,0.000056951,0.00019508397,0.000036129964,0.000047893845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000031418604,0.00016599464,0.00024836464,0.000011590695,0.00009379515,0.000027810353,0.00010144752,0.000059679867,0.000005695778],"category_scores_gemma":[0.000005447108,0.00018432733,0.00005767286,0.00018932916,0.000013668789,0.00008168203,0.000040096344,0.00033460156,0.000007247806],"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":[0.000004906325,0.000018255936,0.000030892395,0.00010382814,0.000023871018,0.000003750343,0.0002126697,0.008965279,0.98505515,0.0013143546,0.00008678237,0.0041802838],"study_design_scores_gemma":[0.00018463019,0.000006038754,0.000117531716,0.000044047454,0.000011004296,0.0000019926247,0.000038050694,0.004004721,0.9943515,0.0007546362,0.00028830755,0.00019754421],"about_ca_topic_score_codex":2.3749094e-7,"about_ca_topic_score_gemma":1.475341e-7,"teacher_disagreement_score":0.011295799,"about_ca_system_score_codex":0.000025617986,"about_ca_system_score_gemma":0.000017718601,"threshold_uncertainty_score":0.75166506},"labels":[],"label_agreement":null},{"id":"W3166550029","doi":"10.1007/978-981-15-2848-4_115-1","title":"Programming Neuromorphics Using the Neural Engineering Framework","year":2021,"lang":"en","type":"book-chapter","venue":"Handbook of Neuroengineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Neuromorphic engineering; Computer science; Computer architecture; Artificial neural network; Porting; Software; Artificial intelligence; Variety (cybernetics); Deep learning; Compiler; Embedded system; Programming language","score_opus":0.03238488704309718,"score_gpt":0.22476649947986552,"score_spread":0.19238161243676832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166550029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0413349,0.0680282,0.8605153,0.000093951065,0.013236691,0.002712306,0.00010834942,0.0050997254,0.008870607],"genre_scores_gemma":[0.75328547,0.013211671,0.1829882,0.00080099993,0.011661393,0.00017435304,0.00021437439,0.0073525053,0.030311013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977065,0.00001506379,0.0007445579,0.00047762235,0.0004227367,0.00063352473],"domain_scores_gemma":[0.9984076,0.00040694542,0.00017190163,0.0007578574,0.00009913671,0.00015657484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000094824085,0.00080203696,0.000785398,0.00021585553,0.00012950388,0.000080642065,0.0004921185,0.00029187507,0.000039287286],"category_scores_gemma":[0.000095046016,0.0007797202,0.00038158792,0.00015864515,0.000075640026,0.00015111086,0.00022664231,0.0018976899,0.000004179747],"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":[0.000005478974,0.0000050641875,0.0000024015142,0.0006839168,0.00010628657,0.00036983006,0.00006943241,0.9577064,0.03320192,0.0028251656,0.000018213155,0.0050059166],"study_design_scores_gemma":[0.00034474404,0.00012381087,0.000010404385,0.0063936966,0.00034869078,0.0011039308,0.000016454844,0.90000314,0.04279543,0.00032337886,0.046787135,0.001749176],"about_ca_topic_score_codex":5.5024066e-7,"about_ca_topic_score_gemma":3.9610123e-7,"teacher_disagreement_score":0.7119506,"about_ca_system_score_codex":0.000066699366,"about_ca_system_score_gemma":0.000028863626,"threshold_uncertainty_score":0.99946535},"labels":[],"label_agreement":null},{"id":"W3167468823","doi":"10.3390/appliedmath2020011","title":"Gradient-Free Neural Network Training via Synaptic-Level Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"AppliedMath","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"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 Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Artificial intelligence; Artificial neural network; Connectionism; Reinforcement learning; Synaptic weight; Gradient descent; Machine learning; Competitive learning; Types of artificial neural networks; Recurrent neural network; Time delay neural network","score_opus":0.02981287971162394,"score_gpt":0.21556465302048777,"score_spread":0.18575177330886383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167468823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7909077,0.00046100645,0.1920025,0.000073552044,0.0014977481,0.0005102892,0.0000046714235,0.0017355715,0.012806984],"genre_scores_gemma":[0.99733955,0.0000068062736,0.0018540104,0.00016832736,0.000263687,0.00008136827,0.00002030673,0.000054702985,0.00021121772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987195,0.000025865556,0.00025985052,0.00022289678,0.00022574098,0.0005461266],"domain_scores_gemma":[0.99948525,0.000089811656,0.000056081153,0.00027384533,0.000007592862,0.00008740199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021643727,0.00019535101,0.00020191548,0.000046207984,0.0005742416,0.000019463365,0.00032297216,0.00002705237,0.00011189535],"category_scores_gemma":[0.00001214486,0.00022186038,0.00006533271,0.00023937716,0.000020171306,0.00008039062,0.0003178273,0.0005996157,0.000017188455],"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":[0.000013248534,0.0000042736665,0.000021161779,0.000018446097,0.000027147868,0.00002328899,0.0007620892,0.9779994,0.0027459294,0.0023592548,0.00040479703,0.015621001],"study_design_scores_gemma":[0.00071196276,0.00013945726,0.00011620505,0.000013478477,0.000023791978,0.0001047617,0.00075471535,0.9781577,0.00080631237,0.002503258,0.016194776,0.0004735825],"about_ca_topic_score_codex":0.0000017425119,"about_ca_topic_score_gemma":0.0000011702239,"teacher_disagreement_score":0.20643191,"about_ca_system_score_codex":0.00008939418,"about_ca_system_score_gemma":0.0000071054505,"threshold_uncertainty_score":0.9047203},"labels":[],"label_agreement":null},{"id":"W3172281394","doi":"10.1016/j.sse.2021.108122","title":"Modeling current and voltage peaks generation in complementary resistive switching devices","year":2021,"lang":"en","type":"article","venue":"Solid-State Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Joule heating; Electromigration; Resistive random-access memory; Resistive touchscreen; Materials science; Voltage; Current (fluid); Coupling (piping); Optoelectronics; Electrical engineering; Engineering","score_opus":0.025775552069748835,"score_gpt":0.2871871212384757,"score_spread":0.2614115691687268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172281394","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8857425,0.010243434,0.103637956,0.000025881638,0.0001332492,0.00008397205,0.0000045757956,0.000078720695,0.000049703318],"genre_scores_gemma":[0.99747455,0.0016751735,0.0006105901,0.000044606837,0.00009576773,0.0000052133882,0.000061941915,0.000024166908,0.000007978674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904054,0.00003160686,0.0002425593,0.00022760431,0.000091198766,0.00036647118],"domain_scores_gemma":[0.9997699,0.000029706103,0.000022336018,0.000100399775,0.00003234237,0.000045287215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012479498,0.00014363971,0.0001548401,0.000055228647,0.00011657,0.00003771026,0.00005512196,0.00002273654,0.0000044641843],"category_scores_gemma":[0.000012623193,0.0001648232,0.000023628256,0.00014244136,0.0000055081805,0.00018534287,0.00004999293,0.0003575288,0.0000014495247],"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":[0.0000068296786,0.000010372812,0.00009232684,0.000067845096,0.000013448456,0.000019438194,0.0005182406,0.88217604,0.093889624,0.00023074925,0.000014586601,0.022960473],"study_design_scores_gemma":[0.00026077047,0.00001825856,0.00005120539,0.00004494448,0.000008373962,0.0000083993755,0.000114859045,0.94865066,0.04849319,0.0013179808,0.00084866397,0.00018266264],"about_ca_topic_score_codex":0.000005199624,"about_ca_topic_score_gemma":0.0009408169,"teacher_disagreement_score":0.11173205,"about_ca_system_score_codex":0.00013558773,"about_ca_system_score_gemma":0.000038025144,"threshold_uncertainty_score":0.6721295},"labels":[],"label_agreement":null},{"id":"W3173029518","doi":"10.3389/fncir.2021.610446","title":"Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review","year":2021,"lang":"en","type":"review","venue":"Frontiers in Neural Circuits","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":52,"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":"Medical Research Council; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; UK Research and Innovation; National Science Foundation","keywords":"Neuromorphic engineering; Computer science; Signal processing; Neuroscience; Olfactory system; SIGNAL (programming language); Artificial intelligence; Artificial neural network; Digital signal processing; Psychology; Computer hardware","score_opus":0.03844538310742789,"score_gpt":0.29800583772042755,"score_spread":0.25956045461299965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173029518","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003366503,0.99387336,0.0043266676,0.000021425765,0.0006042775,0.00073128956,0.000004484957,0.000067207606,0.0000346336],"genre_scores_gemma":[0.005946786,0.9932474,0.00021281415,0.00023289773,0.00023799115,0.00002557427,0.000022560413,0.0000691784,0.0000047975404],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977916,0.00044513817,0.0006398837,0.00048475858,0.00021708851,0.00042151797],"domain_scores_gemma":[0.9993719,0.00018417099,0.00016106023,0.00018742478,0.000020239817,0.00007523772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045268593,0.0004892819,0.001445585,0.00020850248,0.000112328555,0.00007600099,0.00017432096,0.00018413326,0.0000023724347],"category_scores_gemma":[0.00004949053,0.00039462122,0.00014310236,0.000570276,0.00007834667,0.00016430469,0.000046330857,0.0010357213,4.97025e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.426868e-7,0.0000072770995,0.000011577654,0.039253082,0.000008577798,0.0002309868,0.00007443891,0.0002630907,0.0000073448305,4.6586197e-7,0.00047589012,0.9596666],"study_design_scores_gemma":[0.0007840337,0.00008315152,0.00007970864,0.26899925,0.00045489916,0.0007112438,0.00028028837,0.030196413,0.000010333833,0.00006744278,0.69657946,0.0017537812],"about_ca_topic_score_codex":8.072127e-7,"about_ca_topic_score_gemma":0.0000031765217,"teacher_disagreement_score":0.95791286,"about_ca_system_score_codex":0.00012726801,"about_ca_system_score_gemma":0.00008232107,"threshold_uncertainty_score":0.9998506},"labels":[],"label_agreement":null},{"id":"W3175542906","doi":"10.1016/j.mee.2021.111706","title":"Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing","year":2022,"lang":"en","type":"article","venue":"Microelectronic Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Neuromorphic engineering; Crossbar switch; Memristor; Computer science; Fabrication; CMOS; Computer architecture; Voltage; Process (computing); Resistive random-access memory; Electronic engineering; Artificial neural network; Materials science; Electrical engineering; Optoelectronics; Engineering; Telecommunications; Artificial intelligence","score_opus":0.005836960069177837,"score_gpt":0.20619452733755653,"score_spread":0.2003575672683787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175542906","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8049295,0.0019078861,0.19090143,0.000053436313,0.00071971706,0.000544125,0.000020676618,0.00078299275,0.00014021172],"genre_scores_gemma":[0.9955,0.0000057341567,0.003937264,0.000072534254,0.00014196648,0.00012568198,0.000038283666,0.00011182937,0.0000666528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981947,0.000022193399,0.0003842053,0.00034560682,0.0001507876,0.0009025294],"domain_scores_gemma":[0.9994066,0.00021250409,0.000054790613,0.00022660155,0.000025724941,0.000073783565],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019029777,0.00030123882,0.00032993162,0.00027937905,0.00026845207,0.000031657037,0.00029744513,0.000050370334,0.000028190798],"category_scores_gemma":[0.000029721,0.0003957823,0.00012853216,0.00049915694,0.000015139577,0.000080889586,0.0000752792,0.00067403284,0.0000037489658],"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":[0.000017891609,0.000016223583,0.000014766058,0.00012178847,0.00001443697,0.000014999457,0.00013158286,0.75002676,0.24494217,0.00006894619,0.00011114855,0.0045193015],"study_design_scores_gemma":[0.0011505957,0.00013137556,0.00008273485,0.000040682316,0.000011217969,0.00003627269,0.00006392489,0.7520157,0.23391424,0.00003391753,0.012056396,0.00046291796],"about_ca_topic_score_codex":0.0000049794185,"about_ca_topic_score_gemma":0.000010035644,"teacher_disagreement_score":0.19057053,"about_ca_system_score_codex":0.00094519946,"about_ca_system_score_gemma":0.000072357536,"threshold_uncertainty_score":0.9998494},"labels":[],"label_agreement":null},{"id":"W3175594783","doi":"","title":"A Case Study of Processing-in-Memory in off-the-Shelf Systems","year":2021,"lang":"en","type":"article","venue":"USENIX Annual Technical Conference","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Off the shelf; Software engineering","score_opus":0.03689934778720167,"score_gpt":0.28169101538200736,"score_spread":0.2447916675948057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175594783","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997786,0.00045627487,0.0007647829,0.000025469668,0.00009651105,0.0003286844,0.000007760907,0.00016176779,0.00037273508],"genre_scores_gemma":[0.9997313,0.000014173005,0.00011651529,0.000013645959,0.000026738071,0.000034279976,0.0000010416177,0.000017164157,0.00004511545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878305,0.0000861977,0.00046121437,0.00025523934,0.00016123254,0.00025305152],"domain_scores_gemma":[0.9993479,0.00014526748,0.000050026138,0.0002975764,0.00011213975,0.00004706101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021091735,0.00015367755,0.00031035274,0.000077787125,0.00004240296,0.000025149922,0.00019691054,0.000083629224,0.00000426488],"category_scores_gemma":[0.00012949745,0.00013112804,0.000030231951,0.0005276586,0.000050060753,0.00011777383,0.00011257727,0.00047699886,0.0000014635783],"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":[0.00018902533,0.0035889568,0.013870124,0.002744492,0.0000654648,0.10332616,0.038847435,0.4235629,0.16891272,0.0019424547,0.00019075224,0.24275951],"study_design_scores_gemma":[0.015297563,0.0032819463,0.027539317,0.0050008814,0.00023368723,0.04252635,0.34148106,0.4433782,0.111455075,0.0020260897,0.0025172837,0.005262539],"about_ca_topic_score_codex":0.000046150857,"about_ca_topic_score_gemma":0.00061306386,"teacher_disagreement_score":0.3026336,"about_ca_system_score_codex":0.000039259077,"about_ca_system_score_gemma":0.000052196363,"threshold_uncertainty_score":0.53472465},"labels":[],"label_agreement":null},{"id":"W3175838994","doi":"10.1088/1361-6528/ac0e67","title":"Oxygen vacancy engineering of TaO <sub>x</sub> -based resistive memories by Zr doping for improved variability and synaptic behavior","year":2021,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Institut quantique; Université de Sherbrooke","funders":"Canada First Research Excellence Fund; Comissão Nacional de Energia Nuclear; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Universidade Federal de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Materials science; Doping; X-ray photoelectron spectroscopy; Pulsed laser deposition; Oxygen; Optoelectronics; Resistive random-access memory; Analytical Chemistry (journal); Nanotechnology; Thin film; Chemical engineering; Electrode; Physical chemistry","score_opus":0.006035578052100633,"score_gpt":0.2058020202065801,"score_spread":0.19976644215447945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175838994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8670499,0.0004457969,0.13151038,0.000087575965,0.00021070821,0.0002731293,0.00004313501,0.0003748184,0.000004598723],"genre_scores_gemma":[0.99002576,0.000037005448,0.009748866,0.000017684906,0.000024904986,0.0000932351,0.0000148866875,0.000034667417,0.0000029615094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902815,0.000016858026,0.0002864019,0.00031170118,0.000049508264,0.000307403],"domain_scores_gemma":[0.9992247,0.00032438315,0.000057970647,0.000266851,0.000086575994,0.0000395029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012999361,0.00018321292,0.00033483273,0.000081791346,0.000068996764,0.0000075434014,0.00011034894,0.00023831401,0.0000016936021],"category_scores_gemma":[0.0004933962,0.00021067534,0.00005538554,0.00023388938,0.000070490205,0.000067069785,0.000065258726,0.00025146286,3.158148e-7],"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":[0.000016302198,0.000017751387,0.000064282605,0.00026779337,0.000028773547,0.000006012975,0.000016214168,0.0036738259,0.98455095,0.00021629465,0.000008858961,0.011132919],"study_design_scores_gemma":[0.0004272848,0.00007456531,0.00026551346,0.00005510552,0.000046828714,0.0000118099915,0.000018557934,0.02409724,0.97437954,0.00026945263,0.00016184377,0.00019224237],"about_ca_topic_score_codex":8.831896e-7,"about_ca_topic_score_gemma":0.0000025342129,"teacher_disagreement_score":0.12297593,"about_ca_system_score_codex":0.000065024265,"about_ca_system_score_gemma":0.00003117539,"threshold_uncertainty_score":0.8591091},"labels":[],"label_agreement":null},{"id":"W3177447790","doi":"10.1145/3477145.3477267","title":"Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche; Fonds de recherche du Québec – Nature et technologies; European Commission","keywords":"Reservoir computing; Computer science; Neuromorphic engineering; Spiking neural network; Spike (software development); Encoding (memory); Artificial intelligence; Convolutional neural network; Benchmark (surveying); Benchmarking; Pattern recognition (psychology); Gesture; Gesture recognition; Artificial neural network; Recurrent neural network","score_opus":0.0635333838316235,"score_gpt":0.27460956188761737,"score_spread":0.21107617805599388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177447790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8949471,0.00055761624,0.1022936,0.00023456791,0.0006450665,0.000702038,0.00002555199,0.00044265663,0.00015178],"genre_scores_gemma":[0.9810114,0.00005702755,0.01789405,0.0002541599,0.0003589237,0.000024815296,0.00018750456,0.0000784446,0.00013367551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985847,0.000047670277,0.00036233763,0.0005585944,0.00011770093,0.0003290255],"domain_scores_gemma":[0.999083,0.0002795485,0.00006809227,0.00027293465,0.00015747847,0.00013898354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019419745,0.00033716595,0.000434302,0.00011324945,0.0001791308,0.00022288549,0.00014341861,0.00025421538,0.00002070098],"category_scores_gemma":[0.00015631798,0.0003539688,0.00009137331,0.00015111308,0.00002428684,0.00008567974,0.00038628085,0.0005503419,0.000002841321],"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":[0.000041718005,0.000013077053,0.000019101322,0.0011615931,0.00004892647,0.00004457005,0.0004722827,0.77885306,0.1951994,0.0000061312094,0.0006593472,0.02348081],"study_design_scores_gemma":[0.00089518866,0.00019645187,0.0010441402,0.0026708674,0.00012031138,0.000105185034,0.00026103985,0.4432677,0.5462177,0.0027595083,0.001065213,0.0013967012],"about_ca_topic_score_codex":0.000005038576,"about_ca_topic_score_gemma":0.000015567442,"teacher_disagreement_score":0.35101828,"about_ca_system_score_codex":0.000031117885,"about_ca_system_score_gemma":0.000017469458,"threshold_uncertainty_score":0.9998912},"labels":[],"label_agreement":null},{"id":"W3178968559","doi":"10.1021/acs.nanolett.1c01614","title":"CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning","year":2021,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":54,"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":"Fonds de recherche du Québec – Nature et technologies","keywords":"Materials science; Resistor; CMOS; Optoelectronics; Electrolyte; Nanotechnology; Electrical engineering; Voltage; Electrode; Engineering; Chemistry","score_opus":0.008773055222929411,"score_gpt":0.2201987972862015,"score_spread":0.2114257420632721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178968559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8702459,0.0005274801,0.12450338,0.0011265024,0.0004721251,0.0014175597,0.0000033938088,0.0010826858,0.0006209805],"genre_scores_gemma":[0.9904707,0.000007759197,0.0074587413,0.0012612144,0.00010837399,0.00037144398,0.000033548637,0.00007046743,0.00021773913],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987233,0.000039970673,0.00020726737,0.00034500452,0.00014875994,0.0005356935],"domain_scores_gemma":[0.99944663,0.00014294246,0.00005215217,0.0002440793,0.00004194887,0.00007224513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013794444,0.000204711,0.0002257412,0.000067237575,0.00020581773,0.00004764971,0.00013344226,0.00005782764,0.000015694417],"category_scores_gemma":[0.00006216593,0.00021755639,0.00012256966,0.0003275576,0.000020222342,0.000089355635,0.000015591753,0.00026644612,0.00001690514],"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":[0.000070550275,0.000018922781,0.00014699106,0.00011277967,0.00001689665,0.000017236716,0.000024366691,0.3384963,0.6532567,0.00002121678,0.0003407153,0.007477307],"study_design_scores_gemma":[0.00074268784,0.00015196526,0.00010541768,0.00006563528,0.000018189521,0.0000043548143,0.0000069203165,0.1571728,0.79790133,0.00002099669,0.043536805,0.00027289265],"about_ca_topic_score_codex":0.0000017076491,"about_ca_topic_score_gemma":0.0000057759858,"teacher_disagreement_score":0.1813235,"about_ca_system_score_codex":0.00014897078,"about_ca_system_score_gemma":0.000022962702,"threshold_uncertainty_score":0.8871692},"labels":[],"label_agreement":null},{"id":"W3181535323","doi":"10.3390/electronics10141614","title":"Communication Failure Resilient Distributed Neural Network for Edge Devices","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Defense Acquisition Program Administration; Agency for Defense Development","keywords":"Computer science; Distributed computing; Redundancy (engineering); Artificial neural network; Overhead (engineering); Edge device; Fault tolerance; Enhanced Data Rates for GSM Evolution; Telecommunications network; Computer network; Artificial intelligence","score_opus":0.011106355542927993,"score_gpt":0.24312118417358133,"score_spread":0.23201482863065334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181535323","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8417338,0.07075804,0.084889196,0.000841485,0.00040564642,0.0003587108,0.000029868632,0.0006922589,0.0002909933],"genre_scores_gemma":[0.994759,0.00037810858,0.0042242776,0.000099373094,0.0001595895,0.000016934162,0.00028276417,0.000023371578,0.000056548342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934924,0.000026187236,0.00013192024,0.000114680144,0.000049945706,0.00032805183],"domain_scores_gemma":[0.9995347,0.00012531423,0.00002473669,0.00023321119,0.000047959056,0.000034048582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006879605,0.00009380618,0.000105514984,0.00000850312,0.00015586586,0.000024194238,0.00012121851,0.000043762444,0.0000057140737],"category_scores_gemma":[0.000027439786,0.000102414524,0.000046409936,0.00017483786,0.000008970648,0.000079688296,0.000033628578,0.00020844302,0.0000027064605],"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":[0.000016056709,0.000015212911,0.00009818496,0.000067906025,0.000031676656,0.0000032662251,0.000062597326,0.9664163,0.009692625,0.0028567393,0.0085790455,0.012160395],"study_design_scores_gemma":[0.0005183237,0.00008020731,0.0004560225,0.000058056572,0.00003613324,0.000029568328,0.000072167495,0.23994263,0.09348893,0.0032932411,0.66165864,0.00036607744],"about_ca_topic_score_codex":1.0133893e-7,"about_ca_topic_score_gemma":0.000089495814,"teacher_disagreement_score":0.7264737,"about_ca_system_score_codex":0.00006841334,"about_ca_system_score_gemma":0.000020119867,"threshold_uncertainty_score":0.4176343},"labels":[],"label_agreement":null},{"id":"W3181878054","doi":"10.1038/s41598-022-10082-6","title":"Theoretical modeling of dendrite growth from conductive wire electro-polymerization","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Materials science; Neuromorphic engineering; Nanotechnology; Electrical conductor; Chemical physics; Biological system; Computer science; Chemistry; Composite material; Artificial neural network","score_opus":0.00986736894317799,"score_gpt":0.21175420478185886,"score_spread":0.20188683583868086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181878054","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97497755,0.00022399207,0.020613179,0.0000119929355,0.0029164471,0.00009342249,0.000004577318,0.0001250001,0.0010338626],"genre_scores_gemma":[0.9993017,0.0000015711438,0.00054131594,0.000008199935,0.000032655626,0.000007860843,0.000053066884,0.000015755591,0.000037928996],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989526,0.000032995395,0.00028200017,0.00029539395,0.00026540604,0.00017156039],"domain_scores_gemma":[0.99958,0.000028736114,0.000068299225,0.00023604624,0.00004694417,0.000039963463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025899708,0.000082219136,0.00012250406,0.00007463609,0.00025240134,0.000023506343,0.00008070312,0.000025143498,0.00016292089],"category_scores_gemma":[0.000041925996,0.00008729606,0.000044959237,0.0003380862,0.000094168776,0.00012814347,0.00006683337,0.00021188102,8.880894e-7],"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":[0.0000046333776,0.00001048642,0.00006243434,0.000007907375,0.000009767027,0.000051734998,0.00034063152,0.37281856,0.62188107,0.004415188,0.00007055634,0.00032705415],"study_design_scores_gemma":[0.000041359366,0.00001290723,0.00000443886,0.0000075501375,0.000008522166,0.00005159428,0.00015445935,0.4354909,0.49751607,0.06656463,0.000056618017,0.000090936934],"about_ca_topic_score_codex":0.000003388626,"about_ca_topic_score_gemma":2.6484201e-7,"teacher_disagreement_score":0.12436497,"about_ca_system_score_codex":0.000036073743,"about_ca_system_score_gemma":0.000022632541,"threshold_uncertainty_score":0.355983},"labels":[],"label_agreement":null},{"id":"W3184031043","doi":"10.37394/23201.2021.20.22","title":"Impact of Membrane Resistance on Width and Amplitude of Spikes in Different Injected Currents in One Spiking Neural Model","year":2021,"lang":"en","type":"article","venue":"WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stimulus (psychology); Time constant; Biological system; Neuron; Neuroscience; Amplitude; Axon; Physics; Membrane potential; Computer science; Chemistry; Control theory (sociology); Artificial intelligence; Biology; Engineering; Psychology; Optics; Electrical engineering","score_opus":0.04271329314185381,"score_gpt":0.2689149115197191,"score_spread":0.22620161837786526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184031043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.985305,0.00079975,0.013475956,0.000004182942,0.000105716885,0.0001591609,0.000030180983,0.000025331226,0.000094709656],"genre_scores_gemma":[0.9997884,0.00014508862,0.000012539717,0.0000019187382,0.000011280333,0.000008071151,0.0000025974196,0.000014796179,0.000015274818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991118,0.00005053189,0.00034478112,0.00019320547,0.00013142172,0.00016827963],"domain_scores_gemma":[0.9996248,0.00011073781,0.000048505182,0.0001336283,0.000028148994,0.000054213648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006585069,0.00014681956,0.00036536885,0.00015922678,0.00003194542,0.000014446847,0.00004188499,0.000050189148,0.0000019321358],"category_scores_gemma":[0.000010439489,0.00013450946,0.000049269926,0.0002248187,0.000021138045,0.0000840419,0.0000020848622,0.00020222033,7.392971e-8],"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":[0.000037383626,0.00013959694,0.0031748833,0.0006766946,0.000018893266,0.0000068629965,0.00038761538,0.91361386,0.07833256,0.00009158973,2.2134496e-7,0.0035198294],"study_design_scores_gemma":[0.0028742373,0.00029106776,0.19553769,0.004184489,0.00004595117,0.000024076837,0.00025597095,0.755282,0.04076076,0.0002161065,0.0000022030158,0.0005254884],"about_ca_topic_score_codex":0.000028376697,"about_ca_topic_score_gemma":0.00010568299,"teacher_disagreement_score":0.1923628,"about_ca_system_score_codex":0.00005651757,"about_ca_system_score_gemma":0.0000128536785,"threshold_uncertainty_score":0.54851365},"labels":[],"label_agreement":null},{"id":"W3184525714","doi":"10.1016/j.procs.2021.06.009","title":"Dynamical properties of spiking neural networks with small world topologies","year":2021,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Carleton University","funders":"","keywords":"Network topology; Small-world network; Computer science; Random graph; Spiking neural network; Topology (electrical circuits); Artificial neural network; Dissipative system; Graph; Complex network; Theoretical computer science; Mathematics; Artificial intelligence; Physics; Combinatorics","score_opus":0.025178865135923646,"score_gpt":0.20900812003514907,"score_spread":0.1838292548992254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184525714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6333191,0.00030812173,0.36562625,0.00003813728,0.00036262907,0.000053641914,1.3521523e-7,0.00018075107,0.000111267815],"genre_scores_gemma":[0.9566696,0.0000064820174,0.043104667,0.00006848521,0.00012282921,0.0000034932898,3.2919132e-7,0.000009723565,0.000014367881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908775,0.000008998059,0.00015892346,0.00026422687,0.00015530476,0.00032481708],"domain_scores_gemma":[0.9995852,0.00003866188,0.000032537188,0.00016705212,0.00011591666,0.00006063833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009772954,0.00012226687,0.00015516714,0.00007416425,0.00010709047,0.000058443413,0.00029906057,0.000018676725,0.000001442613],"category_scores_gemma":[0.000022661408,0.000094470684,0.000023770812,0.00079478254,0.00028554015,0.00024831737,0.00019705812,0.00017552776,5.3660165e-7],"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":[0.0000075702005,0.000011382362,0.0009794006,0.000096808,0.00000459626,0.000025456142,0.00022868677,0.9270339,0.01776427,0.00027231037,0.0000032914259,0.053572316],"study_design_scores_gemma":[0.000079223755,0.000033651035,0.00065880234,0.00009407067,0.0000031375746,0.000054553893,0.000020203135,0.874647,0.124216594,0.000040252984,0.000023461625,0.00012903097],"about_ca_topic_score_codex":6.679873e-7,"about_ca_topic_score_gemma":0.000017259757,"teacher_disagreement_score":0.32335055,"about_ca_system_score_codex":0.000025390616,"about_ca_system_score_gemma":0.00004190144,"threshold_uncertainty_score":0.3852403},"labels":[],"label_agreement":null},{"id":"W3185446944","doi":"10.1016/j.jssc.2021.122448","title":"Tellurium vacancy in two-dimensional Si2Te3 for resistive random-access memory","year":2021,"lang":"en","type":"article","venue":"Journal of Solid State Chemistry","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Resistive random-access memory; Vacancy defect; Condensed matter physics; Materials science; Lattice (music); Resistive touchscreen; Density functional theory; Diffusion; Band gap; Optoelectronics; Chemistry; Computer science; Computational chemistry; Voltage; Physics; Thermodynamics","score_opus":0.01690219573071801,"score_gpt":0.30307883304882466,"score_spread":0.28617663731810666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185446944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951088,0.0012147403,0.0025672053,0.00007262382,0.00036293216,0.00006531559,0.000025557407,0.00002364323,0.0005592138],"genre_scores_gemma":[0.9975513,0.00009725845,0.0012942638,0.000060910166,0.00038761058,0.000003889981,0.000006863532,0.000030067606,0.0005677979],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998943,0.000015899486,0.0005045796,0.00012979329,0.00015748908,0.0002492451],"domain_scores_gemma":[0.99918216,0.00025590629,0.00015043694,0.000101319456,0.00020637744,0.000103805214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020714097,0.00014668342,0.0003276237,0.000035040594,0.000045718163,0.000021712227,0.00015278875,0.00003840821,0.00004838174],"category_scores_gemma":[0.00015904932,0.00014440305,0.00012603748,0.00013311564,0.00002212383,0.0002025819,0.0000462719,0.0003729681,8.4747535e-7],"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":[0.0002517389,0.000025068934,0.000035948662,0.00024036983,0.00003798994,0.00037338914,0.000094603056,0.43057066,0.5667753,3.3873468e-7,0.00045166447,0.0011429015],"study_design_scores_gemma":[0.0029995914,0.000012430917,0.00012420787,0.00023548515,0.00001627189,0.0002232944,0.000088533954,0.005311238,0.9898082,0.00072218094,0.0002938873,0.00016470117],"about_ca_topic_score_codex":4.6603807e-7,"about_ca_topic_score_gemma":0.0000019878255,"teacher_disagreement_score":0.4252594,"about_ca_system_score_codex":0.00009896679,"about_ca_system_score_gemma":0.00009482833,"threshold_uncertainty_score":0.58885854},"labels":[],"label_agreement":null},{"id":"W3187811670","doi":"10.1002/aelm.202100512","title":"Interfacial Control via Reversible Ionic Motion in Battery‐Like Magnetic Tunnel Junctions","year":2021,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Army Research Office; National Natural Science Foundation of China; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; National Science Foundation","keywords":"Materials science; Magnetoresistance; Quantum tunnelling; Ion; Optoelectronics; Spintronics; Electric field; Tunnel magnetoresistance; Battery (electricity); Ionic bonding; Voltage; Polarization (electrochemistry); Magnetic field; Nanotechnology; Condensed matter physics; Electrical engineering; Chemistry; Physics; Ferromagnetism; Power (physics)","score_opus":0.004457666496270254,"score_gpt":0.19912511526646373,"score_spread":0.19466744877019349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187811670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94642895,0.0011888713,0.04972719,0.00009265004,0.0017939297,0.0002625858,0.000011581691,0.0002957934,0.00019841363],"genre_scores_gemma":[0.9987572,0.00023608796,0.00021114369,0.00015778166,0.0001363547,0.000044681477,0.000031738837,0.00004079339,0.00038421433],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984791,0.000082796345,0.00038550855,0.0003149797,0.00009912069,0.0006384855],"domain_scores_gemma":[0.9995654,0.000050871735,0.000048514423,0.00024308857,0.000039189137,0.000052938474],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013362737,0.00021858652,0.00031954667,0.000092806506,0.000083184445,0.000032519456,0.00011239094,0.000081133265,0.0005598618],"category_scores_gemma":[0.000032885364,0.0002517107,0.00005078214,0.00027066952,0.000020481155,0.0002955919,0.00003334094,0.00023207763,0.000086234606],"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":[0.000047176694,0.000020111529,0.000017479968,0.000052560514,0.000011971935,0.000019037423,0.000031421976,0.07454654,0.9193942,0.00017589227,0.000038374194,0.005645266],"study_design_scores_gemma":[0.0018557932,0.00013913485,0.00066106947,0.00008588489,0.000026939126,0.00009249438,0.000036850775,0.0017309383,0.9885824,0.0033419016,0.0030517867,0.00039484366],"about_ca_topic_score_codex":0.0000037295322,"about_ca_topic_score_gemma":0.000059586935,"teacher_disagreement_score":0.0728156,"about_ca_system_score_codex":0.0002729935,"about_ca_system_score_gemma":0.000037512487,"threshold_uncertainty_score":0.9999935},"labels":[],"label_agreement":null},{"id":"W3193490351","doi":"10.11159/icert21.001","title":"Improving Battery Performance via Mechanical Activation EnhancedSynthesis","year":2021,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Battery (electricity); Computer science; Power (physics); Physics","score_opus":0.011271740244363919,"score_gpt":0.2099745350110761,"score_spread":0.19870279476671218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193490351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99521035,0.00009287082,0.0007204044,0.0010166379,0.00048255106,0.00013092598,5.9919046e-7,0.0012783934,0.0010672931],"genre_scores_gemma":[0.9966104,0.000050406215,0.002427162,0.000046041256,0.00004096128,0.000013273406,1.9651276e-7,0.000023022743,0.00078854983],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991733,0.000002144555,0.0002135163,0.00022012695,0.00016015592,0.00023078555],"domain_scores_gemma":[0.9995271,0.00009888625,0.00011729632,0.00017605047,0.000061544306,0.000019133924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006400271,0.00016738947,0.0001965742,0.00011377156,0.00010566724,0.000029672507,0.00043137692,0.00008189641,0.000006647452],"category_scores_gemma":[0.00025676299,0.00013222992,0.00006544276,0.00052854005,0.000056897185,0.00027139124,0.00022837051,0.0003598088,0.0000031700558],"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":[0.000013566489,0.00000832566,0.00010838811,0.00010464636,0.000013808209,3.9663198e-7,0.000008814324,0.00053421,0.8138722,0.0006236944,0.00026796263,0.18444394],"study_design_scores_gemma":[0.00010887208,0.000021690996,0.000111390684,0.00036649994,0.000010222028,0.0000057078,0.00010004418,0.006109402,0.99104226,0.001419612,0.0005565728,0.00014772764],"about_ca_topic_score_codex":4.9049726e-7,"about_ca_topic_score_gemma":0.0000014241765,"teacher_disagreement_score":0.18429622,"about_ca_system_score_codex":0.000052217674,"about_ca_system_score_gemma":0.000010915764,"threshold_uncertainty_score":0.53921795},"labels":[],"label_agreement":null},{"id":"W3194109001","doi":"10.1109/icip42928.2021.9506331","title":"Human Vision-Like Robust Object Recognition","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cognitive neuroscience of visual object recognition; Artificial intelligence; Generality; Robustness (evolution); Artificial neural network; Spiking neural network; 3D single-object recognition; Pattern recognition (psychology); Computer vision; Object (grammar); Machine vision; Machine learning","score_opus":0.033935955416532666,"score_gpt":0.26047442467994436,"score_spread":0.2265384692634117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194109001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9065374,0.00016382645,0.029456656,0.000032416112,0.00060532865,0.000054556996,0.0000017109473,0.0008107422,0.06233733],"genre_scores_gemma":[0.9938363,0.000012735125,0.0045107957,0.00013838039,0.00011855445,0.0000018667698,0.000017661783,0.000016551367,0.0013471479],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996303,0.000009969654,0.00009147352,0.00010450189,0.000048836566,0.000114947026],"domain_scores_gemma":[0.9998235,0.000021814976,0.0000067935403,0.000092211325,0.00002384344,0.00003181857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000030864507,0.00006409753,0.00006564789,0.000019385614,0.00007226796,0.000017039541,0.000030274026,0.000027438975,0.00039006944],"category_scores_gemma":[0.000009067422,0.00006480741,0.000029877889,0.00010248074,0.000005294258,0.00010424536,0.000019605312,0.00009720776,0.00009636521],"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":[0.0000030332023,0.00003804353,0.000042081796,0.00010754128,0.000027651213,0.00017702724,0.00011846812,0.22401622,0.6523966,0.0002387435,0.0061795977,0.11665498],"study_design_scores_gemma":[0.00051508134,0.000059534916,0.0010856035,0.000119987184,0.000016152782,0.00010556342,0.00020853261,0.029359061,0.9576932,0.0026131894,0.0077141277,0.00050994725],"about_ca_topic_score_codex":5.518244e-7,"about_ca_topic_score_gemma":0.000008381877,"teacher_disagreement_score":0.3052966,"about_ca_system_score_codex":0.000014061798,"about_ca_system_score_gemma":0.000003031652,"threshold_uncertainty_score":0.4270987},"labels":[],"label_agreement":null},{"id":"W3194955979","doi":"10.1063/5.0045257","title":"Improved uniformity and threshold voltage in NbOx-ZrO2 selectors","year":2021,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"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":"National Natural Science Foundation of China; University of Guelph","keywords":"Materials science; Threshold voltage; Optoelectronics; Schottky diode; Quantum tunnelling; Thermal conduction; Voltage; Work (physics); Thermal stability; Nanotechnology; Electrical engineering; Chemistry; Transistor; Composite material; Physics","score_opus":0.00880493330629913,"score_gpt":0.1998885342502685,"score_spread":0.19108360094396937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194955979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9872169,0.000039788803,0.011584971,0.00004078285,0.00008891095,0.00009086602,0.0000018651899,0.00012702508,0.00080888666],"genre_scores_gemma":[0.99874926,0.000009822319,0.00038253315,0.0006905523,0.00011603419,0.000008637009,0.000009468507,0.00002566625,0.000008005126],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939036,0.0000040136692,0.00011769395,0.00018850811,0.000059050188,0.00024039748],"domain_scores_gemma":[0.99975026,0.00004303001,0.00001702031,0.00014170006,0.000006479105,0.000041527026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035894256,0.00013751011,0.00014810506,0.000018893348,0.00004341454,0.000019293544,0.00005888899,0.00003206356,0.000002716755],"category_scores_gemma":[0.0000021519702,0.00015368545,0.000024186182,0.00022175015,0.000023352703,0.00008797188,0.000042955293,0.0002608403,0.0000033484378],"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":[0.0000042742727,0.000007687792,0.00030509965,0.00003827601,0.000009608757,0.00001322016,0.00012191962,0.030040033,0.9644533,0.0010313753,0.00006234837,0.003912907],"study_design_scores_gemma":[0.00055443146,0.0000054940556,0.0011952897,0.000015950276,0.000008393937,0.0000042198094,0.000046393616,0.011157649,0.98560137,0.0008367909,0.00026084564,0.00031317084],"about_ca_topic_score_codex":0.0000018185203,"about_ca_topic_score_gemma":0.000005452524,"teacher_disagreement_score":0.021148121,"about_ca_system_score_codex":0.000031702704,"about_ca_system_score_gemma":0.0000057286297,"threshold_uncertainty_score":0.6267111},"labels":[],"label_agreement":null},{"id":"W3195770122","doi":"10.1088/2058-8585/ac1fd7","title":"Sinter-free inkjet-printed PEDOT:PSS/WO <sub>3</sub> /PEDOT:PSS flexible valency change memory","year":2021,"lang":"en","type":"article","venue":"Flexible and Printed Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Bayerische Forschungsallianz; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"PEDOT:PSS; Valency; Materials science; Inkjet printing; Nanotechnology; Composite material; Layer (electronics); Inkwell","score_opus":0.022482680421879234,"score_gpt":0.2401785462629364,"score_spread":0.21769586584105716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195770122","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93814766,0.020353124,0.029957786,0.00040279553,0.0012327882,0.00062687113,0.0000347173,0.0023050616,0.0069391686],"genre_scores_gemma":[0.98964286,0.006610297,0.0009819939,0.00055221084,0.00062740955,0.00007906295,0.00008400799,0.00018083255,0.0012413078],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968474,0.00010476592,0.0006029991,0.0007958538,0.0003231282,0.0013258803],"domain_scores_gemma":[0.9982848,0.000138556,0.000114052185,0.00096357515,0.00019682394,0.00030219546],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027567163,0.0006146864,0.00059536647,0.00024158314,0.0002988064,0.00011893375,0.0005114856,0.00028093465,0.000117626434],"category_scores_gemma":[0.00015259434,0.0006769718,0.00020978373,0.0008295586,0.00008718981,0.00048912485,0.0004915621,0.0012855734,0.000098094344],"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":[0.00019841475,0.00019448856,0.00023925022,0.0009641951,0.00024868725,0.0002325998,0.0010481238,0.0062318323,0.8148195,0.004081273,0.0017539816,0.16998762],"study_design_scores_gemma":[0.001081386,0.00024157505,0.0003262929,0.00027046102,0.000075122414,0.00026058216,0.00015595685,0.010717185,0.95881784,0.003805956,0.023443293,0.0008043385],"about_ca_topic_score_codex":0.000007780368,"about_ca_topic_score_gemma":0.00008696094,"teacher_disagreement_score":0.16918328,"about_ca_system_score_codex":0.00022503074,"about_ca_system_score_gemma":0.0001380498,"threshold_uncertainty_score":0.99956816},"labels":[],"label_agreement":null},{"id":"W3196539000","doi":"10.1021/acsami.1c09775","title":"Induced Complementary Resistive Switching in Forming-Free TiO<sub><i>x</i></sub>/TiO<sub>2</sub>/TiO<sub><i>x</i></sub> Memristors","year":2021,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Stacking; Resistive random-access memory; Heterojunction; Memristor; Optoelectronics; Layer (electronics); Voltage; Active matrix; Nanotechnology; Electronic engineering; Electrical engineering","score_opus":0.01578437823455695,"score_gpt":0.22762986685028674,"score_spread":0.2118454886157298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196539000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936711,0.00044449058,0.00064501725,0.0001725587,0.0023313554,0.0010315616,0.0001718927,0.0008673051,0.00066470716],"genre_scores_gemma":[0.99714494,0.0004187985,0.00049608573,0.00045173813,0.0006581188,0.0002806246,0.00025369207,0.0002898722,0.0000061030955],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99435055,0.00022890746,0.0018582316,0.001383054,0.0006644175,0.0015148125],"domain_scores_gemma":[0.99741876,0.00045768337,0.00054340827,0.0011506645,0.00016289043,0.00026659307],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009078373,0.0011703308,0.0014073506,0.00043321957,0.00052571035,0.0003498391,0.0009919821,0.00041994028,0.000045676545],"category_scores_gemma":[0.00016459331,0.0013319377,0.00012649769,0.00070430327,0.00012183898,0.0007980062,0.0010967712,0.000954593,0.00017247794],"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":[0.00032258645,0.000108830915,0.00001937178,0.00048650373,0.00019439624,0.00013757731,0.0012494419,0.0034246289,0.9848852,0.00035644489,0.00082202675,0.007993001],"study_design_scores_gemma":[0.0017768454,0.00012384503,0.0003972808,0.0005701735,0.00012470958,0.000058297945,0.0011332348,0.000038554834,0.9906654,0.003629107,0.00013265424,0.0013499386],"about_ca_topic_score_codex":0.00003559424,"about_ca_topic_score_gemma":0.00048750188,"teacher_disagreement_score":0.0066430625,"about_ca_system_score_codex":0.00058600336,"about_ca_system_score_gemma":0.00011053445,"threshold_uncertainty_score":0.99891305},"labels":[],"label_agreement":null},{"id":"W3196680848","doi":"10.1016/j.jpdc.2021.08.003","title":"Energy efficient spiking neural network processing using approximate arithmetic units and variable precision weights","year":2021,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Computer science; Variable (mathematics); Field-programmable gate array; Neuromorphic engineering; Double-precision floating-point format; Artificial neural network; Algorithm; Arbitrary-precision arithmetic; Energy (signal processing); Computation; Computer hardware; Efficient energy use; Parallel computing; Artificial intelligence; Mathematics","score_opus":0.018092310941967553,"score_gpt":0.23212678540745604,"score_spread":0.21403447446548848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196680848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4777273,0.0043797567,0.51760656,0.000008524409,0.00019676001,0.000021948092,0.0000017114021,0.00003161023,0.000025836689],"genre_scores_gemma":[0.9580295,0.00007302976,0.04146815,0.000028340635,0.00037051033,2.0021288e-7,0.0000072929442,0.000019895371,0.00000309936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863005,0.00007043996,0.00054121326,0.00019076835,0.00018733078,0.0003802116],"domain_scores_gemma":[0.9991185,0.00017443081,0.00024412734,0.000082715706,0.00022454625,0.00015565302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028884594,0.00020647659,0.00037032185,0.0000663004,0.00036495173,0.00015261852,0.00008831122,0.00007163955,0.0000017849558],"category_scores_gemma":[0.000057407455,0.00018548695,0.00004207889,0.00053959497,0.000032983226,0.00016407833,0.00013083176,0.00031829078,5.5228305e-8],"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":[0.000020483169,0.000014695046,0.00022291597,0.000099051074,0.000023342633,0.00011907203,0.00008451657,0.9766819,0.004122246,0.00025605466,0.000010201636,0.018345537],"study_design_scores_gemma":[0.00055571884,0.000035754136,0.0003293846,0.00061323826,0.000049369773,0.0014140477,0.00011612238,0.9944009,0.0011023829,0.0010212444,0.00015790117,0.00020392961],"about_ca_topic_score_codex":0.0000016216609,"about_ca_topic_score_gemma":3.8050956e-7,"teacher_disagreement_score":0.48030218,"about_ca_system_score_codex":0.000042852582,"about_ca_system_score_gemma":0.000046716337,"threshold_uncertainty_score":0.75639385},"labels":[],"label_agreement":null},{"id":"W3197843460","doi":"10.1109/itw48936.2021.9611431","title":"Power-Efficient Deep Neural Networks with Noisy Memristor Implementation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Memristor; Computation; Artificial neural network; Karush–Kuhn–Tucker conditions; Computer science; Nonlinear system; Covariance; Inference; Power (physics); Algorithm; Artificial intelligence; Control theory (sociology); Mathematics; Mathematical optimization; Electronic engineering; Statistics; Engineering","score_opus":0.009865408630291601,"score_gpt":0.24837596418363425,"score_spread":0.23851055555334266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197843460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49272868,0.00050239335,0.50386536,0.000021468777,0.0010669653,0.00024413694,0.0000021841836,0.00044415,0.0011246252],"genre_scores_gemma":[0.9948581,0.000016723761,0.004639535,0.00008976944,0.00018009466,0.000027341159,0.00009609446,0.000060323073,0.000032016174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878764,0.000027872287,0.00028471486,0.00037854267,0.00017305207,0.00034818432],"domain_scores_gemma":[0.99941427,0.000041993706,0.00006779245,0.00032805582,0.000057099252,0.00009077727],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000069285925,0.0003126498,0.0002748267,0.00005705029,0.000080708036,0.000081811595,0.00015997447,0.00011529883,0.00027239192],"category_scores_gemma":[0.000003617765,0.000282761,0.00008806394,0.0001266386,0.000017039783,0.000054836586,0.0002248047,0.0006221676,0.0000028590532],"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":[0.0000075230823,0.000008402945,0.000047565267,0.000058765654,0.00003520429,0.00004117254,0.00021895126,0.9934352,0.00040313034,0.000021643813,0.00004866307,0.0056737806],"study_design_scores_gemma":[0.00024722412,0.000038074686,0.0003772563,0.00004284716,0.000027360815,0.000023464074,0.00055774476,0.99345714,0.004667643,0.0000063347584,0.00014219366,0.00041274013],"about_ca_topic_score_codex":0.0000075710923,"about_ca_topic_score_gemma":0.000063831736,"teacher_disagreement_score":0.50212944,"about_ca_system_score_codex":0.00012654492,"about_ca_system_score_gemma":0.000014117481,"threshold_uncertainty_score":0.99996245},"labels":[],"label_agreement":null},{"id":"W3197894387","doi":"10.1126/scirobotics.abk3268","title":"Spiking neural networks take control","year":2021,"lang":"en","type":"letter","venue":"Science Robotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Ontario Brain Institute","funders":"","keywords":"Artificial neural network; Computer science; Control (management); Architecture; Artificial intelligence; Control engineering; Nervous system network models; Cognitive science; Neuroscience; Types of artificial neural networks; Engineering; Recurrent neural network; Psychology","score_opus":0.019566924116960833,"score_gpt":0.2312930119900812,"score_spread":0.21172608787312036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197894387","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011390995,0.002180829,0.8716162,0.103210784,0.017940246,0.00045236727,0.0000110823285,0.0012281035,0.0022213121],"genre_scores_gemma":[0.38926244,0.00009300454,0.009621897,0.58628166,0.013776838,0.000010826688,0.000056774625,0.00021605197,0.00068052905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791616,0.000026553163,0.00027826268,0.00044790146,0.00043645946,0.00089464633],"domain_scores_gemma":[0.9991686,0.0001497124,0.00007023913,0.00042646794,0.0000926613,0.0000923438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000213816,0.00030865852,0.00034784555,0.00012292383,0.00028890435,0.00019928922,0.0006169883,0.00026708777,0.000015178304],"category_scores_gemma":[0.000077754834,0.00031068816,0.00010290988,0.0006898001,0.00025129342,0.00024655208,0.00010249851,0.0020553526,0.000008865183],"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":[3.8582084e-7,0.0000012136054,0.000010410594,0.000034752768,0.000005526521,0.00035830736,0.000011425179,0.9735105,0.00055440754,0.00001615908,0.023109123,0.002387757],"study_design_scores_gemma":[0.000114047165,0.000013810443,0.000017025346,0.000086601794,0.000021969958,0.00007394543,0.00000694642,0.972294,0.00032700863,0.00004819295,0.02661138,0.0003850879],"about_ca_topic_score_codex":4.847819e-7,"about_ca_topic_score_gemma":7.109773e-7,"teacher_disagreement_score":0.86199427,"about_ca_system_score_codex":0.00011543398,"about_ca_system_score_gemma":0.000051468796,"threshold_uncertainty_score":0.9999345},"labels":[],"label_agreement":null},{"id":"W3199003835","doi":"10.3389/fncel.2021.727336","title":"Calcium and Spike Timing-Dependent Plasticity","year":2021,"lang":"en","type":"review","venue":"Frontiers in Cellular Neuroscience","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":"McGill University","funders":"Fondation pour la Recherche Médicale; Agence Nationale de la Recherche","keywords":"Plasticity; Synaptic plasticity; Neuroscience; Neuroplasticity; Metaplasticity; Extracellular; Calcium; Homosynaptic plasticity; Spike-timing-dependent plasticity; Synaptic scaling; Nonsynaptic plasticity; In vivo; Biology; Mechanism (biology); Chemistry; Cell biology; Biochemistry; Physics; Receptor; Genetics","score_opus":0.06442319163350085,"score_gpt":0.29248077717235055,"score_spread":0.2280575855388497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199003835","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013045188,0.87865794,0.11718783,0.0000014462155,0.0035719455,0.00022015808,0.000009189284,0.00010001425,0.00012103838],"genre_scores_gemma":[0.00036062265,0.99770266,0.0015465462,0.000024483918,0.00009621486,0.000012682382,0.0000049849295,0.000051780436,0.00020003285],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822825,0.00008044471,0.00039627258,0.0006270361,0.00021911299,0.00044885467],"domain_scores_gemma":[0.99947053,0.0000790433,0.00007591656,0.0002358722,0.000010314265,0.00012831483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013961735,0.00036831087,0.0009420066,0.0002007712,0.000099277815,0.0000721181,0.00036057856,0.00015086932,0.0000029573514],"category_scores_gemma":[0.0001138035,0.0003635227,0.00011453527,0.0005005916,0.00011869439,0.00013251962,0.00019753784,0.00069721224,0.0000028304178],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014480067,0.00003009776,0.000025965863,0.008557439,0.00000862337,0.0013578,0.000056735556,0.012222605,0.0014416444,0.000030904448,0.0005876744,0.97567904],"study_design_scores_gemma":[0.00012173466,0.000028972308,0.0000038414996,0.0037503862,0.00008717578,0.0001364065,0.00001592624,0.022408655,0.0016779657,0.000028219012,0.9710262,0.0007145048],"about_ca_topic_score_codex":6.645077e-7,"about_ca_topic_score_gemma":4.0047655e-7,"teacher_disagreement_score":0.97496456,"about_ca_system_score_codex":0.000092733244,"about_ca_system_score_gemma":0.000048259524,"threshold_uncertainty_score":0.9998817},"labels":[],"label_agreement":null},{"id":"W3200123501","doi":"10.5772/intechopen.99634","title":"Development of Compute-in-Memory Memristive Crossbar Architecture with Composite Memory Cells","year":2021,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Carleton University; York University","funders":"","keywords":"Crossbar switch; Memristor; Computer science; Memistor; Von Neumann architecture; Computer architecture; Semiconductor memory; Computer memory; Registered memory; Memory architecture; Memory management; In-Memory Processing; Parallel computing; Computer hardware; Resistive random-access memory; Electronic engineering; Electrical engineering; Engineering; Search engine; Voltage; Telecommunications","score_opus":0.013319855551195008,"score_gpt":0.2158993275869222,"score_spread":0.2025794720357272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200123501","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030607125,0.0007592047,0.032718297,0.000007909556,0.0007193736,0.0011315162,0.00004857342,0.00042852675,0.93357944],"genre_scores_gemma":[0.4489776,0.000055713892,0.1714541,0.00039267543,0.0006106453,0.00009799786,0.00018429657,0.0009810249,0.37724596],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979824,0.000019518562,0.00074259046,0.0005422991,0.00032483507,0.00038838372],"domain_scores_gemma":[0.99894416,0.0001385243,0.00021247525,0.00046899583,0.00011897,0.00011685142],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013577452,0.0006502264,0.0009244625,0.00025725158,0.00009499468,0.000038896862,0.00045669937,0.00029734962,0.000073536474],"category_scores_gemma":[0.000003821495,0.0006199381,0.00014014088,0.00004032634,0.0001783642,0.00004388913,0.00029106674,0.0012960372,0.000027645954],"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":[0.00045948825,0.000061231876,0.0000030640979,0.0019669451,0.0009468591,0.0025104464,0.008321187,0.19666362,0.3755652,0.0018156042,0.00026286882,0.4114235],"study_design_scores_gemma":[0.0008864722,0.00008160666,0.000010080954,0.0041577974,0.000055967696,0.000118775926,0.00014920702,0.00035126877,0.95478916,0.00054434664,0.03769808,0.0011572416],"about_ca_topic_score_codex":0.0000035941664,"about_ca_topic_score_gemma":0.00009217397,"teacher_disagreement_score":0.579224,"about_ca_system_score_codex":0.00019316495,"about_ca_system_score_gemma":0.00014334469,"threshold_uncertainty_score":0.9996252},"labels":[],"label_agreement":null},{"id":"W3200424893","doi":"10.1109/mwscas47672.2021.9531835","title":"A Novel Architecture for Memristor-Based Logic","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"XNOR gate; NAND gate; Logic gate; NOR gate; NAND logic; AND-OR-Invert; Pass transistor logic; Memristor; Computer science; NOR logic; Logic synthesis; Spice; AND gate; Digital electronics; OR gate; Voltage; Algorithm; Logic family; Electronic engineering; Electronic circuit; Electrical engineering; Engineering","score_opus":0.029025439926908158,"score_gpt":0.24868403131817188,"score_spread":0.21965859139126373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200424893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011165071,0.00013374451,0.9842011,0.00017576247,0.0002326951,0.00006512196,0.0000041843864,0.00029790014,0.0037244286],"genre_scores_gemma":[0.7954125,0.0000015140124,0.20278388,0.0008422766,0.00014836212,0.000013458055,0.000012857271,0.000024637779,0.00076053967],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996734,0.0000026810767,0.00006656722,0.000096722535,0.000033613353,0.00012698928],"domain_scores_gemma":[0.99977666,0.000076520235,0.0000059418126,0.00009188695,0.000019067513,0.000029899216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000021723006,0.00006723836,0.00007411687,0.000015275135,0.00003576475,0.0000068127283,0.00003986595,0.000025790461,0.00003642968],"category_scores_gemma":[0.000029929568,0.000060392864,0.00004768994,0.000066678054,0.0000056332265,0.000015238949,0.000008064812,0.00007098674,0.000003699483],"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":[0.000005726764,0.000011979801,0.0000024422773,0.00007744414,0.000006736442,0.0000072866087,0.000019839717,0.47625586,0.5156204,0.0016352688,0.0004136393,0.0059433975],"study_design_scores_gemma":[0.0005933629,0.000031448708,0.000022016511,0.00002032067,0.000008145704,0.000024732812,0.00002213623,0.09991293,0.8554358,0.0030160747,0.040701505,0.0002115437],"about_ca_topic_score_codex":1.8608532e-7,"about_ca_topic_score_gemma":0.0000056655385,"teacher_disagreement_score":0.7842474,"about_ca_system_score_codex":0.000014079256,"about_ca_system_score_gemma":0.00000869184,"threshold_uncertainty_score":0.24627496},"labels":[],"label_agreement":null},{"id":"W3200768682","doi":"10.3389/fnano.2021.779070","title":"Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review","year":2021,"lang":"en","type":"preprint","venue":"Frontiers in Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Neuromorphic engineering; Scalability; Computer science; Neuroscience; Computer architecture; Synaptic plasticity; Artificial neural network; Plasticity; Spike-timing-dependent plasticity; Artificial intelligence; Materials science; Biology","score_opus":0.031731351630451106,"score_gpt":0.24949522927558745,"score_spread":0.21776387764513636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200768682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74317783,0.10046237,0.14981075,0.00024189854,0.0042320522,0.0011368227,0.000035556797,0.0008721186,0.000030608022],"genre_scores_gemma":[0.9417856,0.017038787,0.040466096,0.00020129525,0.0000712053,0.00023889888,0.000059762253,0.00010544169,0.000032967826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715453,0.00013318326,0.0008488952,0.00092303474,0.0001666054,0.00077372376],"domain_scores_gemma":[0.9990032,0.00015320758,0.00020346716,0.0005089233,0.000060497154,0.00007071701],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00021611183,0.00057807565,0.0013758105,0.0007231738,0.000062335544,0.000033894878,0.0007337016,0.00089044933,0.000017125374],"category_scores_gemma":[0.00057491666,0.0006752522,0.00013109935,0.0007241462,0.0001529274,0.00013614132,0.00076736533,0.0029443337,0.0000063443745],"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":[0.00016524237,0.0005636521,0.009611339,0.04386881,0.00065028557,0.021704623,0.0009350738,0.80409425,0.024167039,0.000041922096,0.0017904383,0.09240729],"study_design_scores_gemma":[0.0032323094,0.00024237925,0.0019590594,0.042957086,0.00044689217,0.000810748,0.0011670397,0.8896949,0.04643356,0.00094651384,0.0082284,0.0038810826],"about_ca_topic_score_codex":0.00002788955,"about_ca_topic_score_gemma":0.00014102756,"teacher_disagreement_score":0.19860773,"about_ca_system_score_codex":0.0003768513,"about_ca_system_score_gemma":0.00010729628,"threshold_uncertainty_score":0.9995699},"labels":[],"label_agreement":null},{"id":"W3201645160","doi":"10.1109/ijcnn52387.2021.9533834","title":"Comparative Study on Quantization-Aware Training of Memristor Crossbars for Reducing Inference Power of Neural Networks at The Edge","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Neurosciences Research Foundation","keywords":"Crossbar switch; Computer science; Memristor; Convolutional neural network; Edge computing; Quantization (signal processing); Inference; Edge device; Artificial neural network; Artificial intelligence; Kernel (algebra); Cloud computing; Enhanced Data Rates for GSM Evolution; Computer architecture; Algorithm; Electronic engineering; Engineering; Telecommunications; Mathematics; Operating system","score_opus":0.08475422436098824,"score_gpt":0.34033661091502126,"score_spread":0.25558238655403304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201645160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92253214,0.000110708,0.07635556,0.000013768152,0.0003055949,0.0002446667,0.0000059013987,0.00004627612,0.0003853876],"genre_scores_gemma":[0.9995736,0.0000020176121,0.00024718908,0.000020283589,0.000032610238,0.000009163814,0.000006951311,0.000013335658,0.00009485675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929357,0.000039398314,0.00027894136,0.00014937829,0.000089201065,0.00014954052],"domain_scores_gemma":[0.99901927,0.0006003688,0.00007178523,0.00017698716,0.00010726125,0.000024347264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010582374,0.00011227875,0.00025051704,0.000022329466,0.00011971663,0.000009402732,0.00009231539,0.000026870208,0.000027779233],"category_scores_gemma":[0.00006149044,0.00008638243,0.0000570679,0.0001785666,0.00003969037,0.00006516346,0.000041355084,0.00011470253,3.797244e-7],"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":[0.00002830396,0.00003116387,0.0003387103,0.00002064943,0.000028753246,0.0000013454719,0.0076196976,0.98234665,0.008847597,0.00013686532,0.00007062623,0.0005296625],"study_design_scores_gemma":[0.000642315,0.0003386974,0.0036319778,0.00009192708,0.000026035665,0.0000036346985,0.0154931145,0.8147629,0.16472396,0.000031898886,0.000052056246,0.00020145871],"about_ca_topic_score_codex":0.0000016574777,"about_ca_topic_score_gemma":0.000021724609,"teacher_disagreement_score":0.1675837,"about_ca_system_score_codex":0.000023487697,"about_ca_system_score_gemma":0.000013349198,"threshold_uncertainty_score":0.35225734},"labels":[],"label_agreement":null},{"id":"W3201849703","doi":"10.1016/j.neunet.2021.09.021","title":"Extreme neural machines","year":2021,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Recurrent neural network; Artificial neural network; Key (lock); Path (computing); Learning rule; Variety (cybernetics); Machine learning; Pattern recognition (psychology)","score_opus":0.023392515877724573,"score_gpt":0.22989342681723554,"score_spread":0.20650091093951098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201849703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97004485,0.0046201474,0.017785858,0.00019737873,0.0030458197,0.00009456178,0.0000023303605,0.0010561608,0.0031528897],"genre_scores_gemma":[0.9978928,0.000066607034,0.0003720835,0.000449278,0.00084572803,0.0000038167036,0.000014793958,0.000040895047,0.00031398988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990963,0.00003385073,0.00018787922,0.00021503014,0.000096166135,0.00037081793],"domain_scores_gemma":[0.9995498,0.00008798426,0.000020037041,0.0002187076,0.000028096414,0.00009534044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034039116,0.00018384811,0.00017421928,0.000022728402,0.00010244454,0.000040263454,0.000120516204,0.00006746797,0.00010322979],"category_scores_gemma":[0.00001733326,0.00017946718,0.00008653814,0.00024125047,0.000019848656,0.0001586374,0.00006414629,0.00039238823,0.00001087148],"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":[0.0000050818185,0.0000053249782,0.00039376473,0.00001305243,0.000007332846,0.00018462542,0.000014516373,0.9090868,0.005411311,0.000049607042,0.0007767808,0.08405175],"study_design_scores_gemma":[0.00016826272,0.000013039278,0.0009761467,0.00001224628,0.00000776219,0.00013823913,0.0000066423895,0.99353164,0.0029884747,0.0001042698,0.0018517604,0.00020152792],"about_ca_topic_score_codex":6.927255e-7,"about_ca_topic_score_gemma":0.0000076116025,"teacher_disagreement_score":0.08444478,"about_ca_system_score_codex":0.000015234843,"about_ca_system_score_gemma":0.0000028348625,"threshold_uncertainty_score":0.731846},"labels":[],"label_agreement":null},{"id":"W3203454415","doi":"10.5772/intechopen.100246","title":"Mitigating State-Drift in Memristor Crossbar Arrays for Vector Matrix Multiplication","year":2021,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke; University of Toronto; York University","funders":"","keywords":"Memristor; Crossbar switch; Resistive random-access memory; Overhead (engineering); Computer science; Inference; Matrix multiplication; Artificial neural network; Electronic engineering; Computer engineering; Voltage; Computer architecture; Engineering; Artificial intelligence; Electrical engineering","score_opus":0.021656227876719062,"score_gpt":0.2692866106579693,"score_spread":0.24763038278125027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203454415","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031301793,0.0031498307,0.1497625,0.000113340946,0.0041484446,0.0072964933,0.00059077534,0.002706906,0.8009299],"genre_scores_gemma":[0.54322726,0.00005851403,0.028808272,0.00015277883,0.0009461486,0.00051534,0.00025131332,0.0007105375,0.42532983],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984161,0.0000108160775,0.0005997062,0.00047401633,0.00014650008,0.00035282533],"domain_scores_gemma":[0.99903506,0.00022500093,0.00015391024,0.0003973753,0.00010402851,0.00008460154],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014958602,0.00041624717,0.00049383164,0.00014251124,0.00009928227,0.00007321709,0.00028752934,0.00027391803,0.000043273714],"category_scores_gemma":[0.00006840308,0.00048276054,0.00018099262,0.000021688453,0.000053247015,0.00008291088,0.00009434894,0.0007161662,0.000034837005],"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":[0.00027152814,0.00003911021,0.000015512946,0.0031400218,0.000408346,0.0002673885,0.0027924515,0.066300444,0.6183521,0.0709403,0.0014215516,0.2360512],"study_design_scores_gemma":[0.0018822696,0.00014547516,0.000017689877,0.0035805563,0.0000776889,0.00004313561,0.00013552552,0.018504826,0.58750516,0.018580876,0.36723995,0.0022868174],"about_ca_topic_score_codex":0.000005074198,"about_ca_topic_score_gemma":0.00006094204,"teacher_disagreement_score":0.51192546,"about_ca_system_score_codex":0.00025522397,"about_ca_system_score_gemma":0.000045403827,"threshold_uncertainty_score":0.9997624},"labels":[],"label_agreement":null},{"id":"W3203926970","doi":"10.1109/iccv48922.2021.00444","title":"Event Stream Super-Resolution via Spatiotemporal Constraint Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Event (particle physics); Artificial intelligence; Asynchronous communication; Constraint (computer-aided design); Frame (networking); Computer vision; Real-time computing; Image resolution; Mathematics","score_opus":0.02762742856943593,"score_gpt":0.28644593617137953,"score_spread":0.2588185076019436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203926970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3191317,0.00006415825,0.67086405,0.00085148006,0.0044085784,0.00016191207,0.000021928887,0.00031837038,0.0041777976],"genre_scores_gemma":[0.99314547,0.0000899555,0.0049056625,0.00018708639,0.0007801543,0.000008932006,0.00017240296,0.000034813078,0.0006755512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980266,0.0001189472,0.00049431244,0.0005208102,0.000502026,0.00033728665],"domain_scores_gemma":[0.9990039,0.00013279781,0.00009829125,0.0002519401,0.0003659383,0.00014716812],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001660738,0.00031598622,0.00028251187,0.0001498974,0.00016232142,0.00017371116,0.00029451252,0.00012311648,0.0010359862],"category_scores_gemma":[0.000031862743,0.0003361724,0.00015580244,0.0001811139,0.000047625224,0.00029175717,0.00013122201,0.0006157092,0.0002382339],"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":[0.000063188316,0.00018449826,0.00019663369,0.000046626606,0.00012635517,0.00036149478,0.00024658768,0.5681606,0.08386336,0.0078861015,0.0015975613,0.33726698],"study_design_scores_gemma":[0.00055888813,0.00020263772,0.00053074467,0.00031701408,0.000009064847,0.00009490926,0.00006713584,0.95138514,0.039895456,0.0008519815,0.0057024905,0.00038453224],"about_ca_topic_score_codex":0.00000855403,"about_ca_topic_score_gemma":0.000015491554,"teacher_disagreement_score":0.67401373,"about_ca_system_score_codex":0.00021380947,"about_ca_system_score_gemma":0.000070746624,"threshold_uncertainty_score":0.99990904},"labels":[],"label_agreement":null},{"id":"W3204012403","doi":"10.1109/jssc.2021.3113354","title":"Bidirectional Peripheral Nerve Interface With 64 Second-Order Opamp-Less ΔΣ ADCs and Fully Integrated Wireless Power/Data Transmission","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Solid-State Circuits","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":46,"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":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Algorithm; Mathematics","score_opus":0.024133108597985006,"score_gpt":0.2625418163281497,"score_spread":0.2384087077301647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204012403","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86686015,0.0017461979,0.13034068,0.000068081936,0.00064545823,0.000068233174,0.000061293096,0.00006482109,0.00014504867],"genre_scores_gemma":[0.9980817,0.00039451107,0.00095153775,0.000058368114,0.00015677522,9.582867e-7,0.000011319457,0.000058890524,0.00028596589],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984964,0.000081925085,0.00053332373,0.0002801595,0.00027287586,0.00033527543],"domain_scores_gemma":[0.9988925,0.00009381651,0.0001712193,0.00021494593,0.0003959274,0.00023159117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023001761,0.00028102103,0.0004311442,0.00010796546,0.00012244676,0.00008486888,0.00027278022,0.000083007224,0.00010047348],"category_scores_gemma":[0.000016422484,0.00023618188,0.000060633374,0.00034960607,0.00006367709,0.00071194343,0.000035957422,0.000734147,0.0000018645013],"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":[0.0001818555,0.00009023236,0.00020014583,0.00025466405,0.00030637652,0.001262576,0.001857111,0.15097553,0.7301657,0.00000491376,0.00065430376,0.114046566],"study_design_scores_gemma":[0.0041184863,0.000686297,0.0016992372,0.0022338494,0.0001394746,0.012028442,0.0025432499,0.052823674,0.90788364,0.00021074686,0.014441223,0.0011916937],"about_ca_topic_score_codex":0.0000017188396,"about_ca_topic_score_gemma":0.000024930041,"teacher_disagreement_score":0.1777179,"about_ca_system_score_codex":0.00008565635,"about_ca_system_score_gemma":0.00016515452,"threshold_uncertainty_score":0.9631217},"labels":[],"label_agreement":null},{"id":"W3205986684","doi":"10.1145/3477145.3477165","title":"A Flexible FPGA Implementation of Morris-Lecar Neuron for Reproducing Different Neuronal Behaviors","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Field-programmable gate array; Computer science; Biological neuron model; MATLAB; Neuromorphic engineering; VHDL; Embedded system; Artificial neural network; Computer hardware; Artificial intelligence","score_opus":0.02642371405546041,"score_gpt":0.30244086817500193,"score_spread":0.27601715411954153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205986684","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9614933,0.000051765877,0.037578255,0.000039194245,0.00033586926,0.0001620676,0.000008280748,0.000164899,0.00016635383],"genre_scores_gemma":[0.99799234,0.000010497622,0.0016438764,0.00003934735,0.00007624864,0.000016616914,0.00002243374,0.000024236606,0.0001744091],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926984,0.000013086317,0.00023027041,0.00022826274,0.000083727755,0.00017480525],"domain_scores_gemma":[0.99964875,0.0000518815,0.00003234393,0.00019116396,0.00004134356,0.00003454399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000448886,0.00010188879,0.00013535698,0.000033786022,0.000046141864,0.00000962199,0.00005187343,0.000018818457,0.00007849503],"category_scores_gemma":[0.000017275845,0.000102062986,0.00006628161,0.000093824405,0.0000067012174,0.000084119216,0.00003259283,0.000073325224,8.599827e-7],"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":[0.000008947987,0.000021929663,0.00080427533,0.00011871259,0.000009935118,0.0000042732636,0.00013977941,0.03244002,0.92429084,0.00012848097,0.0002156489,0.04181719],"study_design_scores_gemma":[0.0002822978,0.00006210204,0.00958643,0.000010441571,0.000018810706,0.0000065001377,0.00015654134,0.0022155247,0.987226,0.00009401955,0.00024399928,0.000097336306],"about_ca_topic_score_codex":0.000003672428,"about_ca_topic_score_gemma":0.000010407773,"teacher_disagreement_score":0.06293519,"about_ca_system_score_codex":0.000018662098,"about_ca_system_score_gemma":0.000009229572,"threshold_uncertainty_score":0.4162008},"labels":[],"label_agreement":null},{"id":"W3206867805","doi":"10.1088/1361-6528/ac2e78","title":"Zinc phthalocyanine conjugated cellulose nanocrystals for memory device applications","year":2021,"lang":"en","type":"article","venue":"Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; National Research Council Canada; Canada Foundation for Innovation; FPInnovations; University of Alberta; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Materials science; Phthalocyanine; Conjugated system; Nanocrystal; Hysteresis; Zinc; Electrode; Covalent bond; Thin film; HOMO/LUMO; Optoelectronics; Molecular orbital; Nanotechnology; Polymer; Molecule; Organic chemistry; Physical chemistry; Composite material; Metallurgy; Condensed matter physics","score_opus":0.01689556944554829,"score_gpt":0.2524612216750656,"score_spread":0.23556565222951728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206867805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65340453,0.002842736,0.33855474,0.00049769884,0.00038081873,0.0006748283,0.000033780507,0.002302169,0.0013086846],"genre_scores_gemma":[0.9895677,0.000054499167,0.0091965655,0.00014676935,0.00008458572,0.00018998377,0.000045806708,0.00004417838,0.0006699079],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991218,0.000012471312,0.00023097561,0.00026928555,0.000054671174,0.00031079547],"domain_scores_gemma":[0.99935675,0.0001332316,0.00003851901,0.00033311863,0.00009564159,0.000042754804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060536488,0.00015183144,0.00022044736,0.00007583975,0.00010356588,0.0000098238925,0.00017251685,0.00023713618,0.000043731467],"category_scores_gemma":[0.00006322829,0.00017133614,0.000058569494,0.00038131647,0.000052863248,0.000059199017,0.000062521285,0.0002148256,0.000042874584],"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":[0.000005364554,0.000018679686,0.0000034085565,0.00008804157,0.000021542965,0.000023403616,0.000016646823,0.0029836344,0.9705824,0.003008963,0.0002242203,0.023023674],"study_design_scores_gemma":[0.00038084833,0.00002648365,0.0000022011063,0.000013453939,0.000015798683,0.00009332376,0.00004723344,0.0033074685,0.9036635,0.0019335884,0.09034438,0.00017171452],"about_ca_topic_score_codex":6.2145523e-7,"about_ca_topic_score_gemma":0.000005545533,"teacher_disagreement_score":0.33616316,"about_ca_system_score_codex":0.00004427452,"about_ca_system_score_gemma":0.000023851426,"threshold_uncertainty_score":0.6986885},"labels":[],"label_agreement":null},{"id":"W3207435186","doi":"10.1145/3477145.3477264","title":"Drone Virtual Fence Using a Neuromorphic Camera","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Defence Research and Development Canada; National Research Council Canada","funders":"","keywords":"Drone; Computer science; Neuromorphic engineering; Computer vision; Artificial intelligence; Situation awareness; SIGNAL (programming language); Real-time computing; Engineering","score_opus":0.041435867131510366,"score_gpt":0.2369316207996937,"score_spread":0.19549575366818334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207435186","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.920755,0.00012247196,0.07731067,0.000023518096,0.00031681635,0.000023259778,6.9994945e-7,0.00025785354,0.001189705],"genre_scores_gemma":[0.99637353,0.000014548895,0.0029878148,0.00016643431,0.00008095779,5.589343e-7,0.0000012149022,0.000016317177,0.00035864854],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995438,0.000012053589,0.00009579974,0.00012224523,0.0000639969,0.00016212447],"domain_scores_gemma":[0.99977154,0.00003820452,0.000008085188,0.00011798289,0.000017787956,0.000046431705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000022481796,0.00007960933,0.000088038214,0.000017200935,0.000055605153,0.000015895168,0.00004934756,0.00002138702,0.000112016416],"category_scores_gemma":[0.000019616786,0.00008405395,0.00002517723,0.00015545051,0.000012388628,0.000102762046,0.00003630705,0.00012671082,0.00002480137],"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":[0.0000012565826,0.0000054685256,0.0000352562,0.000010771355,0.0000049167165,0.00017674934,0.00004856828,0.28480002,0.71043086,0.00036970156,0.000042362528,0.0040740683],"study_design_scores_gemma":[0.00020625928,0.000025352658,0.00021156312,0.000034236244,0.000008835583,0.00032041685,0.00012748087,0.40576282,0.5916831,0.00015418003,0.0012071535,0.0002586264],"about_ca_topic_score_codex":0.0000014736216,"about_ca_topic_score_gemma":0.000002181896,"teacher_disagreement_score":0.12096279,"about_ca_system_score_codex":0.000016275753,"about_ca_system_score_gemma":0.00001074081,"threshold_uncertainty_score":0.34276205},"labels":[],"label_agreement":null},{"id":"W3211124320","doi":"10.1038/s41593-021-00970-x","title":"Author Correction: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits","year":2021,"lang":"en","type":"erratum","venue":"Nature Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Ottawa; Canadian Institute for Advanced Research; Mila - Quebec Artificial Intelligence Institute; University of Toronto; General Dynamics (Canada); Université de Montréal; The Scarborough Hospital","funders":"","keywords":"Neuroscience; Synaptic plasticity; Neuronal circuits; Plasticity; Neuroplasticity; Biological neural network; Spike-timing-dependent plasticity; Metaplasticity; Biology; Psychology; Physics","score_opus":0.013966265666735972,"score_gpt":0.25722890603844745,"score_spread":0.24326264037171147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211124320","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1288771,0.0072122007,0.02306709,0.0014706128,0.81216997,0.0014832624,0.0001015506,0.0040663504,0.02155189],"genre_scores_gemma":[0.9561794,0.00013665695,0.000023357321,0.00035650536,0.0010005683,0.0000147924675,0.000025901962,0.00008121195,0.04218164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997115,0.00015452795,0.0003823377,0.0009537433,0.0006298293,0.00076457165],"domain_scores_gemma":[0.99901754,0.00033257864,0.00010706337,0.00025866696,0.000072589384,0.00021157277],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00020644965,0.00046464294,0.00051408936,0.00031114102,0.0003012724,0.0001342397,0.0005621393,0.00091034325,0.000014306819],"category_scores_gemma":[0.0020445965,0.0004989789,0.00010254505,0.0011726608,0.00013135953,0.00016258634,0.00020649485,0.012543763,0.0000050216036],"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":[0.000020985346,0.0000879963,0.00021043597,0.00061491656,0.000017039742,0.004176156,0.0003024102,0.85586905,0.04641987,0.00013141685,0.070791535,0.021358173],"study_design_scores_gemma":[0.0007140201,0.00040833314,0.010048588,0.0019782502,0.00007031459,0.0016470953,0.00010895888,0.74897486,0.010454887,0.00017447537,0.2230592,0.0023609863],"about_ca_topic_score_codex":0.000004613672,"about_ca_topic_score_gemma":0.00006623474,"teacher_disagreement_score":0.8273023,"about_ca_system_score_codex":0.00026459448,"about_ca_system_score_gemma":0.00020343382,"threshold_uncertainty_score":0.9997462},"labels":[],"label_agreement":null},{"id":"W3213395180","doi":"10.1109/sips52927.2021.00050","title":"Design and Implementation of a Highly Accurate Stochastic Spiking Neural Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"MNIST database; Computer science; Spiking neural network; Stochastic computing; Artificial neural network; ENCODE; Encoder; Field-programmable gate array; Artificial intelligence; Encoding (memory); Pattern recognition (psychology); Computer engineering; Computer hardware","score_opus":0.027031166698959316,"score_gpt":0.27972034524200917,"score_spread":0.25268917854304984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213395180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46110263,0.00016304631,0.5385102,0.000010169569,0.00007947868,0.000048957212,2.1414124e-7,0.00005409418,0.000031185413],"genre_scores_gemma":[0.98555297,0.000008730532,0.01433163,0.00003097797,0.000051816096,0.00000219106,0.0000017628413,0.000008843218,0.0000111063855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996248,0.000016742106,0.00012255514,0.00007797055,0.00003324063,0.00012471147],"domain_scores_gemma":[0.99978924,0.00009911138,0.000019351133,0.000051553718,0.000017213823,0.000023535446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050276227,0.00006062148,0.00008201658,0.000012603358,0.000034375287,0.000010275909,0.00002231621,0.000012923666,0.000017839802],"category_scores_gemma":[0.0000053838835,0.000060275608,0.00001202172,0.00010041241,0.0000059747254,0.00007126545,0.00002229587,0.000048538168,5.4294134e-7],"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":[0.0000025656695,8.9546637e-7,0.00002615044,0.000019274263,0.00000634795,0.0000052457776,0.00006796696,0.9380724,0.046970334,0.00017932868,0.000017702803,0.014631798],"study_design_scores_gemma":[0.00038761026,0.000044734417,0.0011223376,0.00003511517,0.000018782861,0.000032623364,0.00024373984,0.724227,0.2729207,0.0007849557,0.000023158527,0.00015922812],"about_ca_topic_score_codex":0.000001148842,"about_ca_topic_score_gemma":0.000004776236,"teacher_disagreement_score":0.5244503,"about_ca_system_score_codex":0.000006265163,"about_ca_system_score_gemma":0.0000052752234,"threshold_uncertainty_score":0.24579678},"labels":[],"label_agreement":null},{"id":"W3213745200","doi":"10.1109/sips52927.2021.00049","title":"Hartley Stochastic Computing For Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolution (computer science); Convolutional neural network; Speedup; Fourier transform; Algorithm; Parallel computing; Fast Fourier transform; FLOPS; Field-programmable gate array; Computational science; Artificial neural network; Artificial intelligence; Computer hardware; Mathematics","score_opus":0.018228222466774045,"score_gpt":0.24047252192201563,"score_spread":0.22224429945524157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213745200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10685609,0.0003353212,0.8912861,0.00003817339,0.0007265239,0.00007732173,0.000002399755,0.00031040225,0.00036769197],"genre_scores_gemma":[0.9936896,0.0000012487902,0.005508519,0.0001804374,0.00039293422,0.0000032737937,0.000021569704,0.000018941268,0.00018346208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994248,0.000008202488,0.00013826904,0.00013457157,0.000051388473,0.0002427305],"domain_scores_gemma":[0.9995798,0.00022529875,0.0000127814965,0.00007998219,0.00004979627,0.000052368385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044449924,0.00009452843,0.00010950798,0.000015110743,0.00010871184,0.000019446645,0.000050706796,0.000032968674,0.000038992173],"category_scores_gemma":[0.000033788856,0.000099481666,0.000055610966,0.00008736274,0.000013690172,0.00006518177,0.000030148667,0.0001154678,0.0000044623657],"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":[0.0000031620984,0.000003706327,0.000021004415,0.000014449906,0.000008651043,0.000004276554,0.000010282242,0.99289495,0.0019059719,0.0009866124,0.00042842064,0.0037185212],"study_design_scores_gemma":[0.00024599594,0.000010163837,0.00018292143,0.000012240322,0.000005686203,0.000034893765,0.00002021353,0.9964732,0.0023353186,0.00030046672,0.0002567,0.00012221467],"about_ca_topic_score_codex":1.7539018e-7,"about_ca_topic_score_gemma":0.0000014583078,"teacher_disagreement_score":0.88683355,"about_ca_system_score_codex":0.000019805508,"about_ca_system_score_gemma":0.0000069738066,"threshold_uncertainty_score":0.40567446},"labels":[],"label_agreement":null},{"id":"W3216562638","doi":"10.1088/2632-2153/ac34db","title":"Miniaturizing neural networks for charge state autotuning in quantum dots","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Institut quantique; Université de Sherbrooke; Perimeter Institute; University of Waterloo","funders":"","keywords":"Computer science; Qubit; Artificial neural network; Quantum computer; Quantum dot; Task (project management); Electronic engineering; Quantum; Artificial intelligence; Nanotechnology; Physics; Engineering; Materials science; Quantum mechanics","score_opus":0.05128894798691666,"score_gpt":0.180235017977844,"score_spread":0.12894606999092734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216562638","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.911792,0.00016823696,0.08712076,0.000011747786,0.00039506058,0.00009530467,0.0000036989463,0.00019915964,0.0002140791],"genre_scores_gemma":[0.99924815,0.00006317014,0.00019404994,0.000050999584,0.000066922024,4.5939956e-7,0.000009517818,0.000025693816,0.00034105257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915236,0.000028031485,0.00012897183,0.0002887816,0.000023223234,0.00037865725],"domain_scores_gemma":[0.99957,0.00012742828,0.000031817584,0.00016382891,0.00003181629,0.0000751039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079437385,0.00014309058,0.00016851137,0.00007737189,0.000094527226,0.00001829931,0.00012236719,0.00005610492,0.00001157978],"category_scores_gemma":[0.000023166604,0.00018273616,0.00007217216,0.00042657348,0.000016869742,0.00024607577,0.00005688477,0.00023490487,0.0000044796725],"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":[0.000022365852,0.000009881585,0.00066457805,0.000034206205,0.000011141072,0.0003384426,0.00008682383,0.99275666,0.0033252847,0.0018306553,0.000022814162,0.00089713075],"study_design_scores_gemma":[0.00053547416,0.000017708147,0.00044716324,0.000038424434,0.000009222093,0.0000067721726,0.00007415863,0.9939736,0.003663755,0.00065906043,0.0003665985,0.0002080661],"about_ca_topic_score_codex":0.000001934768,"about_ca_topic_score_gemma":0.000016001335,"teacher_disagreement_score":0.087456174,"about_ca_system_score_codex":0.00006649796,"about_ca_system_score_gemma":0.000011078176,"threshold_uncertainty_score":0.74517643},"labels":[],"label_agreement":null},{"id":"W3216758665","doi":"10.1109/tsp.2021.3130488","title":"Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":24,"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":"Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research; Semiconductor Research Corporation","keywords":"Quantization (signal processing); Computer science; Converters; Noise reduction; Digital signal processing; Algorithm; Signal processing; Electronic engineering; Computer hardware; Artificial intelligence; Power (physics); Engineering","score_opus":0.017476936749527713,"score_gpt":0.30746658848243485,"score_spread":0.28998965173290714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216758665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15859039,0.00097382715,0.8397162,0.000043028525,0.00011383771,0.000089677524,0.0000016588903,0.00014918909,0.00032220874],"genre_scores_gemma":[0.971657,0.000005911845,0.027960923,0.00019552986,0.00008279335,0.000011964038,4.7119525e-7,0.000042722244,0.000042704312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99831736,0.0001538737,0.0004917049,0.000370456,0.00025552904,0.00041106791],"domain_scores_gemma":[0.9991561,0.0004086101,0.000086451226,0.00016034782,0.00010548848,0.00008298249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036679846,0.00025390528,0.0003126233,0.00020069818,0.00035761017,0.000057693564,0.00023450825,0.000074168944,0.000023199113],"category_scores_gemma":[0.000008844439,0.0002193472,0.000098258,0.0011563981,0.00007639053,0.00011702924,0.0000052533037,0.00054286316,0.0000015945167],"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":[0.000012311662,0.000024213872,5.703663e-7,0.000091936534,0.0000039333754,0.000006145057,0.0006410688,0.420457,0.20127662,9.815221e-7,5.4379797e-7,0.37748468],"study_design_scores_gemma":[0.000095064184,0.0000350622,0.000011412585,0.00040556412,0.000012343949,0.000028761813,0.00026151887,0.30771527,0.6911222,0.00010681521,0.000033019172,0.00017298863],"about_ca_topic_score_codex":0.0000046145983,"about_ca_topic_score_gemma":0.000014660656,"teacher_disagreement_score":0.8130666,"about_ca_system_score_codex":0.00006125671,"about_ca_system_score_gemma":0.000102270395,"threshold_uncertainty_score":0.8944719},"labels":[],"label_agreement":null},{"id":"W3217151447","doi":"10.1016/j.mtadv.2021.100192","title":"Adjustable Leaky-Integrate-and-fire neurons based on memristor-coupled capacitors","year":2021,"lang":"en","type":"article","venue":"Materials Today Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Memristor; Neuromorphic engineering; Capacitor; Computer science; Electronic circuit; Von Neumann architecture; Artificial neural network; Electronic engineering; Capacitive sensing; Bottleneck; Artificial intelligence; Topology (electrical circuits); Electrical engineering; Voltage; Engineering; Embedded system","score_opus":0.01068332426108342,"score_gpt":0.21946569322127837,"score_spread":0.20878236896019495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217151447","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99258137,0.0014443418,0.0015614164,0.00010630389,0.0027368362,0.00012588291,0.000032554497,0.00045897526,0.000952329],"genre_scores_gemma":[0.9976746,0.00030919694,0.001192909,0.00019596086,0.00028393982,0.000020796051,0.000025050627,0.000052344178,0.0002451963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988118,0.00006555802,0.00028250596,0.00033884737,0.0001457304,0.00035558533],"domain_scores_gemma":[0.99940145,0.00014893203,0.000052932104,0.00026101552,0.00004113789,0.00009454619],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000114142276,0.00025897977,0.00034909035,0.000044922042,0.00016736051,0.0000713229,0.00010799053,0.00006799979,0.00023907587],"category_scores_gemma":[0.00009974062,0.00025013098,0.000044648074,0.00014839804,0.000047496836,0.0002706488,0.000030295038,0.00015332972,0.000028860248],"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":[0.00005645273,0.000024982102,0.000034504366,0.00022632477,0.00001047167,0.00011018769,0.000068730755,0.114667036,0.87856245,0.00017962845,0.00014978842,0.0059094583],"study_design_scores_gemma":[0.0003370879,0.000066421606,0.00014612809,0.000121290366,0.000015274942,0.000016096486,0.00010162018,0.009764898,0.9749288,0.00025409012,0.013924694,0.00032362383],"about_ca_topic_score_codex":0.0000065321624,"about_ca_topic_score_gemma":0.000010825495,"teacher_disagreement_score":0.10490214,"about_ca_system_score_codex":0.000047068035,"about_ca_system_score_gemma":0.000019968811,"threshold_uncertainty_score":0.9999951},"labels":[],"label_agreement":null},{"id":"W4200370458","doi":"10.1002/aelm.202100961","title":"Flexible Diodes with Low Breakdown Voltage for Steep Slope Transistors and One Diode‐One Resistor Applications","year":2021,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Simon Fraser University","funders":"Ministry of Trade, Industry and Energy","keywords":"Materials science; Diode; Optoelectronics; Schottky diode; Transistor; Resistor; Rectification; Breakdown voltage; Equivalent series resistance; Schottky barrier; Voltage; Electrical engineering","score_opus":0.007349280124176107,"score_gpt":0.21381460907134137,"score_spread":0.20646532894716527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200370458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5366139,0.00398184,0.4573842,0.000094972274,0.00012189319,0.0009106473,0.000088064866,0.0005540439,0.0002504435],"genre_scores_gemma":[0.9905867,0.00063939305,0.0072065084,0.000065278335,0.00015266382,0.00046621313,0.00009156148,0.000082904546,0.00070875423],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860805,0.000023294398,0.00029707185,0.00039464797,0.0001095956,0.00056732877],"domain_scores_gemma":[0.99937415,0.000106673884,0.00006246084,0.00030468134,0.00006853606,0.00008351375],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011133488,0.00023991505,0.0004039315,0.00004629176,0.00019702455,0.00004399019,0.00010871198,0.000066283654,0.000052983345],"category_scores_gemma":[0.000016803713,0.00025044748,0.000038531103,0.00018257811,0.00004624461,0.0002304965,0.000020198102,0.00011655035,0.000004358262],"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":[0.0001565629,0.000033906133,0.0000017577677,0.00047987752,0.000061761726,0.0000021294825,0.000056551435,0.0061338707,0.97899646,0.0031981233,0.000010373599,0.010868613],"study_design_scores_gemma":[0.00089300907,0.00013129081,0.000053911022,0.00010027738,0.00005762709,0.000018272569,0.000029603902,0.000049442227,0.983443,0.0022812497,0.012608584,0.00033373618],"about_ca_topic_score_codex":0.0000014511635,"about_ca_topic_score_gemma":0.000036878155,"teacher_disagreement_score":0.45397285,"about_ca_system_score_codex":0.00013209625,"about_ca_system_score_gemma":0.00006436122,"threshold_uncertainty_score":0.99999475},"labels":[],"label_agreement":null},{"id":"W4200470052","doi":"10.1002/aelm.202101018","title":"Depth Gradient Reduced Graphene Oxide Layer via Intense Pulsed Light Annealing Process for the Flexible Resistive Random Access Memory Device","year":2021,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Korea Evaluation Institute of Industrial Technology; Ministry of Trade, Industry and Energy","keywords":"Materials science; Resistive random-access memory; Optoelectronics; Graphene; Electrode; X-ray photoelectron spectroscopy; Annealing (glass); Oxide; Layer (electronics); Nanotechnology; Composite material; Chemical engineering","score_opus":0.02498626714194762,"score_gpt":0.30235024403695937,"score_spread":0.27736397689501174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200470052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9660103,0.0037178588,0.027473455,0.00022431777,0.0008887081,0.0010489335,0.000018081926,0.00050275284,0.00011562004],"genre_scores_gemma":[0.9979214,0.0004606117,0.00028981184,0.00028214377,0.0002374693,0.00047067314,0.000052009946,0.000104660096,0.00018119316],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975562,0.00010568145,0.00058383186,0.00055132166,0.00018624333,0.0010167389],"domain_scores_gemma":[0.99856305,0.00044907903,0.00016826953,0.0004533207,0.00026802617,0.00009824114],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043959307,0.0003993504,0.0005630315,0.0000787449,0.00039751292,0.000107258216,0.00039675296,0.00009486841,0.0000427135],"category_scores_gemma":[0.00022816377,0.00032581674,0.0001323495,0.00046937444,0.000044919772,0.00046506745,0.000081807775,0.00025264948,0.000005301221],"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":[0.000736063,0.000012955608,0.000001640575,0.0002470011,0.00011481818,0.000012029046,0.0001879769,0.11738952,0.87905127,0.00013756583,0.00002958732,0.0020795912],"study_design_scores_gemma":[0.0018921132,0.000060904906,0.0000544343,0.00014179092,0.00009482925,0.00004823257,0.00021578535,0.0012879586,0.9912509,0.0036238916,0.00091868336,0.00041048328],"about_ca_topic_score_codex":0.0000060108946,"about_ca_topic_score_gemma":0.000062310835,"teacher_disagreement_score":0.11610156,"about_ca_system_score_codex":0.00016359416,"about_ca_system_score_gemma":0.0000942255,"threshold_uncertainty_score":0.9999194},"labels":[],"label_agreement":null},{"id":"W4200489234","doi":"10.33965/celda2021_202108l001","title":"POSTSYNAPTIC SIMULATOR: AN OPEN-SOURCE VISUAL INTERACTIVE SIMULATION FOR TEACHING ACTION POTENTIALS","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Queen's University","funders":"","keywords":"Computer science; Postsynaptic potential; Action (physics); Human–computer interaction; Open source; Software; Simulation; Work (physics); Engineering; Programming language","score_opus":0.03898374254163506,"score_gpt":0.375114538618545,"score_spread":0.33613079607690993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200489234","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5298335,0.000009144937,0.469301,0.000009614685,0.00022222048,0.00016206547,9.845312e-7,0.00020970423,0.00025179956],"genre_scores_gemma":[0.99516904,0.000001093528,0.00416196,0.0001052202,0.00018238956,0.000007525224,0.000033750966,0.00003887086,0.00030012324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930716,0.000054803484,0.00018757384,0.00021257409,0.00006575706,0.00017216058],"domain_scores_gemma":[0.99936485,0.00036102804,0.000036695437,0.00012090873,0.00005908302,0.000057431458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012977821,0.00011906619,0.0001424199,0.000037543603,0.00017909458,0.0001054998,0.00009514226,0.000050263847,0.0000443548],"category_scores_gemma":[0.00013923412,0.00012513166,0.000041226845,0.000057368845,0.00000636115,0.00094793155,0.00007101064,0.00016625959,0.0000074634668],"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":[0.00004461349,0.000025711066,0.0000048209426,0.000016269658,0.00001911013,0.0000020162008,0.0001641289,0.77533334,0.18694676,0.00007332944,0.000007040002,0.03736285],"study_design_scores_gemma":[0.00048716096,0.00008629023,0.000058524834,0.000020197273,0.000014653276,0.000007251931,0.0005485074,0.8952664,0.102038026,0.00019821941,0.0011250004,0.00014975126],"about_ca_topic_score_codex":0.0000031214456,"about_ca_topic_score_gemma":0.000007909866,"teacher_disagreement_score":0.46533558,"about_ca_system_score_codex":0.000064438864,"about_ca_system_score_gemma":0.000008706198,"threshold_uncertainty_score":0.5102721},"labels":[],"label_agreement":null},{"id":"W4200493393","doi":"10.1021/acsaelm.1c00951","title":"Mechanism and Application of Capacitive-Coupled Memristive Behavior Based on a Biomaterial Developed Memristive Device","year":2021,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"Central University Basic Research Fund of China","keywords":"Capacitive sensing; Materials science; Nanotechnology; Electronics; Polyvinylidene fluoride; Resistive random-access memory; Mechanism (biology); Dielectric; Electrical engineering; Electronic engineering; Optoelectronics; Voltage; Engineering","score_opus":0.00926581522312575,"score_gpt":0.23338517315473087,"score_spread":0.22411935793160512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200493393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.970183,0.00004172357,0.028709305,0.000013034985,0.00013007162,0.00054286176,0.00005671041,0.00017866744,0.0001446138],"genre_scores_gemma":[0.9988134,0.00002852414,0.0005418255,0.00006265597,0.0000797615,0.00029252845,0.00012332271,0.000050575596,0.000007442546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864596,0.000043537184,0.0003812421,0.00036973922,0.0001525839,0.00040691908],"domain_scores_gemma":[0.9994212,0.00009730043,0.00012567201,0.00022287617,0.000078132485,0.000054825065],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020551427,0.00026879658,0.00041493462,0.00006964287,0.000096974145,0.0000315932,0.000108070555,0.00012077077,0.000057506415],"category_scores_gemma":[0.000022458833,0.00028090097,0.000022767386,0.00019637898,0.000044175795,0.000051971198,0.000042994376,0.00010531067,0.000010932405],"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":[0.00016478323,0.000030251302,0.0000013653904,0.0001038926,0.00002412183,0.000005620012,0.000081993334,0.0004170928,0.98332125,0.01508478,0.0000023704163,0.0007624737],"study_design_scores_gemma":[0.0007162705,0.000084852305,0.00017025784,0.000028315462,0.00005841664,0.000010243118,0.000048609483,0.00035503326,0.9963264,0.0018541819,0.000071187205,0.0002762744],"about_ca_topic_score_codex":0.000009427196,"about_ca_topic_score_gemma":0.000009016089,"teacher_disagreement_score":0.028630352,"about_ca_system_score_codex":0.00012985272,"about_ca_system_score_gemma":0.00007951092,"threshold_uncertainty_score":0.9999643},"labels":[],"label_agreement":null},{"id":"W4205624512","doi":"10.1039/d1nh00481f","title":"Versatile memristor for memory and neuromorphic computing","year":2022,"lang":"en","type":"article","venue":"Nanoscale Horizons","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Neuromorphic engineering; Memristor; Computer architecture; Computer science; Artificial intelligence; Electronic engineering; Artificial neural network; Engineering","score_opus":0.018513189009012092,"score_gpt":0.21789287854066114,"score_spread":0.19937968953164906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205624512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9844619,0.00056995626,0.011487386,0.00009871182,0.0015196791,0.00034603412,0.000052432886,0.00048952975,0.00097437325],"genre_scores_gemma":[0.998391,0.000008155284,0.001088898,0.000078439254,0.00016046793,0.00003289689,0.000011544633,0.000039006678,0.00018960376],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999246,0.000028830033,0.00015249451,0.00020457974,0.00010939842,0.00025868788],"domain_scores_gemma":[0.99954927,0.00020314757,0.00002867779,0.00013406161,0.000013184935,0.000071676805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011468887,0.00012499581,0.00015506278,0.000057002057,0.0004906281,0.000013545597,0.00011517928,0.000025942289,0.00003126587],"category_scores_gemma":[0.00002732473,0.00014897804,0.00005034828,0.0001363238,0.0000286545,0.000061024068,0.000113881724,0.00022884493,0.000002503593],"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":[0.00014235088,0.000119587996,0.00038195902,0.00045104203,0.000095975236,0.00009934335,0.0022107952,0.30456716,0.51588756,0.001790035,0.023277165,0.15097705],"study_design_scores_gemma":[0.00445335,0.001959188,0.0014414226,0.000060167014,0.00015111484,0.00039460705,0.0019199761,0.51697224,0.14253947,0.0016318576,0.32661432,0.0018622725],"about_ca_topic_score_codex":0.0000014761337,"about_ca_topic_score_gemma":0.0000014497035,"teacher_disagreement_score":0.3733481,"about_ca_system_score_codex":0.00005412552,"about_ca_system_score_gemma":0.0000097433,"threshold_uncertainty_score":0.6075148},"labels":[],"label_agreement":null},{"id":"W4206217096","doi":"10.36227/techrxiv.14485215.v1","title":"CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Encoding (memory); Computer science; Scheme (mathematics); Resistive touchscreen; Resistive random-access memory; Computer architecture; Electronic engineering; Computer hardware; Electrical engineering; Engineering; Mathematics; Artificial intelligence; Voltage; Telecommunications; Operating system","score_opus":0.1042182481214511,"score_gpt":0.3506830489022152,"score_spread":0.2464648007807641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206217096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018924957,0.00030442583,0.9762264,0.00003479143,0.0013013646,0.0006882699,0.000013461029,0.0007458488,0.0017604716],"genre_scores_gemma":[0.50376743,0.00001443572,0.4949136,0.00019253023,0.0002743878,0.00010137566,0.000019972596,0.00010920098,0.00060708896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975962,0.00017666242,0.00055396394,0.00081116083,0.00030443678,0.00055759394],"domain_scores_gemma":[0.9984777,0.00045581738,0.00008926548,0.000581839,0.00014822699,0.0002471464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005652856,0.00051343994,0.0006036273,0.00015642423,0.00020267737,0.00024963263,0.0004353247,0.000273886,0.00014524338],"category_scores_gemma":[0.00027247044,0.00055360986,0.00014533632,0.0002677417,0.000018700537,0.00019454765,0.00085188996,0.00095033844,0.00004393171],"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":[0.000030300282,0.000010254634,0.0000022234444,0.00016527243,0.00008404195,0.00006135505,0.0005426892,0.9154972,0.08023385,0.00006612187,0.00047618482,0.00283054],"study_design_scores_gemma":[0.00067249406,0.00013140349,0.00018683427,0.002057007,0.00017026614,0.000033445653,0.0006434144,0.2752535,0.71642256,0.0016777869,0.0005028284,0.0022484567],"about_ca_topic_score_codex":0.000007394838,"about_ca_topic_score_gemma":0.0000045815013,"teacher_disagreement_score":0.64024365,"about_ca_system_score_codex":0.00034240232,"about_ca_system_score_gemma":0.0000888512,"threshold_uncertainty_score":0.99969155},"labels":[],"label_agreement":null},{"id":"W4206372195","doi":"10.36227/techrxiv.14485215.v2","title":"CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","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":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Encoding (memory); Computer science; Scheme (mathematics); Resistive touchscreen; Resistive random-access memory; Computer architecture; Electronic engineering; Computer hardware; Electrical engineering; Engineering; Mathematics; Telecommunications; Voltage; Artificial intelligence; Operating system","score_opus":0.1042182481214511,"score_gpt":0.3506830489022152,"score_spread":0.2464648007807641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206372195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018924957,0.00030442583,0.9762264,0.00003479143,0.0013013646,0.0006882699,0.000013461029,0.0007458488,0.0017604716],"genre_scores_gemma":[0.50376743,0.00001443572,0.4949136,0.00019253023,0.0002743878,0.00010137566,0.000019972596,0.00010920098,0.00060708896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975962,0.00017666242,0.00055396394,0.00081116083,0.00030443678,0.00055759394],"domain_scores_gemma":[0.9984777,0.00045581738,0.00008926548,0.000581839,0.00014822699,0.0002471464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005652856,0.00051343994,0.0006036273,0.00015642423,0.00020267737,0.00024963263,0.0004353247,0.000273886,0.00014524338],"category_scores_gemma":[0.00027247044,0.00055360986,0.00014533632,0.0002677417,0.000018700537,0.00019454765,0.00085188996,0.00095033844,0.00004393171],"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":[0.000030300282,0.000010254634,0.0000022234444,0.00016527243,0.00008404195,0.00006135505,0.0005426892,0.9154972,0.08023385,0.00006612187,0.00047618482,0.00283054],"study_design_scores_gemma":[0.00067249406,0.00013140349,0.00018683427,0.002057007,0.00017026614,0.000033445653,0.0006434144,0.2752535,0.71642256,0.0016777869,0.0005028284,0.0022484567],"about_ca_topic_score_codex":0.000007394838,"about_ca_topic_score_gemma":0.0000045815013,"teacher_disagreement_score":0.64024365,"about_ca_system_score_codex":0.00034240232,"about_ca_system_score_gemma":0.0000888512,"threshold_uncertainty_score":0.99969155},"labels":[],"label_agreement":null},{"id":"W4206727444","doi":"10.1088/2634-4386/ac4c38","title":"Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Horizon 2020 Framework Programme; Electronic Components and Systems for European Leadership; European Commission","keywords":"Neuromorphic engineering; Computer science; Activity recognition; Hyperparameter; Artificial intelligence; Edge computing; Enhanced Data Rates for GSM Evolution; Artificial neural network; Wearable computer; Machine learning; Classifier (UML); Edge device; Software deployment; Energy consumption; Human–computer interaction; Embedded system; Engineering; Software engineering","score_opus":0.055065594683705775,"score_gpt":0.24533307540327304,"score_spread":0.19026748071956726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206727444","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8901193,0.0000691384,0.10842066,0.00001819892,0.0002055157,0.0005094683,0.000050559167,0.00045283852,0.00015429255],"genre_scores_gemma":[0.997701,0.0000032246685,0.001916145,0.000027501199,0.0001428941,0.00012100219,0.000026485948,0.000054855333,0.000006928015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988472,0.000041690528,0.00028094114,0.00037066077,0.00015262686,0.00030690304],"domain_scores_gemma":[0.9992401,0.0003136032,0.000066167144,0.00026102696,0.000032642685,0.00008646357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002757621,0.0002228368,0.00029709464,0.00012673874,0.00040410843,0.000017986487,0.00016880492,0.00003808245,0.0000065286167],"category_scores_gemma":[0.00003527611,0.00027543752,0.000086723165,0.00030918323,0.000033131426,0.000052946267,0.00013033772,0.0005051551,4.528276e-7],"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":[0.00001573073,0.00009487303,0.00006783156,0.00051013386,0.00001910561,0.0000034793998,0.000095272364,0.8872514,0.09839294,0.0001995656,0.000031409527,0.013318307],"study_design_scores_gemma":[0.0007394661,0.00038465468,0.0028554194,0.000034415883,0.000032744374,0.00011235165,0.000036765545,0.98210514,0.012130276,0.00015728256,0.00095960265,0.0004518729],"about_ca_topic_score_codex":0.000002127785,"about_ca_topic_score_gemma":1.4961378e-7,"teacher_disagreement_score":0.10758164,"about_ca_system_score_codex":0.000040440827,"about_ca_system_score_gemma":0.0000082138695,"threshold_uncertainty_score":0.9999698},"labels":[],"label_agreement":null},{"id":"W4206870755","doi":"","title":"Observation of Highly Nonlinear Resistive Switching of Al2O3/TiO2-based Memristors at Cryogenic Temperature (1.5 K)","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Memristor; Materials science; Cryogenic temperature; Optoelectronics; Nonlinear system; Resistive touchscreen; Cryogenics; Temperature measurement; Electrical engineering; Physics; Composite material; Thermodynamics; Engineering","score_opus":0.013837253072844296,"score_gpt":0.21647859827668053,"score_spread":0.20264134520383623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206870755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9504078,0.001049859,0.04440268,0.0004080518,0.00036418898,0.00039885167,0.000081190454,0.00021996802,0.0026674091],"genre_scores_gemma":[0.9541161,0.00013454011,0.04428444,0.000023982218,0.000027034592,0.000014730505,0.00033809716,0.00006386648,0.000997196],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971188,0.0010471651,0.00069261406,0.00047786377,0.00041792958,0.0002456647],"domain_scores_gemma":[0.9954018,0.0009958117,0.00057946297,0.0013702139,0.0015696704,0.00008302802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018738293,0.0003317064,0.0005196442,0.00018822616,0.00014519521,0.00003731596,0.00067611225,0.00031198215,0.000022648686],"category_scores_gemma":[0.0006441966,0.0003659615,0.00024382921,0.0003342771,0.00006949211,0.000106057894,0.0004729553,0.0006666644,0.0000069547314],"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":[0.00004874621,0.00014752259,0.0013307884,0.0014793454,0.00011319719,0.0000025890147,0.0030655006,0.12267595,0.8667701,0.0013531648,0.0002755914,0.0027375093],"study_design_scores_gemma":[0.00041296752,8.049445e-7,0.0023894892,0.002445517,0.00004588854,0.0000015500115,0.000044790442,0.082918696,0.90933156,0.0003257909,0.00172876,0.00035417403],"about_ca_topic_score_codex":0.00008761044,"about_ca_topic_score_gemma":0.00021762167,"teacher_disagreement_score":0.04256147,"about_ca_system_score_codex":0.00025952948,"about_ca_system_score_gemma":0.00015953799,"threshold_uncertainty_score":0.99987924},"labels":[],"label_agreement":null},{"id":"W4210327302","doi":"10.1017/s0269888921000151","title":"Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots","year":2022,"lang":"en","type":"article","venue":"The Knowledge Engineering Review","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Norges Forskningsråd","keywords":"Neuroevolution; Computer science; Pruning; Modular design; Robustness (evolution); Artificial intelligence; Adaptability; Artificial neural network; Robot; Generalization; Machine learning; Mathematics","score_opus":0.018389255615080743,"score_gpt":0.22979279034478745,"score_spread":0.2114035347297067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210327302","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04661364,0.7231191,0.22623317,0.0004303718,0.0011212241,0.0015084799,0.00000966132,0.00065505755,0.00030929307],"genre_scores_gemma":[0.9875985,0.009200047,0.0023160907,0.00010106603,0.00021024958,0.000310931,0.0000052848595,0.000094599156,0.00016327744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991798,0.00003876794,0.00022367637,0.00020291717,0.000084002866,0.00027087118],"domain_scores_gemma":[0.9994983,0.00020437379,0.000032219832,0.0001766986,0.00002274079,0.0000656373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048651343,0.00019173119,0.00030844082,0.000048903206,0.0003268914,0.000017140645,0.00013863904,0.000016020878,0.000008920211],"category_scores_gemma":[0.00012201997,0.00016541943,0.0000638031,0.00021410204,0.000020048166,0.000044411976,0.00014723981,0.0002609261,0.0000015315085],"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":[0.0000029708006,0.0000044637018,9.943415e-7,0.0016855906,0.000019652836,0.00000184838,0.00012605559,0.975649,0.0020427187,0.0003052628,0.000115217284,0.020046197],"study_design_scores_gemma":[0.00029262158,0.00002428331,0.000050017545,0.0005458673,0.00006443538,0.0000632387,0.000018550854,0.96496624,0.00014689584,0.000013492927,0.033619955,0.00019441427],"about_ca_topic_score_codex":2.9391865e-7,"about_ca_topic_score_gemma":1.2938621e-7,"teacher_disagreement_score":0.9409848,"about_ca_system_score_codex":0.000057939957,"about_ca_system_score_gemma":0.000009382696,"threshold_uncertainty_score":0.6745609},"labels":[],"label_agreement":null},{"id":"W4210651373","doi":"10.1063/5.0073285","title":"Van der Pol oscillator based on NbO2 volatile memristor: A simulation analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Science Foundation of Tianjin City; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Memristor; Nonlinear system; Van der Pol oscillator; Capacitance; Oscillation (cell signaling); Physics; Control theory (sociology); Computer science; Electronic engineering; Engineering; Quantum mechanics; Chemistry","score_opus":0.01236681950235921,"score_gpt":0.23634717064462302,"score_spread":0.2239803511422638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210651373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7656883,0.000037713613,0.23126405,0.000010551824,0.0002955571,0.000105768195,0.00001033452,0.00007384314,0.002513859],"genre_scores_gemma":[0.9985298,8.1121306e-7,0.0010340988,0.000121766294,0.0002747783,0.0000023269959,0.000004254476,0.00002244726,0.000009726727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918044,0.000017134826,0.00026245,0.00008901331,0.00031881503,0.00013212519],"domain_scores_gemma":[0.9994551,0.00014422205,0.0001731396,0.00013471542,0.000033015553,0.000059788814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012276242,0.00011442141,0.00023920384,0.00012794933,0.0001471737,0.000012496085,0.00011240515,0.000019211071,0.000077772645],"category_scores_gemma":[0.000004692723,0.00011480514,0.00017285316,0.0005420384,0.00000792093,0.000065375556,0.000020354224,0.00034675948,0.0000032725482],"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":[0.00007880784,0.00002906918,0.000039888608,0.00000823498,0.00011545362,0.0000043793198,0.00012512255,0.9856024,0.011077199,0.00007477225,0.00009703689,0.0027476684],"study_design_scores_gemma":[0.00035720784,0.000063054715,0.00009389993,0.000004092458,0.00017273685,8.838666e-7,0.000041246483,0.98828113,0.0074430797,0.00095573155,0.0024648763,0.000122058045],"about_ca_topic_score_codex":1.8999529e-7,"about_ca_topic_score_gemma":1.6144648e-7,"teacher_disagreement_score":0.23284148,"about_ca_system_score_codex":0.00015604199,"about_ca_system_score_gemma":0.000019550787,"threshold_uncertainty_score":0.4681618},"labels":[],"label_agreement":null},{"id":"W4212913440","doi":"10.1038/s41467-022-28580-6","title":"A self-driving laboratory advances the Pareto front for material properties","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":199,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of British Columbia","funders":"Natural Resources Canada; Canada Research Chairs","keywords":"Coating; Combustion; Materials science; Yield (engineering); Multi-objective optimization; Palladium; Nafion; Work (physics); Fuel efficiency; Fabrication; Composite material; Chemical engineering; Computer science; Nanotechnology; Mechanical engineering; Automotive engineering; Mathematical optimization; Mathematics; Chemistry; Electrochemistry; Organic chemistry; Catalysis; Engineering","score_opus":0.015541965386025052,"score_gpt":0.2551439210780871,"score_spread":0.23960195569206202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212913440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7226208,0.23753248,0.0052572708,0.010146424,0.008025603,0.0044568377,0.0006218554,0.0055190944,0.005819645],"genre_scores_gemma":[0.99213344,0.00032198426,0.00675637,0.00019487736,0.00009473332,0.0004233767,0.00002504335,0.000021667885,0.000028519324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995109,0.000077672295,0.00011990018,0.00008194314,0.00007839429,0.00013119745],"domain_scores_gemma":[0.9990915,0.0001608889,0.000032225773,0.0006549291,0.000042212625,0.000018214163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001435538,0.0000817432,0.000079961115,0.000022854445,0.0009740976,0.000024979547,0.0007414871,0.000035901856,0.000009710553],"category_scores_gemma":[0.000042607808,0.000063819374,0.000032205866,0.00010937034,0.000030442532,0.000121202,0.00028754075,0.00055650156,0.0000016377998],"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":[0.00028226286,0.0006454979,0.0019095006,0.0011148998,0.0006397544,0.000007669777,0.030040532,0.45184255,0.37098554,0.0450572,0.05082044,0.046654183],"study_design_scores_gemma":[0.00011427493,0.000022540005,0.00008572009,0.00001094979,0.00001527343,0.000003656727,0.0005894158,0.0201422,0.00976957,0.00019512647,0.96891904,0.00013224555],"about_ca_topic_score_codex":3.2902145e-7,"about_ca_topic_score_gemma":0.000030854633,"teacher_disagreement_score":0.91809857,"about_ca_system_score_codex":0.0000635369,"about_ca_system_score_gemma":0.000017369734,"threshold_uncertainty_score":0.74920696},"labels":[],"label_agreement":null},{"id":"W4214519772","doi":"10.1007/978-3-030-96878-6_8","title":"Event-Based Looming Objects Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Looming; Asynchronous communication; Event (particle physics); Computer science; Artificial intelligence; Computer vision; Telecommunications; Physics; Optics","score_opus":0.026995332752361045,"score_gpt":0.26548564461994,"score_spread":0.23849031186757896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214519772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021438587,0.0010895394,0.66333115,0.00013976103,0.0014467001,0.0007292722,0.000021036378,0.000617976,0.3304807],"genre_scores_gemma":[0.9885473,0.00079747423,0.009977761,0.00032517037,0.000037970192,0.000024801646,0.000043113094,0.00001608677,0.00023032143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918276,0.000011891277,0.00035502313,0.00011142346,0.00019866286,0.00014020894],"domain_scores_gemma":[0.9990252,0.00012120337,0.00009771413,0.00064799574,0.0000626721,0.000045203262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036656475,0.00013566377,0.00012743607,0.00054694,0.00046972456,0.00009761951,0.0006363941,0.000049650185,0.000019349298],"category_scores_gemma":[0.000015256294,0.00015716108,0.000028647173,0.00023606047,0.0001912703,0.0021804862,0.00043997928,0.0004583162,0.000013032366],"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":[0.000003742845,0.0000059281733,0.0000061908386,0.000083770494,0.000004295706,6.334023e-7,0.0008375508,0.4167745,0.00020256109,0.020388085,0.000025440617,0.56166726],"study_design_scores_gemma":[0.00015404512,0.000026980424,0.00006897417,0.00008430592,0.000003357015,0.000010591887,0.000018218263,0.91139764,0.0004360378,0.0005999852,0.086991586,0.00020826753],"about_ca_topic_score_codex":0.000001328114,"about_ca_topic_score_gemma":0.0000053933663,"teacher_disagreement_score":0.98640347,"about_ca_system_score_codex":0.00018363714,"about_ca_system_score_gemma":0.000054515764,"threshold_uncertainty_score":0.6408843},"labels":[],"label_agreement":null},{"id":"W4220663817","doi":"10.1002/advs.202200168","title":"Mixed‐Dimensional Formamidinium Bismuth Iodides Featuring In‐Situ Formed Type‐I Band Structure for Convolution Neural Networks","year":2022,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Formamidinium; Neuromorphic engineering; Memristor; Materials science; Bismuth; Optoelectronics; Transistor; Artificial neural network; Computer science; Electronic engineering; Voltage; Electrical engineering; Chemistry; Artificial intelligence; Engineering; Halide","score_opus":0.015462907550658937,"score_gpt":0.237217495059737,"score_spread":0.22175458750907806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220663817","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9872945,0.00042773204,0.009564336,0.000023425086,0.0021440322,0.00030752562,0.000012945442,0.0001503664,0.00007512485],"genre_scores_gemma":[0.99658114,0.0000066755147,0.0031624022,0.00008864639,0.00007105005,0.000026710351,0.000021608677,0.00001849586,0.000023260138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862605,0.000014734734,0.00021107469,0.0003242277,0.0002501823,0.000573747],"domain_scores_gemma":[0.9995103,0.00012313553,0.000054052118,0.0001650934,0.000064033055,0.00008338613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022742695,0.00016259031,0.0001619233,0.0001522201,0.00069305964,0.000027459719,0.00028811587,0.000030316321,0.0000069353755],"category_scores_gemma":[0.000074686795,0.00016525983,0.000034561348,0.00096309587,0.00010792952,0.00073995703,0.00012407488,0.00034930932,4.2654298e-7],"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":[0.000045679055,0.0000036746856,0.00009108481,0.0000123559375,0.0000013167828,0.0000030212775,0.000056934936,0.77672124,0.21883851,0.00022639414,0.000016865875,0.003982953],"study_design_scores_gemma":[0.0007850069,0.00015469674,0.0029454166,0.000021609136,0.0000051073316,0.000056352324,0.0001641422,0.82730985,0.16614273,0.0014801256,0.0005998357,0.00033513657],"about_ca_topic_score_codex":6.732862e-7,"about_ca_topic_score_gemma":0.000012504021,"teacher_disagreement_score":0.052695777,"about_ca_system_score_codex":0.0002075463,"about_ca_system_score_gemma":0.0000369719,"threshold_uncertainty_score":0.67391},"labels":[],"label_agreement":null},{"id":"W4220703584","doi":"10.1016/j.isci.2022.104119","title":"A 3D-printed neuromorphic humanoid hand for grasping unknown objects","year":2022,"lang":"en","type":"article","venue":"iScience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Humanoid robot; Computer science; Artificial intelligence; Robot; Biomimetics; Robotics; SIGNAL (programming language); Artificial neural network","score_opus":0.03732095050782882,"score_gpt":0.25080270067424,"score_spread":0.21348175016641116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220703584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94653213,0.00025929906,0.05134555,0.000037872254,0.0008071252,0.0001978802,0.0000026608939,0.00023792512,0.0005795563],"genre_scores_gemma":[0.9987518,0.00000256052,0.00079730025,0.00014808311,0.000048707218,0.000039416234,0.0000014120207,0.000014029795,0.00019667542],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992809,0.000014153853,0.000109308,0.00020486949,0.00012639696,0.00026435236],"domain_scores_gemma":[0.9997095,0.00007248379,0.000022867109,0.00013597074,0.00001550504,0.000043711054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013602569,0.00008564757,0.00008581306,0.00005868541,0.00066426414,0.000034801582,0.0002432236,0.000009506876,0.000020024567],"category_scores_gemma":[0.000050536928,0.000091972935,0.000031888445,0.00030738747,0.000053181175,0.000117517506,0.00012359103,0.00015426149,0.000003946292],"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":[0.0000050547224,0.000007785919,0.000017262682,0.000026415995,0.0000016821423,0.000013289109,0.00037623072,0.4594321,0.5342131,0.00022981719,0.000097040225,0.005580198],"study_design_scores_gemma":[0.00054626266,0.00027659297,0.00064860797,0.000033209424,0.000008996724,0.000103856466,0.00019214429,0.685257,0.2908552,0.0011148098,0.02052423,0.00043904103],"about_ca_topic_score_codex":9.0515493e-7,"about_ca_topic_score_gemma":0.000002445782,"teacher_disagreement_score":0.24335791,"about_ca_system_score_codex":0.000037145448,"about_ca_system_score_gemma":0.000016326934,"threshold_uncertainty_score":0.510905},"labels":[],"label_agreement":null},{"id":"W4220847857","doi":"10.1002/aisy.202200001","title":"Memristors with Initial Low‐Resistive State for Efficient Neuromorphic Systems","year":2022,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Key Laboratory of Advanced Functional Materials of Jiangsu Province; Collaborative Innovation Center of Suzhou Nano Science and Technology; King Abdullah University of Science and Technology; Priority Academic Program Development of Jiangsu Higher Education Institutions; State Administration of Foreign Experts Affairs; Technology Agency of the Czech Republic; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Memristor; Initialization; Computer science; Artificial neural network; Resistive touchscreen; Process (computing); Electronic engineering; Computer architecture; Artificial intelligence; Engineering","score_opus":0.02395605729753216,"score_gpt":0.24069489455032042,"score_spread":0.21673883725278825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220847857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5397571,0.0026643144,0.44711247,0.000009962359,0.006613467,0.0023807192,0.0002078489,0.0007741037,0.0004800212],"genre_scores_gemma":[0.9983076,0.00002395912,0.00017424478,0.000023931463,0.0001651499,0.00068599475,0.000041982894,0.000115483024,0.00046168183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997936,0.000109416455,0.0005594193,0.0004512478,0.00038762458,0.0005563078],"domain_scores_gemma":[0.99893314,0.00030744215,0.00017357341,0.00033898753,0.0001059877,0.00014089346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002482706,0.0003453993,0.00043832388,0.00015567122,0.00045056545,0.000047700098,0.0002825421,0.00003193275,0.0000107396345],"category_scores_gemma":[0.000030573403,0.00033048788,0.00009243308,0.00033478288,0.000046135945,0.00010471219,0.00007386842,0.0003500229,0.000015410824],"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":[0.00021137441,0.000036820824,0.0000112394255,0.00037826688,0.00005252409,0.000057573827,0.00031978812,0.9929354,0.0045799227,0.00037660284,0.00014040738,0.00090006134],"study_design_scores_gemma":[0.0013113398,0.0012841284,0.000019891588,0.00039493392,0.000060258586,0.00029474474,0.0031221528,0.8788985,0.044275176,0.00005018462,0.06912659,0.0011621079],"about_ca_topic_score_codex":0.000012762818,"about_ca_topic_score_gemma":0.0000020698608,"teacher_disagreement_score":0.45855048,"about_ca_system_score_codex":0.00038537473,"about_ca_system_score_gemma":0.000030475054,"threshold_uncertainty_score":0.9999147},"labels":[],"label_agreement":null},{"id":"W4220855081","doi":"10.3389/felec.2022.825077","title":"Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo; York University; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; CHIST-ERA; Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Agence Nationale de la Recherche; Canada Research Chairs; Government of Canada; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Computer science; Robustness (evolution); Overfitting; Resistive random-access memory; Artificial neural network; Memristor; Neuromorphic engineering; Implementation; Artificial intelligence; Spiking neural network; MNIST database; Inefficiency; Deep learning; Computer engineering; Computer architecture; Machine learning; Electronic engineering; Engineering; Voltage; Electrical engineering; Software engineering","score_opus":0.006919338303344787,"score_gpt":0.20907404987737493,"score_spread":0.20215471157403014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220855081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65631425,0.005548614,0.3367692,0.000015516844,0.0006850718,0.00025308813,0.000011971723,0.00013217548,0.00027010005],"genre_scores_gemma":[0.99115795,0.00006653223,0.0084954435,0.000013058113,0.00005509163,0.0000646832,0.000017191931,0.000042037085,0.00008801314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989008,0.000046297784,0.0002912445,0.00017718422,0.00014679061,0.00043772778],"domain_scores_gemma":[0.9996168,0.00015262705,0.00008241892,0.00010173021,0.00002130456,0.000025133471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039347008,0.00014040136,0.00026546343,0.00015378668,0.00028513462,0.000008407522,0.00015639563,0.000025907118,0.0000032917426],"category_scores_gemma":[0.00009480545,0.00016831877,0.00006702561,0.00025264538,0.000017278755,0.000055503075,0.00007103181,0.00054763455,9.543023e-8],"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":[0.000075263095,0.000011118261,0.00046506213,0.000108985485,0.000023187898,0.0000026322728,0.001567437,0.9836467,0.006707925,0.00038011273,0.00010115574,0.006910418],"study_design_scores_gemma":[0.00054543244,0.00023940235,0.000037992402,0.000038003127,0.000014811192,0.000004926988,0.0044182152,0.9340548,0.054201685,0.0020754093,0.004127475,0.00024187799],"about_ca_topic_score_codex":0.0000036265646,"about_ca_topic_score_gemma":0.000005580952,"teacher_disagreement_score":0.33484367,"about_ca_system_score_codex":0.00034525237,"about_ca_system_score_gemma":0.000034826648,"threshold_uncertainty_score":0.686384},"labels":[],"label_agreement":null},{"id":"W4220918847","doi":"10.1038/s42256-022-00452-0","title":"Biological underpinnings for lifelong learning machines","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":225,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lifelong learning; Computer science; Artificial intelligence; Set (abstract data type); Bridge (graph theory); Biological organism; Perspective (graphical); Cognitive science; Human–computer interaction; Biochemical engineering; Engineering; Psychology; Biology; Biological materials","score_opus":0.01665808754806814,"score_gpt":0.27982117242493176,"score_spread":0.26316308487686363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220918847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29255846,0.009771495,0.6908637,0.00051149033,0.002282123,0.00054851075,0.000036919402,0.0016742215,0.0017530612],"genre_scores_gemma":[0.99591905,0.00005506483,0.00303233,0.0004501233,0.00021942121,0.00004972818,0.0000387588,0.000035482426,0.00020005701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990201,0.000047596674,0.00021403965,0.00027000593,0.00014301557,0.00030526205],"domain_scores_gemma":[0.9993679,0.00037767194,0.000043972635,0.00012566226,0.000027412325,0.000057396057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025060333,0.0001904278,0.00018153388,0.00008472853,0.00044872798,0.00002035909,0.00032105538,0.00009995804,0.00012963041],"category_scores_gemma":[0.00022431741,0.00017348028,0.00009771766,0.000288515,0.000028416101,0.000065494954,0.00015661387,0.0017182855,0.0000061011997],"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":[0.00005938659,0.000019734236,0.0010928041,0.00004847387,0.00002462629,0.000019318493,0.00021625235,0.8722271,0.009721805,0.003733508,0.0003969866,0.112440035],"study_design_scores_gemma":[0.00021697966,0.00045821883,0.00039392902,0.000027942973,0.000016192069,0.00017216039,0.00023329713,0.81289124,0.054600958,0.012792448,0.11744101,0.0007555996],"about_ca_topic_score_codex":0.0000024159417,"about_ca_topic_score_gemma":0.0000016444139,"teacher_disagreement_score":0.70336056,"about_ca_system_score_codex":0.00006182606,"about_ca_system_score_gemma":0.0000073134124,"threshold_uncertainty_score":0.7465191},"labels":[],"label_agreement":null},{"id":"W4220988501","doi":"10.1088/1674-1056/ac5e95","title":"Memristor hyperchaos in a generalized Kolmogorov-type system with extreme multistability","year":2022,"lang":"en","type":"article","venue":"Chinese Physics B","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Multistability; Memristor; Computer science; Chaotic; Bistability; Bifurcation diagram; Topology (electrical circuits); Control theory (sociology); Bifurcation; Statistical physics; Applied mathematics; Physics; Mathematics; Artificial intelligence; Nonlinear system; Quantum mechanics","score_opus":0.023473467574442096,"score_gpt":0.22373397623771635,"score_spread":0.20026050866327424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220988501","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992946,0.00021810368,0.0054168925,0.000007600161,0.00037455736,0.00021240168,0.00001078995,0.00033576114,0.00047785082],"genre_scores_gemma":[0.99885267,0.0000024861695,0.00084640895,0.000018772143,0.00014224926,0.000046952475,0.000016022559,0.000041651012,0.000032758664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999171,0.000058499714,0.00017226869,0.00022415425,0.00015730917,0.00021672566],"domain_scores_gemma":[0.99957126,0.000057185844,0.000030043982,0.00026946087,0.00002343489,0.000048583268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010136854,0.00017914186,0.00024535277,0.00003284658,0.00012936581,0.000008875542,0.00014859645,0.00001649827,0.000014956444],"category_scores_gemma":[0.0000106569405,0.00015477995,0.000041197527,0.00045193208,0.000019024428,0.00010490813,0.000080799844,0.0002929335,0.0000053470585],"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":[0.00010812478,0.00005961769,0.0046953075,0.00021680762,0.00001647046,0.00006243522,0.00090292864,0.93009883,0.061202597,0.00043488917,0.00002829017,0.0021737283],"study_design_scores_gemma":[0.0022944019,0.00014788362,0.0048965714,0.000055749322,0.000017316788,0.000067649424,0.00043198076,0.98287755,0.0066682315,0.00092884793,0.00082169427,0.0007921505],"about_ca_topic_score_codex":0.000029025872,"about_ca_topic_score_gemma":0.000023190958,"teacher_disagreement_score":0.05453437,"about_ca_system_score_codex":0.00025766133,"about_ca_system_score_gemma":0.000015550875,"threshold_uncertainty_score":0.63117427},"labels":[],"label_agreement":null},{"id":"W4221104088","doi":"10.1002/anie.202116175","title":"Estimating Phosphorescent Emission Energies in Ir<sup>III</sup> Complexes Using Large‐Scale Quantum Computing Simulations**","year":2022,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 British Columbia; OTI Lumionics (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Qubit; Quantum computer; Phosphorescence; Quantum; Ab initio; Coupled cluster; Computer science; Density functional theory; Quantum chemistry; Scale (ratio); Quantum algorithm; Statistical physics; Computational science; Algorithm; Chemistry; Physics; Computational chemistry; Quantum mechanics; Molecule","score_opus":0.019591859157511212,"score_gpt":0.27455607787055414,"score_spread":0.2549642187130429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221104088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9275365,0.0001349141,0.070413254,0.000057432466,0.0012405602,0.00011781955,0.000075618176,0.00022559335,0.00019830973],"genre_scores_gemma":[0.9950634,0.0000071040927,0.0034353589,0.00008910369,0.0008363072,0.000009830533,0.00050106755,0.00003432107,0.00002345805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986192,0.000030255298,0.0004125064,0.0002682018,0.00037891022,0.00029092302],"domain_scores_gemma":[0.9994982,0.00015592296,0.00010729543,0.000117461575,0.00006726533,0.00005385534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019092047,0.00018752988,0.00018430557,0.00016748886,0.00036494544,0.00004684976,0.00020291205,0.000046688474,0.00017917356],"category_scores_gemma":[0.000056644138,0.000224388,0.00006663384,0.00025266514,0.000023285103,0.00032101004,0.00019456248,0.00036800923,0.0000019029878],"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":[0.000020676953,0.00004592929,0.00053123786,0.000026108828,0.000012304396,0.000012003026,0.00096328126,0.87054044,0.12695657,0.000053328145,0.00043162264,0.0004064983],"study_design_scores_gemma":[0.0004592043,0.000010781218,0.00015744506,0.00009345875,0.0000055098726,0.00001997527,0.0006495346,0.685471,0.3119198,0.0004095692,0.0006402676,0.00016345593],"about_ca_topic_score_codex":0.000023852648,"about_ca_topic_score_gemma":0.0000046737,"teacher_disagreement_score":0.18506944,"about_ca_system_score_codex":0.0004461447,"about_ca_system_score_gemma":0.000017757111,"threshold_uncertainty_score":0.91502774},"labels":[],"label_agreement":null},{"id":"W4221154635","doi":"10.1109/ted.2023.3244133","title":"Memristor-Based Cryogenic Programmable DC Sources for Scalable In Situ Quantum-Dot Control","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; École Centrale de Lyon; Institut National des Sciences Appliquées de Lyon; Canada First Research Excellence Fund; Natural Science Foundation of Beijing Municipality; Centre National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Indian National Science Academy","keywords":"Memristor; Quantum dot; Cryostat; Optoelectronics; Computer science; Scalability; Voltage; Biasing; Physics; Electrical engineering; Materials science; Nanotechnology; Electronic engineering; Engineering; Superconductivity; Condensed matter physics","score_opus":0.016095747478171957,"score_gpt":0.2528493248458857,"score_spread":0.2367535773677137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221154635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76373214,0.00033047676,0.23390041,0.00011110499,0.00028657087,0.00057115924,0.000011023374,0.0009850347,0.00007208516],"genre_scores_gemma":[0.99902904,0.00003130079,0.0002580093,0.00007997226,0.000051904713,0.0003030764,0.0000064384335,0.000056141133,0.0001841449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998646,0.000034156506,0.00026499745,0.00027837444,0.00013743914,0.00063902244],"domain_scores_gemma":[0.9993899,0.00030427473,0.000038593553,0.00016628733,0.000029616558,0.00007133433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019793893,0.00021684208,0.00025469458,0.00027285458,0.00020063792,0.000038056525,0.0001500649,0.000084235064,0.000011781764],"category_scores_gemma":[0.000004456812,0.00022617615,0.00012991716,0.0006537965,0.00002395338,0.00017372935,3.9110563e-7,0.00028973186,0.00004392989],"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":[0.000109874185,0.000045576602,0.0000187881,0.00012041304,0.00003241592,0.0000036850854,0.00004852953,0.792697,0.20228063,0.000015256334,0.00003705734,0.004590752],"study_design_scores_gemma":[0.0013074466,0.00028770644,0.00006455305,0.000056326404,0.000040089173,0.000002657144,0.000050333492,0.24759418,0.7469179,0.0001420693,0.003250025,0.000286715],"about_ca_topic_score_codex":0.000011209743,"about_ca_topic_score_gemma":0.00044875182,"teacher_disagreement_score":0.54510283,"about_ca_system_score_codex":0.00011658361,"about_ca_system_score_gemma":0.000033582714,"threshold_uncertainty_score":0.9223196},"labels":[],"label_agreement":null},{"id":"W4221167508","doi":"10.3389/fnins.2022.983950","title":"Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Waterloo; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; European Research Council; Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Hebbian theory; Synaptic plasticity; Plasticity; Neuroscience; Probabilistic logic; Computer science; Artificial intelligence; Psychology; Chemistry; Artificial neural network; Materials science","score_opus":0.011695143744638637,"score_gpt":0.20503120701481536,"score_spread":0.19333606327017672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221167508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74040014,0.000019296886,0.25355077,0.00006760121,0.004778128,0.00045854243,0.000053847183,0.00041394887,0.0002577399],"genre_scores_gemma":[0.9985822,0.000004559403,0.0006047864,0.00058421254,0.000051112653,0.000060357946,0.0000033855458,0.000046211582,0.000063189385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975549,0.00012746846,0.000339832,0.0006852772,0.00062125013,0.0006712655],"domain_scores_gemma":[0.99932873,0.00017353776,0.0000554357,0.00025649363,0.000015747588,0.0001700295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018223778,0.00029075044,0.00029351772,0.00031817466,0.000512474,0.000061061466,0.0006533557,0.000040969,0.00003959314],"category_scores_gemma":[0.00036786738,0.00032647152,0.000072022805,0.00076834473,0.00016080297,0.00018513345,0.00019441098,0.00080196245,0.0000039934644],"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":[0.00007068609,0.000093091265,0.00025981135,0.000042805656,0.0000015721355,0.00028294208,0.000035393976,0.89151734,0.10697167,0.000022921295,0.00015768415,0.0005440999],"study_design_scores_gemma":[0.00060974737,0.000272235,0.0016728034,0.000019386409,0.000012714945,0.000031220956,0.00004741912,0.9887716,0.007784876,0.00015549794,0.00030418922,0.0003183156],"about_ca_topic_score_codex":0.0000033429471,"about_ca_topic_score_gemma":0.0000022504737,"teacher_disagreement_score":0.25818208,"about_ca_system_score_codex":0.00022025203,"about_ca_system_score_gemma":0.000058967627,"threshold_uncertainty_score":0.99991876},"labels":[],"label_agreement":null},{"id":"W4224085244","doi":"10.1088/2058-8585/ac6783","title":"Cu <sub> <i>x</i> </sub> S thin films for printed memory cells and temperature sensors","year":2022,"lang":"en","type":"article","venue":"Flexible and Printed Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Bayerische Forschungsallianz; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Materials science; Analytical Chemistry (journal); Chemistry","score_opus":0.007963479037939451,"score_gpt":0.2125583206075675,"score_spread":0.20459484156962804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224085244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99430215,0.0029005352,0.001181607,0.00007840475,0.00028225154,0.00044216766,0.00003243809,0.00045319734,0.00032727423],"genre_scores_gemma":[0.99740255,0.00093735603,0.00066036615,0.00029548703,0.000080534,0.00006650549,0.000035909365,0.00006649774,0.00045478027],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988146,0.000040831907,0.00021068432,0.00032449685,0.00011552147,0.0004938897],"domain_scores_gemma":[0.99952275,0.00012448237,0.000041599174,0.00018500286,0.000031863397,0.00009429935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019423792,0.00023254882,0.00022870355,0.000083548686,0.00037940542,0.000039120285,0.00011868243,0.00007859906,0.000014548772],"category_scores_gemma":[0.000017772085,0.00024591477,0.00005839574,0.00022550378,0.000028725233,0.000102126134,0.00012740244,0.0007244348,0.000002118292],"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":[0.00008591568,0.000014983682,0.0000059372496,0.00013706248,0.000038674134,0.000006209978,0.00024119254,0.097413294,0.8949314,0.00041858794,0.0012826418,0.005424099],"study_design_scores_gemma":[0.00054020603,0.00020829703,0.00001589567,0.000020257638,0.000022971995,0.00007319645,0.00016953918,0.047366977,0.93054265,0.00059031695,0.020150268,0.000299401],"about_ca_topic_score_codex":8.279808e-7,"about_ca_topic_score_gemma":0.0000033546148,"teacher_disagreement_score":0.050046314,"about_ca_system_score_codex":0.000059852886,"about_ca_system_score_gemma":0.000034121804,"threshold_uncertainty_score":0.9999993},"labels":[],"label_agreement":null},{"id":"W4224127714","doi":"10.21203/rs.3.rs-1548156/v1","title":"Medium-Temperature-Oxidized GeO Resistive-Switching Random-Access Memory and Its Applicability in Processing-in-Memory Computing","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Seoul National University; National Research Foundation","keywords":"Resistive random-access memory; Von Neumann architecture; Computer science; Interconnection; Latency (audio); Non-volatile memory; Computer architecture; In-Memory Processing; Reliability (semiconductor); Embedded system; Computer hardware; Electrical engineering; Power (physics); Engineering; Operating system; Voltage; Computer network; Search engine; Telecommunications","score_opus":0.05025410407982473,"score_gpt":0.38330024157435627,"score_spread":0.33304613749453155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224127714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862394,0.008819823,0.00036062425,0.00016346783,0.0003573324,0.002738736,0.000027403268,0.00041936344,0.0008739035],"genre_scores_gemma":[0.9982051,0.00048466958,0.00026245468,0.000025195543,0.00034765858,0.0004325722,0.000058648253,0.00012632964,0.000057365338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99400675,0.0011529537,0.00096134073,0.0014313379,0.0011651274,0.0012825087],"domain_scores_gemma":[0.9972658,0.001435007,0.0001408191,0.0006637932,0.0002250337,0.0002695371],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0057039987,0.0005995413,0.0011108466,0.0009381563,0.0005790682,0.0003325698,0.0010620337,0.00041445455,0.00006346558],"category_scores_gemma":[0.0010871547,0.00063965627,0.00013573299,0.0013007316,0.00011538248,0.00036723356,0.0034130488,0.007356039,0.0000037872464],"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":[0.0015145887,0.00027627574,0.0032170345,0.027601665,0.000053704665,0.0008137979,0.0056424984,0.83725464,0.090409376,0.00004142856,0.00009266704,0.033082332],"study_design_scores_gemma":[0.020785969,0.00034460556,0.12170459,0.016826747,0.00006676484,0.000087368644,0.008400218,0.71476525,0.103821784,0.0071144886,0.00086442893,0.0052177887],"about_ca_topic_score_codex":0.00009351711,"about_ca_topic_score_gemma":0.00016532905,"teacher_disagreement_score":0.12248939,"about_ca_system_score_codex":0.0007912952,"about_ca_system_score_gemma":0.00031975252,"threshold_uncertainty_score":0.9996055},"labels":[],"label_agreement":null},{"id":"W4224261208","doi":"10.5121/csit.2022.121010","title":"An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Artificial neural network; Nervous system network models; Interpretability; Spiking neural network; Computer science; Physical neural network; Artificial Intelligence System; Biological neural network; Expansive; Types of artificial neural networks; Time delay neural network; Deep learning; Machine learning","score_opus":0.06579722510460273,"score_gpt":0.2958524534794572,"score_spread":0.23005522837485448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224261208","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8893874,0.011196386,0.09598163,0.00029993473,0.0021494667,0.0004995137,0.000014637886,0.00038497063,0.000086016866],"genre_scores_gemma":[0.9915795,0.0006506173,0.0048874714,0.0010855667,0.0017138609,0.000023401586,0.000026101885,0.000030444548,0.0000030551557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977492,0.00020935675,0.0007558822,0.0005184487,0.0002158296,0.00055125606],"domain_scores_gemma":[0.9990617,0.0002670368,0.00009629,0.00036129425,0.00004159516,0.00017207238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056571583,0.00027324498,0.00046492452,0.000052912856,0.00028411337,0.000029285462,0.00039922848,0.000049927883,0.00044912612],"category_scores_gemma":[0.00009415003,0.00026073665,0.00008243338,0.00061756384,0.00008252394,0.0001560104,0.00035264314,0.00053732353,0.000006563918],"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":[0.000047203786,0.000018216047,0.00013144429,0.000080377904,0.000008645405,0.000012373514,0.00007500569,0.77461994,0.005918063,0.0018139,0.00014618805,0.21712866],"study_design_scores_gemma":[0.00003531815,0.0010117021,0.00069554633,0.0006308468,0.00005747015,0.0000617391,0.0006129573,0.93568313,0.032406732,0.025049878,0.002570528,0.0011841727],"about_ca_topic_score_codex":0.000012479102,"about_ca_topic_score_gemma":0.000014976205,"teacher_disagreement_score":0.21594448,"about_ca_system_score_codex":0.000042075168,"about_ca_system_score_gemma":0.0000105495765,"threshold_uncertainty_score":0.9999845},"labels":[],"label_agreement":null},{"id":"W4226227766","doi":"10.1109/tcsii.2022.3157789","title":"CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"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; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encoding (memory); Reduction (mathematics); Thresholding; Range (aeronautics); Effective number of bits; Gaussian; Artificial intelligence; Computer hardware; Algorithm; Image (mathematics); Electronic engineering; Mathematics; CMOS; Engineering","score_opus":0.061229102749464,"score_gpt":0.2913411391087209,"score_spread":0.2301120363592569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226227766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0149765555,0.00019699488,0.9793772,0.00001952531,0.0029394927,0.0011951077,0.00017613666,0.0007787464,0.000340206],"genre_scores_gemma":[0.9954373,0.000003976681,0.0022998655,0.000118841446,0.00016101223,0.0008739397,0.0000036828837,0.0001281097,0.0009732283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99696267,0.00043967244,0.0006569241,0.00067451654,0.00061084284,0.0006554009],"domain_scores_gemma":[0.99850756,0.0004895532,0.00011228393,0.0005341917,0.00008768706,0.00026871185],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00069552177,0.00041288752,0.00049383094,0.0003023106,0.0018606769,0.00011913097,0.00047039677,0.000093924806,0.000086873304],"category_scores_gemma":[0.00001849613,0.00049679424,0.00013864045,0.00063310046,0.000028798746,0.0003852718,0.00001397505,0.0007340566,0.00003362371],"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":[0.000046958536,0.000047572772,2.8128767e-7,0.00006320227,0.000065321474,0.000025840896,0.0016475237,0.7645441,0.2303877,0.00003656861,0.0004124483,0.0027224293],"study_design_scores_gemma":[0.003583351,0.0016821512,0.000038577837,0.00123152,0.0003073296,0.0004827474,0.0033222898,0.22381316,0.742568,0.0001444015,0.019615667,0.0032107944],"about_ca_topic_score_codex":0.000032232627,"about_ca_topic_score_gemma":0.0000021129838,"teacher_disagreement_score":0.98046076,"about_ca_system_score_codex":0.000589939,"about_ca_system_score_gemma":0.00006362007,"threshold_uncertainty_score":0.99974835},"labels":[],"label_agreement":null},{"id":"W4226340880","doi":"10.1145/3514253","title":"SyncNN: Evaluating and Accelerating Spiking Neural Networks on FPGAs","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Reconfigurable Technology and Systems","topic":"Advanced Memory and Neural Computing","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":"Simon Fraser University","funders":"","keywords":"Computer science; Spiking neural network; Asynchronous communication; Field-programmable gate array; Scalability; Parallel computing; Quantization (signal processing); Encoding (memory); Computation; Artificial neural network; Embedded system; Artificial intelligence; Algorithm","score_opus":0.03839060559782862,"score_gpt":0.26256308181053833,"score_spread":0.22417247621270972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226340880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97400105,0.0018788582,0.020655526,0.00024400758,0.0010725225,0.0003839246,0.0000069768253,0.0009272528,0.0008299089],"genre_scores_gemma":[0.99927676,0.00006697519,0.0002192294,0.000068624926,0.000043399752,0.00014989307,0.0000022037002,0.000033015564,0.00013988189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883616,0.00008048321,0.0003036031,0.00033222826,0.00011448304,0.00033303964],"domain_scores_gemma":[0.99927264,0.0002795898,0.00006375487,0.00031601265,0.000018393985,0.000049589064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033175683,0.00020481404,0.00026531867,0.00033629304,0.0011363309,0.000050184437,0.00018434174,0.00012596812,0.000039836956],"category_scores_gemma":[0.000029338606,0.00021722462,0.00003346596,0.00041420636,0.000048782655,0.00011259708,0.000011145405,0.0010121927,0.0000018374319],"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":[0.000016442023,0.0000098273085,0.000049815735,0.00003972828,0.000026372138,0.000012160672,0.000056768415,0.83380175,0.003433786,0.00033108797,0.000007153449,0.16221513],"study_design_scores_gemma":[0.00043716535,0.00046987867,0.000039124297,0.00009713333,0.00002483366,0.00047878543,0.0012728748,0.99138236,0.0046900865,0.00043526661,0.00037710412,0.00029536212],"about_ca_topic_score_codex":0.0000049146734,"about_ca_topic_score_gemma":0.0000024012286,"teacher_disagreement_score":0.16191977,"about_ca_system_score_codex":0.00005686293,"about_ca_system_score_gemma":0.000005538409,"threshold_uncertainty_score":0.8858163},"labels":[],"label_agreement":null},{"id":"W4226382868","doi":"10.22215/etd/2022-14857","title":"Domain-Specific Analog Accelerators for Artificial Intelligent Algorithms Implementation","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Implementation; Computer science; Algorithm; Analogue electronics; Artificial neural network; Computer engineering; Artificial intelligence; Electronic circuit; Engineering; Electrical engineering","score_opus":0.040219865842606325,"score_gpt":0.33375046793124313,"score_spread":0.2935306020886368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226382868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5617576,0.0012941059,0.4177015,0.00001855635,0.010601325,0.0021770592,0.00020312855,0.0010219944,0.0052246773],"genre_scores_gemma":[0.81091505,0.0022358005,0.096877985,0.00042450745,0.007979,0.0037572458,0.06376139,0.0014133134,0.012635681],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998799,0.000015579993,0.0004299949,0.00029424217,0.0001711497,0.0002900349],"domain_scores_gemma":[0.99960965,0.00007083186,0.00007927296,0.00014341187,0.00004306369,0.000053792493],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000112173766,0.00025779352,0.00023346102,0.00016957107,0.00024118248,0.00005043245,0.00015100809,0.000088124194,0.0015143356],"category_scores_gemma":[0.0000015564871,0.00028362067,0.00014407851,0.00018868115,0.000005214238,0.0000948848,0.000015163925,0.0002662176,0.000012890551],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013265568,0.00004034457,0.0000067019782,0.00035701762,0.00014801086,0.000014350765,0.0023903355,0.024861673,0.066849306,0.01642558,0.006223889,0.8825501],"study_design_scores_gemma":[0.00028723097,0.0001555679,0.00007389558,0.000022201551,0.00003893972,0.0000033460738,0.016656954,0.0017066302,0.525682,0.0059173377,0.44865832,0.000797581],"about_ca_topic_score_codex":0.000002791827,"about_ca_topic_score_gemma":0.00008870091,"teacher_disagreement_score":0.88175255,"about_ca_system_score_codex":0.00016238749,"about_ca_system_score_gemma":0.000019151095,"threshold_uncertainty_score":0.9999616},"labels":[],"label_agreement":null},{"id":"W4229447288","doi":"10.3390/fi14050146","title":"A Survey on Memory Subsystems for Deep Neural Network Accelerators","year":2022,"lang":"en","type":"article","venue":"Future Internet","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"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":"Computer science; Artificial neural network; Computer architecture; Application-specific integrated circuit; Memory map; In-Memory Processing; Computation; Deep learning; Artificial intelligence; Computer engineering; Embedded system; Semiconductor memory; Computer hardware; Programming language","score_opus":0.02223901424306333,"score_gpt":0.2382716479618115,"score_spread":0.21603263371874817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229447288","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9819197,0.0009042598,0.0034761291,0.00004274322,0.012077774,0.00042835716,0.000055812303,0.00046456396,0.000630649],"genre_scores_gemma":[0.9966896,0.0000021474198,0.00009497701,0.00031350672,0.0023136938,0.0000759891,0.00008637406,0.00005153585,0.00037217175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905974,0.00008808887,0.00020224074,0.00020957115,0.00012374383,0.00031664825],"domain_scores_gemma":[0.99952114,0.00018479205,0.000041036827,0.00017455302,0.000019416791,0.00005904617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023854089,0.00017069903,0.00018903713,0.00004041633,0.0001240869,0.000027492175,0.00025491542,0.00003974113,0.00010357993],"category_scores_gemma":[0.00001498097,0.00016876882,0.00008061693,0.00016396419,0.000007275744,0.000056920388,0.00007385961,0.00035268452,0.000009706784],"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":[0.00015798361,0.0000150740425,0.0005508483,0.000047784924,0.00004250778,0.000022342541,0.0003844584,0.929496,0.00036528008,0.00015158503,0.05772887,0.011037274],"study_design_scores_gemma":[0.0014780684,0.0009041322,0.0099520385,0.000051709747,0.000026356764,0.00006962427,0.0005335326,0.76863813,0.007915331,0.0001607511,0.20917802,0.0010922858],"about_ca_topic_score_codex":0.000007927096,"about_ca_topic_score_gemma":0.0000781724,"teacher_disagreement_score":0.16085784,"about_ca_system_score_codex":0.0000756897,"about_ca_system_score_gemma":0.0000038222724,"threshold_uncertainty_score":0.68821925},"labels":[],"label_agreement":null},{"id":"W4230012243","doi":"10.3410/f.14071956.15726105","title":"Faculty Opinions recommendation of Distinct neuronal coding schemes in memory revealed by selective erasure of fast synchronous synaptic transmission.","year":2012,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Erasure; Coding (social sciences); Computer science; Erasure code; Transmission (telecommunications); Neuroscience; Computer network; Biology; Telecommunications; Decoding methods; Mathematics","score_opus":0.020472449110523663,"score_gpt":0.30714595539376827,"score_spread":0.2866735062832446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230012243","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001913405,0.0030352941,0.00066668633,0.009166796,0.0005215203,0.0010023888,0.9855038,0.00005633849,0.000028034641],"genre_scores_gemma":[0.0011941389,0.00036269016,0.0006530109,0.0002930455,0.00014195213,0.000085177926,0.99715036,0.000036106598,0.000083537474],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99641937,0.00036242531,0.0015796843,0.00045899674,0.0007845261,0.00039497775],"domain_scores_gemma":[0.99656415,0.00027715997,0.0008679784,0.00064760115,0.0014237852,0.00021931659],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082927727,0.0005232533,0.0010657132,0.00028904004,0.0001309407,0.000028695824,0.0008819648,0.00048319303,0.0001581405],"category_scores_gemma":[0.0019324335,0.00037326376,0.0003828899,0.0016003808,0.00020000178,0.00033728126,0.00016736174,0.0013478424,0.000006220082],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000148712625,0.00020490398,0.000006795822,0.012073689,0.00011081099,2.3731978e-7,0.00013249472,0.000012128687,0.00015120079,0.000016795373,0.9815243,0.0057517854],"study_design_scores_gemma":[0.0005324811,0.00008333754,0.0006315651,0.012406043,0.00012434309,0.000026121681,0.000018984427,0.00016516846,0.0005369742,0.0000066894117,0.9851369,0.00033135994],"about_ca_topic_score_codex":0.000009375738,"about_ca_topic_score_gemma":0.0000015440187,"teacher_disagreement_score":0.011646532,"about_ca_system_score_codex":0.00014410753,"about_ca_system_score_gemma":0.00016743212,"threshold_uncertainty_score":0.9998719},"labels":[],"label_agreement":null},{"id":"W4233443202","doi":"10.1109/aspdac.2018.8297384","title":"Fully parallel RRAM synaptic array for implementing binary neural network with (+1, −1) weights and (+1, 0) neurons","year":2018,"lang":"en","type":"article","venue":"2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":78,"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; National Science Foundation","keywords":"Computer science; Resistive random-access memory; Binary number; Convolutional neural network; Sense amplifier; Artificial neural network; MNIST database; Offset (computer science); Efficient energy use; Deep learning; Artificial intelligence; Computer hardware; Voltage; Electrical engineering; Mathematics; Engineering","score_opus":0.03485248842197626,"score_gpt":0.24045849676865808,"score_spread":0.2056060083466818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233443202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36299178,0.00013240319,0.6336566,0.00013002714,0.00039046613,0.0008539516,0.000015609961,0.00063168124,0.0011974777],"genre_scores_gemma":[0.96654445,0.000024041032,0.03271458,0.00004575446,0.00033590166,0.000073753356,0.000025171063,0.000054228836,0.00018211709],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980749,0.00011033553,0.00042020326,0.0004964748,0.00017542165,0.0007226956],"domain_scores_gemma":[0.9990214,0.00023108984,0.00016761343,0.00026771115,0.00012204847,0.00019011182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032369097,0.00038987584,0.0003680105,0.00010976604,0.0006676814,0.00020972075,0.00014919847,0.00011474181,0.00004443775],"category_scores_gemma":[0.00003258493,0.00033917755,0.000048873102,0.00020353623,0.00018827397,0.00043466248,0.000043381373,0.00020473597,0.000015990017],"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":[0.0040442008,0.00047466438,0.021225309,0.004603752,0.0025822646,0.00032333538,0.088864125,0.2197891,0.32942787,0.040124267,0.023722174,0.26481894],"study_design_scores_gemma":[0.0016026681,0.0012199837,0.0040425216,0.00026288547,0.00012367357,0.00011068954,0.0024688328,0.98185134,0.002742114,0.0019653046,0.0026689125,0.0009410578],"about_ca_topic_score_codex":0.0000011303771,"about_ca_topic_score_gemma":0.0000037186621,"teacher_disagreement_score":0.76206225,"about_ca_system_score_codex":0.000022229067,"about_ca_system_score_gemma":0.00004170362,"threshold_uncertainty_score":0.999906},"labels":[],"label_agreement":null},{"id":"W4233804525","doi":"10.1007/978-3-319-56782-2_1149-2","title":"Remote Memory","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science","score_opus":0.028529250607151165,"score_gpt":0.23658415194345347,"score_spread":0.2080549013363023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233804525","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000191292,0.0005032516,0.0030958056,0.000011474564,0.00074913487,0.000074889285,0.000002511157,0.0005836091,0.9949602],"genre_scores_gemma":[0.0017355456,0.00019781811,0.0011758786,0.000049657265,0.00048515273,1.8747804e-7,0.0000055333894,0.00008104246,0.99626917],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994804,0.0000010465836,0.00013109844,0.00016043519,0.00008137658,0.00014561719],"domain_scores_gemma":[0.99942017,0.000025072404,0.000040165778,0.00044566437,0.0000138940795,0.000055036006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032718275,0.00021871942,0.00021369748,0.000040507966,0.00008542401,0.000019775156,0.00019369925,0.00016472713,0.0005256512],"category_scores_gemma":[0.0000069333737,0.0002122001,0.00008692984,0.0000017144403,0.000026219546,0.000061742656,0.00005202527,0.00035179008,0.00049586507],"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":[0.0000071544528,0.0000012232608,9.41117e-8,0.00028472306,0.00010610939,0.00031320722,0.00004218086,0.005564619,0.0019115807,0.027828278,0.015460496,0.9484803],"study_design_scores_gemma":[0.00013136616,0.000016060338,0.000001312322,0.00026910068,0.000026012609,0.00004272935,0.0000012285034,0.0038520398,0.006703695,0.0310206,0.9573277,0.0006081034],"about_ca_topic_score_codex":6.6056833e-7,"about_ca_topic_score_gemma":0.0000037714433,"teacher_disagreement_score":0.9478722,"about_ca_system_score_codex":0.000025638563,"about_ca_system_score_gemma":0.00000547187,"threshold_uncertainty_score":0.86532694},"labels":[],"label_agreement":null},{"id":"W4237618376","doi":"10.22215/etd/2016-11685","title":"Ionically-Coupled Non-Linear Variable Resistors for Neuromorphic Applications","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"U.S. Department of Energy","keywords":"Neuromorphic engineering; Memristor; Resistor; Spice; Materials science; Hysteresis; Electronic engineering; Fabrication; Optoelectronics; Computer science; Voltage; Electrical engineering; Artificial neural network; Engineering; Physics; Condensed matter physics; Artificial intelligence","score_opus":0.018521463799933618,"score_gpt":0.2584650991545887,"score_spread":0.2399436353546551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237618376","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019101119,0.00026877905,0.9399596,0.00005114796,0.0019120942,0.0019802647,0.000052981377,0.0011869444,0.035487056],"genre_scores_gemma":[0.42081282,0.00074250344,0.12518439,0.0006756949,0.0059426166,0.0058424957,0.0055567864,0.0012564574,0.43398625],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900556,0.000005569716,0.00032159063,0.00030180186,0.0000984343,0.00026707456],"domain_scores_gemma":[0.9992078,0.00025148634,0.00007001145,0.00029805757,0.000093161754,0.00007950848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077959274,0.00024033552,0.00026839925,0.000075180316,0.00017024991,0.000014162114,0.00020575145,0.00020360405,0.00010564704],"category_scores_gemma":[0.000043778,0.00020943524,0.000100662524,0.00013666875,0.000009717867,0.00007126658,0.00001005375,0.00021093687,0.00005294451],"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":[0.00030719547,0.000108447144,0.000007079836,0.003046023,0.00024071652,0.0000074143736,0.00011782834,0.068557866,0.88964903,0.011184687,0.009605592,0.017168133],"study_design_scores_gemma":[0.003259936,0.00030484484,0.00024953278,0.001069738,0.00046833255,0.00001631447,0.00011525395,0.24215493,0.22633342,0.015971819,0.5066086,0.0034472581],"about_ca_topic_score_codex":0.0000020921543,"about_ca_topic_score_gemma":0.000006828586,"teacher_disagreement_score":0.8147752,"about_ca_system_score_codex":0.00004623305,"about_ca_system_score_gemma":0.00003806649,"threshold_uncertainty_score":0.8540521},"labels":[],"label_agreement":null},{"id":"W4238673269","doi":"10.4018/978-1-7998-1754-3.ch041","title":"Single SNN Architecture for Classical and Operant Conditioning Using Reinforcement Learning","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Operant conditioning; Reinforcement learning; Computer science; Field-programmable gate array; Spiking neural network; Artificial neural network; Reinforcement; Scalability; Adaptation (eye); Artificial intelligence; Computer architecture; Computer hardware; Neuroscience; Engineering; Psychology","score_opus":0.024199819850046916,"score_gpt":0.2458411751743824,"score_spread":0.2216413553243355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238673269","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010021308,0.00076664815,0.15740545,0.000017186538,0.00088871474,0.0010108136,0.00004928207,0.0005383646,0.82930225],"genre_scores_gemma":[0.9679058,0.000005740946,0.0053884275,0.00033546772,0.0007421285,0.000011289315,0.0000287207,0.00017350259,0.025408927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988991,0.000007435851,0.00029346714,0.00031996323,0.00014608249,0.00033395452],"domain_scores_gemma":[0.9995542,0.000077024684,0.00008785759,0.0001530754,0.000037163245,0.00009065483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000053008556,0.00036934996,0.00039325457,0.000047636942,0.00017506206,0.00006629768,0.00009493169,0.00023731416,0.0000080134],"category_scores_gemma":[0.0000149088855,0.0003808669,0.000121469835,0.000009026763,0.000053009055,0.000044492077,0.00008208549,0.00046472694,0.000007353351],"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":[0.000054343254,0.0000022269023,0.0000025742218,0.0003725842,0.00011786886,0.000028259801,0.000104795356,0.6815437,0.012386522,0.2884527,0.00008998499,0.016844442],"study_design_scores_gemma":[0.004740494,0.0016010246,0.0000074802438,0.0057566976,0.0006775653,0.0007379052,0.00011374995,0.39013025,0.022088692,0.23086898,0.33850706,0.004770093],"about_ca_topic_score_codex":0.0000011821578,"about_ca_topic_score_gemma":0.0000032704195,"teacher_disagreement_score":0.9578845,"about_ca_system_score_codex":0.00019376166,"about_ca_system_score_gemma":0.000032347136,"threshold_uncertainty_score":0.99986434},"labels":[],"label_agreement":null},{"id":"W4238893459","doi":"10.1017/9781108989817.034","title":"Technologies for the Network AI Architecture","year":2021,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Architecture; Computer science; Content (measure theory); Computer architecture; World Wide Web; Computer network; Telecommunications; Art; Visual arts; Mathematics","score_opus":0.017246526195715817,"score_gpt":0.19190478906964117,"score_spread":0.17465826287392536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238893459","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030717136,0.0029884067,0.103257716,0.00006497885,0.00067081966,0.0005734888,0.000099798686,0.0012594694,0.89105463],"genre_scores_gemma":[0.0012680528,0.00027763288,0.0006144985,0.00007044914,0.0003227819,0.0000019004298,0.000022714878,0.00006463225,0.9973573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992771,0.000006876694,0.00010198155,0.0002580449,0.0000837751,0.0002721775],"domain_scores_gemma":[0.9992144,0.0002302759,0.000049501185,0.00041960622,0.000053897365,0.000032286505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000038871294,0.00027556208,0.00025714163,0.000033869783,0.00026517932,0.000026258225,0.00041302363,0.0002694876,8.273907e-7],"category_scores_gemma":[0.000009350072,0.00025303615,0.00018892027,0.000009262895,0.000118184435,0.000027950193,0.0002510793,0.00071686873,0.000001096524],"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":[0.00004082348,0.0000012943466,1.6424585e-7,0.00022588416,0.00024958787,0.0001608546,0.000020311609,0.061832044,0.00019031041,0.8580835,0.041528255,0.03766697],"study_design_scores_gemma":[0.00016160044,0.00001571392,4.985279e-7,0.00016019098,0.000114418355,0.000018250032,0.000026693502,0.0014640718,0.0019364635,0.00010385571,0.9957086,0.0002896455],"about_ca_topic_score_codex":9.3222667e-7,"about_ca_topic_score_gemma":9.87355e-7,"teacher_disagreement_score":0.95418036,"about_ca_system_score_codex":0.000060925915,"about_ca_system_score_gemma":0.00001831039,"threshold_uncertainty_score":0.9999922},"labels":[],"label_agreement":null},{"id":"W4241146674","doi":"10.1101/450825","title":"Pre- and postsynaptically expressed spiking-timing-dependent plasticity contribute differentially to neuronal learning","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Montreal General Hospital","funders":"National Institute on Aging; Fundação para a Ciência e a Tecnologia; Canadian Institutes of Health Research; Engineering and Physical Sciences Research Council; Biotechnology and Biological Sciences Research Council; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Postsynaptic potential; Neuroscience; Nonsynaptic plasticity; Synaptic plasticity; Metaplasticity; Spike-timing-dependent plasticity; Homeostatic plasticity; Plasticity; Synaptic scaling; Biology; Synapse; Neuroplasticity; Homosynaptic plasticity; Inhibitory postsynaptic potential; Synaptic augmentation; Excitatory postsynaptic potential; Physics; Genetics","score_opus":0.011949974254409372,"score_gpt":0.21682590089196344,"score_spread":0.20487592663755405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241146674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91841125,0.00015454933,0.07817296,0.000045219873,0.0012687239,0.0006267314,0.000102262835,0.0012104345,0.000007854362],"genre_scores_gemma":[0.99491525,0.000068318965,0.003846669,0.000113867034,0.0007735617,0.00007510376,4.811691e-7,0.0002011297,0.0000055974574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99680454,0.00012089795,0.000627836,0.0011182099,0.00043703857,0.0008914761],"domain_scores_gemma":[0.9981237,0.00026158776,0.00019457156,0.00056033913,0.0003035882,0.0005561803],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002667057,0.00078301836,0.0007473355,0.00023013112,0.0002883853,0.00031605613,0.000527095,0.0004408504,0.000029307472],"category_scores_gemma":[0.00057453394,0.0008832635,0.0001159565,0.00020225736,0.00010822617,0.00016122867,0.0010576576,0.0014936315,0.000037387876],"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":[0.000098478624,0.000040232964,0.0011087934,0.00041522196,0.00012274516,0.000065857676,0.000021073025,0.035255678,0.9627424,0.00008374379,0.00003757152,0.000008206739],"study_design_scores_gemma":[0.0009260989,0.00031139932,0.12771268,0.0010051745,0.0001992581,1.5980083e-7,0.0000019543702,0.03432772,0.83274645,0.0000065655836,0.0010397263,0.0017228279],"about_ca_topic_score_codex":0.0000047564977,"about_ca_topic_score_gemma":0.0000014142305,"teacher_disagreement_score":0.12999596,"about_ca_system_score_codex":0.00019050289,"about_ca_system_score_gemma":0.00009524596,"threshold_uncertainty_score":0.9993618},"labels":[],"label_agreement":null},{"id":"W4242178602","doi":"10.22215/etd/2014-10379","title":"The Drift Diffusion Simulation of Coupled Ionic -Electronic Devices","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Electrolyte; PEDOT:PSS; Materials science; Ionic bonding; Diffusion; Ionic conductivity; Ion; Poisson's equation; Solver; Polystyrene sulfonate; Lithium (medication); Memristor; Chemical physics; Molecular dynamics; Electronic engineering; Polymer; Electrode; Thermodynamics; Chemistry; Physical chemistry; Computer science; Composite material; Physics; Computational chemistry; Engineering; Quantum mechanics","score_opus":0.0060975167555127,"score_gpt":0.24818135096456498,"score_spread":0.24208383420905227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242178602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9848579,0.0011610321,0.010039643,0.000005101011,0.0005819471,0.00019948128,4.1176736e-7,0.00020403112,0.0029504343],"genre_scores_gemma":[0.9979528,0.00021608891,0.000026685359,0.0000082684155,0.000089745285,0.00000543369,0.000095671734,0.000033378736,0.0015719245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992495,0.000017302087,0.0002677759,0.00012231262,0.00013498562,0.00020812331],"domain_scores_gemma":[0.9993064,0.00034517972,0.00010728277,0.00016750133,0.000052814896,0.00002084762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010064341,0.00016157441,0.00017985381,0.00004084231,0.0001370502,0.000015762706,0.00013884754,0.00011603488,0.000021254418],"category_scores_gemma":[0.00003122572,0.00011477026,0.00006859399,0.000096815515,0.000008134862,0.00005059752,0.000008096168,0.00024202542,0.00000897449],"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":[0.000031727777,0.000005159638,0.000010562416,0.00022335397,0.000027114003,1.8828187e-7,0.00009834259,0.96122533,0.021648772,0.00097626913,0.000013737339,0.015739439],"study_design_scores_gemma":[0.00015921553,0.000041091476,0.000605431,0.000109044355,0.00003060906,3.319352e-7,0.00011100169,0.9730909,0.021970442,0.00068735704,0.003008021,0.00018654249],"about_ca_topic_score_codex":0.000003056666,"about_ca_topic_score_gemma":0.00029191762,"teacher_disagreement_score":0.015552897,"about_ca_system_score_codex":0.000031740354,"about_ca_system_score_gemma":0.00001643335,"threshold_uncertainty_score":0.46801952},"labels":[],"label_agreement":null},{"id":"W4244450602","doi":"10.1002/0471221562.ch1","title":"Introduction","year":2002,"lang":"en","type":"other","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Very-large-scale integration; CMOS; Electrical engineering; Computer science; Electronic engineering; Power (physics); Engineering; Physics","score_opus":0.008952883706700282,"score_gpt":0.19506125375453048,"score_spread":0.1861083700478302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244450602","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031873356,0.000408948,0.0085335765,0.000019763567,0.0009289712,0.000031056887,5.4101884e-7,0.0015984387,0.9884755],"genre_scores_gemma":[0.00009925147,0.000120573626,0.0010675319,0.000010791521,0.003015307,9.101379e-7,0.0000028164277,0.00020873596,0.9954741],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999808,0.0000015826367,0.000034734505,0.000067762514,0.000024381077,0.00006353917],"domain_scores_gemma":[0.9998915,0.0000021350834,0.0000063162784,0.000086426204,0.0000011633897,0.00001245663],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000050106205,0.00006903572,0.00006085226,0.000040746672,0.0000052287714,0.0000026947428,0.00002810857,0.00005221789,0.012423232],"category_scores_gemma":[0.0000016316854,0.00006409501,0.00001352703,0.000032783588,0.0000034805535,0.000010353853,0.0000043945092,0.00007840201,0.0007699694],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.0119695e-8,6.360456e-7,5.5029055e-8,0.000020072355,0.0000032600703,9.6349e-7,0.0000013424199,0.0022869231,0.00015160607,0.000053028398,0.98813355,0.009348504],"study_design_scores_gemma":[0.00001831492,0.0000019236404,2.1371467e-7,0.000008273797,0.0000018724057,0.0000031413094,7.428764e-7,0.0011481105,0.00085464027,0.000010254684,0.9978754,0.000077147735],"about_ca_topic_score_codex":3.7313902e-7,"about_ca_topic_score_gemma":8.817585e-7,"teacher_disagreement_score":0.011653263,"about_ca_system_score_codex":0.0000067026035,"about_ca_system_score_gemma":2.1156349e-7,"threshold_uncertainty_score":0.98966557},"labels":[],"label_agreement":null},{"id":"W4244786884","doi":"10.3410/f.1025193.298769","title":"Faculty Opinions recommendation of Unitary IPSPs drive precise thalamic spiking in a circuit required for learning.","year":2005,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Unitary state; Psychology; Neuroscience; Computer science; Artificial intelligence; Political science; Law","score_opus":0.03972374562430681,"score_gpt":0.33685030776656,"score_spread":0.2971265621422532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244786884","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007548775,0.0014851793,0.00047313166,0.01797332,0.0008296585,0.001715234,0.97737134,0.00010006263,0.000044517128],"genre_scores_gemma":[0.0001139459,0.0004951562,0.0009349473,0.00061767624,0.000366929,0.0002479087,0.9968792,0.000045996505,0.00029824863],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965069,0.00027816373,0.0015731002,0.000549456,0.00066738704,0.00042495568],"domain_scores_gemma":[0.99610406,0.00032613205,0.0008524507,0.0008192962,0.001730583,0.00016745333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010744184,0.00051367184,0.0009220097,0.00045316937,0.00018188784,0.000059388618,0.001098399,0.0005707391,0.000079777616],"category_scores_gemma":[0.0045847343,0.00039626952,0.00046516504,0.0016868334,0.00014153335,0.0004520418,0.00025316214,0.0014029719,0.0000109098255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010586678,0.000109135304,0.0000030748631,0.007380133,0.00006433875,3.099382e-7,0.00015724893,0.00010018986,0.000015574142,0.000075049335,0.9822285,0.009855836],"study_design_scores_gemma":[0.0006775835,0.000090556794,0.00029801452,0.013600063,0.00008630921,0.000022867076,0.000032494714,0.0004439711,0.00007324017,0.0000521335,0.9842606,0.00036217397],"about_ca_topic_score_codex":0.000007975788,"about_ca_topic_score_gemma":0.0000072277494,"teacher_disagreement_score":0.019507844,"about_ca_system_score_codex":0.0001965948,"about_ca_system_score_gemma":0.00016666771,"threshold_uncertainty_score":0.9998489},"labels":[],"label_agreement":null},{"id":"W4245096176","doi":"10.1149/ma2008-02/30/2120","title":"Printed Organic Memory Devices","year":2008,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Materials science; Computer science","score_opus":0.01957373306260218,"score_gpt":0.22675592113986218,"score_spread":0.20718218807726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245096176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9109998,0.00024334225,0.000051395153,0.000019709825,0.0003983506,0.00006546887,5.216089e-7,0.0008094853,0.087411925],"genre_scores_gemma":[0.99804854,0.000023216295,0.0013547578,0.00005585702,0.00023992905,0.0000020630243,0.0000023454827,0.000040768977,0.00023250731],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991305,0.0000139365175,0.000256081,0.00017207993,0.00012759888,0.0002997912],"domain_scores_gemma":[0.99952316,0.0001342683,0.0000544184,0.00016592613,0.000029696486,0.00009255762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013088701,0.00015513251,0.00014540131,0.00004803648,0.00017306716,0.000012989645,0.00013889262,0.000058636302,0.000033551092],"category_scores_gemma":[0.00013779908,0.00015983295,0.000039215553,0.00014604148,0.000025823378,0.00013350068,0.000033430442,0.0002723734,0.00019559785],"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":[0.0000043039254,0.00001434071,0.0005942291,0.00006889173,0.000016207248,0.00015410682,0.0003917242,0.4985386,0.49919143,0.0000011096263,0.00027012578,0.00075492373],"study_design_scores_gemma":[0.0001922323,0.000014894867,0.019251583,0.000103620296,0.000007007491,0.00016323979,0.00007146944,0.0026022864,0.97489065,0.000034509936,0.002400506,0.00026802413],"about_ca_topic_score_codex":0.0000036707727,"about_ca_topic_score_gemma":0.000004531637,"teacher_disagreement_score":0.49593633,"about_ca_system_score_codex":0.00003349176,"about_ca_system_score_gemma":0.000013173763,"threshold_uncertainty_score":0.65177983},"labels":[],"label_agreement":null},{"id":"W4245657312","doi":"10.1504/ijcvr.2019.098006","title":"Bio-inspired visual attention process using spiking neural networks controlling a camera","year":2019,"lang":"en","type":"article","venue":"International Journal of Computational Vision and Robotics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Computer science; Stimulus (psychology); Artificial intelligence; Spiking neural network; Implementation; Artificial neural network; Computer vision; Psychology","score_opus":0.0122549793155862,"score_gpt":0.3042663193083429,"score_spread":0.2920113399927567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245657312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64174825,0.000180253,0.35660827,0.00008200641,0.0012883277,0.00005230191,0.0000010772054,0.00002327799,0.000016241287],"genre_scores_gemma":[0.993568,0.000031772943,0.005809143,0.00012972632,0.0004302819,2.003518e-7,0.0000074931845,0.00001907857,0.0000043214072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988623,0.000026627391,0.00048121586,0.00009533457,0.00039823996,0.00013626232],"domain_scores_gemma":[0.9990536,0.00018847368,0.0002370937,0.000031705655,0.0004158421,0.000073263895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015347783,0.00013757496,0.0002097456,0.00018605932,0.0000676811,0.0000941505,0.00013718898,0.000051627165,0.000009388262],"category_scores_gemma":[0.000026357182,0.00012206382,0.0000892322,0.0001028552,0.00002500036,0.00039749584,0.00003257294,0.00024165823,0.0000034541472],"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":[0.00006718745,0.000022680317,0.0023715284,0.00001860965,0.00006405547,0.000021863956,0.000064674685,0.988626,0.0022747954,0.00011428081,0.00000474067,0.0063495766],"study_design_scores_gemma":[0.0009987298,0.00009403221,0.003153938,0.00021364671,0.00001894874,0.00030059228,0.000098181874,0.99434346,0.000109932254,0.00052390073,0.000016454327,0.00012815675],"about_ca_topic_score_codex":3.34252e-7,"about_ca_topic_score_gemma":2.243149e-7,"teacher_disagreement_score":0.35181975,"about_ca_system_score_codex":0.000055014345,"about_ca_system_score_gemma":0.000023377388,"threshold_uncertainty_score":0.49776182},"labels":[],"label_agreement":null},{"id":"W4247708926","doi":"10.1007/s12274-016-1291-7","title":"A highly sensitive chemical gas detecting transistor based on highly crystalline CVD-grown MoSe2 films","year":2016,"lang":"en","type":"article","venue":"Nano Research","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Semiconductor; Fabrication; Optoelectronics; Materials science; Field-effect transistor; Chemical vapor deposition; Transistor; Valence (chemistry); Nanotechnology; Band gap; Chemistry; Voltage; Electrical engineering","score_opus":0.03852015411254177,"score_gpt":0.2948590091361636,"score_spread":0.2563388550236218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247708926","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98398787,0.000057604917,0.01251716,0.0005932093,0.00015444329,0.00027624116,0.000021382086,0.0004247917,0.001967287],"genre_scores_gemma":[0.99754083,0.000016375208,0.0016724786,0.000068537214,0.00026142274,0.000015727244,0.000004342658,0.00006747042,0.0003528195],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977643,0.00014938717,0.0002621058,0.00042039048,0.0006111505,0.00079264765],"domain_scores_gemma":[0.99781215,0.0014459516,0.000022775872,0.00033802586,0.00017669692,0.000204398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006637434,0.00021848096,0.00024146354,0.00022241777,0.0001963945,0.000029567154,0.00021819909,0.00014636964,0.000053477637],"category_scores_gemma":[0.00042804854,0.00016660256,0.00009916278,0.000465696,0.000112662914,0.00012406474,0.000046796013,0.0005997867,0.00010701479],"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":[0.00017464567,0.000028247745,0.000004322413,0.000057386525,0.000011024631,0.00009152309,0.00008774067,0.005003768,0.97461325,0.00003913826,0.0003851918,0.019503776],"study_design_scores_gemma":[0.0008216061,0.00018359709,0.000019120504,0.0002418659,0.0000041334133,0.000007781515,0.000036570458,0.031191736,0.9656125,0.00012066708,0.001531822,0.00022860176],"about_ca_topic_score_codex":0.000005452807,"about_ca_topic_score_gemma":0.0000042037477,"teacher_disagreement_score":0.026187968,"about_ca_system_score_codex":0.00023104336,"about_ca_system_score_gemma":0.000045092653,"threshold_uncertainty_score":0.67938554},"labels":[],"label_agreement":null},{"id":"W4252443816","doi":"10.36227/techrxiv.12601991.v1","title":"Towards a Programming Paradigm for Reconfigurable Computing: Asynchronous Graph Programming","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Control reconfiguration; Computer science; Asynchronous communication; Parallel computing; Programming paradigm; Distributed computing; Software; Graph; Programming language; Computer architecture; Theoretical computer science; Embedded system","score_opus":0.04085989690775829,"score_gpt":0.2790959464131949,"score_spread":0.23823604950543661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252443816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016589029,0.001346,0.9669421,0.00039220144,0.0022102462,0.0033914743,0.000024266881,0.0050448533,0.004059858],"genre_scores_gemma":[0.79792464,0.0000633119,0.20012343,0.00013752519,0.0009602029,0.00033625137,0.00017383158,0.0002232797,0.000057518268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711937,0.000034686072,0.0007407324,0.0009083217,0.00021939912,0.0009775228],"domain_scores_gemma":[0.9988073,0.00016133509,0.00018410412,0.00047920752,0.00007274213,0.00029530434],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002664266,0.00071822666,0.0008574882,0.00014467181,0.00024335312,0.00027982274,0.00055916695,0.00037476022,0.000021432152],"category_scores_gemma":[0.00008064652,0.00075132656,0.00046140485,0.00027714574,0.00005652312,0.000121691184,0.00024540708,0.0011582709,0.000014906623],"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":[0.00003327865,0.000043328753,0.000013998115,0.0030309309,0.00023242584,0.000034251465,0.0005748691,0.22485214,0.00085995666,0.0015613146,0.0004456129,0.7683179],"study_design_scores_gemma":[0.0021367583,0.0007664677,0.00007277789,0.0018998885,0.00039006473,0.00013176764,0.00049560197,0.7041147,0.07396996,0.03696043,0.1744845,0.004577102],"about_ca_topic_score_codex":0.00001314021,"about_ca_topic_score_gemma":0.000008242276,"teacher_disagreement_score":0.7813356,"about_ca_system_score_codex":0.00015038151,"about_ca_system_score_gemma":0.00009552034,"threshold_uncertainty_score":0.9994938},"labels":[],"label_agreement":null},{"id":"W4253117913","doi":"10.1007/978-1-4471-7452-3_8","title":"Associative Memory Networks","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Content-addressable memory; Computer science; Bidirectional associative memory; Artificial neural network; Associative property; Cognitive science; Artificial intelligence; Neuroscience; Psychology; Mathematics","score_opus":0.012562618684448723,"score_gpt":0.19930501178008825,"score_spread":0.18674239309563953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253117913","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000319067,0.00073236186,0.010820827,0.000006272767,0.0010678228,0.00014467032,0.0000041823805,0.0005297591,0.9866622],"genre_scores_gemma":[0.006942709,0.00018542475,0.00023281165,0.00014866547,0.0005397815,0.0000010383187,0.000018744726,0.000103205304,0.9918276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.999351,0.0000034024276,0.00016945951,0.00017752661,0.00009927529,0.0001992868],"domain_scores_gemma":[0.99958026,0.000122316,0.00005005661,0.00018038445,0.000022975013,0.000043989145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004677531,0.00025499385,0.000296143,0.00003978123,0.000031967495,0.000010971193,0.000109105684,0.0002684477,0.00053116836],"category_scores_gemma":[0.000004487337,0.0002499753,0.00011055119,0.000011821124,0.000012590054,0.00005589945,0.000046185105,0.000569286,0.00044426683],"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":[0.000010118314,0.000003943241,0.0000041804387,0.00016356223,0.00039954289,0.00006829727,0.00011100718,0.7995727,0.0001859119,0.09313128,0.030595507,0.07575396],"study_design_scores_gemma":[0.0010319592,0.00016658202,0.000046365287,0.0009665619,0.00022333178,0.000033175525,0.00004801794,0.25755948,0.0030194882,0.023606429,0.7093368,0.0039618206],"about_ca_topic_score_codex":1.9690205e-7,"about_ca_topic_score_gemma":0.0000019624583,"teacher_disagreement_score":0.6787413,"about_ca_system_score_codex":0.00008703136,"about_ca_system_score_gemma":0.0000085616,"threshold_uncertainty_score":0.99999523},"labels":[],"label_agreement":null},{"id":"W4254917525","doi":"10.1149/ma2009-01/42/1444","title":"EIS Studies on Printed Organic Memories","year":2009,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Materials science","score_opus":0.0257767154513257,"score_gpt":0.2692919229669353,"score_spread":0.24351520751560962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254917525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9714282,0.00037050014,0.000013252269,0.00011921143,0.00068245287,0.00007454825,7.0932083e-7,0.0007347148,0.026576366],"genre_scores_gemma":[0.9986446,0.000039100367,0.00073837675,0.00013620066,0.00027058276,0.0000013228105,0.0000016587287,0.000023107374,0.0001450627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991255,0.000018143213,0.00027383017,0.00018072918,0.00012795549,0.00027388838],"domain_scores_gemma":[0.9994187,0.00025260498,0.000059790997,0.00016596341,0.000040521023,0.000062427054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024496534,0.00017856562,0.00018760678,0.000051476214,0.00013280402,0.000022964943,0.00010608707,0.000046459416,0.000007417289],"category_scores_gemma":[0.0006452614,0.00016742037,0.000037292797,0.00014295329,0.000018996641,0.00009006628,0.000018225172,0.00027001032,0.000081506194],"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":[0.000013685091,0.000017922523,0.000023197148,0.00004049363,0.000025229288,0.000048489877,0.0006092838,0.66818535,0.32909733,0.000020086149,0.00023500132,0.0016839228],"study_design_scores_gemma":[0.00014333994,0.000076094366,0.0028843223,0.00021431957,0.0000090731155,0.000012104565,0.00021291747,0.00075534056,0.9939458,0.00054835336,0.0009873078,0.00021106006],"about_ca_topic_score_codex":2.8289764e-7,"about_ca_topic_score_gemma":0.0000013806541,"teacher_disagreement_score":0.66743,"about_ca_system_score_codex":0.000053303305,"about_ca_system_score_gemma":0.0000060101784,"threshold_uncertainty_score":0.6827204},"labels":[],"label_agreement":null},{"id":"W4256581587","doi":"10.36227/techrxiv.12601991","title":"Towards a Programming Paradigm for Reconfigurable Computing: Asynchronous Graph Programming","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Control reconfiguration; Computer science; Asynchronous communication; Parallel computing; Programming paradigm; Distributed computing; Software; Graph; Programming language; Computer architecture; Theoretical computer science; Embedded system","score_opus":0.04085989690775829,"score_gpt":0.2790959464131949,"score_spread":0.23823604950543661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256581587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016589029,0.001346,0.9669421,0.00039220144,0.0022102462,0.0033914743,0.000024266881,0.0050448533,0.004059858],"genre_scores_gemma":[0.79792464,0.0000633119,0.20012343,0.00013752519,0.0009602029,0.00033625137,0.00017383158,0.0002232797,0.000057518268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711937,0.000034686072,0.0007407324,0.0009083217,0.00021939912,0.0009775228],"domain_scores_gemma":[0.9988073,0.00016133509,0.00018410412,0.00047920752,0.00007274213,0.00029530434],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002664266,0.00071822666,0.0008574882,0.00014467181,0.00024335312,0.00027982274,0.00055916695,0.00037476022,0.000021432152],"category_scores_gemma":[0.00008064652,0.00075132656,0.00046140485,0.00027714574,0.00005652312,0.000121691184,0.00024540708,0.0011582709,0.000014906623],"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":[0.00003327865,0.000043328753,0.000013998115,0.0030309309,0.00023242584,0.000034251465,0.0005748691,0.22485214,0.00085995666,0.0015613146,0.0004456129,0.7683179],"study_design_scores_gemma":[0.0021367583,0.0007664677,0.00007277789,0.0018998885,0.00039006473,0.00013176764,0.00049560197,0.7041147,0.07396996,0.03696043,0.1744845,0.004577102],"about_ca_topic_score_codex":0.00001314021,"about_ca_topic_score_gemma":0.000008242276,"teacher_disagreement_score":0.7813356,"about_ca_system_score_codex":0.00015038151,"about_ca_system_score_gemma":0.00009552034,"threshold_uncertainty_score":0.9994938},"labels":[],"label_agreement":null},{"id":"W4281397325","doi":"10.1088/2634-4386/ac724c","title":"Computational properties of multi-compartment LIF neurons with passive dendrites","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Computer science; Context (archaeology); Artificial neural network; Layer (electronics); Universality (dynamical systems); Spiking neural network; Neuron; Topology (electrical circuits); Biological system; Neuroscience; Artificial intelligence; Physics; Mathematics; Biology; Nanotechnology; Materials science","score_opus":0.025744353698138295,"score_gpt":0.20070920643315449,"score_spread":0.1749648527350162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281397325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97599316,0.00044151794,0.022737212,0.000036421025,0.0002607418,0.00014592626,0.0000068525374,0.00036541242,0.0000127319645],"genre_scores_gemma":[0.9968237,0.000006389293,0.0030307628,0.000030879495,0.000042962598,0.000010605418,0.0000052087667,0.000042736257,0.000006726613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990635,0.000028695542,0.00024355824,0.00021735161,0.00019203305,0.00025486512],"domain_scores_gemma":[0.99962616,0.00011135486,0.000049847084,0.000113149625,0.000027630085,0.00007187884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066990746,0.00019680044,0.0002343472,0.00010927129,0.00021357193,0.000019266254,0.0001153735,0.000015826645,0.0000047812964],"category_scores_gemma":[0.000014437607,0.00019517571,0.0000341211,0.0001864314,0.00003480062,0.000059303013,0.00013724402,0.0003466271,5.892425e-7],"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":[0.000009537163,0.000025813071,0.00022354822,0.00012327323,0.000022767807,0.000032096465,0.00030121088,0.97025347,0.027710447,0.00007634805,0.000013139477,0.0012083736],"study_design_scores_gemma":[0.0004857563,0.0001399736,0.0022424273,0.00006940927,0.000014659185,0.00025997698,0.00007146797,0.9887179,0.0074559427,0.000006225134,0.0003163573,0.00021987915],"about_ca_topic_score_codex":0.000002259974,"about_ca_topic_score_gemma":3.206755e-7,"teacher_disagreement_score":0.020830546,"about_ca_system_score_codex":0.000026291253,"about_ca_system_score_gemma":0.000011679076,"threshold_uncertainty_score":0.79590344},"labels":[],"label_agreement":null},{"id":"W4281777130","doi":"10.22541/au.165459422.27259911/v1","title":"In-Memory Memristive Transformation Stage of Gaussian Random Number Generator","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Dot product; Computer science; Robustness (evolution); Gaussian; Random number generation; Non-volatile memory; Parallel computing; Algorithm; Computer engineering; Computer hardware; Mathematics","score_opus":0.01586181980849626,"score_gpt":0.26413494544875,"score_spread":0.24827312564025372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281777130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8784956,0.00020186842,0.059532158,0.000039063045,0.0013528285,0.00064657384,0.0000973263,0.0002551371,0.059379477],"genre_scores_gemma":[0.99680835,0.00010310644,0.0014391978,0.000043954406,0.00009017268,0.00005664355,0.00005131608,0.000036816007,0.0013704479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896,0.000054428976,0.00045296893,0.00019232873,0.00015907167,0.00018116106],"domain_scores_gemma":[0.9995999,0.00006495745,0.00007177167,0.00020461557,0.000017619668,0.00004117139],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018273003,0.00021155014,0.00037240126,0.00009810421,0.000036825462,0.00000964538,0.0001566766,0.00010510938,0.0018666165],"category_scores_gemma":[0.000010014712,0.00021716287,0.00010737692,0.00012345739,0.000016570337,0.00013461409,0.000101608326,0.0006638497,0.000008184726],"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":[0.0000608693,0.000016510692,0.000030007941,0.000712677,0.000030110494,0.000019492249,0.0013539705,0.9895843,0.0062011653,0.00041089303,0.00013197392,0.0014480323],"study_design_scores_gemma":[0.005643061,0.000057235633,0.00044761633,0.0002880837,0.00006200541,0.000016816874,0.0024068726,0.25890702,0.71677434,0.001951962,0.01189963,0.0015453608],"about_ca_topic_score_codex":0.00001981119,"about_ca_topic_score_gemma":0.000014362928,"teacher_disagreement_score":0.7306773,"about_ca_system_score_codex":0.00011086413,"about_ca_system_score_gemma":0.000026558077,"threshold_uncertainty_score":0.9990458},"labels":[],"label_agreement":null},{"id":"W4281806468","doi":"10.3389/fmats.2022.813407","title":"Engineering Silicon Oxide by Argon Ion Implantation for High Performance Resistance Switching","year":2022,"lang":"en","type":"article","venue":"Frontiers in Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"McMaster University","funders":"Engineering and Physical Sciences Research Council; Royal Academy of Engineering; Leverhulme Trust","keywords":"Electroforming; Argon; Materials science; Silicon; Optoelectronics; Ion implantation; Reset (finance); Oxide; Ion; Deposition (geology); Voltage; Layer (electronics); Nanotechnology; Electrical engineering; Chemistry; Metallurgy","score_opus":0.004811556578935263,"score_gpt":0.18825147828647124,"score_spread":0.18343992170753598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281806468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95281196,0.00022099382,0.043972325,0.0000109982275,0.0024958344,0.00024573813,0.000059398015,0.00016830338,0.00001447601],"genre_scores_gemma":[0.9931375,0.00004189416,0.0063903597,0.000025981653,0.00008860933,0.00012668742,0.00007724391,0.000037147533,0.000074574964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992072,0.000023437544,0.00025903617,0.00016847257,0.00008820116,0.00025365374],"domain_scores_gemma":[0.9997834,0.00003480447,0.00004996113,0.00010243371,0.0000069827993,0.000022403563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025307288,0.0001273064,0.00019510621,0.00008897983,0.00012584943,0.000024122925,0.000118269396,0.000030454241,0.000008817833],"category_scores_gemma":[0.000018421299,0.00015374928,0.000014661563,0.00011203508,0.0000036848687,0.00018238995,0.00002940902,0.00010438145,8.652967e-7],"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":[0.000074309966,0.000003544792,0.00006554683,0.00017150195,0.000005472032,0.0000017567588,0.00007821714,0.22942343,0.76808393,0.00003220754,0.0013148326,0.0007452643],"study_design_scores_gemma":[0.00039469934,0.000022160399,0.0004541909,0.000044148444,0.000005143183,0.0000034834209,0.00006741225,0.0076022926,0.9885873,0.00019899016,0.0024177362,0.00020244674],"about_ca_topic_score_codex":0.000002717108,"about_ca_topic_score_gemma":5.7774463e-7,"teacher_disagreement_score":0.22182113,"about_ca_system_score_codex":0.00015722253,"about_ca_system_score_gemma":0.000004562415,"threshold_uncertainty_score":0.62697136},"labels":[],"label_agreement":null},{"id":"W4282914796","doi":"10.1371/journal.pcbi.1009409","title":"Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Montreal General Hospital","funders":"National Institute on Aging; Fundação para a Ciência e a Tecnologia; Biotechnology and Biological Sciences Research Council; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canada Foundation for Innovation","keywords":"Postsynaptic potential; Nonsynaptic plasticity; Neuroscience; Synaptic plasticity; Metaplasticity; Spike-timing-dependent plasticity; Synaptic scaling; Plasticity; Biology; Homosynaptic plasticity; Post-tetanic potentiation; Synapse; Neuroplasticity; Inhibitory postsynaptic potential; Synaptic augmentation; Homeostatic plasticity; Excitatory postsynaptic potential; Physics; Genetics; Receptor","score_opus":0.01602049155136766,"score_gpt":0.23887928321238944,"score_spread":0.22285879166102177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282914796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87399536,0.00003699722,0.12518859,0.00012073054,0.0001784659,0.00016186481,0.00005101875,0.00021376654,0.000053181448],"genre_scores_gemma":[0.99743533,0.0000028034501,0.0020879114,0.00024051066,0.00007825782,0.000032544383,0.00008076131,0.00002019894,0.0000217061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897325,0.00010985377,0.00022100299,0.00028087955,0.00014539433,0.0002695924],"domain_scores_gemma":[0.9991007,0.00066405325,0.000037387017,0.000048563084,0.00004298122,0.00010634891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058376576,0.00014954493,0.00019911678,0.00008157694,0.00027708494,0.000019152341,0.00013126413,0.000034707777,0.000075863456],"category_scores_gemma":[0.00011811129,0.00016195161,0.000029751307,0.00008035923,0.00003356132,0.00004048836,0.0002783236,0.00037057462,0.000008370243],"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":[0.0000810658,0.000029292809,0.0006947796,0.000013893133,0.00003424454,0.000008956072,0.000094102594,0.82750523,0.16942917,0.001144525,0.000016570675,0.00094819564],"study_design_scores_gemma":[0.0011530493,0.0009788558,0.034275588,0.000018600465,0.000038092305,0.00007442976,0.000038389502,0.94770277,0.010080199,0.00422172,0.00090975384,0.0005085414],"about_ca_topic_score_codex":0.0000010459868,"about_ca_topic_score_gemma":6.430579e-7,"teacher_disagreement_score":0.15934896,"about_ca_system_score_codex":0.000049129438,"about_ca_system_score_gemma":0.000015626782,"threshold_uncertainty_score":0.6604195},"labels":[],"label_agreement":null},{"id":"W4282920391","doi":"10.1039/d2nh00163b","title":"Applications of biomemristors in next generation wearable electronics","year":2022,"lang":"en","type":"review","venue":"Nanoscale Horizons","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":43,"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":"Fundamental Research Funds for the Central Universities; National Science and Technology Infrastructure Program; Sichuan Province Science and Technology Support Program; Fujian Normal University","keywords":"Wearable computer; Wearable technology; Electronics; Computer science; Engineering; Human–computer interaction; Embedded system; Electrical engineering","score_opus":0.05558133206837528,"score_gpt":0.29373964159849203,"score_spread":0.23815830953011674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282920391","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006137042,0.99516106,0.003116325,0.0000013606308,0.00030260914,0.000556837,0.000025160645,0.000117111296,0.00065819704],"genre_scores_gemma":[0.00018057755,0.99864393,0.00031899358,0.0000018946391,0.00015224898,0.00037815014,0.00011091101,0.00005898518,0.00015432322],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998833,0.0000564774,0.00047990974,0.00024214316,0.00012654765,0.0002619381],"domain_scores_gemma":[0.99944144,0.000102835824,0.00011006121,0.00029455216,0.0000100416055,0.000041071693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011439014,0.00022492018,0.00065641216,0.00029001062,0.00008472549,0.000009909044,0.00023009951,0.00014846514,0.00006219379],"category_scores_gemma":[0.000012815861,0.00024296199,0.00017326296,0.0010138812,0.00001975409,0.00007719606,0.000049616712,0.00052296236,0.00001275594],"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":[3.794126e-7,0.000021177111,1.6762684e-7,0.0019310301,0.000015166261,0.0000015028983,0.0000133506,0.0012589442,0.00056192453,0.00014244876,0.00014362046,0.9959103],"study_design_scores_gemma":[0.00005018705,0.00004071417,4.465378e-8,0.0003271103,0.000058297093,0.000007691211,0.0000068367813,0.00069702347,0.00035749932,0.000032600175,0.99821794,0.00020406072],"about_ca_topic_score_codex":0.0000027313868,"about_ca_topic_score_gemma":0.000013913383,"teacher_disagreement_score":0.9980743,"about_ca_system_score_codex":0.00032245993,"about_ca_system_score_gemma":0.0000870426,"threshold_uncertainty_score":0.9907702},"labels":[],"label_agreement":null},{"id":"W4283380468","doi":"10.1063/5.0097106","title":"Investigation of multi-photoconductance state induced by light-sensitive defect in TiO<i>x</i>-based memristor","year":2022,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":31,"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":"Foundation for Fundamental Research of China","keywords":"Memristor; Materials science; Resistive random-access memory; Optoelectronics; Excited state; Photoconductivity; Protein filament; Substrate (aquarium); Doping; Nanotechnology; Electrode; Chemistry; Physics; Composite material; Atomic physics","score_opus":0.020141627425848015,"score_gpt":0.2139729860929174,"score_spread":0.19383135866706938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283380468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99067175,0.000012845797,0.008497543,0.000057710717,0.000100677884,0.00045785212,0.000021491469,0.00010210672,0.00007799514],"genre_scores_gemma":[0.9980998,4.290574e-7,0.0007007051,0.00094978337,0.000023982348,0.00015341511,0.000034793076,0.000035250196,0.0000018269925],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992072,0.000040879433,0.00020532035,0.00020433441,0.0001447277,0.0001975509],"domain_scores_gemma":[0.9996412,0.000078791796,0.00008489376,0.00015028197,0.000009741542,0.000035114797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008951636,0.00015170044,0.00018835967,0.000049347855,0.00007049942,0.0000049528617,0.00009924591,0.00001745342,0.0000022391591],"category_scores_gemma":[0.0000026609891,0.00018793467,0.000046972084,0.00033794358,0.00002628861,0.00005647581,0.000029806233,0.00032500585,0.0000023018324],"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":[0.000014637177,0.000008700775,0.000025686792,0.00002528947,0.000008860718,0.0000024879819,0.0005076593,0.24642809,0.7523633,0.000025394058,0.000113660506,0.0004762033],"study_design_scores_gemma":[0.0006415631,0.0000165858,0.00010113869,0.00001060695,0.000005783332,3.2104282e-7,0.000051664996,0.0070920065,0.9917143,0.00008170056,0.00008718716,0.00019711879],"about_ca_topic_score_codex":0.000008143141,"about_ca_topic_score_gemma":0.000002672154,"teacher_disagreement_score":0.23935099,"about_ca_system_score_codex":0.00012875881,"about_ca_system_score_gemma":0.000012341795,"threshold_uncertainty_score":0.76637536},"labels":[],"label_agreement":null},{"id":"W4283386115","doi":"10.3390/micro2030024","title":"Molecular-Scale Hardware Encryption Using Tunable Self-Assembled Nanoelectronic Networks","year":2022,"lang":"en","type":"article","venue":"Micro","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Hardware security module; Computer science; Encryption; Electronic circuit; Nanotechnology; Physical unclonable function; Wafer; Embedded system; Materials science; Cryptography; Computer hardware; Computer network; Electrical engineering; Engineering; Computer security","score_opus":0.006428969286901266,"score_gpt":0.20405453705672044,"score_spread":0.19762556776981918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283386115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9039136,0.0012774746,0.093662366,0.000007753469,0.00032374376,0.00011721296,0.0000010782139,0.0004303002,0.00026642508],"genre_scores_gemma":[0.9974929,0.000019568817,0.0022251715,0.000078407415,0.00007636507,0.000013755605,0.000008057163,0.000041728734,0.00004406049],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991882,0.000038566533,0.00013619062,0.00017668269,0.000088229615,0.0003721407],"domain_scores_gemma":[0.9997549,0.000015673686,0.000026531436,0.00014986662,0.000013041106,0.000039963958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008852343,0.0001271711,0.00012383853,0.00004627153,0.00027670633,0.000020477624,0.00012651912,0.000033002045,0.000054106273],"category_scores_gemma":[0.0000015726068,0.00015665765,0.000059037353,0.00024553042,0.000002778102,0.00008568586,0.0000887822,0.00031277284,0.0000057358325],"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":[0.0000030347142,0.000005855149,0.000018678456,0.000009340558,0.000007200887,0.000010551053,0.000034673973,0.49211133,0.5073179,0.000010437402,0.00004648279,0.00042450457],"study_design_scores_gemma":[0.00024348915,0.000037428552,0.000027084927,0.0000108043605,0.000018854664,0.00007497479,0.00004633692,0.40732875,0.5871154,0.000101345744,0.004766688,0.0002288625],"about_ca_topic_score_codex":0.0000028035138,"about_ca_topic_score_gemma":0.000001269831,"teacher_disagreement_score":0.09357924,"about_ca_system_score_codex":0.00027226703,"about_ca_system_score_gemma":0.000016715207,"threshold_uncertainty_score":0.6388314},"labels":[],"label_agreement":null},{"id":"W4283510959","doi":"10.1002/aelm.202200353","title":"Hydrogen Atom Doping—A Versatile Method for Modulated Interface Resistive Switching","year":2022,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Materials science; Doping; Oxide; Schottky barrier; Optoelectronics; Hydrogen; Resistive touchscreen; Memristor; Layer (electronics); Atom (system on chip); Metal; Interface (matter); Schottky diode; Nanotechnology; Electronic engineering; Electrical engineering; Composite material; Diode; Computer science","score_opus":0.007664367243724964,"score_gpt":0.2686807914221709,"score_spread":0.2610164241784459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283510959","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76184607,0.0005631808,0.23595105,0.00003202017,0.00047353894,0.00052805775,0.000046571415,0.0004894151,0.000070069174],"genre_scores_gemma":[0.9918438,0.000029991714,0.0074265576,0.00006991042,0.00009050965,0.00026534195,0.000046916553,0.000085345084,0.00014163209],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983266,0.00010865537,0.00035873096,0.00036668745,0.00013440063,0.0007049377],"domain_scores_gemma":[0.99937844,0.00018756102,0.00010357912,0.0002520948,0.00002607697,0.00005226445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038721226,0.000246371,0.0003536608,0.000076510856,0.00036417355,0.000024415353,0.00025546097,0.000040855186,0.00021180412],"category_scores_gemma":[0.000031334275,0.00028153943,0.00006934325,0.00016022219,0.000008330374,0.00018368434,0.00012470747,0.00023321316,0.000008240874],"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":[0.00013891669,0.0000059283166,3.5824928e-7,0.000042988173,0.000039306655,0.0000017987717,0.00012610851,0.3227264,0.67324764,0.00067201845,0.000029178143,0.0029693777],"study_design_scores_gemma":[0.0006933057,0.0001855128,0.0000046953337,0.000019519426,0.000023169932,0.000021308206,0.000085478634,0.010428261,0.96357936,0.006125739,0.018517,0.00031666417],"about_ca_topic_score_codex":0.0000054075226,"about_ca_topic_score_gemma":0.0000031180928,"teacher_disagreement_score":0.31229815,"about_ca_system_score_codex":0.0004301908,"about_ca_system_score_gemma":0.00003305674,"threshold_uncertainty_score":0.9999637},"labels":[],"label_agreement":null},{"id":"W4283797178","doi":"10.1609/aaai.v36i2.20061","title":"SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":48,"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":"National Key Research and Development Program of China; Shanghai Jiao Tong University; National Natural Science Foundation of China; Institute for Catastrophic Loss Reduction","keywords":"Spiking neural network; Computer science; Artificial neural network; Artificial intelligence; Spike (software development); Inference; Pattern recognition (psychology); Machine learning","score_opus":0.06553404433713134,"score_gpt":0.2765627105121829,"score_spread":0.21102866617505156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283797178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.961408,0.00014924661,0.035487644,0.00056481536,0.0014709546,0.00051612896,0.000006402115,0.00020265665,0.00019415373],"genre_scores_gemma":[0.99901015,0.000021199863,0.0001072227,0.00027165547,0.00050842226,0.000026870812,0.0000027261237,0.000043641056,0.000008095572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977255,0.000061971026,0.00065325876,0.00050757284,0.0004258812,0.0006258001],"domain_scores_gemma":[0.998857,0.0003135192,0.0002845634,0.00027600632,0.00013161481,0.00013724901],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067318336,0.00035939578,0.0003698222,0.000100086516,0.0011343916,0.00020432236,0.0009936884,0.00011122885,0.00007698248],"category_scores_gemma":[0.00011386033,0.00027884493,0.00013274717,0.000600022,0.0003045093,0.00022938989,0.0005413785,0.0014594718,0.0000028564828],"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":[0.00009985534,0.00003825232,0.000540724,0.00003733248,0.000019050694,0.0000018027026,0.0009409986,0.90392953,0.0036763547,0.015289323,0.000016127658,0.075410634],"study_design_scores_gemma":[0.00002874702,0.00021748403,0.00023141179,0.000088718305,0.000036461286,0.0000131292845,0.0016195776,0.97115254,0.021043831,0.005234474,0.000023453873,0.00031016648],"about_ca_topic_score_codex":0.0000110610845,"about_ca_topic_score_gemma":0.0000023717555,"teacher_disagreement_score":0.07510047,"about_ca_system_score_codex":0.00007440434,"about_ca_system_score_gemma":0.0000127116355,"threshold_uncertainty_score":0.9999664},"labels":[],"label_agreement":null},{"id":"W4283800840","doi":"10.1609/aaai.v36i1.19923","title":"Event-Image Fusion Stereo Using Cross-Modality Feature Propagation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Agency for Defense Development; National Research Foundation of Korea; National Research Foundation","keywords":"Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Stereopsis; Event (particle physics); Visual odometry; Pixel; Pattern recognition (psychology); Motion blur; Image (mathematics); Robot","score_opus":0.07048611108253894,"score_gpt":0.3204239899933323,"score_spread":0.2499378789107934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283800840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99207175,0.000020872818,0.0050322106,0.0002499992,0.00053864,0.00035929304,0.000011722246,0.0001334445,0.0015820689],"genre_scores_gemma":[0.99910235,0.000005481886,0.00057059206,0.000048689522,0.00008171462,0.000017139324,0.000001446809,0.000021950473,0.00015063857],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871266,0.00001825404,0.0003530111,0.000276141,0.00037479447,0.00026516474],"domain_scores_gemma":[0.9993641,0.000033097986,0.00018943522,0.00014655449,0.00021874467,0.00004808602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034881127,0.00018484039,0.0001802577,0.00006996814,0.00048293336,0.0000930718,0.000526504,0.0000487601,0.0001448525],"category_scores_gemma":[0.00010660784,0.00015811811,0.00008992775,0.00042818597,0.00011178186,0.00029712138,0.0002776958,0.0005275552,0.000008853898],"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":[0.000090379894,0.000047978167,0.00015140208,0.00009817873,0.000007712915,7.865716e-7,0.0005255564,0.03732893,0.93351746,0.015421453,0.000033217457,0.0127769755],"study_design_scores_gemma":[0.00002152606,0.000069628346,0.00011675114,0.000071691866,0.000008306461,0.000008064176,0.00044051028,0.20699774,0.7775462,0.0145126805,0.00004834342,0.00015852577],"about_ca_topic_score_codex":0.000005333216,"about_ca_topic_score_gemma":0.0000012114964,"teacher_disagreement_score":0.16966881,"about_ca_system_score_codex":0.00011580915,"about_ca_system_score_gemma":0.00002885004,"threshold_uncertainty_score":0.64478695},"labels":[],"label_agreement":null},{"id":"W4283814179","doi":"10.1186/s11671-022-03701-8","title":"Medium-Temperature-Oxidized GeOx Resistive-Switching Random-Access Memory and Its Applicability in Processing-in-Memory Computing","year":2022,"lang":"en","type":"article","venue":"Nanoscale Research Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Ministry of Education, Science and Technology; National Research Foundation of Korea; Seoul National University; National Research Foundation","keywords":"Resistive random-access memory; Von Neumann architecture; Computer science; Interconnection; Non-volatile memory; Latency (audio); Materials science; Reliability (semiconductor); Computer architecture; Embedded system; Computer hardware; Electrical engineering; Power (physics); Operating system; Voltage; Telecommunications; Engineering","score_opus":0.03177602259474303,"score_gpt":0.32364508769909994,"score_spread":0.2918690651043569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283814179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953643,0.0015800201,0.00033491186,0.0010930335,0.00019388317,0.0010054869,0.000005398075,0.00020468392,0.00021828852],"genre_scores_gemma":[0.99905074,0.000046444733,0.0001922061,0.0002857366,0.00015883816,0.00017370396,0.0000073083575,0.00005795174,0.000027047729],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99610144,0.0007830362,0.00054331776,0.0007213449,0.00083973835,0.0010111385],"domain_scores_gemma":[0.9985428,0.0008826762,0.0000588221,0.00028411124,0.00006118115,0.00017040117],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0036349732,0.00027929223,0.0004968025,0.00055551145,0.0007165607,0.00012632842,0.00059296837,0.00008880184,0.00002381738],"category_scores_gemma":[0.00035678814,0.00029947702,0.00006165408,0.0013685211,0.000111990616,0.00041170605,0.0007261735,0.0024696155,0.0000028435402],"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":[0.0003749468,0.000058131118,0.0009242235,0.0005595333,0.0000069189896,0.00019010702,0.0009191143,0.060959782,0.92850935,0.0000047060516,0.00015986513,0.007333322],"study_design_scores_gemma":[0.021854095,0.0002610775,0.05473553,0.0012389029,0.000024628618,0.00014951806,0.0028105958,0.33180934,0.5827205,0.00056775165,0.0013436364,0.0024844052],"about_ca_topic_score_codex":0.000040938325,"about_ca_topic_score_gemma":0.00004682645,"teacher_disagreement_score":0.34578884,"about_ca_system_score_codex":0.0004526625,"about_ca_system_score_gemma":0.00006969163,"threshold_uncertainty_score":0.99994576},"labels":[],"label_agreement":null},{"id":"W4284990878","doi":"10.1002/aelm.202200449","title":"Soft Biomaterials Based Flexible Artificial Synapse for Neuromorphic Computing","year":2022,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Neuromorphic engineering; Materials science; Synapse; Computer science; Memristor; Long-term potentiation; Neural facilitation; Artificial neural network; Computer architecture; Nanotechnology; Excitatory postsynaptic potential; Artificial intelligence; Neuroscience; Electronic engineering; Engineering; Chemistry","score_opus":0.020857616809481186,"score_gpt":0.24704932463672755,"score_spread":0.22619170782724635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284990878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93808526,0.0002123159,0.05784187,0.000075359145,0.0016471182,0.0007874457,0.0001472388,0.0011808272,0.000022535249],"genre_scores_gemma":[0.9981013,0.000008082211,0.00078691886,0.00024839118,0.00028388636,0.00024001417,0.00017304368,0.00011622623,0.000042157044],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976381,0.00012634638,0.0005841612,0.00043652233,0.00018876804,0.00102609],"domain_scores_gemma":[0.99919707,0.00023007923,0.00014895995,0.00031538963,0.000034748853,0.00007372793],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051669864,0.00032421746,0.00046959074,0.00012626563,0.0005912025,0.00006541571,0.00031757052,0.00005140768,0.0004212423],"category_scores_gemma":[0.00006150861,0.00037507346,0.00009112254,0.00025804836,0.000032051335,0.00015730494,0.00010544683,0.0001301005,0.000014657319],"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":[0.00017597627,0.000020405536,5.5952097e-7,0.0000946152,0.000015224609,0.0000058083974,0.00001830204,0.26579353,0.73036337,0.0011533748,0.00005930352,0.0022995195],"study_design_scores_gemma":[0.0006629126,0.0003449892,0.000006343322,0.000016592849,0.000021039064,0.000032218195,0.000021145883,0.006350467,0.98176014,0.003320891,0.0070571406,0.0004061245],"about_ca_topic_score_codex":0.0000017897645,"about_ca_topic_score_gemma":0.0000011373097,"teacher_disagreement_score":0.25944307,"about_ca_system_score_codex":0.00021994679,"about_ca_system_score_gemma":0.00006755902,"threshold_uncertainty_score":0.9998701},"labels":[],"label_agreement":null},{"id":"W4285032045","doi":"10.1109/prime55000.2022.9816797","title":"A Low-Resource Digital Implementation of the Fitzhugh-Nagumo Neuron","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Neuromorphic engineering; Field-programmable gate array; Computer science; Acceleration; Component (thermodynamics); Hardware acceleration; Implementation; Resource (disambiguation); Computer architecture; Biological neuron model; Computer hardware; Clock rate; Computer engineering; Artificial neural network; Artificial intelligence; Telecommunications","score_opus":0.007845601426656981,"score_gpt":0.22261425394580242,"score_spread":0.21476865251914545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285032045","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99606466,0.000012112995,0.00092747045,0.00004720314,0.000119803815,0.000081623075,0.000009180199,0.00008561196,0.0026523506],"genre_scores_gemma":[0.99966,3.4007743e-7,0.000019831305,0.00007603621,0.000018776684,0.000004522562,0.0000030596798,0.000008803499,0.00020861826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996645,0.000010047657,0.000095042946,0.000056628145,0.00008917509,0.000084595515],"domain_scores_gemma":[0.99984574,0.00002729854,0.000018387856,0.000093807714,0.0000033505194,0.000011384287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000024073353,0.000044480767,0.00004225368,0.000013828205,0.000080639395,0.000006877836,0.0000994676,0.0000041220474,0.00012663357],"category_scores_gemma":[0.000003282541,0.00003562449,0.00003258546,0.00011012688,0.0000066530765,0.00006361976,0.00010565357,0.00008336107,0.0000015037605],"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":[0.000009080267,0.000020094003,0.0012251514,0.000046921155,0.000013347861,0.0000037359584,0.00064342684,0.8130166,0.10277376,0.0005390239,0.0019383865,0.079770505],"study_design_scores_gemma":[0.00081253395,0.00017145256,0.009067975,0.000012055586,0.000013914238,0.00003982494,0.004046419,0.028342504,0.91013485,0.00048094615,0.046544876,0.00033267296],"about_ca_topic_score_codex":0.0000010676182,"about_ca_topic_score_gemma":0.0000012645896,"teacher_disagreement_score":0.80736107,"about_ca_system_score_codex":0.000015833326,"about_ca_system_score_gemma":0.0000029789098,"threshold_uncertainty_score":0.14527245},"labels":[],"label_agreement":null},{"id":"W4285126417","doi":"10.1109/mcas.2022.3169854","title":"Contents","year":2022,"lang":"en","type":"paratext","venue":"IEEE Circuits and Systems Magazine","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Alberta","funders":"","keywords":"Computer science","score_opus":0.03546901885181342,"score_gpt":0.24605028581276547,"score_spread":0.21058126696095206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285126417","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061108254,0.09738945,0.008859953,0.00002476968,0.09461706,0.0019451322,0.0010025654,0.0009655207,0.7340873],"genre_scores_gemma":[0.73885703,0.0012956071,0.0000023456453,0.000057850404,0.0014672421,0.000076018645,0.00013872435,0.00013384987,0.25797132],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986024,0.000055064676,0.0004109604,0.000350672,0.000217166,0.0003637345],"domain_scores_gemma":[0.9993967,0.000071963805,0.00009957329,0.00027319387,0.000035806213,0.00012278667],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000115475974,0.00036008438,0.00060201867,0.00012297073,0.00015720057,0.00007692646,0.0001836496,0.00015781261,0.0006103449],"category_scores_gemma":[0.000007304375,0.00036020682,0.00007302131,0.00013403594,0.000025774223,0.00008288941,0.000042154003,0.0005742057,0.0015995507],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004991966,0.000018431005,0.000009978877,0.0027993876,0.00017338645,0.00014529834,0.00015902387,0.15680052,0.03179592,0.000057038076,0.7965836,0.011452397],"study_design_scores_gemma":[0.00038520232,0.00005526082,0.000023137041,0.00026587263,0.000027747225,0.00018644116,0.000036576705,0.0056043463,0.0003585725,0.000005015682,0.99251693,0.00053490937],"about_ca_topic_score_codex":0.000004369593,"about_ca_topic_score_gemma":8.4471395e-7,"teacher_disagreement_score":0.6777488,"about_ca_system_score_codex":0.000079300065,"about_ca_system_score_gemma":0.000011228878,"threshold_uncertainty_score":0.99988496},"labels":[],"label_agreement":null},{"id":"W4285218612","doi":"10.1109/tcsii.2022.3187623","title":"A High-Accuracy Digital Implementation of the Morris–Lecar Neuron With Variable Physiological Parameters","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"CORDIC; Field-programmable gate array; Computer science; Acceleration; Rotation (mathematics); Computer hardware; Variable (mathematics); State (computer science); Artificial intelligence; Algorithm; Mathematics","score_opus":0.0180889104765702,"score_gpt":0.22385979406834278,"score_spread":0.2057708835917726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285218612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7852878,0.00003321508,0.21264908,0.000010129914,0.0010300067,0.0005103459,0.00022078175,0.00019275723,0.000065873566],"genre_scores_gemma":[0.99952656,0.000004026419,0.000057811845,0.000037840193,0.00003694477,0.00021346853,0.000007037923,0.00003921705,0.000077068675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986701,0.000107244356,0.00034788082,0.00027828044,0.00031942734,0.00027708602],"domain_scores_gemma":[0.99928296,0.00015311489,0.0001232123,0.00035548856,0.000031437386,0.000053776082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007428059,0.00020450371,0.00025829687,0.000059555005,0.0005251475,0.000040616407,0.0002688461,0.000035098077,0.00003383298],"category_scores_gemma":[0.0000034938382,0.00016354363,0.000088850145,0.0003544493,0.000043695723,0.00028905657,0.000007284952,0.0003755806,0.0000014363571],"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":[0.000014956324,0.0000464038,0.0000062785934,0.00005561915,0.000042071228,0.0000032660405,0.00034884232,0.88213825,0.11463284,0.000053628635,0.000059888407,0.0025979616],"study_design_scores_gemma":[0.00616097,0.0035432945,0.0015702716,0.00056734827,0.00037376132,0.0006029577,0.0072257165,0.055794604,0.91105527,0.00024867328,0.010538969,0.0023181394],"about_ca_topic_score_codex":0.00008479146,"about_ca_topic_score_gemma":0.0000018550828,"teacher_disagreement_score":0.82634366,"about_ca_system_score_codex":0.00009292444,"about_ca_system_score_gemma":0.000029648187,"threshold_uncertainty_score":0.6669116},"labels":[],"label_agreement":null},{"id":"W4285252396","doi":"10.1109/tetci.2022.3174905","title":"Modulating STDP With Back-Propagated Error Signals to Train SNNs for Audio Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Spiking neural network; Artificial intelligence; Backpropagation; Deep learning; Artificial neural network; Speech recognition; Learning rule; Machine learning; Pattern recognition (psychology)","score_opus":0.07404502785326134,"score_gpt":0.31946304388177227,"score_spread":0.24541801602851093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285252396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09344926,0.000012290683,0.90499353,0.00035839475,0.00049272383,0.00043924164,0.00003106524,0.0001425698,0.0000809265],"genre_scores_gemma":[0.9622593,0.0000018431952,0.03716591,0.00015795926,0.0000578127,0.00019335483,0.000012822311,0.00003240736,0.00011860175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880666,0.00004747143,0.00035871647,0.00030117473,0.00024067884,0.0002453053],"domain_scores_gemma":[0.9993826,0.00029367505,0.000048337493,0.00012708636,0.000088828085,0.00005945512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018944607,0.00016459699,0.00015417706,0.0002365963,0.00032601508,0.00002073196,0.00018026783,0.00002970961,0.000101314276],"category_scores_gemma":[0.000010464442,0.0001859248,0.000053346364,0.0005961483,0.000022373295,0.00010964557,0.0000028426152,0.00033671584,0.000007864392],"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":[0.000046096986,0.000045132434,0.000005958614,0.000031189094,0.00001540092,0.00000239146,0.0005975193,0.9547947,0.0024141353,0.00036157636,0.000018718278,0.041667197],"study_design_scores_gemma":[0.00014074253,0.00017781382,0.000089578374,0.000041364547,0.000006754188,0.000006617524,0.00039375783,0.97790587,0.01852059,0.0022102438,0.00027967358,0.00022699115],"about_ca_topic_score_codex":0.0000038062005,"about_ca_topic_score_gemma":0.000016440625,"teacher_disagreement_score":0.86881006,"about_ca_system_score_codex":0.00020640514,"about_ca_system_score_gemma":0.000027133863,"threshold_uncertainty_score":0.75817937},"labels":[],"label_agreement":null},{"id":"W4285289251","doi":"10.2316/j.2022.201-0213","title":"NETWORKED STATE ESTIMATION OVER LOSSY COMMUNICATION CHANNELS WITH DATA RATE LIMITATION, 115-121.","year":2022,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lossy compression; Estimation; Computer science; State (computer science); High data rate; Telecommunications; Algorithm; Engineering; Wireless; Artificial intelligence; Systems engineering","score_opus":0.015265704867860032,"score_gpt":0.21636803315718636,"score_spread":0.20110232828932634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285289251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3103778,0.009676525,0.6779203,0.00013586199,0.0005163108,0.0008801144,0.000071351256,0.0003420294,0.00007972915],"genre_scores_gemma":[0.99924576,0.00008167054,0.00016485622,0.00004911398,0.000060541784,0.00010508529,0.00013894607,0.000027612787,0.00012640709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990544,0.0001651434,0.00023015846,0.00020123277,0.00013123096,0.0002178753],"domain_scores_gemma":[0.99920636,0.00014463151,0.0000971175,0.0004817283,0.0000260175,0.00004416313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005318934,0.00013340253,0.00019423704,0.000042891428,0.00032299221,0.00007011554,0.00022393426,0.000020023974,0.000007979963],"category_scores_gemma":[0.000010189053,0.0001235808,0.000014047255,0.00012043187,0.0000144723035,0.0003040691,0.00009113681,0.00018808561,0.0000026656971],"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":[0.000039483537,0.000006352714,0.000037148206,0.00004621417,0.000046839465,0.000002743097,0.00016371683,0.9946088,0.00044639554,0.00068263855,0.00016017903,0.0037594738],"study_design_scores_gemma":[0.001241864,0.00007282654,0.00010302986,0.00004400519,0.000029367102,0.000021847984,0.00020922207,0.99411374,0.000046604924,0.00031076244,0.0036528201,0.00015391958],"about_ca_topic_score_codex":0.000023416758,"about_ca_topic_score_gemma":0.000010440157,"teacher_disagreement_score":0.688868,"about_ca_system_score_codex":0.00006596975,"about_ca_system_score_gemma":0.000016821761,"threshold_uncertainty_score":0.5039479},"labels":[],"label_agreement":null},{"id":"W4285309784","doi":"10.1109/tvlsi.2022.3170596","title":"Hardware-Efficient, On-the-Fly, On-Implant Spike Sorter Dedicated to Brain-Implantable Microsystems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University","funders":"","keywords":"Spike (software development); Brain implant; Channel (broadcasting); Computer science; CMOS; Computer hardware; Algorithm; Artificial intelligence; Pattern recognition (psychology); Electrical engineering; Engineering; Telecommunications","score_opus":0.016065468236795107,"score_gpt":0.23174697515845163,"score_spread":0.21568150692165652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285309784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3433849,0.00007799624,0.6422109,0.00042572967,0.008902177,0.0014890417,0.0012611735,0.00086617155,0.0013819415],"genre_scores_gemma":[0.99602807,0.000006402449,0.000030644922,0.0008090143,0.00021013593,0.00051651977,0.000049391565,0.00008955624,0.002260289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972055,0.00027680534,0.00068828213,0.0005557784,0.00062688324,0.0006467818],"domain_scores_gemma":[0.99858946,0.00044467772,0.0001154609,0.0005694504,0.000073326664,0.00020764321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054768135,0.0004402234,0.00043673062,0.00038463078,0.0012052329,0.00014919997,0.0004098014,0.00011615187,0.00020326854],"category_scores_gemma":[0.000012117037,0.00036881876,0.00021010062,0.0006782695,0.000027505244,0.00012775838,0.0000064252577,0.0009574217,0.00049099367],"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":[0.00011790592,0.00021258408,0.0000028921938,0.000057510082,0.00007179279,0.000025690999,0.0008515927,0.9231757,0.051732887,0.00013245648,0.022680493,0.00093852106],"study_design_scores_gemma":[0.001352506,0.0009379546,0.000018469347,0.0006608818,0.00007539155,0.0006897258,0.0032660107,0.5835239,0.31538907,0.000009406022,0.092962585,0.0011140662],"about_ca_topic_score_codex":0.00003927377,"about_ca_topic_score_gemma":0.000067082845,"teacher_disagreement_score":0.65264314,"about_ca_system_score_codex":0.00041450688,"about_ca_system_score_gemma":0.000026278267,"threshold_uncertainty_score":0.9998764},"labels":[],"label_agreement":null},{"id":"W4285594718","doi":"10.1145/3546790.3546803","title":"Efficient Spike Encoding Algorithms for Neuromorphic Speech Recognition","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Neuromorphic engineering; Encoding (memory); Spike (software development); Spiking neural network; ENCODE; Artificial intelligence; Context (archaeology); Speech recognition; Convolutional neural network; Decoding methods; Pattern recognition (psychology); Artificial neural network; Algorithm","score_opus":0.09515590085426741,"score_gpt":0.2842254641095999,"score_spread":0.18906956325533253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285594718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75260806,0.00026921427,0.2297405,0.00006429748,0.0069656423,0.0013917796,0.00016998347,0.0017814086,0.0070091463],"genre_scores_gemma":[0.91272134,0.000114188,0.08367324,0.00022879505,0.0013147593,0.00044943052,0.0005584131,0.00023276122,0.00070707477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987389,0.000025811965,0.0003112472,0.0004297991,0.00017017813,0.00032409572],"domain_scores_gemma":[0.99939024,0.00017031192,0.000066377805,0.0002662425,0.000039537124,0.00006731014],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002156182,0.00027756294,0.00027249224,0.00012750583,0.00016281263,0.00003943071,0.00021668557,0.000110427696,0.00031233206],"category_scores_gemma":[0.000048440732,0.00031060947,0.00016579684,0.00011357573,0.000012210819,0.000024266858,0.0003561455,0.0007603294,0.000017695616],"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":[0.000008328194,0.00001644781,0.0000018439613,0.00033525357,0.000016983693,0.000029048426,0.000051281066,0.9125913,0.005848867,0.000016791326,0.00020258577,0.08088132],"study_design_scores_gemma":[0.0003028662,0.00006625311,0.000017135128,0.000103871265,0.000041203122,0.000040063765,0.00006483261,0.93211645,0.06163741,0.0017655692,0.0032592427,0.00058510836],"about_ca_topic_score_codex":0.000002557099,"about_ca_topic_score_gemma":8.7940833e-7,"teacher_disagreement_score":0.1601133,"about_ca_system_score_codex":0.00011715613,"about_ca_system_score_gemma":0.000016338643,"threshold_uncertainty_score":0.9999346},"labels":[],"label_agreement":null},{"id":"W4288069828","doi":"10.1016/j.trechm.2022.06.004","title":"MXenes: promising 2D memristor materials for neuromorphic computing components","year":2022,"lang":"en","type":"article","venue":"Trends in Chemistry","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Science and Engineering Research Board; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Neuromorphic engineering; Memristor; MXenes; Scalability; Computer science; Von Neumann architecture; Artificial neural network; Computer architecture; Artificial intelligence; Materials science; Nanotechnology; Electronic engineering; Engineering","score_opus":0.04760697681498047,"score_gpt":0.2666196468788987,"score_spread":0.21901267006391825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288069828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99777836,0.00009490174,0.00038326622,0.000027735157,0.00052622164,0.00008475584,0.00004414725,0.00027380112,0.0007868052],"genre_scores_gemma":[0.99876976,0.0000015895937,0.00063778047,0.000028274892,0.00018225986,0.000031964875,0.00009554576,0.000047208414,0.00020563345],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989455,0.000021223868,0.0003083014,0.0002522488,0.00015812537,0.0003146358],"domain_scores_gemma":[0.999615,0.000086053704,0.00006348121,0.00017665478,0.000012915673,0.00004588268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020649364,0.000171623,0.0002486537,0.0000543904,0.00018462313,0.00001950272,0.00020999511,0.000037607213,0.00020456176],"category_scores_gemma":[0.000021554359,0.000212492,0.000051712304,0.00020188604,0.00001793468,0.000048745143,0.000120342556,0.0002360975,0.0000011365304],"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":[0.000028321132,0.000024993411,0.000037650727,0.00019292503,0.000007492703,0.000023626795,0.00012604296,0.07814989,0.9173002,0.0000031871027,0.00024104943,0.0038645938],"study_design_scores_gemma":[0.0009516155,0.00002259135,0.00024826312,0.000051762152,0.000010408736,0.00007179465,0.00008155688,0.030773222,0.9640629,0.000076319855,0.0033098734,0.00033968882],"about_ca_topic_score_codex":0.0000018226988,"about_ca_topic_score_gemma":3.0665637e-7,"teacher_disagreement_score":0.047376674,"about_ca_system_score_codex":0.00019598444,"about_ca_system_score_gemma":0.00000752464,"threshold_uncertainty_score":0.86651725},"labels":[],"label_agreement":null},{"id":"W4288092562","doi":"","title":"Evidence of Normal-Isolant-Superconducting Tunnel Junction in TiN/TiO2/Pt Memristors","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Memristor; Tin; Superconductivity; Materials science; Electrical engineering; Condensed matter physics; Physics; Engineering; Metallurgy","score_opus":0.030789563382539725,"score_gpt":0.23747650484596627,"score_spread":0.20668694146342656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288092562","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89413327,0.0033521554,0.09159755,0.00036902697,0.00093114923,0.0004020895,0.000008587737,0.00031279481,0.008893351],"genre_scores_gemma":[0.9896469,0.0009557774,0.007987785,0.00001624062,0.000033101845,0.000020819232,0.00005243615,0.00005161747,0.0012353163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969747,0.0012627662,0.00066524546,0.0005105391,0.00026152658,0.00032522355],"domain_scores_gemma":[0.9965523,0.0013498655,0.000290859,0.0011762657,0.00054783357,0.000082869745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028847323,0.0003070407,0.0004481681,0.0002502345,0.000107972235,0.000067969886,0.0006813284,0.0002403975,0.000051633975],"category_scores_gemma":[0.0011204897,0.00035464225,0.00015703042,0.00034879957,0.00008174069,0.0003428937,0.0006004434,0.00094337354,0.000025698402],"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":[0.000069492446,0.00041280393,0.020186061,0.0051371497,0.00016727956,0.000020339148,0.0275198,0.4377241,0.40005603,0.005112652,0.0005418755,0.10305239],"study_design_scores_gemma":[0.00086908997,0.0000019304753,0.009307999,0.019949516,0.000081516126,0.000036246915,0.00069861993,0.25602686,0.70791477,0.0011674005,0.0025702545,0.0013757868],"about_ca_topic_score_codex":0.00037187187,"about_ca_topic_score_gemma":0.00068071485,"teacher_disagreement_score":0.3078587,"about_ca_system_score_codex":0.00018326196,"about_ca_system_score_gemma":0.00008497439,"threshold_uncertainty_score":0.99989057},"labels":[],"label_agreement":null},{"id":"W4289926190","doi":"10.1109/newcas52662.2022.9842225","title":"Input-Layer Neuron Implementation Using Delta-Sigma Modulators","year":2022,"lang":"en","type":"article","venue":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Spiking neural network; Artificial neural network; Electronic circuit; Spike (software development); Synapse; Biological neural network; Biological neuron model; Encoding (memory); Topology (electrical circuits); Artificial intelligence; Neuroscience; Electrical engineering; Engineering; Machine learning","score_opus":0.09255892129936916,"score_gpt":0.3205586961590829,"score_spread":0.22799977485971373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289926190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9485593,0.0001374711,0.04658521,0.00021679413,0.0030632415,0.00039408912,0.000075918906,0.0004136938,0.0005543092],"genre_scores_gemma":[0.9979566,0.000025457894,0.0005479512,0.00059791043,0.00036906474,0.00007394554,0.00009837109,0.00009517971,0.00023556546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973071,0.00016019215,0.0006250782,0.0006160303,0.0006072093,0.0006843832],"domain_scores_gemma":[0.9990094,0.000114414004,0.00017578671,0.00040778393,0.00009390204,0.0001987355],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022380533,0.00044977642,0.0003782103,0.00030020115,0.00052935205,0.00008915421,0.00053189875,0.00007317928,0.001505233],"category_scores_gemma":[0.000017168835,0.00052227854,0.00019249655,0.00049298984,0.000066211025,0.00049884507,0.0003081521,0.0008193886,0.000029987923],"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":[0.00016367748,0.00009573019,0.0009776269,0.000086386644,0.00015563553,0.0003093996,0.0014689564,0.7777364,0.17111929,0.0030156919,0.007499692,0.037371468],"study_design_scores_gemma":[0.0016800608,0.000451076,0.0010037618,0.00008376426,0.00009378014,0.00085906073,0.0025920977,0.91525334,0.04392335,0.0022263692,0.030380392,0.0014529419],"about_ca_topic_score_codex":0.000107449225,"about_ca_topic_score_gemma":0.00008315417,"teacher_disagreement_score":0.1375169,"about_ca_system_score_codex":0.00042021667,"about_ca_system_score_gemma":0.00012334369,"threshold_uncertainty_score":0.9997229},"labels":[],"label_agreement":null},{"id":"W4290679165","doi":"","title":"Integration and Electrical Characterization of Indium-Oxide Nanoparticles in Oxide Resistive Random-Access Memories","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Indium; Random access; Characterization (materials science); Materials science; Nanoparticle; Random access memory; Resistive touchscreen; Oxide; Resistive random-access memory; Optoelectronics; Nanotechnology; Computer science; Chemistry; Electrode; Metallurgy; Computer network; Computer hardware","score_opus":0.012846389314591146,"score_gpt":0.2320177732264492,"score_spread":0.21917138391185806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290679165","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89686286,0.0002767297,0.10127652,0.0003200868,0.00007659126,0.00028279854,0.000020996917,0.00012769342,0.00075572974],"genre_scores_gemma":[0.9961727,0.00053349056,0.0029220118,0.000011740783,0.000011959745,0.000037321057,0.00009237735,0.000026978807,0.00019138954],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975109,0.0013152735,0.00047585357,0.00033516533,0.0001603496,0.00020242385],"domain_scores_gemma":[0.9978277,0.0010135146,0.0002440588,0.00044711772,0.00040352496,0.000064104854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001563006,0.00020941801,0.00033578425,0.0001941509,0.00008402643,0.00010171825,0.00034386263,0.00014925965,0.0000061832948],"category_scores_gemma":[0.0010353508,0.00019795739,0.000054694956,0.00027125707,0.00010941635,0.00025809967,0.0003534145,0.00033627547,0.0000013282145],"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":[0.00006178927,0.000047701007,0.001276421,0.00013399936,0.000018340497,0.0000021489777,0.001706646,0.00025684334,0.97416764,0.003307271,0.0000063825073,0.019014822],"study_design_scores_gemma":[0.0004970735,2.8748997e-7,0.03586795,0.0013530727,0.000013393342,0.0000025072718,0.00001969479,0.01154552,0.9478225,0.002630157,0.000053710886,0.00019409448],"about_ca_topic_score_codex":0.00004701739,"about_ca_topic_score_gemma":0.00022696011,"teacher_disagreement_score":0.09930987,"about_ca_system_score_codex":0.00007920229,"about_ca_system_score_gemma":0.000045137,"threshold_uncertainty_score":0.8072468},"labels":[],"label_agreement":null},{"id":"W4291414035","doi":"10.3390/chips1020008","title":"Integrated Sensor Electronic Front-Ends with Self-X Capabilities","year":2022,"lang":"en","type":"article","venue":"Chips","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"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 Food Inspection Agency","keywords":"Computer science; Interfacing; Robustness (evolution); System on a chip; Integrated circuit; Computer architecture; Application-specific integrated circuit; CMOS; Embedded system; Neuromorphic engineering; Electronic engineering; Computer hardware; Engineering; Artificial intelligence; Artificial neural network","score_opus":0.007209879915644142,"score_gpt":0.18278967908703173,"score_spread":0.17557979917138758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291414035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99493045,0.00032835544,0.0012678641,0.000027260268,0.00015577865,0.00008620903,0.0000061620112,0.0007759237,0.0024220056],"genre_scores_gemma":[0.9986209,0.000007928764,0.0006350381,0.000045660712,0.000059063233,0.00002279343,0.000011269605,0.000029814522,0.00056751864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993721,0.000032535034,0.000091357,0.00013142006,0.000088959205,0.00028360842],"domain_scores_gemma":[0.9997626,0.00004291959,0.000013908492,0.0001356823,0.000010534988,0.00003435712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006558842,0.000113869435,0.00011056997,0.000038641152,0.00016796165,0.000009381898,0.00009462076,0.0000147819555,0.00016727275],"category_scores_gemma":[0.0000062508643,0.00010059014,0.000025652998,0.00012216119,0.000014045273,0.00005829359,0.000028807328,0.0004283135,0.000011552737],"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":[0.00020023732,0.000114219736,0.0018196746,0.0002545851,0.0002535857,0.00011834162,0.008536622,0.9170853,0.053834736,0.002838152,0.002128136,0.012816396],"study_design_scores_gemma":[0.0038264028,0.002253155,0.002134425,0.00010100892,0.00015967374,0.001089983,0.019771019,0.24996199,0.23198347,0.0048718345,0.48086762,0.0029794131],"about_ca_topic_score_codex":0.0000026674259,"about_ca_topic_score_gemma":0.000009944723,"teacher_disagreement_score":0.6671233,"about_ca_system_score_codex":0.00016091656,"about_ca_system_score_gemma":0.00002190933,"threshold_uncertainty_score":0.4101947},"labels":[],"label_agreement":null},{"id":"W4292070340","doi":"10.1109/newcas52662.2022.9842017","title":"Towards Current-Mode Analog Implementation of Deep Neural Network Functions","year":2022,"lang":"en","type":"article","venue":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"MNIST database; Softmax function; Computer science; Convolutional neural network; Subthreshold conduction; Artificial neural network; CMOS; Analog multiplier; Electronic engineering; Multiplier (economics); Artificial intelligence; Transistor; Computer hardware; Voltage; Electrical engineering; Analog signal; Engineering","score_opus":0.05008286536193704,"score_gpt":0.3221522631923185,"score_spread":0.2720693978303814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292070340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8425571,0.0011347916,0.14384308,0.00041800158,0.008955786,0.00060356123,0.00023972223,0.00049709744,0.0017508732],"genre_scores_gemma":[0.99849373,0.000055090342,0.00026931576,0.00015211014,0.0005304407,0.00011251666,0.00021592503,0.000048788526,0.00012210498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977116,0.00013816895,0.0006390294,0.00043714553,0.00049639144,0.0005776802],"domain_scores_gemma":[0.9990797,0.00011250974,0.00020005277,0.0003312321,0.00012199613,0.0001545174],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018588119,0.0003438021,0.00038498256,0.0002030276,0.00037025643,0.000037770144,0.00046646842,0.0000499938,0.0017688554],"category_scores_gemma":[0.000013439174,0.00038695996,0.00022653371,0.0005785152,0.000081547136,0.00031083977,0.00023161192,0.00075840484,0.000021806803],"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":[0.00013110405,0.00007980234,0.0012877043,0.00009106132,0.00013702209,0.00004200102,0.0009376686,0.73010653,0.0065928577,0.0032932784,0.011702119,0.24559885],"study_design_scores_gemma":[0.0022575313,0.0009149187,0.004447271,0.00011593963,0.00019304719,0.00045897797,0.00500502,0.92722785,0.006512918,0.005787948,0.04552471,0.0015538842],"about_ca_topic_score_codex":0.00008253293,"about_ca_topic_score_gemma":0.00031533404,"teacher_disagreement_score":0.24404497,"about_ca_system_score_codex":0.00019169052,"about_ca_system_score_gemma":0.00009835018,"threshold_uncertainty_score":0.99985826},"labels":[],"label_agreement":null},{"id":"W4292070648","doi":"10.1109/newcas52662.2022.9842182","title":"Activity-Adaptive Architectures for Energy-Efficient Scalable Neural Recording Microsystems: A Review of Current and Future Directions","year":2022,"lang":"en","type":"review","venue":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Scalability; Microsystem; Artificial neural network; Wireless; Transmission (telecommunications); Reduction (mathematics); Efficient energy use; Energy (signal processing); Power (physics); Embedded system; Artificial intelligence; Electrical engineering; Telecommunications; Engineering","score_opus":0.10710725517038555,"score_gpt":0.3386172789565396,"score_spread":0.23151002378615404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292070648","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024084761,0.9860298,0.0066145645,0.000067635,0.0048530623,0.0013314001,0.0004480749,0.00020110818,0.0002135246],"genre_scores_gemma":[0.0014320768,0.99611545,0.00017609204,0.000053304706,0.0010316097,0.0007842247,0.00010984953,0.00012940023,0.00016800317],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966976,0.0003536087,0.0010341483,0.00089548144,0.0004039093,0.0006152693],"domain_scores_gemma":[0.9976467,0.0008660904,0.0006419784,0.00049971364,0.00013762774,0.00020787421],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000344465,0.0008837194,0.0022102015,0.00042659303,0.00034108706,0.000056061308,0.0005648308,0.00018533549,0.00015031145],"category_scores_gemma":[0.000074101954,0.0008049984,0.0008625219,0.0006279386,0.00012063703,0.000079534155,0.0002495657,0.0012581747,0.0000026081213],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038752427,0.000055156925,2.2149013e-7,0.04407573,0.00018486737,0.000012375311,0.00008625815,0.0007497314,0.000040428597,0.0003835764,0.001620207,0.9527527],"study_design_scores_gemma":[0.0002140357,0.0001712707,2.7551542e-7,0.040564895,0.0003958403,0.00046610122,0.000058813122,0.01348406,0.000075395554,0.00006847152,0.9437908,0.00071004283],"about_ca_topic_score_codex":0.000026824388,"about_ca_topic_score_gemma":0.000031675274,"teacher_disagreement_score":0.95204264,"about_ca_system_score_codex":0.00031019826,"about_ca_system_score_gemma":0.00024337118,"threshold_uncertainty_score":0.9994401},"labels":[],"label_agreement":null},{"id":"W4292616153","doi":"10.1021/acs.jpclett.2c01906","title":"Ag/HfO<sub><i>x</i></sub>/Pt Unipolar Memristor for High-Efficiency Logic Operation","year":2022,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"Natural Science Foundation of Chongqing; Central University Basic Research Fund of China; Natural Science Foundation of Guizhou Province; National Natural Science Foundation of China","keywords":"Memristor; Reset (finance); Materials science; Voltage; Optoelectronics; Power (physics); Work (physics); Electrical conductor; Resistive random-access memory; Computer science; Electrical engineering; Nanotechnology; Physics; Thermodynamics; Engineering; Composite material","score_opus":0.00876235231516905,"score_gpt":0.2086645462723652,"score_spread":0.19990219395719613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292616153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97472525,0.00009749172,0.02394605,0.00089542294,0.00017987413,0.00008030714,0.000007766894,0.000039370538,0.00002845913],"genre_scores_gemma":[0.99867815,0.0000074749555,0.00011428424,0.0003890047,0.000773631,0.000005381922,0.000004454961,0.000022226513,0.000005388618],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922264,0.00003938506,0.00022411397,0.00008536253,0.00022738446,0.00020109356],"domain_scores_gemma":[0.9994744,0.00019005542,0.00011970572,0.0001321444,0.000029390903,0.00005431418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022594836,0.0001324881,0.00018096053,0.000016261349,0.00026387293,0.000013638822,0.0003094767,0.00001668004,0.000008856252],"category_scores_gemma":[0.000027746473,0.00010330678,0.00011817812,0.00011230179,0.00004101964,0.00010083101,0.000049721326,0.0004912645,0.0000015133146],"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":[0.000025805546,0.000021333532,2.8741232e-7,0.000025124169,0.000012523397,0.0000048008883,0.00011697971,0.3748964,0.6240767,0.000014338263,0.00051976263,0.00028589394],"study_design_scores_gemma":[0.00032794193,0.00006846562,0.000005887923,0.0000105677,0.00004223967,0.000061025843,0.00007241875,0.014841737,0.9837884,0.00024557047,0.00040766463,0.00012813207],"about_ca_topic_score_codex":2.8096176e-7,"about_ca_topic_score_gemma":3.8681275e-8,"teacher_disagreement_score":0.36005467,"about_ca_system_score_codex":0.00013479393,"about_ca_system_score_gemma":0.000013032473,"threshold_uncertainty_score":0.4212728},"labels":[],"label_agreement":null},{"id":"W4293701563","doi":"10.1021/acsami.2c12850","title":"Voltage-Controlled Conversion from CDS to MDS in an Azobenzene-Based Organic Memristor for Information Storage and Logic Operations","year":2022,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Memristor; Materials science; Capacitance; Voltage; Optoelectronics; Azobenzene; Nanotechnology; Electronic engineering; Electrode; Electrical engineering; Physical chemistry; Chemistry","score_opus":0.0112634492446947,"score_gpt":0.22645038641543244,"score_spread":0.21518693717073775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293701563","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881292,0.00003552308,0.010132176,0.000050246537,0.00045226168,0.0008993419,0.00012990511,0.0001500455,0.000021299973],"genre_scores_gemma":[0.9981238,0.0000026601047,0.0009927818,0.00033330722,0.000048599588,0.00034224358,0.00012779191,0.00002132388,0.0000074639674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992232,0.00002904328,0.0003284901,0.00016547664,0.000085837855,0.00016800391],"domain_scores_gemma":[0.9996805,0.00007561757,0.000046669848,0.00013400702,0.000021797365,0.00004140096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021063568,0.00015289233,0.00028215037,0.00011172984,0.00017064133,0.00009988894,0.00014606318,0.000041731466,0.0002097927],"category_scores_gemma":[0.000021491265,0.00015304124,0.000008470729,0.00008510096,0.000008960194,0.00027366792,0.00008730011,0.00008064478,0.000010644838],"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":[0.00042558034,0.000011193662,0.0000027459155,0.000029613782,0.000007078562,5.0342055e-7,0.0006518959,0.16869484,0.82977515,0.00009222249,0.000042944266,0.00026621463],"study_design_scores_gemma":[0.0024145087,0.00016587162,0.00004752537,0.000010151493,0.000015684787,5.514119e-7,0.0006543889,0.0037587099,0.9923289,0.00013647441,0.00026534425,0.0002019032],"about_ca_topic_score_codex":0.000017049308,"about_ca_topic_score_gemma":0.00003100666,"teacher_disagreement_score":0.16493613,"about_ca_system_score_codex":0.00010410784,"about_ca_system_score_gemma":0.000012459277,"threshold_uncertainty_score":0.62408406},"labels":[],"label_agreement":null},{"id":"W4293812222","doi":"10.1109/icjece.2022.3182711","title":"Electronically Tunable Flux-Controlled Resistorless Memristor Emulator","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Memristor; Capacitor; Breadboard; Computer science; Electrical engineering; Electronic engineering; Voltage; Topology (electrical circuits); Engineering","score_opus":0.003905069004805841,"score_gpt":0.15711351464718698,"score_spread":0.15320844564238115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293812222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8819097,0.01229869,0.103715606,0.00012518621,0.0014209364,0.00019305531,0.0000037217173,0.00012004853,0.00021305546],"genre_scores_gemma":[0.99813664,0.000014717541,0.0012693146,0.00008960178,0.0003699983,0.0000052977525,0.0000010024185,0.000030745887,0.00008269982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887276,0.000029537372,0.0003656019,0.00012139334,0.00014382428,0.00046687722],"domain_scores_gemma":[0.99920285,0.00014523414,0.000056844998,0.00008833726,0.00004265194,0.0004640621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019840129,0.00016261978,0.0003738628,0.000305269,0.0002019065,0.000040844396,0.00021990658,0.00003602085,0.000038697734],"category_scores_gemma":[0.000035001038,0.00016577971,0.00011279782,0.00030274797,0.000011126661,0.00010132927,0.00002245531,0.0006714318,8.6698975e-7],"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":[0.000054724074,0.000009287088,0.000083332125,0.000026611258,0.00009843634,0.00031672473,0.00008824699,0.9844069,0.004905376,0.001130385,0.0018908014,0.00698917],"study_design_scores_gemma":[0.0023845604,0.00059082237,0.0007162095,0.000032670792,0.000053186828,0.0010712809,0.000008072097,0.91593045,0.0015407075,0.00022156964,0.07696918,0.0004812941],"about_ca_topic_score_codex":0.000016478405,"about_ca_topic_score_gemma":0.000026772615,"teacher_disagreement_score":0.11622692,"about_ca_system_score_codex":0.00036495947,"about_ca_system_score_gemma":0.00016635023,"threshold_uncertainty_score":0.67603004},"labels":[],"label_agreement":null},{"id":"W4294990794","doi":"10.1371/journal.pcbi.1010461","title":"Constructing functional models from biophysically-detailed neurons","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Air Force Office of Scientific Research; Ontario Innovation Trust","keywords":"Computer science; Forgetting; Artificial neural network; Neuroscience; Artificial intelligence; Biology","score_opus":0.03489078806935814,"score_gpt":0.21897998484835446,"score_spread":0.18408919677899632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294990794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8560237,0.00006880319,0.1418327,0.000109055334,0.0005481248,0.0000857724,0.000153382,0.00036103977,0.00081741234],"genre_scores_gemma":[0.9933422,0.0000010575765,0.005716912,0.00021744194,0.0001935369,0.00002581533,0.00047268564,0.000019623742,0.000010726893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920267,0.00006992053,0.00019680458,0.00023179762,0.000113743285,0.0001850425],"domain_scores_gemma":[0.9993229,0.00048047665,0.000041084386,0.00007844894,0.00003207826,0.000045035235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003167394,0.0001224517,0.00015642925,0.00006231468,0.00024427506,0.000008611906,0.000116100724,0.000026721047,0.00024485285],"category_scores_gemma":[0.000014078937,0.00013676568,0.00005384536,0.00014142906,0.000051745617,0.00007816143,0.00011992229,0.0002895598,0.000023676552],"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":[0.000014766326,0.000021918817,0.00029476167,0.000003269733,0.000042004325,0.000004693358,0.00003059246,0.95989907,0.022503106,0.015000618,0.000055986082,0.0021292244],"study_design_scores_gemma":[0.00027380523,0.00004194292,0.00039027812,0.0000023573525,0.0000096162,0.000018025748,0.00005681424,0.90341777,0.0014120273,0.094093755,0.00013065647,0.00015294914],"about_ca_topic_score_codex":0.0000015370136,"about_ca_topic_score_gemma":6.3690806e-7,"teacher_disagreement_score":0.13731848,"about_ca_system_score_codex":0.00006508163,"about_ca_system_score_gemma":0.000020702655,"threshold_uncertainty_score":0.5577143},"labels":[],"label_agreement":null},{"id":"W4295119433","doi":"10.1088/2634-4386/ac86ef","title":"A superconducting nanowire-based architecture for neuromorphic computing","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Neuromorphic engineering; Computer science; Bridge (graph theory); Nanowire; Computer architecture; Network topology; Artificial neural network; Circuit design; Topology (electrical circuits); Electronic engineering; Computer engineering; Artificial intelligence; Electrical engineering; Nanotechnology; Embedded system; Engineering; Materials science","score_opus":0.02765823275297155,"score_gpt":0.21563337829901438,"score_spread":0.18797514554604283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295119433","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8631465,0.00041928465,0.13284701,0.00015434556,0.0015118988,0.00038709978,0.000020985908,0.0014769676,0.000035934314],"genre_scores_gemma":[0.9924756,0.0000043045106,0.0066324333,0.00027209226,0.00039099107,0.00002325553,0.000026927097,0.00016354074,0.000010819994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977716,0.00007218474,0.00048431515,0.0005975383,0.00026805457,0.0008062791],"domain_scores_gemma":[0.99854374,0.00085516006,0.00007533506,0.00030195684,0.000038188242,0.00018561944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042931308,0.00046352064,0.0004599122,0.00027608528,0.0009092534,0.00008864592,0.00031532417,0.00006449186,0.000012173758],"category_scores_gemma":[0.00012727802,0.000561627,0.00014944245,0.0004898257,0.000039797902,0.000091780974,0.0002631947,0.0010463572,0.0000012811546],"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":[0.00001405417,0.00001636811,0.00008341334,0.00028245038,0.000018942985,0.00005440278,0.00032628016,0.90988487,0.08025851,0.00009344248,0.000066075016,0.0089011835],"study_design_scores_gemma":[0.0008940793,0.0002029727,0.00028612316,0.00008955114,0.00002860158,0.0004998809,0.00010131412,0.9875499,0.005243129,0.00004879535,0.004462925,0.000592746],"about_ca_topic_score_codex":0.0000040289824,"about_ca_topic_score_gemma":7.235704e-7,"teacher_disagreement_score":0.12932917,"about_ca_system_score_codex":0.00007158298,"about_ca_system_score_gemma":0.000025590924,"threshold_uncertainty_score":0.9996835},"labels":[],"label_agreement":null},{"id":"W4295786315","doi":"10.1145/3555819.3555857","title":"Efficient Process Arrival Pattern Aware Collective Communication for Deep Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Scalability; Process (computing); Context (archaeology); Distributed computing; Premise; Artificial intelligence; Data science; Machine learning","score_opus":0.014235156176713401,"score_gpt":0.2536917634747152,"score_spread":0.23945660729800183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295786315","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64876586,0.00008787525,0.34928948,0.00002582623,0.00009599679,0.00025726124,0.0000024791648,0.0003309978,0.0011442155],"genre_scores_gemma":[0.9992217,0.0000012431764,0.00034258486,0.000036934587,0.000019565123,0.00012675306,0.000012434931,0.00001876856,0.0002200625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995792,0.000028665594,0.000091792004,0.00009394577,0.00007409404,0.00013231335],"domain_scores_gemma":[0.99972254,0.0001199214,0.000021546519,0.000088162655,0.000026604923,0.00002123071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008759218,0.00006559083,0.000071740615,0.00003156822,0.0004987583,0.000009149354,0.000115422525,0.000011036654,0.00005050042],"category_scores_gemma":[0.000017096472,0.00007083845,0.000027061624,0.00013465599,0.000006815247,0.000020062498,0.00005849591,0.00020149676,0.0000017149719],"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":[0.000007518289,0.000008719629,0.000113454866,0.000026743039,0.000005589604,4.5847088e-7,0.0010418935,0.98981756,0.000622108,0.000014847047,0.000016266,0.00832487],"study_design_scores_gemma":[0.00023403183,0.000055332577,0.00012088112,0.0000058922687,0.0000040371265,0.000004697148,0.0018060283,0.99116397,0.005865143,0.00012911926,0.0005068946,0.00010397159],"about_ca_topic_score_codex":7.6930905e-7,"about_ca_topic_score_gemma":0.00000286473,"teacher_disagreement_score":0.3504558,"about_ca_system_score_codex":0.00009786776,"about_ca_system_score_gemma":0.0000073352935,"threshold_uncertainty_score":0.38360956},"labels":[],"label_agreement":null},{"id":"W4295837020","doi":"10.1088/2058-8585/ac9190","title":"Fully printed ZnO-based valency-change memories for flexible and transparent applications","year":2022,"lang":"en","type":"article","venue":"Flexible and Printed Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"PEDOT:PSS; Materials science; Resistive random-access memory; Optoelectronics; Polystyrene sulfonate; Nanotechnology; Fabrication; Electrode; Layer (electronics); Chemistry","score_opus":0.04298179579809205,"score_gpt":0.2728729292649732,"score_spread":0.22989113346688114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295837020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20892966,0.020312188,0.7639598,0.00062189234,0.00038620355,0.0027795376,0.00014773123,0.0019347722,0.0009281941],"genre_scores_gemma":[0.9949169,0.0006845636,0.0022322447,0.00022201739,0.000104439096,0.0013294785,0.000088701054,0.000057811925,0.0003638543],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989139,0.0000239093,0.00021688218,0.00030199098,0.000112854286,0.00043046265],"domain_scores_gemma":[0.99953336,0.00010757378,0.00003979381,0.00019493302,0.000037436595,0.00008689623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014905052,0.00019802572,0.00020299316,0.00010167307,0.00042736335,0.000027830905,0.00013710526,0.000045458586,0.00003366701],"category_scores_gemma":[0.000008296775,0.00021930052,0.000054444226,0.00026756464,0.000036020752,0.00009804025,0.000053884814,0.00034055975,0.0000013195158],"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":[0.0010816606,0.00042361004,0.0010499484,0.0040564714,0.00043584532,0.000013681377,0.0029663728,0.3572462,0.13937117,0.059405923,0.0009813212,0.43296778],"study_design_scores_gemma":[0.001944654,0.00094331877,0.00033559796,0.00006369547,0.00011971645,0.00004851714,0.0002882919,0.16001938,0.3311529,0.010461594,0.49375945,0.0008629128],"about_ca_topic_score_codex":0.0000019890294,"about_ca_topic_score_gemma":0.000008548026,"teacher_disagreement_score":0.78598726,"about_ca_system_score_codex":0.00009595297,"about_ca_system_score_gemma":0.0000436604,"threshold_uncertainty_score":0.89428157},"labels":[],"label_agreement":null},{"id":"W4295911650","doi":"10.1101/2022.09.13.507796","title":"Simulating Short-Term Synaptic Plasticity on SpiNNaker Neuromorphic Hardware","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"University of Manchester","keywords":"Neuromorphic engineering; Computer science; Context (archaeology); Neuroscience; Neuromodulation; Synaptic plasticity; Synapse; Postsynaptic potential; Spiking neural network; Computer architecture; Artificial neural network; Artificial intelligence; Psychology; Stimulation; Biology","score_opus":0.029328793747157775,"score_gpt":0.2322497626500476,"score_spread":0.20292096890288983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295911650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896261,0.00030493934,0.0040905233,0.0000249023,0.0028522916,0.00067003357,0.00019277072,0.0022079088,0.000030544346],"genre_scores_gemma":[0.9977953,0.000056708646,0.0008524634,0.00015092781,0.0006708701,0.0001321501,8.879938e-7,0.00033742303,0.0000032734054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658483,0.00013659133,0.00070789683,0.0011595447,0.0005378994,0.0008732205],"domain_scores_gemma":[0.9980092,0.00029463248,0.00018732945,0.0010673292,0.00013007852,0.0003114172],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0002696418,0.00090216415,0.0007704747,0.0003031251,0.00041762774,0.00017022272,0.00071975804,0.00036302803,0.00016386154],"category_scores_gemma":[0.00025812146,0.0010715366,0.0002358256,0.00046736,0.000072234136,0.00016476431,0.00080266944,0.002631827,0.000060990114],"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":[0.000030580974,0.00006506963,0.0015137335,0.0007414318,0.00015891252,0.0005403139,0.000009712621,0.6465613,0.35018852,0.00012207584,0.000059839313,0.0000084705525],"study_design_scores_gemma":[0.0013019717,0.00051196746,0.10667347,0.002506232,0.00047096796,3.8876203e-7,0.000009371171,0.44641,0.4317781,0.0000086211885,0.004694332,0.0056345933],"about_ca_topic_score_codex":0.0000020513162,"about_ca_topic_score_gemma":4.2353426e-7,"teacher_disagreement_score":0.20015134,"about_ca_system_score_codex":0.00043429705,"about_ca_system_score_gemma":0.00010866598,"threshold_uncertainty_score":0.99966913},"labels":[],"label_agreement":null},{"id":"W4295935088","doi":"10.1109/jxcdc.2022.3206778","title":"Scalable 2T2R Logic Computation Structure: Design From Digital Logic Circuits to 3-D Stacked Memory Arrays","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Exploratory Solid-State Computational Devices and Circuits","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"XNOR gate; Computer science; Logic gate; Digital electronics; Resistive random-access memory; Semiconductor memory; CMOS; Sense amplifier; Scalability; Electronic circuit; Pass transistor logic; Computer architecture; Parallel computing; NAND gate; Computer hardware; Electronic engineering; Engineering; Electrical engineering; Algorithm","score_opus":0.041753487408809326,"score_gpt":0.26169675951657384,"score_spread":0.21994327210776451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295935088","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58419836,0.0005503944,0.41285703,0.00010572897,0.0014330886,0.00031712753,0.00023214608,0.00021330515,0.0000928197],"genre_scores_gemma":[0.99645096,0.000030966527,0.0010649281,0.0018313299,0.0003910304,0.000021218631,0.00010751918,0.000079449455,0.00002257022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719536,0.00024306917,0.00072000845,0.000518941,0.0007793506,0.0005432497],"domain_scores_gemma":[0.9983818,0.00053907186,0.00027374108,0.00014865742,0.00022050913,0.00043623213],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000315135,0.0004555264,0.0004781233,0.0003280086,0.0009775406,0.00041483666,0.00033141382,0.00006594765,0.000072831186],"category_scores_gemma":[0.00003173775,0.00047141733,0.0001051544,0.00051133166,0.000053405096,0.00092196447,0.00007685166,0.00080268324,0.000046205187],"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":[0.00003736377,0.000051962466,0.00004523358,0.000030928368,0.00008693514,0.00017163232,0.0011294188,0.9722216,0.005475035,0.000053472937,0.00070879585,0.01998763],"study_design_scores_gemma":[0.0040597324,0.0019777871,0.003512156,0.00032318555,0.000116382646,0.0009229131,0.0029857717,0.79591304,0.009556464,0.17549793,0.0024057,0.0027289111],"about_ca_topic_score_codex":8.202801e-7,"about_ca_topic_score_gemma":0.0000013140019,"teacher_disagreement_score":0.41225263,"about_ca_system_score_codex":0.0002838107,"about_ca_system_score_gemma":0.000110675646,"threshold_uncertainty_score":0.99977374},"labels":[],"label_agreement":null},{"id":"W4296182691","doi":"10.31234/osf.io/m2x6y","title":"Open design and validation of a reproducible videogame controller for MRI and MEG","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Institut Universitaire de Gériatrie de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Courtois Foundation; Canada Research Chairs; European Commission","keywords":"Computer science; Scanner; License; Neuroimaging; Open source; Controller (irrigation); Latency (audio); Human–computer interaction; Artificial intelligence; Software; Psychology; Operating system","score_opus":0.06574934941122242,"score_gpt":0.3087063090938104,"score_spread":0.24295695968258801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296182691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092863195,0.0008713506,0.90226215,0.0001139161,0.0002666916,0.0024025717,0.000015374966,0.00013184798,0.0010729117],"genre_scores_gemma":[0.89121515,0.00024925114,0.10733837,0.000046192094,0.000068502595,0.00027622606,0.00002019855,0.000042334654,0.0007437581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922085,0.000037113714,0.00021665571,0.00037015261,0.000054389646,0.00010085396],"domain_scores_gemma":[0.99939156,0.0002497137,0.00006951019,0.00023137893,0.000028985212,0.000028866154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056558184,0.00012530645,0.00029831385,0.00004873973,0.00005569199,0.000043683325,0.0001505991,0.000051952677,0.000029126651],"category_scores_gemma":[0.00008530052,0.00012297303,0.000027244769,0.000037741964,0.000012997649,0.000093536175,0.0006491567,0.00015498938,1.4788769e-7],"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":[0.00012227317,0.000008055507,0.000027915019,0.00043652777,0.00006170216,8.698615e-7,0.00021036745,0.9736004,0.018568786,0.00042631463,0.00043929555,0.0060975137],"study_design_scores_gemma":[0.0021634588,0.00021219112,0.00011133246,0.00015009008,0.00010899473,0.000011350946,0.00014367605,0.48456463,0.4839089,0.02327271,0.0048233205,0.0005293428],"about_ca_topic_score_codex":0.000006912402,"about_ca_topic_score_gemma":3.869209e-7,"teacher_disagreement_score":0.79835194,"about_ca_system_score_codex":0.000017151051,"about_ca_system_score_gemma":0.000012140242,"threshold_uncertainty_score":0.5014695},"labels":[],"label_agreement":null},{"id":"W4296742994","doi":"10.1155/2022/2673191","title":"Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Neuromorphic engineering; Computer vision; Computer science; Artificial intelligence; Thresholding; Frame rate; SIGNAL (programming language); Frame (networking); Real-time computing; Artificial neural network; Telecommunications","score_opus":0.03328577465054408,"score_gpt":0.2578374604748471,"score_spread":0.22455168582430304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296742994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6661761,0.0002246086,0.33212265,0.000036482463,0.0010260807,0.0003170169,0.000019891051,0.00007516337,0.000002011571],"genre_scores_gemma":[0.98812383,0.0000060761345,0.0115837,0.000020876958,0.00016126757,0.000014849238,0.000024186911,0.000060909442,0.0000043061855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778324,0.00009242682,0.0008840936,0.00021505039,0.00073490624,0.0002902685],"domain_scores_gemma":[0.9988626,0.00011624648,0.0004985606,0.0001222566,0.00028536914,0.000114922106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006333799,0.00022524758,0.00034698463,0.00017639945,0.0005368641,0.000040056937,0.00015479093,0.000048344024,0.000011650631],"category_scores_gemma":[0.000021859756,0.00023314214,0.00023518347,0.00029302124,0.000012855663,0.00050770753,0.0000052062423,0.00038172427,6.116427e-7],"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":[0.00026788234,0.000030748994,0.000016938806,0.000153341,0.000016878852,0.000025050194,0.00009697513,0.53720003,0.4601921,0.0000061346054,0.000007559796,0.0019863313],"study_design_scores_gemma":[0.0037099659,0.001347943,0.0013903013,0.00073806813,0.00017908025,0.000064894986,0.00092439144,0.5612869,0.42893776,0.00006766969,0.0009496176,0.00040338116],"about_ca_topic_score_codex":6.608715e-7,"about_ca_topic_score_gemma":0.0000025992083,"teacher_disagreement_score":0.32194775,"about_ca_system_score_codex":0.0004514529,"about_ca_system_score_gemma":0.000056804907,"threshold_uncertainty_score":0.95072603},"labels":[],"label_agreement":null},{"id":"W4296777401","doi":"10.1063/5.0102076","title":"Self-selective analogue FeO<i>x</i>-based memristor induced by the electron transport in the defect energy level","year":2022,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":25,"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":"Natural Science Foundation of Guizhou Province; Natural Science Foundation of Chongqing","keywords":"Homojunction; Memristor; Materials science; Neuromorphic engineering; Quantum tunnelling; Optoelectronics; Electron; Resistive random-access memory; Sputter deposition; Nanotechnology; Electrode; Sputtering; Thin film; Doping; Electrical engineering; Chemistry; Computer science; Physics","score_opus":0.012999579592640125,"score_gpt":0.19954654129682495,"score_spread":0.18654696170418483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296777401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9768096,0.00005519559,0.021398805,0.0003479137,0.000098341596,0.0002800797,0.000017628483,0.00018041772,0.0008120034],"genre_scores_gemma":[0.99413085,0.0000019418821,0.00006806153,0.005335662,0.000107395565,0.0002518559,0.00006254687,0.00003971923,0.0000019705149],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989325,0.00008175337,0.00015696531,0.00023438684,0.00023571176,0.0003587254],"domain_scores_gemma":[0.9994868,0.00020270827,0.00004577278,0.0002367501,0.000005510705,0.000022436765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017362992,0.0002022332,0.00016170673,0.00003135011,0.00030717242,0.000010899477,0.00036908427,0.000024095767,0.0000020036498],"category_scores_gemma":[9.997515e-7,0.0001602796,0.0000937769,0.0005723805,0.000022768003,0.000036742924,0.000016564914,0.000647536,0.0000012139992],"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":[0.000028420696,0.000044015815,0.00001905476,0.000011131468,0.000040673858,0.00000542359,0.00092245854,0.33764654,0.6575863,0.0011315311,0.00091372075,0.0016507449],"study_design_scores_gemma":[0.0016955888,0.00015082619,0.00076230906,0.000007741862,0.00011302592,0.000009871049,0.0003678731,0.010694409,0.9684044,0.00091420714,0.015869856,0.001009884],"about_ca_topic_score_codex":0.000029321549,"about_ca_topic_score_gemma":0.000022234432,"teacher_disagreement_score":0.32695213,"about_ca_system_score_codex":0.00018153194,"about_ca_system_score_gemma":0.000019680665,"threshold_uncertainty_score":0.6536012},"labels":[],"label_agreement":null},{"id":"W4296906038","doi":"10.1016/j.mtchem.2022.101169","title":"A flexible resistive switching device for logical operation applications in wearable systems","year":2022,"lang":"en","type":"article","venue":"Materials Today Chemistry","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Fujian Normal University","keywords":"Memristor; Conductance; Materials science; Molecule; Neuromorphic engineering; Schottky diode; Resistive random-access memory; Vacancy defect; Nanotechnology; Chemical physics; Oxygen; Optoelectronics; Computer science; Chemistry; Electronic engineering; Physics; Electrode; Crystallography; Condensed matter physics; Physical chemistry; Engineering","score_opus":0.019820571134451778,"score_gpt":0.2530208256901719,"score_spread":0.23320025455572013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296906038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987971,0.00025776488,0.010203718,0.000017685825,0.00017874579,0.00040200131,0.000050262526,0.00020052976,0.0007182587],"genre_scores_gemma":[0.99839056,0.0000053084686,0.0003729521,0.000014343591,0.00014345779,0.000733686,0.00006895287,0.000019008688,0.00025174985],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993552,0.000018570854,0.00021992749,0.00017047742,0.000067342655,0.00016848742],"domain_scores_gemma":[0.9997499,0.00006118636,0.000033886816,0.00011632441,0.000012453195,0.000026245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019955986,0.000096414646,0.00015045465,0.000014841698,0.00018980679,0.00004271411,0.00009828924,0.000036495432,0.00008588452],"category_scores_gemma":[0.000015801059,0.000103202765,0.000017534723,0.000080916274,0.0000046882246,0.000058332975,0.000050730014,0.000095866955,0.000002739363],"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":[0.000014994941,0.000009556442,0.0000068676964,0.00021263168,0.000002789442,0.0000012801532,0.000044327076,0.1858132,0.8137597,0.000048527898,0.000033043354,0.000053063035],"study_design_scores_gemma":[0.00019621744,0.000007948622,0.00002345305,0.000027791535,0.0000041318563,0.000007228445,0.00023796118,0.0039245,0.99175787,0.00015844603,0.0035236692,0.0001307857],"about_ca_topic_score_codex":0.0000061823434,"about_ca_topic_score_gemma":3.62801e-7,"teacher_disagreement_score":0.18188871,"about_ca_system_score_codex":0.00011166153,"about_ca_system_score_gemma":0.00001103583,"threshold_uncertainty_score":0.42084867},"labels":[],"label_agreement":null},{"id":"W4297535945","doi":"10.1016/j.isci.2022.105240","title":"Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory","year":2022,"lang":"en","type":"article","venue":"iScience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Guizhou Province; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Chongqing","keywords":"Content-addressable memory; Memristor; Computer science; Resistive random-access memory; Capacitance; Materials science; Computer hardware; Electrical engineering; Physics; Voltage; Artificial neural network; Electrode; Engineering; Artificial intelligence","score_opus":0.015623237867809019,"score_gpt":0.2347871776389947,"score_spread":0.21916393977118567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297535945","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92058617,0.0006903627,0.07274969,0.00031591352,0.0012836636,0.00037472093,0.00031698297,0.00024652568,0.0034359796],"genre_scores_gemma":[0.997581,0.0000014153416,0.00032512826,0.0009918613,0.00009404197,0.00007524521,0.000013732853,0.000022836417,0.00089471356],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983089,0.00011097447,0.00024225382,0.00043059097,0.0004468119,0.00046047973],"domain_scores_gemma":[0.9991819,0.00028978114,0.00009007565,0.0002931897,0.000058630118,0.00008638527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046098433,0.00018492008,0.00017121063,0.000062507475,0.0011288173,0.000050272716,0.0005623221,0.000024889745,0.00032999658],"category_scores_gemma":[0.00010692515,0.0001707604,0.000048909205,0.00069911114,0.00004511859,0.0003189425,0.00018688396,0.00047333958,0.00002396505],"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":[0.000023741744,0.00002858264,0.0005594062,0.000018996687,0.000054945176,0.00002182736,0.016039299,0.12995839,0.8056602,0.00010452747,0.03396311,0.013566998],"study_design_scores_gemma":[0.0035799267,0.0006884011,0.075829156,0.00035119377,0.00017427446,0.00009792843,0.12583238,0.12723672,0.5683313,0.008309683,0.084641755,0.004927259],"about_ca_topic_score_codex":0.00008128913,"about_ca_topic_score_gemma":0.00014058556,"teacher_disagreement_score":0.23732886,"about_ca_system_score_codex":0.0007439227,"about_ca_system_score_gemma":0.000045049816,"threshold_uncertainty_score":0.86820644},"labels":[],"label_agreement":null},{"id":"W4297619716","doi":"10.1145/3546790.3546814","title":"Think Fast: Time Control in Varying Paradigms of Spiking Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"U.S. Department of Energy; National Science Foundation","keywords":"Spiking neural network; Computer science; Artificial intelligence; Deep learning; Artificial neural network; Machine learning; Construct (python library); Inference; Process (computing); Task (project management); Engineering","score_opus":0.007494890112077407,"score_gpt":0.1989567625280966,"score_spread":0.1914618724160192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297619716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93035513,0.000486291,0.062677816,0.00003905546,0.00040316416,0.00020744298,0.0000024293352,0.0003350931,0.0054935548],"genre_scores_gemma":[0.9994862,0.0000026900975,0.00023083735,0.00014465716,0.00005490044,0.0000048848037,0.0000021774304,0.000018664268,0.000054977536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931324,0.00003519872,0.00021697248,0.0001129025,0.00009243964,0.0002292325],"domain_scores_gemma":[0.9997087,0.00012067969,0.000030269222,0.000107489825,0.000004676069,0.000028168775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013089525,0.000096649055,0.0001775162,0.000060704,0.00007168483,0.000006019878,0.00012456687,0.000019471208,0.0001459576],"category_scores_gemma":[0.0000063019083,0.00010119211,0.00004291657,0.0001997878,0.000009384877,0.000094788455,0.00005728016,0.0003100512,0.0000018334839],"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":[0.000013295142,0.0000064681076,0.00020288219,0.000008487735,0.0000057444854,0.000019353778,0.00015183461,0.9845593,0.009135316,0.00012452657,0.000023281875,0.0057494924],"study_design_scores_gemma":[0.00040641546,0.000034021814,0.00020724237,0.0000076194174,0.0000034780319,0.000014982502,0.00003950403,0.99801624,0.0009751604,0.000100154815,0.000087794906,0.00010738371],"about_ca_topic_score_codex":0.0000030025574,"about_ca_topic_score_gemma":7.0492945e-7,"teacher_disagreement_score":0.069131054,"about_ca_system_score_codex":0.000034679688,"about_ca_system_score_gemma":0.0000025996023,"threshold_uncertainty_score":0.41264945},"labels":[],"label_agreement":null},{"id":"W4297669577","doi":"10.1145/3546790.3546819","title":"Towards a Laser Warning System in the Visible Spectrum using a Neuromorphic Camera","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","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":"National Research Council Canada","funders":"","keywords":"Neuromorphic engineering; Laser; Optics; Computer science; Artificial intelligence; Laser beams; Lens (geology); Computer vision; Photodiode; Smart camera; Physics; Artificial neural network","score_opus":0.03152262870016731,"score_gpt":0.2325974466725677,"score_spread":0.2010748179724004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297669577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99041045,0.00008218361,0.0058256714,0.00007652618,0.00031512376,0.00014318826,0.0000017206985,0.0002880056,0.0028571477],"genre_scores_gemma":[0.999396,0.0000016101899,0.00028999252,0.0001644805,0.00007311512,0.000013085703,0.0000012860769,0.000022319251,0.000038111782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920774,0.00009039813,0.00015707318,0.00013642877,0.00015985024,0.00024853376],"domain_scores_gemma":[0.99973696,0.00005796711,0.000020670108,0.00015775156,0.0000035898474,0.000023048113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024137291,0.000104824714,0.000119195916,0.000078495206,0.0002155669,0.000024592724,0.00018329824,0.000012722446,0.000060958602],"category_scores_gemma":[0.0000075215685,0.000085579384,0.000037276634,0.00042038734,0.0000072374664,0.000079946,0.00008775728,0.00039478904,0.000005385153],"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":[0.000004865817,0.0000067455576,0.00010988883,0.000041070725,0.0000037467971,0.00018533858,0.0005838939,0.9919696,0.006204678,0.000391814,0.000033858567,0.0004645262],"study_design_scores_gemma":[0.00029646055,0.00005850204,0.0003341617,0.0000313081,0.000008817998,0.0005313909,0.0032878944,0.985398,0.007842036,0.00008624575,0.0019070584,0.00021813088],"about_ca_topic_score_codex":0.000038709113,"about_ca_topic_score_gemma":0.000007449558,"teacher_disagreement_score":0.008985566,"about_ca_system_score_codex":0.00011102866,"about_ca_system_score_gemma":0.000011792881,"threshold_uncertainty_score":0.3489826},"labels":[],"label_agreement":null},{"id":"W4297809233","doi":"10.1007/978-3-031-16770-6_12","title":"Same/Different Concept: An Embodied Spiking Neural Model in a Learning Context","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep du Vieux Montréal; University of Ottawa","funders":"","keywords":"Computer science; Embodied cognition; Artificial intelligence; Spiking neural network; Context (archaeology); Novelty; Reinforcement learning; Robot; Discriminative model; Machine learning; Artificial neural network; Psychology","score_opus":0.02718551329419612,"score_gpt":0.2505099574343493,"score_spread":0.22332444414015318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297809233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055195026,0.0007190802,0.94040054,0.00005019753,0.0013075918,0.00035817435,0.0000049454206,0.0003802189,0.0015842482],"genre_scores_gemma":[0.99258035,0.0000248928,0.0066070226,0.00037850975,0.00023571207,0.000008145094,0.000011701555,0.000068957896,0.00008472491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756867,0.000038835864,0.00045117707,0.00085007946,0.00045786856,0.00063336705],"domain_scores_gemma":[0.99905694,0.00027017685,0.000111529866,0.00040243077,0.000035108613,0.00012380186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026174067,0.0005038958,0.0005625145,0.0004721744,0.00024810372,0.00010958831,0.0008164786,0.00015820225,0.00005033194],"category_scores_gemma":[0.000040536015,0.0005132119,0.00009013691,0.00023517256,0.00022649988,0.00037589367,0.00046128707,0.0021224995,0.0000024346548],"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":[0.0000064628703,0.0000057659277,0.000031730036,0.000022061446,0.0000024276465,0.0000589272,0.0013395676,0.788852,0.0007427831,0.00076892076,4.4228375e-7,0.20816892],"study_design_scores_gemma":[0.0002800478,0.00012130617,0.000024862076,0.00018822147,0.0000043953764,0.000028952449,0.0000028715074,0.98709565,0.0015390514,0.010034877,0.00013355998,0.00054618187],"about_ca_topic_score_codex":0.000008158088,"about_ca_topic_score_gemma":0.000111938745,"teacher_disagreement_score":0.9373853,"about_ca_system_score_codex":0.00039862472,"about_ca_system_score_gemma":0.0000569176,"threshold_uncertainty_score":0.99973196},"labels":[],"label_agreement":null},{"id":"W4304820847","doi":"10.1007/978-3-031-18344-7_14","title":"NeuroTower: A 3D Neuromorphic Architecture with Low-Power TSVs","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dram; Neuromorphic engineering; Computer science; Computer architecture; Pruning; Embedded system; Exploit; Computer hardware; Memory architecture; Chip; Architecture; Artificial neural network; Artificial intelligence; Telecommunications","score_opus":0.010374780915050381,"score_gpt":0.1874004242910746,"score_spread":0.17702564337602422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304820847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028952584,0.11959411,0.6673903,0.0003189223,0.016509078,0.0088757295,0.0001799999,0.003198877,0.15498039],"genre_scores_gemma":[0.996431,0.000569767,0.000091904825,0.0002596686,0.00082162116,0.000059844453,0.00004204016,0.000273862,0.0014502855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982409,0.000053401778,0.00041389745,0.0005658161,0.00025707012,0.00046891614],"domain_scores_gemma":[0.9989466,0.0003778976,0.00012681549,0.00041606574,0.000021152307,0.000111484165],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010958528,0.0006326978,0.0007174372,0.00016835172,0.00014645742,0.000076396675,0.00020984297,0.00034280648,0.00009414697],"category_scores_gemma":[0.000012961895,0.00052811473,0.000083845975,0.00011548844,0.00006506339,0.0000510869,0.000103051,0.0022945446,0.0000018812983],"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":[0.000049433995,0.0000037269647,0.000023064313,0.00020505334,0.000038282138,0.00054491434,0.00009469593,0.9934343,0.000042461117,0.00037657729,0.00004359098,0.0051438883],"study_design_scores_gemma":[0.0016326659,0.0010607736,0.00006768836,0.0035716195,0.00014697913,0.0029350827,0.000008086284,0.61337066,0.000039494174,0.0017331683,0.3723173,0.0031164396],"about_ca_topic_score_codex":0.000005782948,"about_ca_topic_score_gemma":0.00003211175,"teacher_disagreement_score":0.9674784,"about_ca_system_score_codex":0.00005609839,"about_ca_system_score_gemma":0.000014619665,"threshold_uncertainty_score":0.99971706},"labels":[],"label_agreement":null},{"id":"W4306156587","doi":"10.3389/frobt.2022.1007547","title":"Towards the Neuroevolution of Low-level artificial general intelligence","year":2022,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Norges Forskningsråd","keywords":"Neuroevolution; Computer science; Artificial intelligence; Emulation; Generality; Artificial neural network; Benchmark (surveying); Artificial life; Reinforcement learning; Machine learning","score_opus":0.02879897898872569,"score_gpt":0.23548115393890942,"score_spread":0.20668217495018373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306156587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18054447,0.000428291,0.8170423,0.00020929793,0.0015660458,0.00007954872,0.0000071154927,0.000023981096,0.000098979144],"genre_scores_gemma":[0.99125326,0.000056122146,0.008517084,0.000077057644,0.000053455333,0.000003155977,0.0000019755666,0.000008057922,0.000029831537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995784,0.000022962737,0.00013455882,0.00007967359,0.00007424463,0.00011015777],"domain_scores_gemma":[0.9998707,0.000011709661,0.000018162744,0.00007610808,0.000007530818,0.000015795149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008633413,0.000056337532,0.00008475292,0.000039677067,0.00008680899,0.0000062776694,0.00008987528,0.000012556086,0.00000499731],"category_scores_gemma":[0.00001025697,0.000050412935,0.000019689674,0.00013500986,0.000032934233,0.00003294745,0.0000642414,0.00019296803,2.3239579e-7],"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":[0.0000060994403,0.0000070257643,0.00022552378,0.000014173692,0.0000030176175,0.0000028351467,0.000124354,0.9695758,0.0011901681,0.001170574,0.00039647508,0.027283946],"study_design_scores_gemma":[0.00004084936,0.000024073817,0.00079976005,0.0000067688943,0.0000041007083,0.0000047389663,0.00019715448,0.98369384,0.0060505974,0.008799748,0.0003005483,0.00007781471],"about_ca_topic_score_codex":0.0000023587409,"about_ca_topic_score_gemma":0.0000012004339,"teacher_disagreement_score":0.8107088,"about_ca_system_score_codex":0.000023096214,"about_ca_system_score_gemma":0.0000069165885,"threshold_uncertainty_score":0.20557797},"labels":[],"label_agreement":null},{"id":"W4306827193","doi":"10.1088/2634-4386/ac9b85","title":"Plasticity of conducting polymer dendrites to bursts of voltage spikes in phosphate buffered saline","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Université de Sherbrooke","funders":"H2020 European Research Council","keywords":"Bursting; Polarity (international relations); Dendrite (mathematics); Interconnectivity; Computer science; Biological system; Neuroscience; Materials science; Biophysics; Chemistry; Biology; Artificial intelligence; Mathematics","score_opus":0.02725568080290967,"score_gpt":0.22286459470660738,"score_spread":0.19560891390369772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306827193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9876241,0.00057060755,0.011044859,0.000013511926,0.00043867336,0.00011538814,0.000011135067,0.00016355264,0.000018182629],"genre_scores_gemma":[0.9992331,0.000008179315,0.00063840207,0.000017847338,0.000051578656,0.0000033286565,0.0000025395243,0.000038655424,0.00000631345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885243,0.000023096667,0.0004338303,0.0002253441,0.00015977243,0.00030552794],"domain_scores_gemma":[0.999286,0.0004340684,0.00006630949,0.00011534243,0.000020076548,0.00007817809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001761192,0.00019003396,0.00036616556,0.00022512737,0.00007024951,0.000007766965,0.00014360098,0.000027504297,0.000010014023],"category_scores_gemma":[0.00012875028,0.00023100701,0.000040697563,0.00045945728,0.0000186967,0.000052753297,0.00020769253,0.00036211798,3.017519e-7],"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":[0.000010123453,0.000011012433,0.0008124033,0.00013700269,0.000004629834,0.000024525189,0.00028814567,0.5591231,0.4382349,0.000051396477,0.000002869405,0.0012998894],"study_design_scores_gemma":[0.0005335224,0.00016168921,0.0044744085,0.00020994892,0.000012847946,0.0001075336,0.0001490348,0.547623,0.4463597,0.000010217047,0.00005990424,0.00029819613],"about_ca_topic_score_codex":0.000022935083,"about_ca_topic_score_gemma":0.000001651988,"teacher_disagreement_score":0.011609065,"about_ca_system_score_codex":0.000025232224,"about_ca_system_score_gemma":0.000007024589,"threshold_uncertainty_score":0.9420192},"labels":[],"label_agreement":null},{"id":"W4307081146","doi":"","title":"Investigation of analog characteristics of Al2O3/TiO2-x memristors at liquid helium temperature for quantum technology applications.","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Memristor; Liquid helium; Helium; Materials science; Optoelectronics; Quantum; Engineering physics; Nanotechnology; Condensed matter physics; Electrical engineering; Physics; Atomic physics; Engineering; Quantum mechanics","score_opus":0.013124514382928148,"score_gpt":0.22117502531682465,"score_spread":0.2080505109338965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307081146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9067105,0.0012369835,0.08856662,0.0010215482,0.0002892294,0.00086800725,0.0003486856,0.00032812517,0.0006302815],"genre_scores_gemma":[0.97889704,0.0003996858,0.018486235,0.00002066511,0.000027858217,0.0003332894,0.0012853406,0.000059834358,0.0004900335],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979362,0.0005199097,0.0006881862,0.00042950464,0.00020637797,0.0002198388],"domain_scores_gemma":[0.9965728,0.0005594866,0.0006004488,0.001185387,0.0010082726,0.000073641444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012795087,0.00025998484,0.0004597934,0.0003080145,0.00023595212,0.000021260958,0.0007332594,0.00028991973,0.000031487572],"category_scores_gemma":[0.00032679643,0.00031125898,0.00015761892,0.00050027535,0.00020980714,0.000059615002,0.00071995694,0.0005624362,0.0000016433972],"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":[0.000057017623,0.000113673006,0.0010362914,0.0022556777,0.00012971518,0.0000012657316,0.00343187,0.0037127973,0.9440621,0.039982285,0.00041619764,0.004801126],"study_design_scores_gemma":[0.0002955885,0.0000028807206,0.00048678345,0.00070505246,0.00005662083,0.0000059594304,0.0001403825,0.009643572,0.97060823,0.005155679,0.012500628,0.00039861506],"about_ca_topic_score_codex":0.000015578338,"about_ca_topic_score_gemma":0.000035685123,"teacher_disagreement_score":0.07218654,"about_ca_system_score_codex":0.00017397557,"about_ca_system_score_gemma":0.00009447086,"threshold_uncertainty_score":0.99993396},"labels":[],"label_agreement":null},{"id":"W4307404864","doi":"10.1007/s00339-022-06120-9","title":"Temperature-dependent time relaxation of ON and OFF states in NiO$$_{x}$$-based crossbar memory arrays","year":2022,"lang":"en","type":"article","venue":"Applied Physics A","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-blocking I/O; Relaxation (psychology); Crossbar switch; Condensed matter physics; Work (physics); Materials science; Chemistry; Physics; Thermodynamics; Electrical engineering","score_opus":0.0057775938214553995,"score_gpt":0.19632796553088683,"score_spread":0.19055037170943143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307404864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982293,0.000050359802,0.0003768998,0.00000876703,0.000051255087,0.00016032417,0.00001630521,0.00007227273,0.0010345089],"genre_scores_gemma":[0.9996342,0.000004724963,0.00014793278,0.000071153336,0.000032322518,0.00003125827,0.000031195927,0.000023475848,0.000023741652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944437,0.000014745765,0.00013768421,0.00014594792,0.00013210201,0.00012514884],"domain_scores_gemma":[0.9997232,0.00008026779,0.000042886335,0.00012426639,0.000007338908,0.00002205786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008693889,0.00010353686,0.00013727727,0.00003626715,0.000074488285,0.00000798392,0.00006216298,0.000019798186,0.000012175178],"category_scores_gemma":[0.0000024023668,0.000114803675,0.000018900859,0.00014910345,0.000017700937,0.00003358097,0.00003171495,0.00024051484,0.0000043370596],"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":[0.000028375287,0.00002310993,0.00001118896,0.000022799863,0.000003785292,0.0000016289102,0.00020563528,0.662403,0.33390898,0.00036312957,0.000026152324,0.0030022266],"study_design_scores_gemma":[0.0009775335,0.00008951146,0.0003415895,0.000021375965,0.0000069293055,9.777572e-7,0.00017122187,0.10086104,0.8933371,0.0038001554,0.00015142288,0.00024115703],"about_ca_topic_score_codex":0.000001547881,"about_ca_topic_score_gemma":4.8667226e-7,"teacher_disagreement_score":0.561542,"about_ca_system_score_codex":0.00004336555,"about_ca_system_score_gemma":0.000008869749,"threshold_uncertainty_score":0.4681558},"labels":[],"label_agreement":null},{"id":"W4307540780","doi":"","title":"HfOx complementary resistive switches","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Resistive touchscreen; Computer science; Materials science; Optoelectronics; Operating system","score_opus":0.018937680023869932,"score_gpt":0.23321596654657198,"score_spread":0.21427828652270203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307540780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27088946,0.0026925763,0.605155,0.0058988216,0.0010195721,0.0007622999,0.00025641656,0.0018052482,0.11152059],"genre_scores_gemma":[0.97847426,0.0003798606,0.017919254,0.00007292347,0.00007411623,0.000041151638,0.00024516773,0.00007606389,0.0027172188],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99737006,0.001020993,0.0004382285,0.00054439163,0.00024912067,0.0003771973],"domain_scores_gemma":[0.99671245,0.0011174988,0.00018982902,0.0012926067,0.00054171367,0.00014592714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015271801,0.0003523641,0.00034476139,0.00012391074,0.00026093976,0.00011690138,0.0008344307,0.0001675834,0.000127195],"category_scores_gemma":[0.00032361472,0.0003470554,0.00015944835,0.0001519479,0.000124253,0.000118755306,0.0009978338,0.000598932,0.00005552301],"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":[0.000111378984,0.0008228867,0.004799186,0.002858719,0.0011081531,0.000093344715,0.024329852,0.01046068,0.30711395,0.13176426,0.02750543,0.48903218],"study_design_scores_gemma":[0.0010082244,5.546644e-7,0.0035588075,0.005884789,0.000086159904,0.000017360751,0.00018638797,0.018218078,0.8886267,0.03629721,0.04469637,0.0014193639],"about_ca_topic_score_codex":0.00005631042,"about_ca_topic_score_gemma":0.00022502778,"teacher_disagreement_score":0.7075848,"about_ca_system_score_codex":0.00015017555,"about_ca_system_score_gemma":0.000057427565,"threshold_uncertainty_score":0.99989814},"labels":[],"label_agreement":null},{"id":"W4308344324","doi":"10.1016/j.sse.2022.108506","title":"An atomistic modeling framework for valence change memory cells","year":2022,"lang":"en","type":"article","venue":"Solid-State Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Supercomputing Center, Korea Institute of Science and Technology Information; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Werner Siemens-Stiftung","keywords":"Conductance; Quantum tunnelling; Valence (chemistry); Materials science; Resistive random-access memory; Ab initio; Tin; Hysteresis; Condensed matter physics; Kinetic Monte Carlo; Diffusion; Chemical physics; Electrode; Monte Carlo method; Nanotechnology; Chemistry; Optoelectronics; Physics; Thermodynamics","score_opus":0.02973627216023445,"score_gpt":0.2876106646047166,"score_spread":0.2578743924444822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308344324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.341788,0.001802559,0.6546969,0.00002335168,0.0006091775,0.00047881345,0.00005167666,0.00051308516,0.000036432233],"genre_scores_gemma":[0.99345875,0.00027202797,0.005388461,0.0002203245,0.00024962984,0.00023625854,0.000030996427,0.00009414037,0.000049419752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985286,0.000039249804,0.00022694406,0.00030326835,0.00016447468,0.00073746586],"domain_scores_gemma":[0.9994136,0.000116033334,0.00004244428,0.00029841653,0.000027223088,0.00010227912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021787242,0.0002063571,0.00020386268,0.00006348667,0.00040326064,0.000024684467,0.00030423602,0.000044222434,0.000026507765],"category_scores_gemma":[0.000015819485,0.00025144534,0.00007594474,0.00020256039,0.000012453758,0.00017481405,0.000058824317,0.00061541604,0.000005434034],"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":[0.000033318363,0.000018058457,9.626593e-7,0.00007201541,0.000013738728,0.000006671705,0.0006431348,0.9737421,0.017795721,0.001027581,0.000026860467,0.006619803],"study_design_scores_gemma":[0.00019611583,0.00020759483,3.9018346e-7,0.000013967347,0.000015632737,0.000009010746,0.000117840704,0.9267761,0.040951274,0.029746704,0.0016663664,0.0002990195],"about_ca_topic_score_codex":0.000001189033,"about_ca_topic_score_gemma":0.0000033554352,"teacher_disagreement_score":0.65167075,"about_ca_system_score_codex":0.00023848335,"about_ca_system_score_gemma":0.00003467849,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W4309048508","doi":"10.1116/6.0002116","title":"Multiple material stack grayscale patterning using electron-beam lithography and a single plasma etching step","year":2022,"lang":"en","type":"article","venue":"Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Resist; Etching (microfabrication); Electron-beam lithography; Materials science; Plasma etching; Stack (abstract data type); Lithography; Reactive-ion etching; Optoelectronics; Wafer; X-ray lithography; Dry etching; Next-generation lithography; Shadow mask; Optics; Critical dimension; Nanotechnology; Layer (electronics); Computer science; Physics","score_opus":0.014353883919214414,"score_gpt":0.21355191642879978,"score_spread":0.19919803250958537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309048508","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874106,0.00758126,0.004088976,0.00016303129,0.0003585729,0.00018786418,0.0000071545014,0.00019875834,0.0000038013202],"genre_scores_gemma":[0.9963481,0.00046050604,0.0030543997,0.000030321675,0.000059825143,0.000010364931,9.807032e-7,0.00003400241,0.000001533952],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978219,0.00005203079,0.00059759687,0.00040901167,0.00033341584,0.00078600063],"domain_scores_gemma":[0.99916244,0.000028454897,0.00042397875,0.00015577197,0.0001484815,0.00008088191],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014437708,0.00030936117,0.0004979302,0.0012542866,0.0012573241,0.00016486023,0.00040822048,0.00016394569,0.000004538947],"category_scores_gemma":[0.00006147906,0.0003007759,0.000030833547,0.00093356066,0.00059739104,0.00044667153,0.000349904,0.00072912633,1.01362154e-7],"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":[0.000106394575,0.000041086547,0.0002732209,0.000114247414,0.000031072155,0.000010879428,0.00020778229,0.00029708134,0.9772529,0.0001083647,0.0000020693876,0.021554923],"study_design_scores_gemma":[0.00083650154,0.00087048544,0.00003112676,0.00012307503,0.000060379156,0.0012713444,0.00045423844,0.0011550267,0.99200326,0.0021997932,0.00067042734,0.00032432706],"about_ca_topic_score_codex":0.0000018145979,"about_ca_topic_score_gemma":0.000003537835,"teacher_disagreement_score":0.021230595,"about_ca_system_score_codex":0.00031335748,"about_ca_system_score_gemma":0.00018615076,"threshold_uncertainty_score":0.99994445},"labels":[],"label_agreement":null},{"id":"W4309802094","doi":"10.48550/arxiv.2211.11281","title":"Intelligent Computing: The Latest Advances, Challenges and Future","year":2022,"lang":"en","type":"preprint","venue":"Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)","topic":"Advanced Memory and Neural Computing","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 Manitoba","funders":"","keywords":"Cognitive computing; Computer science; End-user computing; Big data; Data science; Intelligent decision support system; Marketing and artificial intelligence; Scope (computer science); Autonomic computing; Human intelligence; Soft computing; Affective computing; Computational intelligence; Cloud computing; Artificial intelligence; Utility computing; Cognition; Artificial neural network; Cloud computing security","score_opus":0.018823625743650832,"score_gpt":0.1988316506986864,"score_spread":0.1800080249550356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309802094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9101045,0.035426047,0.0050083357,0.0012743602,0.0023465971,0.0011923613,0.0003142883,0.00041113002,0.043922365],"genre_scores_gemma":[0.97642416,0.021822846,0.0011804972,0.000009745089,0.0002713578,2.434214e-7,0.000034287525,0.000023492432,0.00023335748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99775195,0.00020625773,0.000384258,0.0006678174,0.0006029528,0.00038674063],"domain_scores_gemma":[0.9981593,0.00023960987,0.00046861725,0.0007680333,0.00020512946,0.00015934119],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000403936,0.0004981684,0.00059337413,0.00013535266,0.0017884792,0.000032436994,0.0015827416,0.0003173575,0.00008031136],"category_scores_gemma":[0.000011005999,0.00049850316,0.0003488546,0.00022676558,0.0017208325,0.00036292872,0.0011441929,0.0018028265,0.000003711921],"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":[0.0005589267,0.0004817723,0.00030232384,0.0030379405,0.0013604867,0.0012636133,0.020783257,0.79805124,0.006589917,0.105218016,0.00032302173,0.062029473],"study_design_scores_gemma":[0.005506255,0.0018427409,0.093137555,0.0035564336,0.0025482744,0.0020869696,0.47256032,0.046266202,0.009272098,0.024368372,0.33258048,0.006274304],"about_ca_topic_score_codex":0.00011656661,"about_ca_topic_score_gemma":0.00014742417,"teacher_disagreement_score":0.75178504,"about_ca_system_score_codex":0.0002570644,"about_ca_system_score_gemma":0.00035160067,"threshold_uncertainty_score":0.9997467},"labels":[],"label_agreement":null},{"id":"W4311224397","doi":"10.1109/icecs202256217.2022.9970933","title":"Digital Realization of Conductance-Based Adaptive Exponential Integrate-and-Fire Neuron Model","year":2022,"lang":"en","type":"article","venue":"2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Neuromorphic engineering; Computer science; Realization (probability); Artificial neuron; Biological neuron model; Field (mathematics); Computer architecture; Exponential function; Computer engineering; Artificial intelligence; Computational science; Artificial neural network; Mathematics","score_opus":0.05939712407497848,"score_gpt":0.25663857887565733,"score_spread":0.19724145480067884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311224397","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.943405,0.0008381448,0.04849066,0.00005269284,0.0013946913,0.00038991836,0.0003001613,0.00014805984,0.0049807173],"genre_scores_gemma":[0.9991948,0.00015766564,0.000006304585,0.000039964187,0.00007946729,0.000049945265,0.00008911745,0.00002999196,0.00035272466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986062,0.000058956717,0.00038114106,0.0003322767,0.00037378384,0.00024761618],"domain_scores_gemma":[0.9994428,0.00006336163,0.00016046726,0.00013688608,0.00013238016,0.000064112464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015807465,0.00020662445,0.000259891,0.00012913955,0.0001530641,0.000082864164,0.00021077637,0.00004731994,0.000037903486],"category_scores_gemma":[0.000020083162,0.00021990764,0.000048020505,0.000118091135,0.00004648369,0.00024153243,0.00003887093,0.0004040723,0.000001302914],"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":[0.00012000074,0.00006726834,0.000092419956,0.00009144273,0.00009373844,0.000012222222,0.0003673405,0.7884397,0.08141332,0.11596906,0.00014827134,0.013185206],"study_design_scores_gemma":[0.00046294025,0.0002937685,0.00002045713,0.00006262125,0.000011514555,0.000023104089,0.0004306932,0.9943922,0.002833569,0.00092705636,0.00032505233,0.00021703473],"about_ca_topic_score_codex":0.000015243315,"about_ca_topic_score_gemma":0.000008396649,"teacher_disagreement_score":0.20595248,"about_ca_system_score_codex":0.00017282575,"about_ca_system_score_gemma":0.000102213045,"threshold_uncertainty_score":0.8967573},"labels":[],"label_agreement":null},{"id":"W4312051479","doi":"10.1109/jxcdc.2022.3217043","title":"Leveraging Ferroelectric Stochasticity and In-Memory Computing for DNN IP Obfuscation","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Exploratory Solid-State Computational Devices and Circuits","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"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":"Universität Stuttgart; York University; New York University Abu Dhabi","keywords":"Obfuscation; Computer science; Computer security","score_opus":0.031240812261565377,"score_gpt":0.266880391366465,"score_spread":0.23563957910489963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312051479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8324194,0.00082811667,0.16581951,0.000057953243,0.0005624859,0.00020920654,0.000010512499,0.00006832627,0.000024500252],"genre_scores_gemma":[0.99894625,0.000052253414,0.0002430767,0.0004778247,0.00020642274,0.000019293067,0.000010958742,0.000036966012,0.0000069750795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985747,0.000101188016,0.0004444118,0.000266755,0.00027716142,0.00033578745],"domain_scores_gemma":[0.99904275,0.00050180405,0.00016518091,0.00005905039,0.000094369614,0.00013684749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047812614,0.00021538322,0.0002624084,0.00031360795,0.00073736033,0.00009939084,0.000114776,0.000028249513,0.0000044243675],"category_scores_gemma":[0.000023712131,0.00024403592,0.00004796808,0.00027952608,0.00002594663,0.00034208706,0.000034011075,0.0005220152,0.0000012254628],"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":[0.000029560473,0.000027038472,0.00030883256,0.00007729319,0.000023294122,0.000025324904,0.001505368,0.9720129,0.0011743555,0.00003190407,0.000044398716,0.024739737],"study_design_scores_gemma":[0.0012731955,0.00023269148,0.0102694575,0.00010859205,0.000016909084,0.00027759318,0.0008134791,0.981085,0.0006236396,0.004705697,0.00020428939,0.00038944246],"about_ca_topic_score_codex":4.921045e-7,"about_ca_topic_score_gemma":0.0000017879139,"teacher_disagreement_score":0.16652684,"about_ca_system_score_codex":0.00018383254,"about_ca_system_score_gemma":0.00005890592,"threshold_uncertainty_score":0.9951496},"labels":[],"label_agreement":null},{"id":"W4312083346","doi":"10.1016/j.nanoen.2022.108117","title":"Evolution between CRS and NRS behaviors in MnO2@TiO2 nanocomposite based memristor for multi-factors-regulated memory applications","year":2022,"lang":"en","type":"article","venue":"Nano Energy","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Fujian Normal University","keywords":"Materials science; Capacitive sensing; Nanocomposite; Electrode; Nanotechnology; Dielectric; Optoelectronics; Resistive random-access memory; Ion; Electron; Memristor; Resistive touchscreen; Electric field; Doping; Electronic engineering; Electrical engineering","score_opus":0.019504964086635507,"score_gpt":0.24712256805015448,"score_spread":0.22761760396351896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312083346","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90961736,0.0004056545,0.08904882,0.000015563492,0.00018765587,0.00034495126,0.00009549979,0.00025317643,0.000031333886],"genre_scores_gemma":[0.99693507,0.0000017446306,0.0020618073,0.000024142742,0.000052736403,0.0005224282,0.0001708743,0.000042406667,0.00018876838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990801,0.00005290884,0.00024171936,0.0002550257,0.00012582549,0.00024439665],"domain_scores_gemma":[0.99960536,0.00009052392,0.00005485764,0.00016341168,0.000021390568,0.00006444209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010550301,0.00015750532,0.00018728577,0.00019231327,0.00039207438,0.0000088954675,0.0001349518,0.00004970516,0.0000071820064],"category_scores_gemma":[0.000004503924,0.0001900631,0.00005736861,0.00031979117,0.000023389346,0.000059133126,0.00006837755,0.00015094274,2.9050105e-7],"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":[0.00002161082,0.000048797967,0.0015314486,0.000027646387,0.000009821175,0.0000016946201,0.00009147136,0.65303373,0.3395443,0.00014118028,0.000046288613,0.005501997],"study_design_scores_gemma":[0.004468652,0.00035993604,0.02701657,0.000041456417,0.00013147804,0.000014455728,0.0004118715,0.49732795,0.434392,0.00030249092,0.034222323,0.0013108086],"about_ca_topic_score_codex":0.0000643153,"about_ca_topic_score_gemma":0.00004035683,"teacher_disagreement_score":0.1557058,"about_ca_system_score_codex":0.00034035183,"about_ca_system_score_gemma":0.000021379328,"threshold_uncertainty_score":0.7750549},"labels":[],"label_agreement":null},{"id":"W4312277017","doi":"10.14778/3551793.3551808","title":"Evaluating persistent memory range indexes","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Dram; Computer science; Scalability; Latency (audio); CAS latency; Range (aeronautics); Embedded system; Memory controller; Computer hardware; Operating system; Semiconductor memory; Telecommunications; Engineering","score_opus":0.037182770645599446,"score_gpt":0.2610079709039207,"score_spread":0.22382520025832126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312277017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99365234,0.00054442254,0.000009838289,0.00013155353,0.00045817636,0.00032310243,0.0000038620287,0.00013220552,0.0047444897],"genre_scores_gemma":[0.9989965,0.000010102584,0.00045335296,0.00007728098,0.00006790605,0.00007674939,4.2108474e-7,0.000021496664,0.00029620618],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903667,0.000007652807,0.00020982754,0.00014816991,0.00038506225,0.00021262318],"domain_scores_gemma":[0.9997278,0.00003138414,0.000086527,0.000081092265,0.000039324168,0.000033908193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033906783,0.00011959898,0.00013912308,0.00004895924,0.0002806662,0.00001189565,0.0003367384,0.0000144093565,0.00006691214],"category_scores_gemma":[0.00003740043,0.00009748541,0.00013910198,0.00020320126,0.000024839692,0.00008534559,0.00034357628,0.00024685453,0.0000018436946],"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":[0.000044318833,0.00005245265,0.0010408337,0.0002698059,0.00008680905,8.475202e-7,0.0026527753,0.23299457,0.7479743,0.00047661382,0.0008624084,0.01354426],"study_design_scores_gemma":[0.0014113858,0.00039790725,0.0009299769,0.00011949559,0.0001226063,0.00008199381,0.0050098337,0.036085255,0.9514213,0.0013803996,0.0025705185,0.00046930398],"about_ca_topic_score_codex":0.0000023892285,"about_ca_topic_score_gemma":1.5248257e-7,"teacher_disagreement_score":0.20344703,"about_ca_system_score_codex":0.00014664744,"about_ca_system_score_gemma":0.000007100522,"threshold_uncertainty_score":0.39753398},"labels":[],"label_agreement":null},{"id":"W4312328889","doi":"10.1109/nano54668.2022.9928711","title":"Analog Resistance Switching in Single Tungsten Oxide Nanoparticle Devices","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 22nd International Conference on Nanotechnology (NANO)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Deutsche Forschungsgemeinschaft","keywords":"Materials science; Neuromorphic engineering; Tungsten; Electrode; Nanoparticle; Nanotechnology; Optoelectronics; Sputtering; Oxide; Nanolithography; Thin film; Fabrication; Computer science; Chemistry; Artificial neural network; Metallurgy","score_opus":0.027259770729970465,"score_gpt":0.2539111720361314,"score_spread":0.22665140130616093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312328889","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99085736,0.00018634557,0.0011101773,0.0010966182,0.001089018,0.00015679543,0.000021824442,0.00045494165,0.00502693],"genre_scores_gemma":[0.99845606,0.000040967734,0.00033799233,0.000352742,0.00004126141,0.00008234198,0.000012683652,0.000032127857,0.0006438305],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983694,0.00006202779,0.00041274307,0.00042525024,0.00032987946,0.00040072098],"domain_scores_gemma":[0.9994189,0.000092268725,0.00010859271,0.00028704447,0.000048109967,0.000045058117],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021835405,0.00022205924,0.00024407024,0.00041692005,0.0002103979,0.000036466296,0.00072417996,0.00010150053,0.000314745],"category_scores_gemma":[0.00006650605,0.0002540225,0.000054267963,0.00056076737,0.000053342934,0.00022050325,0.00018374332,0.0007227182,0.000024960185],"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":[0.0000674237,0.00007112509,0.00042107413,0.00001242594,0.000025547248,0.00008407043,0.000109225395,0.03164308,0.9498718,0.014216517,0.00023117001,0.0032465733],"study_design_scores_gemma":[0.0010999326,0.00024881525,0.0005131507,0.000130598,0.0000123697755,0.0000655114,0.00091467897,0.040234674,0.93240494,0.008967656,0.0147243785,0.00068330346],"about_ca_topic_score_codex":0.0000117497475,"about_ca_topic_score_gemma":0.0002562814,"teacher_disagreement_score":0.017466834,"about_ca_system_score_codex":0.0003501284,"about_ca_system_score_gemma":0.000039086925,"threshold_uncertainty_score":0.9999912},"labels":[],"label_agreement":null},{"id":"W4312341102","doi":"10.1109/iscas48785.2022.9937455","title":"Power Delivery for Ultra-Large-Scale Applications on Si-IF","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Nature; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Neuromorphic engineering; Computer science; Interconnection; Supercomputer; Network topology; Bandwidth (computing); Electronic engineering; Computer architecture; Embedded system; Engineering; Artificial neural network; Telecommunications; Computer network; Artificial intelligence; Parallel computing","score_opus":0.01637634707340285,"score_gpt":0.24646143218366146,"score_spread":0.2300850851102586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312341102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8919596,0.00059007393,0.04579277,0.0004801623,0.011003495,0.002239255,0.0016610959,0.00058992044,0.045683622],"genre_scores_gemma":[0.9970085,0.000038951577,0.000009606536,0.00024665502,0.00045538624,0.00068461406,0.000071105635,0.000045385525,0.001439801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986562,0.000040236762,0.00032764013,0.0003611778,0.00035792863,0.00025682762],"domain_scores_gemma":[0.99935555,0.0002216027,0.00008177432,0.00019918795,0.00005921444,0.000082687504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022763193,0.00019040641,0.00019735262,0.000111224275,0.00040410014,0.000065829394,0.00025699238,0.00004701388,0.00007707644],"category_scores_gemma":[0.0000067703927,0.00020029218,0.000096760836,0.0001171994,0.000013822682,0.000099787394,0.0000301679,0.00025748354,0.000022855062],"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":[0.00006548678,0.00023897694,0.00023977185,0.00015807473,0.00017793303,0.000020148429,0.0010425332,0.73639107,0.23926611,0.013951107,0.005388419,0.0030603698],"study_design_scores_gemma":[0.0025888653,0.00084198825,0.00043986124,0.00017485207,0.000050654795,0.0002703158,0.002475298,0.24719152,0.023532143,0.00039294013,0.7207672,0.0012743323],"about_ca_topic_score_codex":0.0000026276446,"about_ca_topic_score_gemma":0.0000013411914,"teacher_disagreement_score":0.7153788,"about_ca_system_score_codex":0.00018588586,"about_ca_system_score_gemma":0.000008535141,"threshold_uncertainty_score":0.8167678},"labels":[],"label_agreement":null},{"id":"W4312363658","doi":"10.1007/978-3-031-20071-7_34","title":"DVS-Voltmeter: Stochastic Process-Based Event Simulator for Dynamic Vision Sensors","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Voltmeter; Computer science; Noise (video); Event (particle physics); Process (computing); Simulation; Real-time computing; Artificial intelligence; Voltage; Electrical engineering; Physics; Engineering; Image (mathematics)","score_opus":0.011018523372938505,"score_gpt":0.2745387723037066,"score_spread":0.2635202489307681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312363658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048987307,0.00031999135,0.9920957,0.0000507711,0.0015609526,0.0007022234,0.000022621625,0.00029218002,0.00005683935],"genre_scores_gemma":[0.97601974,0.0000036254555,0.023380803,0.00021764946,0.00019208963,0.00002314202,0.000019450508,0.00008901879,0.000054498996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779296,0.0000112411735,0.00038433433,0.00080369523,0.0004912387,0.00051650155],"domain_scores_gemma":[0.9985328,0.0007234356,0.000117546304,0.00043749495,0.00008169441,0.00010703953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025387428,0.0004552329,0.00041433648,0.0004388657,0.0002809396,0.00006827724,0.00063857296,0.00014576637,0.000033908695],"category_scores_gemma":[0.000075679905,0.00045192512,0.00014196924,0.0002667035,0.00015880207,0.0001757318,0.0001621138,0.0006790574,0.0000048254087],"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":[0.000016330532,0.000007673678,4.93639e-7,0.00015200692,0.0000050117455,0.000014141205,0.00009368021,0.922732,0.00064641034,0.00004374822,0.000001056208,0.07628743],"study_design_scores_gemma":[0.00030823302,0.00024526246,0.000003326193,0.00028522455,0.000012648097,0.000012880281,2.1426321e-7,0.98719835,0.0020298662,0.00908183,0.00032601188,0.0004961611],"about_ca_topic_score_codex":4.132233e-7,"about_ca_topic_score_gemma":0.0000046584983,"teacher_disagreement_score":0.971121,"about_ca_system_score_codex":0.00038226042,"about_ca_system_score_gemma":0.000120565775,"threshold_uncertainty_score":0.99979323},"labels":[],"label_agreement":null},{"id":"W4312468999","doi":"10.1109/iscas48785.2022.9937446","title":"Design of A New Memristive-Based Architecture Using VTM Method","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Memristor; Spice; Crossbar switch; NAND logic; CMOS; Diagonal; Computer science; NAND gate; Electronic engineering; Logic gate; Computation; Nonlinear system; Novelty; Comparator; Electrical engineering; Algorithm; Engineering; Mathematics; Physics","score_opus":0.054048819344559,"score_gpt":0.29152841344269187,"score_spread":0.23747959409813288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312468999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14924225,0.00035533422,0.84579873,0.000084181986,0.002940832,0.00039374176,0.00007233517,0.00011877558,0.0009938099],"genre_scores_gemma":[0.99833477,0.000007973973,0.00087258004,0.00007078793,0.00030832487,0.00002694219,0.000010258741,0.000039217713,0.00032916403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985397,0.00018344168,0.00036500278,0.00028104786,0.000437906,0.0001928938],"domain_scores_gemma":[0.9992986,0.00028806805,0.00013458164,0.00015272321,0.000042940068,0.00008313542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038283417,0.00019170286,0.00027411862,0.00016115457,0.00016329315,0.000036531033,0.0002369576,0.00004221256,0.000060543873],"category_scores_gemma":[0.000016108897,0.00019351984,0.00007291623,0.0001765966,0.000015500238,0.000062264415,0.00004251462,0.00030567098,0.000001707362],"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":[0.000024631008,0.000016605829,0.000028741037,0.000048426522,0.000052250638,0.000019374256,0.0003515736,0.7564717,0.24053884,0.000235602,0.00013520967,0.0020770645],"study_design_scores_gemma":[0.0006991644,0.0001786983,0.000039048726,0.00011502437,0.00002837956,0.00017939457,0.00021325241,0.9582427,0.03707687,0.00012705756,0.0028025983,0.0002977816],"about_ca_topic_score_codex":0.00003762243,"about_ca_topic_score_gemma":8.930617e-7,"teacher_disagreement_score":0.8490925,"about_ca_system_score_codex":0.00016527541,"about_ca_system_score_gemma":0.000033504657,"threshold_uncertainty_score":0.78915095},"labels":[],"label_agreement":null},{"id":"W4312578340","doi":"10.14778/3554821.3554897","title":"The past, present and future of indexing on persistent memory","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Simon Fraser University","funders":"","keywords":"Search engine indexing; Computer science; Hash function; Contrast (vision); Data science; Information retrieval; Artificial intelligence; Computer security","score_opus":0.009660911960263854,"score_gpt":0.19781378855625145,"score_spread":0.1881528765959876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312578340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99078846,0.0014869809,0.0000029369744,0.001601184,0.00047646384,0.00033383255,0.000003656926,0.000044197608,0.005262297],"genre_scores_gemma":[0.9993522,0.00008820771,0.000050229002,0.00003588161,0.00026705547,0.000030408712,1.7006666e-7,0.000012662006,0.00016316147],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930644,0.000006070665,0.0001675037,0.00010905308,0.00025902854,0.00015189688],"domain_scores_gemma":[0.9997271,0.000050515533,0.00008921058,0.00007976659,0.000027133106,0.000026301945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017900678,0.0000963272,0.00010772223,0.000028219221,0.00028811055,0.000010244846,0.00024525877,0.000013323823,0.0000053049403],"category_scores_gemma":[0.0000051249467,0.000059784128,0.00007858503,0.000115344694,0.00004007674,0.00003999524,0.00027554465,0.00021144535,1.5554815e-7],"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":[0.00050509727,0.00026299318,0.0022548458,0.0014687354,0.0005517503,0.0000030135461,0.012799822,0.26682597,0.50301313,0.01510985,0.013079726,0.1841251],"study_design_scores_gemma":[0.0014579374,0.00077672396,0.0024563784,0.00017870106,0.00011996151,0.00007397039,0.02207575,0.012619456,0.828133,0.0022268554,0.1294027,0.0004785609],"about_ca_topic_score_codex":0.00000123215,"about_ca_topic_score_gemma":7.2333634e-8,"teacher_disagreement_score":0.3251199,"about_ca_system_score_codex":0.000053392905,"about_ca_system_score_gemma":0.0000034001312,"threshold_uncertainty_score":0.24379261},"labels":[],"label_agreement":null},{"id":"W4312845220","doi":"10.1109/asap54787.2022.00020","title":"Fast Heterogeneous Task Mapping for Reducing Edge DNN Latency","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Initialization; Inference; Latency (audio); Edge computing; Task (project management); Enhanced Data Rates for GSM Evolution; Parallel computing; Distributed computing; Artificial intelligence","score_opus":0.02119805558447708,"score_gpt":0.22487447168565322,"score_spread":0.20367641610117615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312845220","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8438568,0.00040659934,0.1497993,0.000055143555,0.0011599163,0.0003075147,0.000014198887,0.00082260685,0.0035779225],"genre_scores_gemma":[0.9951507,0.0000052399937,0.0037693444,0.00009201117,0.00014882033,0.000047416226,0.000008885585,0.0000325747,0.00074505026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993553,0.000010417195,0.00015221929,0.00016168866,0.000066847744,0.00025353004],"domain_scores_gemma":[0.9997497,0.00005721797,0.000017949687,0.00012456345,0.000009736826,0.00004080654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007523134,0.00010060943,0.0001065293,0.000049931932,0.0002756952,0.0000116472875,0.00011243725,0.000015681593,0.00009432097],"category_scores_gemma":[0.000010017809,0.00010974378,0.000060780825,0.00011386241,0.0000055671794,0.00006052824,0.0000795267,0.00013653893,0.0000065662016],"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":[0.000004504056,0.0000063985967,0.000008053272,0.000044334625,0.000011933793,0.000010445817,0.00025724873,0.8513366,0.12348497,0.00008089889,0.0004457876,0.024308829],"study_design_scores_gemma":[0.0007440673,0.00019211794,0.000047161593,0.00003798657,0.00001728922,0.00024491255,0.0006764314,0.6916809,0.2320747,0.0011679304,0.07236029,0.00075622014],"about_ca_topic_score_codex":0.0000012759099,"about_ca_topic_score_gemma":5.547207e-7,"teacher_disagreement_score":0.15965569,"about_ca_system_score_codex":0.00006394329,"about_ca_system_score_gemma":0.000005354634,"threshold_uncertainty_score":0.44752213},"labels":[],"label_agreement":null},{"id":"W4312856857","doi":"10.1109/icpr56361.2022.9956379","title":"Aggregating Global Features into Local Vision Transformer","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"Toronto Metropolitan University","funders":"","keywords":"Transformer; Computer science; Architecture; Artificial intelligence; Image resolution; Pixel; Pattern recognition (psychology); Engineering; Voltage","score_opus":0.0294381293729458,"score_gpt":0.28724787775458577,"score_spread":0.25780974838163995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312856857","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7790335,0.00016332838,0.1709228,0.0017699965,0.0047977204,0.00049148366,0.0007063408,0.0007795175,0.041335307],"genre_scores_gemma":[0.9977489,0.00004757096,0.00022055792,0.0010625907,0.0001903741,0.00008261568,0.0004629921,0.000028758957,0.0001556518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839985,0.00007522345,0.0003331108,0.0003657796,0.0005577347,0.00026826924],"domain_scores_gemma":[0.99954534,0.000055652083,0.00008626042,0.00013377226,0.0000995655,0.000079388716],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015969489,0.00023714216,0.00017226917,0.00011831984,0.0003008629,0.00007587149,0.000331389,0.00005543786,0.0041832705],"category_scores_gemma":[0.000018348814,0.00026110414,0.00010822863,0.00019019564,0.00004070549,0.00025065008,0.00007893439,0.00057545304,0.00013479193],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009116447,0.000070530245,0.00026153188,0.000038585193,0.000056210756,0.000057390604,0.00027862666,0.0163,0.006006796,0.00049123925,0.0011905232,0.9751574],"study_design_scores_gemma":[0.008340601,0.0029071234,0.011171457,0.0015425663,0.00015554014,0.001147127,0.009223045,0.6907787,0.16061756,0.07482873,0.034356367,0.0049312175],"about_ca_topic_score_codex":0.00003084243,"about_ca_topic_score_gemma":0.000062367646,"teacher_disagreement_score":0.97022617,"about_ca_system_score_codex":0.00034323943,"about_ca_system_score_gemma":0.000025567479,"threshold_uncertainty_score":0.99998415},"labels":[],"label_agreement":null},{"id":"W4312889826","doi":"10.1109/ojsscs.2022.3213633","title":"Sparsity-Aware 25-Gb/s Memory Link With 0.0375-pJ/bit Signaling Efficiency for Machine Learning Hardware","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of the Solid-State Circuits Society","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"MNIST database; Computer science; Efficient energy use; Inference; Computer hardware; Multiplication (music); Computation; Set (abstract data type); Process (computing); Artificial neural network; Parallel computing; Computer engineering; Artificial intelligence; Algorithm; Electrical engineering","score_opus":0.027583603350877967,"score_gpt":0.25598756650189425,"score_spread":0.22840396315101627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312889826","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8655403,0.00083346956,0.12895545,0.00045172466,0.0023008257,0.0011701551,0.00011482783,0.00014189913,0.0004913356],"genre_scores_gemma":[0.99737275,0.000047259033,0.0007717167,0.00034983127,0.0003417502,0.000016000608,0.000006546631,0.00009970739,0.0009944604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767053,0.00021495183,0.0006899879,0.00028963046,0.0005714402,0.0005634546],"domain_scores_gemma":[0.99841815,0.00028347594,0.00060525315,0.0003086731,0.00022531737,0.00015910644],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0016256272,0.00032240557,0.00057379605,0.000053087235,0.0016686779,0.0001731086,0.0015436327,0.000055649707,0.0000711521],"category_scores_gemma":[0.00005782341,0.00024675444,0.0005390898,0.00044901887,0.00007033058,0.00043580338,0.00032501537,0.0016798881,0.0000025290271],"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":[0.000038953556,0.000030905136,0.00015129804,0.00009635823,0.00017121428,0.00003053361,0.0035958933,0.95779526,0.033355117,0.0000032822238,0.00089819,0.003832975],"study_design_scores_gemma":[0.012266096,0.0026549161,0.0004526914,0.001484073,0.00077217526,0.0026101207,0.013669029,0.56434506,0.35500893,0.001756692,0.042048257,0.0029319497],"about_ca_topic_score_codex":0.0000047609233,"about_ca_topic_score_gemma":0.0000026287578,"teacher_disagreement_score":0.39345023,"about_ca_system_score_codex":0.0002977049,"about_ca_system_score_gemma":0.00014764804,"threshold_uncertainty_score":0.99999845},"labels":[],"label_agreement":null},{"id":"W4313191607","doi":"10.1109/iscas48785.2022.9937618","title":"Selective Input Sparsity in Spiking Neural Networks for Pattern Classification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Memory and Neural Computing","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":"University of Windsor","funders":"","keywords":"MNIST database; Inference; Computer science; Artificial neural network; Artificial intelligence; Spiking neural network; Pattern recognition (psychology); Set (abstract data type); Enhanced Data Rates for GSM Evolution; Machine learning","score_opus":0.03510406941949527,"score_gpt":0.25837327422459744,"score_spread":0.22326920480510218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313191607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9622431,0.00017614168,0.029621722,0.00016787497,0.005562077,0.00062341604,0.00006680184,0.00013514153,0.0014037464],"genre_scores_gemma":[0.9989464,0.00002012753,0.0000027456942,0.0001160464,0.00050871726,0.00019712979,0.000046700312,0.00003090775,0.00013121818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874324,0.00007900454,0.00034147935,0.00032988656,0.00026214911,0.00024426123],"domain_scores_gemma":[0.99950904,0.00017674308,0.00010270402,0.00011644575,0.000045069828,0.000049978575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030941592,0.00016656301,0.00020031989,0.000130638,0.00022665816,0.00006575683,0.00019896812,0.0000493308,0.000010286678],"category_scores_gemma":[0.000011762191,0.00018593966,0.000057512585,0.00016323042,0.000012164491,0.00014305685,0.000046317604,0.0003607436,0.0000013741051],"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":[0.0000212478,0.000033065124,0.00519233,0.000046860558,0.000033685934,0.000013116449,0.0003605418,0.9693932,0.016485564,0.00048477648,0.00024787383,0.0076877577],"study_design_scores_gemma":[0.0004860887,0.00009605167,0.0048003206,0.000039101345,0.0000064268797,0.00004497903,0.00021191176,0.9921475,0.0005405564,0.000034152952,0.0013789383,0.00021395225],"about_ca_topic_score_codex":0.000021018559,"about_ca_topic_score_gemma":0.000018790719,"teacher_disagreement_score":0.03670333,"about_ca_system_score_codex":0.00032361475,"about_ca_system_score_gemma":0.000005835692,"threshold_uncertainty_score":0.7582399},"labels":[],"label_agreement":null},{"id":"W4313254039","doi":"10.1016/j.isci.2022.105888","title":"Electronic properties of lithium-ion battery cathodes studied in ion-gated transistor configuration","year":2022,"lang":"en","type":"article","venue":"iScience","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Ministero dell’Istruzione, dell’Università e della Ricerca; Consejo Nacional de Ciencia y Tecnología","keywords":"Electrolyte; Battery (electricity); Materials science; Cathode; Ethylene carbonate; Lithium (medication); Ionic bonding; Ion; Transistor; Lithium-ion battery; Optoelectronics; Nanotechnology; Chemistry; Electrical engineering; Electrode; Physical chemistry; Physics; Voltage; Organic chemistry","score_opus":0.02466446261421271,"score_gpt":0.2303056234825722,"score_spread":0.20564116086835948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313254039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957961,0.000941297,0.0026449924,0.00005181351,0.0001700693,0.000111496214,9.947289e-7,0.000060273967,0.00022298396],"genre_scores_gemma":[0.9998352,0.000017854327,0.000047122317,0.0000245173,0.000012353129,0.000013352716,8.1261817e-7,0.0000052770815,0.00004352713],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994086,0.000037958976,0.0001436406,0.00011956642,0.00012324468,0.00016699386],"domain_scores_gemma":[0.9998565,0.000021691652,0.000023266355,0.000071903094,0.000013695028,0.000012945793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020165929,0.00005872874,0.00008788804,0.000061026658,0.00010491927,0.000005116833,0.0001084421,0.000009577797,0.000010181144],"category_scores_gemma":[0.000017204722,0.00005544262,0.000014843149,0.00030270414,0.000037273483,0.00012707977,0.000018205492,0.00013872224,9.478573e-7],"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":[0.000009124087,0.000009781675,0.000034467983,0.000020995316,9.776448e-7,0.0000012382709,0.0013014559,0.1800514,0.8169747,0.000050279166,0.000006202158,0.0015394002],"study_design_scores_gemma":[0.00015075636,0.00007774587,0.0005727526,0.00002536628,0.000002081143,0.0000052700034,0.0004130213,0.029340006,0.9687282,0.00005707527,0.00053262233,0.00009508949],"about_ca_topic_score_codex":0.000006499665,"about_ca_topic_score_gemma":0.000010917987,"teacher_disagreement_score":0.15175354,"about_ca_system_score_codex":0.000092179296,"about_ca_system_score_gemma":0.00002416561,"threshold_uncertainty_score":0.22608843},"labels":[],"label_agreement":null},{"id":"W4313458650","doi":"10.34133/icomputing.0006","title":"Intelligent Computing: The Latest Advances, Challenges, and Future","year":2023,"lang":"en","type":"article","venue":"Intelligent Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":254,"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":"Cognitive computing; Computer science; End-user computing; Big data; Data science; Intelligent decision support system; Marketing and artificial intelligence; Scope (computer science); Autonomic computing; Human intelligence; Soft computing; Affective computing; Computational intelligence; Cloud computing; Artificial intelligence; Utility computing; Cognition; Artificial neural network; Cloud computing security","score_opus":0.036120386696272065,"score_gpt":0.26956486830498916,"score_spread":0.23344448160871709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313458650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.690366,0.18943699,0.08224161,0.0039479975,0.011471385,0.0014869719,0.000010627012,0.008139804,0.01289861],"genre_scores_gemma":[0.9752982,0.021796374,0.0007567592,0.0001559669,0.001837935,0.0000034666907,0.000011349717,0.000079657235,0.000060240494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980517,0.00007144583,0.0005021396,0.00044743606,0.0002444253,0.00068283215],"domain_scores_gemma":[0.9987072,0.00063340185,0.00009073816,0.0003672635,0.000058051653,0.00014337352],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005118215,0.0003726586,0.00031974,0.00014124771,0.00039690582,0.00007305806,0.00039901686,0.00010354498,0.000011585032],"category_scores_gemma":[0.000045908546,0.00029923322,0.00009995715,0.000484393,0.00009081266,0.0001320151,0.00033714584,0.0006145096,0.00015334679],"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":[0.0000057692614,0.000011304993,0.000054104403,0.00015911889,0.000036804562,0.000036213776,0.0025135619,0.22296697,0.0003631297,0.0021014358,0.00026936608,0.7714822],"study_design_scores_gemma":[0.00023358795,0.0001033239,0.0016604186,0.00034838868,0.000030616702,0.00017815964,0.0040961066,0.60463065,0.013516386,0.002565654,0.3717735,0.0008631623],"about_ca_topic_score_codex":0.0000019151837,"about_ca_topic_score_gemma":0.000006816702,"teacher_disagreement_score":0.77061903,"about_ca_system_score_codex":0.000059723217,"about_ca_system_score_gemma":0.000009566528,"threshold_uncertainty_score":0.999946},"labels":[],"label_agreement":null},{"id":"W4313489850","doi":"10.1088/2634-4386/acad98","title":"Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Spiking neural network; MNIST database; Computer science; Neuromorphic engineering; Spike-timing-dependent plasticity; Artificial intelligence; Spike (software development); Convolutional neural network; Preprocessor; Unsupervised learning; Machine learning; Artificial neural network; Synaptic plasticity; Pattern recognition (psychology); Biology","score_opus":0.015453561443246508,"score_gpt":0.18469614531898995,"score_spread":0.16924258387574345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313489850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93821186,0.00061750243,0.059952375,0.00001843579,0.00043437135,0.00015983502,0.0000038368826,0.00058825134,0.000013548409],"genre_scores_gemma":[0.99969757,0.000020556397,0.0000832762,0.000034497134,0.00007712006,0.000009061672,0.000011213471,0.00005993019,0.0000067616056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838084,0.00006920909,0.00034628008,0.00041762873,0.00021662527,0.0005694287],"domain_scores_gemma":[0.99923694,0.0004746629,0.000052540738,0.000087971464,0.000015373553,0.00013250274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026204577,0.0003028412,0.00032145507,0.00017159099,0.0004012949,0.000058291844,0.0001250112,0.000056034038,0.000009812615],"category_scores_gemma":[0.000076152246,0.00037234707,0.000033920678,0.00035440392,0.000030847295,0.000070093854,0.00028038924,0.0012772515,3.86828e-7],"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":[0.0000186563,0.000016429021,0.000826019,0.000063358995,0.000015983782,0.000081368824,0.00013297495,0.8996859,0.09755531,0.00002995604,0.0000063171387,0.0015677226],"study_design_scores_gemma":[0.0007390492,0.00008398679,0.00335233,0.000056868204,0.000012236188,0.00022690403,0.00006661055,0.99420303,0.0008344936,0.0000029295388,0.000085645515,0.00033590695],"about_ca_topic_score_codex":0.000011344561,"about_ca_topic_score_gemma":6.1578055e-7,"teacher_disagreement_score":0.096720815,"about_ca_system_score_codex":0.0001018237,"about_ca_system_score_gemma":0.000008099993,"threshold_uncertainty_score":0.99987286},"labels":[],"label_agreement":null},{"id":"W4313506031","doi":"10.1007/978-3-031-17425-4_11","title":"Direct Laser Writing of Copper/Copper Oxide Patterns for Emerging Roles in Advanced Electronics","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Copper; Electronics; Laser; Materials science; Copper oxide; Oxide; Metallurgy; Engineering; Electrical engineering; Optics; Physics","score_opus":0.016496737243437868,"score_gpt":0.2476003938423353,"score_spread":0.23110365659889742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313506031","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38040292,0.008860541,0.019020906,0.00014127376,0.0018818782,0.0035568532,0.00048480724,0.004603241,0.5810476],"genre_scores_gemma":[0.89017355,0.002780729,0.002528667,0.00010137597,0.0003109468,0.00008003331,0.00014291257,0.00059467345,0.103287086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99836165,0.00000725892,0.00058749184,0.00037685788,0.000154778,0.0005119558],"domain_scores_gemma":[0.99918395,0.0003540545,0.00011031926,0.0002497963,0.000047868696,0.000054032782],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016612935,0.00039753783,0.00059760886,0.00022755623,0.000050513518,0.000011271232,0.00017778354,0.00019542458,0.000022883063],"category_scores_gemma":[0.000035639743,0.00042844805,0.00019268066,0.00006056897,0.000015659487,0.0001167359,0.000071007125,0.00042758006,0.000010779254],"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":[0.00012203443,0.000021316362,0.00020280523,0.0026261928,0.00026624417,0.00007745881,0.00024536162,0.80074453,0.07595206,0.015246997,0.00075961143,0.10373538],"study_design_scores_gemma":[0.0036953958,0.0004393286,0.00017400306,0.009081599,0.00022000851,0.000026948956,0.0005644939,0.061713237,0.83865464,0.010753022,0.07026566,0.0044116653],"about_ca_topic_score_codex":0.0000019843646,"about_ca_topic_score_gemma":0.00016391977,"teacher_disagreement_score":0.7627026,"about_ca_system_score_codex":0.00010426857,"about_ca_system_score_gemma":0.000018229923,"threshold_uncertainty_score":0.9998167},"labels":[],"label_agreement":null},{"id":"W4313566140","doi":"10.1109/lssc.2022.3231762","title":"Editorial","year":2022,"lang":"nl","type":"editorial","venue":"IEEE Solid-State Circuits Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pleasure; Very-large-scale integration; Computer science; State (computer science); Editorial board; Psychology; Library science; Embedded system","score_opus":0.012818114090097749,"score_gpt":0.2521112616632145,"score_spread":0.23929314757311676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313566140","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01431478,0.00031756822,0.0031619654,0.00016254054,0.97855496,0.00082106504,0.0009429552,0.0010473963,0.0006767605],"genre_scores_gemma":[0.010469618,0.0003818762,0.000032693286,0.00075232144,0.98586804,0.00012655802,0.0005397164,0.00062256004,0.0012066442],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911752,0.00041098183,0.0016011655,0.0018537649,0.0025387534,0.002420169],"domain_scores_gemma":[0.9955945,0.0016903613,0.00063407165,0.0012955017,0.00020298803,0.00058255345],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0008408978,0.0017251305,0.001675435,0.00053925347,0.0010662299,0.0003594132,0.0017318501,0.0008878901,0.00085119583],"category_scores_gemma":[0.00041616458,0.002194494,0.00077133195,0.0008253537,0.00022263896,0.00062928605,0.00033489655,0.0067671454,0.0010324136],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004608975,0.000038076523,0.000001781984,0.0003973739,0.00024206733,0.00036222432,0.00066526845,0.2154304,0.10162507,0.0000024508474,0.6797314,0.0014577915],"study_design_scores_gemma":[0.0017990432,0.00018528853,0.0000036814868,0.0002448452,0.00020350277,0.0000241073,0.000072471754,0.00053086935,0.00857522,0.000095327545,0.98599523,0.0022704287],"about_ca_topic_score_codex":0.00004442036,"about_ca_topic_score_gemma":0.0000052338355,"teacher_disagreement_score":0.3062638,"about_ca_system_score_codex":0.0015334637,"about_ca_system_score_gemma":0.00032145693,"threshold_uncertainty_score":0.99974537},"labels":[],"label_agreement":null},{"id":"W4313592635","doi":"10.1039/d2mh01491b","title":"Intelligent matter endows reconfigurable temperature and humidity sensations for in-sensor computing","year":2023,"lang":"en","type":"article","venue":"Materials Horizons","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Basic Research Program of Jiangsu Province; Nanjing University of Posts and Telecommunications; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Humidity; Materials science; Computer science; Meteorology; Physics","score_opus":0.024803751323599826,"score_gpt":0.2570596928626606,"score_spread":0.2322559415390608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313592635","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972683,0.000026945178,0.00047947306,0.00017340976,0.0009840204,0.0004568951,0.00007217704,0.00032907305,0.0002096656],"genre_scores_gemma":[0.99851596,0.000038640424,0.00082436873,0.00004094584,0.00024043048,0.000039280465,0.000048055957,0.000033808617,0.00021852135],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992001,0.000029443117,0.0002493125,0.00018649684,0.000042375832,0.00029224827],"domain_scores_gemma":[0.9996259,0.00017033171,0.000026541536,0.00011672657,0.000017790851,0.00004267318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018965472,0.00013727248,0.00020317044,0.00009843853,0.00012916683,0.00006688639,0.000055949506,0.00006724868,0.00006391749],"category_scores_gemma":[0.00003259922,0.00013850888,0.00002426848,0.00015058501,0.00001685257,0.00007505597,0.000024607283,0.00009070207,0.00006878887],"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":[0.000007662485,0.0000039921792,0.00007309388,0.00014792044,0.000007638952,0.0000066135954,0.00020734538,0.012762472,0.9845204,0.000058561796,0.0011333474,0.0010709747],"study_design_scores_gemma":[0.00014876098,0.000025684718,0.0012826956,0.00006978261,0.000006109385,0.000013991265,0.0001744651,0.0011565945,0.9935071,0.00033452836,0.003093482,0.00018679095],"about_ca_topic_score_codex":0.0000034451548,"about_ca_topic_score_gemma":0.0000052744417,"teacher_disagreement_score":0.011605877,"about_ca_system_score_codex":0.00002479975,"about_ca_system_score_gemma":0.000004337235,"threshold_uncertainty_score":0.56482285},"labels":[],"label_agreement":null},{"id":"W4313613004","doi":"10.1088/2058-8585/acb0df","title":"Direct laser writing of copper and copper oxide structures on plastic substrates for memristor devices","year":2023,"lang":"en","type":"article","venue":"Flexible and Printed Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Copper; Laser; Fabrication; Laser power scaling; Copper oxide; Optoelectronics; Focused ion beam; Scanning electron microscope; Flexible electronics; X-ray photoelectron spectroscopy; Electrical conductor; Nanotechnology; Composite material; Optics; Chemical engineering; Metallurgy; Chemistry; Ion","score_opus":0.018280392062557996,"score_gpt":0.26173045563224046,"score_spread":0.24345006356968246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313613004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972329,0.0015307904,0.00028258542,0.000017407294,0.00008786161,0.00013470252,0.000012820814,0.00025375272,0.00044714895],"genre_scores_gemma":[0.99912757,0.0004334359,0.00022493556,0.000029400075,0.0000421686,0.000010184667,0.000013394321,0.0000268238,0.000092084716],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992951,0.000010946762,0.00016529099,0.00016865424,0.00006199301,0.00029797087],"domain_scores_gemma":[0.9993979,0.00042123705,0.000031984415,0.000077221885,0.00002585841,0.00004585319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009217107,0.0001383089,0.00018193401,0.00007923502,0.000091791044,0.000018498959,0.000053346033,0.000054519976,0.000003182549],"category_scores_gemma":[0.000050258528,0.0001277077,0.000028447506,0.0001468988,0.000026394611,0.0000645313,0.000020632413,0.00014632203,0.0000016745691],"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":[0.0005348409,0.000031085354,0.0026840547,0.002483076,0.00030658528,0.000012306999,0.0005613346,0.27401328,0.685996,0.009894584,0.002656515,0.020826314],"study_design_scores_gemma":[0.0004192158,0.00022372641,0.0021037913,0.00012337156,0.000027912945,0.0000075710454,0.00010222977,0.019455161,0.9714243,0.0011169396,0.004771556,0.00022424306],"about_ca_topic_score_codex":0.000001426514,"about_ca_topic_score_gemma":0.000015012567,"teacher_disagreement_score":0.28542826,"about_ca_system_score_codex":0.000020281293,"about_ca_system_score_gemma":0.000013196672,"threshold_uncertainty_score":0.52077687},"labels":[],"label_agreement":null},{"id":"W4315631989","doi":"10.1016/b978-0-12-819728-8.00073-5","title":"Organic Electrolyte-Gated Transistors","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Transistor; Electrolyte; Materials science; Nanotechnology; Key (lock); Optoelectronics; Computer science; Electrical engineering; Chemistry; Engineering; Electrode; Voltage","score_opus":0.01419537791874084,"score_gpt":0.20701226263912786,"score_spread":0.19281688472038702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315631989","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041745626,0.0007603102,0.0001093483,0.000010001247,0.0008372913,0.00028242054,0.000011768691,0.0018885273,0.9956829],"genre_scores_gemma":[0.0054791057,0.0001818086,0.00007320594,0.00007478953,0.0003783322,0.000006581664,0.000026284402,0.00036029523,0.9934196],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99869585,0.000008916964,0.00036431305,0.0003402343,0.00018976477,0.0004009083],"domain_scores_gemma":[0.9993867,0.00006475998,0.00005957054,0.00034434182,0.000031557298,0.00011303968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000082117534,0.0004647146,0.0004739953,0.00015322054,0.000094534065,0.000018675906,0.00022531819,0.00029414453,0.00014405798],"category_scores_gemma":[0.000007248943,0.0004955154,0.00020105262,0.00003114226,0.00003970502,0.00003245768,0.000031806983,0.00081407116,0.0006199297],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007699868,0.0000011507589,1.0138563e-7,0.00017865082,0.00012546965,0.00010556915,0.00011141758,0.00041344578,0.011411979,0.00077008514,0.00013214147,0.9867423],"study_design_scores_gemma":[0.00021731327,0.000050038987,0.0000013657216,0.0003786164,0.00010001498,0.000029018902,0.0000024278597,0.00025253644,0.010111587,0.004965087,0.9831469,0.0007451104],"about_ca_topic_score_codex":2.4709632e-8,"about_ca_topic_score_gemma":0.000009206526,"teacher_disagreement_score":0.9859972,"about_ca_system_score_codex":0.00011205672,"about_ca_system_score_gemma":0.000028443179,"threshold_uncertainty_score":0.99974966},"labels":[],"label_agreement":null},{"id":"W4315778706","doi":"10.1088/2634-4386/acb286","title":"Neuromorphic control of a simulated 7-DOF arm using Loihi","year":2023,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs; Canada Foundation for Innovation","keywords":"Neuromorphic engineering; Benchmark (surveying); Computer science; Controller (irrigation); Artificial neural network; Trajectory; Spiking neural network; Node (physics); Artificial intelligence; Engineering","score_opus":0.033318842849774696,"score_gpt":0.22944602428875588,"score_spread":0.19612718143898117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315778706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9653463,0.00022471174,0.032034732,0.000020057705,0.0007023891,0.0001610589,0.000008250897,0.0014666672,0.00003587365],"genre_scores_gemma":[0.9993187,0.000038612943,0.00035944273,0.000031269927,0.00015029463,8.4997015e-7,0.000004886653,0.000089435416,0.000006464951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998502,0.000032076725,0.0004477841,0.00031323664,0.00018541174,0.00051945687],"domain_scores_gemma":[0.99905217,0.00045813195,0.00006938854,0.00023587582,0.000043933116,0.00014050351],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022213846,0.00030875317,0.00043901496,0.00028653146,0.00011224489,0.000029835937,0.00015670295,0.00008489215,0.0000052492414],"category_scores_gemma":[0.00011036767,0.00035047438,0.00008220309,0.0007850242,0.000041432537,0.00009707259,0.00009307188,0.00041176725,0.0000065743243],"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":[0.0000065174045,0.0000074368913,0.00021339225,0.00019103229,0.000022698507,0.00008368933,0.00008031839,0.79320407,0.20496403,0.00004447193,0.000008602405,0.001173715],"study_design_scores_gemma":[0.00064742024,0.00006955685,0.0019271305,0.00017382998,0.000029473835,0.00013437842,0.000012986663,0.9889601,0.007572133,0.00002968994,0.00015242845,0.000290904],"about_ca_topic_score_codex":0.000004696468,"about_ca_topic_score_gemma":1.8734302e-7,"teacher_disagreement_score":0.1973919,"about_ca_system_score_codex":0.00002000059,"about_ca_system_score_gemma":0.000009958204,"threshold_uncertainty_score":0.99989474},"labels":[],"label_agreement":null},{"id":"W4315926782","doi":"10.1021/acsmaterialslett.2c00911","title":"Photoelectric Memristor-Based Machine Vision for Artificial Intelligence Applications","year":2023,"lang":"en","type":"article","venue":"ACS Materials Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"University of Waterloo","funders":"Central University Basic Research Fund of China; Fujian Normal University; Department of Science and Technology of Sichuan Province; Ministry of Science and Technology of the People's Republic of China","keywords":"Memristor; Machine vision; Computer science; Von Neumann architecture; Artificial intelligence; Photoelectric effect; Photoelectric sensor; Applications of artificial intelligence; Computer vision; Engineering; Electronic engineering; Electrical engineering; Physics","score_opus":0.02338725466870895,"score_gpt":0.2729021910575469,"score_spread":0.24951493638883793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315926782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57000655,0.000020856054,0.42796752,0.00045810314,0.0004314408,0.0004063576,0.00003228808,0.00066648005,0.000010411545],"genre_scores_gemma":[0.9974091,0.0000086226555,0.0014980554,0.00046607308,0.00025732347,0.00021466312,0.00010069621,0.000038671344,0.000006778526],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926305,0.000016280414,0.00022529067,0.00017249562,0.00007383729,0.00024904672],"domain_scores_gemma":[0.99961716,0.00013873151,0.000035060777,0.0001621451,0.000013203402,0.000033696455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015361555,0.0001216784,0.00013485258,0.00011093952,0.00011727831,0.000037926926,0.00013507996,0.00003527159,0.000018890838],"category_scores_gemma":[0.00001978572,0.00012546906,0.0000327312,0.00030246508,0.00001757445,0.0000677938,0.000016886257,0.000052879655,0.00011002849],"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":[0.00001668965,0.0000049176924,6.1180543e-7,0.000071336624,0.0000044235467,0.0000020232287,0.000012877102,0.03127665,0.96018517,0.0002094276,0.0005711068,0.0076447707],"study_design_scores_gemma":[0.000046289464,0.000022914845,0.000015682039,0.000011477724,0.000007702077,0.0000010713017,0.0000035142875,0.012477744,0.9841113,0.0008553028,0.0023033016,0.00014370994],"about_ca_topic_score_codex":0.0000017114206,"about_ca_topic_score_gemma":8.6304453e-7,"teacher_disagreement_score":0.4274026,"about_ca_system_score_codex":0.000036542035,"about_ca_system_score_gemma":0.0000042055417,"threshold_uncertainty_score":0.511648},"labels":[],"label_agreement":null},{"id":"W4317749307","doi":"10.1021/acsami.2c16569","title":"In-Depth Physical Mechanism Analysis and Wearable Applications of HfO<i><sub>x</sub></i>-Based Flexible Memristors","year":2023,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Waterloo","funders":"Central University Basic Research Fund of China; Fujian Normal University; Department of Science and Technology of Sichuan Province; Ministry of Science and Technology of the People's Republic of China","keywords":"Memristor; Materials science; Mechanism (biology); Electronics; Crossbar switch; Band diagram; Wearable computer; Nanotechnology; Neuromorphic engineering; Flexible electronics; Optoelectronics; Computer science; Heterojunction; Electrical engineering; Embedded system; Artificial neural network; Engineering; Physics; Artificial intelligence","score_opus":0.010336711249966608,"score_gpt":0.2407345486001406,"score_spread":0.230397837350174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317749307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99508107,0.0000448948,0.003938268,0.00001768814,0.0001003275,0.00025679727,0.000023429167,0.0003127938,0.00022471962],"genre_scores_gemma":[0.9994316,0.00007040717,0.00022717129,0.000021913476,0.00005491092,0.00013324026,0.000014019977,0.000032562017,0.0000141725195],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905187,0.000018522791,0.00030085386,0.0002634622,0.000114866896,0.0002504383],"domain_scores_gemma":[0.99955195,0.000098194185,0.000076551034,0.00021746625,0.00001706751,0.000038797258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016845849,0.00018199484,0.00040954782,0.00029122722,0.000058795187,0.000030333707,0.00014821166,0.00006189461,0.0000125503],"category_scores_gemma":[0.000005358681,0.00018546834,0.000022441145,0.0008047289,0.000045093817,0.00008835684,0.00007500574,0.00009002863,0.00004044357],"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":[0.000020733172,0.000017409118,0.000006301711,0.00012869957,0.000060035545,0.0000011577979,0.00015673494,0.03754944,0.9604602,0.00068168866,0.000019380734,0.0008982225],"study_design_scores_gemma":[0.00019286167,0.000019345638,0.00008691902,0.000022594844,0.000075646785,4.1680426e-7,0.00009016652,0.0005955241,0.99428105,0.00444622,0.000018624332,0.00017062684],"about_ca_topic_score_codex":0.000009421666,"about_ca_topic_score_gemma":0.000011478942,"teacher_disagreement_score":0.036953915,"about_ca_system_score_codex":0.000025668278,"about_ca_system_score_gemma":0.000007040103,"threshold_uncertainty_score":0.7563179},"labels":[],"label_agreement":null},{"id":"W4317882195","doi":"10.1140/epjp/s13360-023-03699-7","title":"A new mix chaotic circuit based on memristor–memcapacitor","year":2023,"lang":"en","type":"article","venue":"The European Physical Journal Plus","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Memristor; Multistability; Chaotic; Capacitor; Inductor; Computer science; Electronic engineering; Chua's circuit; Topology (electrical circuits); Electrical engineering; Engineering; Physics; Nonlinear system; Voltage; Artificial intelligence","score_opus":0.030560572375368127,"score_gpt":0.2355916781604091,"score_spread":0.20503110578504097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317882195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8000983,0.00012639961,0.04793656,0.00093037344,0.004124188,0.0003034049,0.0000070433516,0.0021272635,0.14434648],"genre_scores_gemma":[0.9931751,0.0000075357793,0.000057139376,0.00016043709,0.005869539,4.8042523e-7,0.000001253973,0.00007276012,0.0006557496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989497,0.0001772584,0.00016630582,0.00012767497,0.0002515153,0.0003275281],"domain_scores_gemma":[0.999285,0.00023447294,0.00004620119,0.00023275043,0.000016444954,0.00018515112],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002941745,0.000172839,0.00016496837,0.00006432832,0.00023093635,0.00006015043,0.0003537747,0.000012594321,0.00001003962],"category_scores_gemma":[0.000049465914,0.000119545366,0.00014121213,0.00030348942,0.000027405058,0.00008296141,0.000037944596,0.00068095763,0.0010794406],"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":[0.000028216935,0.00001591727,8.931747e-7,0.000020587482,0.000026732678,0.00038317757,0.0009190526,0.8552905,0.044218354,0.0001636765,0.020576727,0.07835617],"study_design_scores_gemma":[0.004550326,0.0008705068,0.0026338336,0.0008167246,0.00017287467,0.00045751262,0.0004510508,0.8941298,0.0289398,0.023672244,0.041532833,0.0017724787],"about_ca_topic_score_codex":3.7594572e-7,"about_ca_topic_score_gemma":1.0494523e-7,"teacher_disagreement_score":0.1930768,"about_ca_system_score_codex":0.00007709826,"about_ca_system_score_gemma":0.000017430939,"threshold_uncertainty_score":0.99969834},"labels":[],"label_agreement":null},{"id":"W4317951750","doi":"10.1039/d2nh00502f","title":"Transverse magnetoconductance in two-terminal chiral spin-selective devices","year":2023,"lang":"en","type":"article","venue":"Nanoscale Horizons","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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 Alberta","funders":"Agencia Estatal de Investigación; Natural Sciences and Engineering Research Council of Canada; Consejería de Transformación Económica, Industria, Conocimiento y Universidades","keywords":"Terminal (telecommunication); Transverse plane; Spin (aerodynamics); Condensed matter physics; Physics; Materials science; Computer science; Engineering; Telecommunications","score_opus":0.017615229390765798,"score_gpt":0.28440160552790084,"score_spread":0.266786376137135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317951750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932236,0.00013253247,0.00043602777,0.00004164538,0.0005167761,0.00023626271,0.000010582253,0.00060463,0.004797909],"genre_scores_gemma":[0.9988964,0.00002916917,0.0003958104,0.00002255049,0.00016848519,0.000040498417,0.0000073254505,0.000031811418,0.00040791687],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989902,0.000024442867,0.00019997805,0.0002392885,0.000112834816,0.00043327612],"domain_scores_gemma":[0.9996951,0.000064385895,0.000019660793,0.00013661355,0.000014278475,0.00006992913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007865468,0.0001726842,0.00019170722,0.00015086413,0.0000793563,0.000015165483,0.00014850481,0.000050508555,0.000025526644],"category_scores_gemma":[0.000014029705,0.0001847862,0.000059295176,0.0007949891,0.000035144778,0.0002186729,0.000021078065,0.00029925938,0.00011742212],"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":[0.000099040364,0.0000750029,0.0045599667,0.00027990554,0.00004026266,0.0009365639,0.0017701507,0.08426659,0.81621766,0.0014737663,0.001371547,0.08890954],"study_design_scores_gemma":[0.0055374615,0.00090737606,0.06892626,0.0005550908,0.000072786635,0.00021902022,0.0012209959,0.08983666,0.8075405,0.004981059,0.017743552,0.0024592483],"about_ca_topic_score_codex":0.0000056009258,"about_ca_topic_score_gemma":0.00018675235,"teacher_disagreement_score":0.086450286,"about_ca_system_score_codex":0.000055527184,"about_ca_system_score_gemma":0.000014768584,"threshold_uncertainty_score":0.7535362},"labels":[],"label_agreement":null},{"id":"W4318954811","doi":"10.1016/j.simpa.2023.100473","title":"Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"Software Impacts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Crossbar switch; Computer science; Python (programming language); Bottleneck; Convolutional neural network; MATLAB; Artificial neural network; Computer architecture; Deep learning; Software; Artificial intelligence; Embedded system","score_opus":0.030356004717567456,"score_gpt":0.28273118174586814,"score_spread":0.2523751770283007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318954811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49170923,0.00007628391,0.5075664,0.0000019317752,0.00023982285,0.00013039088,0.000023799726,0.00025134595,8.1000115e-7],"genre_scores_gemma":[0.99779993,0.0000073975034,0.0019045508,0.0000076236324,0.00021247388,0.000005894042,0.000034721783,0.000023231663,0.0000041599856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939793,0.0000133250505,0.00017194185,0.00012549249,0.00007947443,0.00021184434],"domain_scores_gemma":[0.9994135,0.00034388367,0.00005396055,0.00006158951,0.00006791927,0.000059136044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009411159,0.00010956063,0.00012420742,0.00006777368,0.00015228227,0.000014412523,0.000026906271,0.00008027946,0.0000013658166],"category_scores_gemma":[0.00024024647,0.0001160164,0.000040298746,0.0001955793,0.00003193887,0.00023297974,0.000015035562,0.00010454003,3.9411728e-7],"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":[0.000054222848,0.0000023670182,0.00096325105,0.00007301731,0.000014913222,7.3251806e-7,0.00010926402,0.9906187,0.0055366317,0.000002548011,0.000011382153,0.0026129351],"study_design_scores_gemma":[0.0003736943,0.00006626797,0.014483639,0.000037606635,0.000017433724,0.0000052023197,0.000027683327,0.9827594,0.0018213853,0.0002943011,0.000019260022,0.000094137045],"about_ca_topic_score_codex":0.0000020873083,"about_ca_topic_score_gemma":0.0000022615634,"teacher_disagreement_score":0.5060907,"about_ca_system_score_codex":0.000049430662,"about_ca_system_score_gemma":0.0000077463055,"threshold_uncertainty_score":0.47310117},"labels":[],"label_agreement":null},{"id":"W4319078223","doi":"10.22541/au.167543650.04886551/v1","title":"SEVDA: Singular Value Decomposition Based Parallel Write Scheme for Memristive CNN Accelerators","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Crossbar switch; Singular value decomposition; Parallel computing; Von Neumann architecture; FLOPS; Artificial neural network; Convolutional neural network; Multiplication (music); Scheme (mathematics); Matrix multiplication; Line (geometry); Algorithm; Artificial intelligence; Mathematics; Quantum","score_opus":0.05296949498889108,"score_gpt":0.3151599784379937,"score_spread":0.2621904834491026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319078223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08438358,0.00017194635,0.91046965,0.00010008266,0.0014224856,0.0008398888,0.000058491594,0.0020185998,0.0005352753],"genre_scores_gemma":[0.695218,0.00003753369,0.3021201,0.00026672884,0.00073258183,0.0002597303,0.0007447232,0.00024672155,0.0003738731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983977,0.00003288143,0.00042683163,0.0005490474,0.00016922782,0.00042430943],"domain_scores_gemma":[0.99903905,0.00029900498,0.00009080939,0.0003619475,0.00009551176,0.000113646514],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020465297,0.00043364032,0.00045452517,0.00015758313,0.00016582619,0.00008520331,0.00025755542,0.00030347702,0.000025274221],"category_scores_gemma":[0.000054708347,0.0004672247,0.00025282265,0.00014458147,0.000023897925,0.00010211338,0.00019625992,0.0005947228,0.000038865455],"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":[0.00003399376,0.00001529851,0.000022399008,0.00050405617,0.00008304897,0.000015890135,0.00004766279,0.98735785,0.0097062085,0.00038722024,0.0008275571,0.0009987836],"study_design_scores_gemma":[0.0005424478,0.000039180723,0.00018535445,0.0003316601,0.0000525343,0.0000018290679,0.00002654683,0.9272039,0.065186575,0.005078075,0.0006930811,0.0006588138],"about_ca_topic_score_codex":0.0000055981286,"about_ca_topic_score_gemma":0.0000029138344,"teacher_disagreement_score":0.6108345,"about_ca_system_score_codex":0.00016622694,"about_ca_system_score_gemma":0.000041348787,"threshold_uncertainty_score":0.999778},"labels":[],"label_agreement":null},{"id":"W4319078291","doi":"10.22541/au.167543651.10481778/v1","title":"A Survey of Ensemble Methods for Mitigating Memristive Neural Network Non-idealities","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"MNIST database; Weighting; Artificial neural network; Computer science; Voting; Ensemble learning; Ensemble forecasting; Work (physics); Ensemble average; Artificial intelligence; Machine learning; Algorithm; Data mining; Pattern recognition (psychology); Engineering","score_opus":0.11111629344766585,"score_gpt":0.3793379323547438,"score_spread":0.26822163890707795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319078291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07453218,0.0005561959,0.920171,0.000015112922,0.0024908078,0.00063928287,0.00009761949,0.00066339003,0.00083440886],"genre_scores_gemma":[0.677582,0.000046569654,0.32083175,0.00003320221,0.0004482487,0.00011663894,0.00018740901,0.00014089025,0.0006132828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982786,0.00018952473,0.00060977566,0.00036544292,0.00009613814,0.0004605189],"domain_scores_gemma":[0.9955449,0.0037262167,0.00017091761,0.00031904993,0.00016927625,0.00006961928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013781929,0.00033508067,0.0007088119,0.00007014523,0.00008992539,0.000027892856,0.00025841568,0.0001993045,0.0000059915915],"category_scores_gemma":[0.00051547243,0.00034265555,0.00019124777,0.00020258356,0.000040439427,0.00004845072,0.0003927214,0.0005121561,0.0000018821921],"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":[0.000016476417,0.0000025548482,0.00018134186,0.00092924753,0.000096274154,0.000001672453,0.000218692,0.981203,0.0017728901,0.000092436174,0.00089388713,0.014591535],"study_design_scores_gemma":[0.00017628714,0.000040295523,0.00326505,0.00036573302,0.00003815576,0.0000015211236,0.00011825151,0.9485661,0.038764756,0.008163198,0.000059912774,0.00044074716],"about_ca_topic_score_codex":0.00021449589,"about_ca_topic_score_gemma":0.00013504097,"teacher_disagreement_score":0.6030498,"about_ca_system_score_codex":0.000041721483,"about_ca_system_score_gemma":0.00003178057,"threshold_uncertainty_score":0.99990255},"labels":[],"label_agreement":null},{"id":"W4319078323","doi":"10.22541/au.167543650.07743954/v1","title":"HUXIN: In-Memory Crossbar Core for Integration of Biologically Inspired Stochastic Neuron Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Memristor; Solver; Crossbar switch; Quantization (signal processing); Nonlinear system; Computer science; Computation; Dynamical systems theory; Stability (learning theory); Mathematics; Algorithm; Electronic engineering; Physics","score_opus":0.14863958186247217,"score_gpt":0.3137285108918835,"score_spread":0.16508892902941136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319078323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.483982,0.000068498055,0.5141718,0.000015716905,0.00061081495,0.0005426359,0.000031225023,0.00040134726,0.00017598795],"genre_scores_gemma":[0.9948912,0.00003140098,0.004577922,0.00002929004,0.00007575161,0.00009883279,0.000095202806,0.000050562674,0.00014978656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988135,0.000015021538,0.00049156236,0.00035161705,0.000086022046,0.00024226807],"domain_scores_gemma":[0.99928004,0.0002636318,0.00009107794,0.00026205866,0.00006368727,0.00003952014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014806417,0.00025829874,0.00040833658,0.00014778093,0.000030768442,0.000014940494,0.00023447421,0.00025076556,0.000004452026],"category_scores_gemma":[0.00011659833,0.00023168651,0.00012060491,0.00012735264,0.000038642982,0.00007371671,0.00021085254,0.00046491466,0.0000032594842],"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":[0.000034396944,0.00001081827,0.000002432624,0.0002310118,0.000008184979,0.0000027164206,0.00011856839,0.95496833,0.03784838,0.00046270405,0.000032058168,0.0062803808],"study_design_scores_gemma":[0.0002787407,0.00008248351,0.00023255507,0.00029756184,0.000008781849,0.0000015400113,0.000045528926,0.96032524,0.010775661,0.027709542,0.0000026621992,0.00023970983],"about_ca_topic_score_codex":0.000009750553,"about_ca_topic_score_gemma":0.000031262567,"teacher_disagreement_score":0.51090926,"about_ca_system_score_codex":0.000051687137,"about_ca_system_score_gemma":0.000021406562,"threshold_uncertainty_score":0.9447902},"labels":[],"label_agreement":null},{"id":"W4319299985","doi":"10.1109/wacv56688.2023.00542","title":"Event-based RGB sensing with structured light","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"","keywords":"Monochrome; Computer science; Computer vision; Projector; Artificial intelligence; RGB color model; Pixel; Brightness; Digital Light Processing; Computer graphics (images); Optics","score_opus":0.016188930273947865,"score_gpt":0.27046898473788544,"score_spread":0.2542800544639376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12618314,0.000015425148,0.87085444,0.00048322612,0.00037698293,0.0005312617,0.000024272089,0.0007728686,0.0007584061],"genre_scores_gemma":[0.9901984,0.000008981127,0.00915338,0.00013915848,0.00019589871,0.000029549059,0.000047082667,0.000050961167,0.00017660877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984977,0.000039488645,0.0004157309,0.00043333916,0.00028542947,0.0003282958],"domain_scores_gemma":[0.998835,0.00013386155,0.00011706961,0.0006277983,0.00016710447,0.00011916725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010747329,0.00029815963,0.00032802895,0.0003038098,0.00013105199,0.0000653571,0.00035986473,0.000091234804,0.00005005191],"category_scores_gemma":[0.0000036802269,0.0002636894,0.00009889726,0.00074611465,0.00006192202,0.000121813595,0.00006379072,0.0002911149,0.00020771174],"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":[0.00012569946,0.0000824718,0.000037336253,0.00025494568,0.00009682514,0.00003249597,0.00029677068,0.5799127,0.18496963,0.001537403,0.0078138765,0.22483979],"study_design_scores_gemma":[0.0007378896,0.00032809228,0.00061133364,0.0005060317,0.000023660275,0.000014434662,0.000058791953,0.8243274,0.16551718,0.0008931857,0.0065012868,0.00048071748],"about_ca_topic_score_codex":0.0000015809632,"about_ca_topic_score_gemma":0.0000068669106,"teacher_disagreement_score":0.8640152,"about_ca_system_score_codex":0.00003869407,"about_ca_system_score_gemma":0.000035873225,"threshold_uncertainty_score":0.9999815},"labels":[],"label_agreement":null},{"id":"W4319791632","doi":"10.1002/aelm.202201017","title":"A Flexible Corn Starch‐Based Biomaterial Device Integrated with Capacitive‐Coupled Memristive Memory, Mechanical Stress Sensing, Synapse, and Logic Operation Functions","year":2023,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Capacitive sensing; Electronics; Wearable technology; Synapse; Nanotechnology; Wearable computer; Computer science; Electrical engineering; Engineering; Embedded system; Neuroscience","score_opus":0.013072637620851748,"score_gpt":0.24236884002896292,"score_spread":0.22929620240811116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319791632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9401999,0.00007341638,0.057163183,0.00005453087,0.0004234623,0.0005662882,0.00014353951,0.0013418021,0.000033865952],"genre_scores_gemma":[0.99806637,0.000057254358,0.0009969814,0.00006269335,0.00010799349,0.000059888633,0.0004907361,0.0000756524,0.000082436534],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981044,0.00012207868,0.0003704547,0.0004751046,0.0001708343,0.00075714046],"domain_scores_gemma":[0.99932325,0.00014468827,0.00009160728,0.00023043143,0.00010616644,0.00010385095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023532957,0.00037592254,0.00044410978,0.00016114363,0.00027035247,0.00009934223,0.00010655451,0.00011331239,0.00011333542],"category_scores_gemma":[0.000059673243,0.00032422534,0.000029964796,0.00045273508,0.00008513486,0.00029178153,0.000042296953,0.0001951817,0.000035576257],"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":[0.00058708625,0.0000127065205,0.0000012228705,0.00010800407,0.000059151756,0.000028212147,0.00006729868,0.045434,0.95249647,0.00039738312,0.000013379598,0.00079508976],"study_design_scores_gemma":[0.0012673883,0.0005915283,0.000041245825,0.00013766557,0.00004899946,0.00003910407,0.00024774365,0.017340198,0.9792552,0.00045134025,0.0001707908,0.00040877968],"about_ca_topic_score_codex":0.000036653488,"about_ca_topic_score_gemma":0.00012540711,"teacher_disagreement_score":0.05786645,"about_ca_system_score_codex":0.00020275518,"about_ca_system_score_gemma":0.00009081942,"threshold_uncertainty_score":0.99992096},"labels":[],"label_agreement":null},{"id":"W4319989685","doi":"10.1007/978-981-16-5540-1_115","title":"Programming Neuromorphics Using the Neural Engineering Framework","year":2023,"lang":"en","type":"book-chapter","venue":"Handbook of Neuroengineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Neuromorphic engineering; Computer science; Computer architecture; Artificial neural network; Porting; Artificial intelligence; Software; Variety (cybernetics); Deep learning; Compiler; Embedded system; Machine learning; Programming language","score_opus":0.045058705824107294,"score_gpt":0.23297733837918955,"score_spread":0.18791863255508226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319989685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04177337,0.02384801,0.87158537,0.00015439777,0.02692989,0.0054225354,0.0002358266,0.019320091,0.010730518],"genre_scores_gemma":[0.76263297,0.015463127,0.1183279,0.0007361504,0.016790897,0.0003522576,0.0002445799,0.01527638,0.07017577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976388,0.000011802886,0.0007520221,0.00045739405,0.00044584938,0.0006941171],"domain_scores_gemma":[0.9983441,0.00055220793,0.00017389769,0.0007060763,0.00007020631,0.00015347461],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012432045,0.00080264936,0.0007287288,0.00032407593,0.00013707454,0.00006604433,0.0005699514,0.00029324368,0.000014881453],"category_scores_gemma":[0.00010670768,0.0007644185,0.00034843214,0.00020290037,0.00008107483,0.00014621815,0.00022968046,0.0018443936,0.000016951075],"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":[0.0000062730815,0.0000029672403,0.0000021181027,0.00062833074,0.00009113329,0.0002317698,0.00007687397,0.97669494,0.015335916,0.0026894764,0.000029773659,0.0042104376],"study_design_scores_gemma":[0.0002492598,0.00011539972,0.000012993054,0.0041933306,0.00023234884,0.00036820912,0.000009873242,0.9639039,0.011833114,0.0007474594,0.017024336,0.0013097556],"about_ca_topic_score_codex":7.352924e-7,"about_ca_topic_score_gemma":4.7625514e-7,"teacher_disagreement_score":0.75325745,"about_ca_system_score_codex":0.000058466834,"about_ca_system_score_gemma":0.000019221721,"threshold_uncertainty_score":0.99948066},"labels":[],"label_agreement":null},{"id":"W4320233626","doi":"10.1016/j.mtcomm.2023.105512","title":"Improved resistive switching performance and in-depth mechanism analysis in Mn-doped SrTiO3-based RRAM","year":2023,"lang":"en","type":"article","venue":"Materials Today Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Fujian Normal University","keywords":"Materials science; Resistive random-access memory; Doping; Memristor; Optoelectronics; Ion; Crystal (programming language); Nanotechnology; Electronic engineering; Computer science; Electrical engineering; Voltage","score_opus":0.02663717531856571,"score_gpt":0.27066902376981566,"score_spread":0.24403184845124995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320233626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997308,0.00005344122,0.0017431604,0.00016639719,0.000078371835,0.00018670868,0.000014999419,0.00025498247,0.00019394347],"genre_scores_gemma":[0.9971451,0.00023076757,0.0023892773,0.000033475964,0.000012545,0.00006665636,0.00007822089,0.000020948475,0.000022987655],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911463,0.000110137786,0.00034131441,0.00015477485,0.00006114338,0.00021797667],"domain_scores_gemma":[0.9990737,0.00019172502,0.000052927804,0.0006294226,0.000018553537,0.000033694265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047650927,0.00012530015,0.0002487587,0.00037579078,0.00015995622,0.000047858164,0.00032570038,0.000052011117,0.000011258595],"category_scores_gemma":[0.000051389718,0.00013557503,0.000026014426,0.0009576768,0.000022752927,0.00014370718,0.00018735885,0.00015905005,0.00000900837],"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":[0.000030050376,0.000024886689,0.0008800955,0.00008757839,0.000042440544,0.0000035870944,0.0006704546,0.059254654,0.937251,0.00036600995,0.0000073284527,0.0013819278],"study_design_scores_gemma":[0.00061608915,0.000025963553,0.06721927,0.00013802222,0.00006228968,0.00000108305,0.0002384379,0.46837047,0.46187982,0.0010002636,0.00009721043,0.00035109816],"about_ca_topic_score_codex":0.000038983646,"about_ca_topic_score_gemma":0.00045328852,"teacher_disagreement_score":0.47537118,"about_ca_system_score_codex":0.000053343785,"about_ca_system_score_gemma":0.000011134866,"threshold_uncertainty_score":0.55285895},"labels":[],"label_agreement":null},{"id":"W4320713208","doi":"10.1109/jetcas.2023.3244775","title":"CTT-Based Scalable Neuromorphic Architecture","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; MNIST database; Scalability; Computer science; Binary number; Emulation; Artificial neural network; Computer architecture; Artificial intelligence; Mathematics; Arithmetic","score_opus":0.03205538313289517,"score_gpt":0.2446920245691291,"score_spread":0.21263664143623395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320713208","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99604434,0.0006184814,0.0013430013,0.00013320554,0.0012994536,0.00008435778,0.0000018895353,0.00017197683,0.0003032694],"genre_scores_gemma":[0.9989951,0.00026661856,0.000010813582,0.00005229136,0.0004487924,0.0000028773375,0.0000018333682,0.000022096232,0.00019956195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990655,0.000070312,0.00026463144,0.00015318401,0.00014431465,0.0003020291],"domain_scores_gemma":[0.9996268,0.00011455179,0.000042862644,0.00007874953,0.000034672375,0.00010236859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023605912,0.00014349159,0.00021528116,0.00025827743,0.0001814865,0.00008584138,0.00006533993,0.0000686915,0.0000015600721],"category_scores_gemma":[0.00003927153,0.00012576867,0.000022929666,0.0004851438,0.0000151925,0.00005288949,0.0000058127043,0.0005868199,0.0000021135454],"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":[0.000008130798,0.000012711199,0.0016280083,0.000263918,0.000022716353,0.00029222888,0.00040498294,0.941333,0.02640107,0.00016432264,0.00040199372,0.0290669],"study_design_scores_gemma":[0.0021424156,0.00039996885,0.014722956,0.0013359996,0.000026586846,0.0012062204,0.00017267713,0.9478068,0.004563514,0.0007955979,0.026001353,0.0008259126],"about_ca_topic_score_codex":0.0000032057892,"about_ca_topic_score_gemma":0.0000036046663,"teacher_disagreement_score":0.028240988,"about_ca_system_score_codex":0.00002371739,"about_ca_system_score_gemma":0.000011875398,"threshold_uncertainty_score":0.5128697},"labels":[],"label_agreement":null},{"id":"W4321109547","doi":"10.1002/cta.3570","title":"Multiplierless low‐cost implementation of Hindmarsh–Rose neuron model in case of large‐scale realization","year":2023,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Carleton University","funders":"","keywords":"Field-programmable gate array; Realization (probability); Computer science; Lookup table; Biological neuron model; Artificial neural network; Computer hardware; Scale (ratio); Artificial intelligence; Mathematics","score_opus":0.018195838284332442,"score_gpt":0.32703641002302314,"score_spread":0.3088405717386907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321109547","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6468219,0.000030156054,0.3528541,0.000008137527,0.00005454999,0.00010274812,0.000046969933,0.000012467085,0.00006897992],"genre_scores_gemma":[0.9996843,0.00012991644,0.00006009673,0.000016290003,0.000056471454,0.000013012534,0.000018395607,0.000010642684,0.000010889483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992672,0.000034634202,0.00043077525,0.0000715532,0.00011366886,0.000082159466],"domain_scores_gemma":[0.9994065,0.0001635374,0.00019126195,0.00006957467,0.00013744387,0.00003167466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039886663,0.000064175176,0.00011800194,0.00021363689,0.000028728762,0.0000073577908,0.000119640026,0.000027901111,0.000006984369],"category_scores_gemma":[0.000019788478,0.00006732451,0.000038515787,0.00018410388,0.000025297377,0.00017373211,0.000023488159,0.0000982538,6.6023614e-7],"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":[0.000052127874,0.000090458656,0.001417196,0.00009810583,0.00004835226,0.000051493935,0.0029858851,0.6028348,0.09652489,0.20749015,0.000019532157,0.08838696],"study_design_scores_gemma":[0.005602815,0.00012069117,0.016726708,0.00047042535,0.00011588787,0.0013606554,0.012295668,0.2460558,0.24563263,0.47039437,0.00064172863,0.0005826097],"about_ca_topic_score_codex":0.0000017474033,"about_ca_topic_score_gemma":0.000014917293,"teacher_disagreement_score":0.35677907,"about_ca_system_score_codex":0.000025715754,"about_ca_system_score_gemma":0.000015857919,"threshold_uncertainty_score":0.27454138},"labels":[],"label_agreement":null},{"id":"W4321764792","doi":"10.22541/au.167727732.20256861/v1","title":"HESSPROP: Mitigating Memristive DNN Weight Mapping Errors with Hessian Backpropagation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"MNIST database; Backpropagation; Hessian matrix; Maxima and minima; Artificial neural network; Gradient descent; Stochastic gradient descent; Robustness (evolution); Computer science; Memristor; Artificial intelligence; Algorithm; Convolutional neural network; Pattern recognition (psychology); Mathematics; Applied mathematics; Electronic engineering; Engineering; Mathematical analysis","score_opus":0.03380868750380305,"score_gpt":0.24014557400234232,"score_spread":0.20633688649853926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321764792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54312223,0.0002923956,0.43363136,0.00034641003,0.0022559138,0.0010911657,0.000019496778,0.0055396683,0.013701343],"genre_scores_gemma":[0.97238034,0.000048742535,0.025693009,0.00004513353,0.00051790115,0.000083036364,0.00010487577,0.00018009116,0.00094690034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981411,0.00004492051,0.00044812483,0.00060993666,0.00027758352,0.00047832954],"domain_scores_gemma":[0.9991165,0.00013529113,0.0001557248,0.00039613686,0.00007446339,0.00012190396],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018029167,0.0004896807,0.00045019484,0.00018225667,0.00019125029,0.00007745954,0.00026687534,0.00022985884,0.000040029845],"category_scores_gemma":[0.000037398757,0.00042184617,0.000099778095,0.0003085195,0.000056629367,0.0001742055,0.00028734762,0.0011271682,0.000093501534],"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":[0.000017349,0.00001342586,0.0005886881,0.0018784915,0.00021792446,0.00017187778,0.0012583769,0.97252804,0.009646637,0.00034894727,0.00046872426,0.0128615005],"study_design_scores_gemma":[0.0009126168,0.000104228355,0.0036301585,0.007062639,0.0001386869,0.000056690413,0.002469306,0.70659256,0.2656316,0.00880552,0.0014813151,0.0031146887],"about_ca_topic_score_codex":0.000014784226,"about_ca_topic_score_gemma":0.000030713465,"teacher_disagreement_score":0.42925805,"about_ca_system_score_codex":0.00018280715,"about_ca_system_score_gemma":0.00004465077,"threshold_uncertainty_score":0.99982333},"labels":[],"label_agreement":null},{"id":"W4322744407","doi":"10.1002/adfm.202213296","title":"Set/Reset Bilaterally Controllable Resistance Switching Ga‐doped Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> Long‐Term Electronic Synapses for Neuromorphic Computing","year":2023,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Memristor; Materials science; Emulation; CMOS; Optoelectronics; Computer science; Artificial neural network; Electronic engineering; Artificial intelligence","score_opus":0.02012308739211997,"score_gpt":0.23296285810253325,"score_spread":0.2128397707104133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322744407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9682148,0.00062531524,0.023348074,0.00015694075,0.0038228603,0.0014951379,0.00028380778,0.0020262923,0.000026740723],"genre_scores_gemma":[0.9960362,0.0005858754,0.00022907434,0.00044959137,0.0012570992,0.00030074027,0.0007632485,0.0003404166,0.00003780731],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99424714,0.0002332724,0.0014528895,0.0013263979,0.0006166879,0.0021236225],"domain_scores_gemma":[0.9969043,0.0012756711,0.000513846,0.0006866248,0.00032132707,0.0002982206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009001223,0.0010229101,0.0012436174,0.00043276962,0.0009776601,0.00031492382,0.00048589,0.00029845312,0.000021366584],"category_scores_gemma":[0.0004299595,0.0011001551,0.00028921943,0.000952941,0.000101584155,0.0010430375,0.00025073165,0.0006418869,0.00020412616],"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":[0.0010392872,0.000029632292,0.000049934388,0.00053239113,0.00015988883,0.000094493495,0.00006398444,0.17371346,0.8202886,0.00027372202,0.00045507672,0.0032995034],"study_design_scores_gemma":[0.0028502343,0.0002667396,0.0021475002,0.0004907969,0.00010308824,0.000103662824,0.000035494293,0.0030667118,0.98614573,0.0033590335,0.00030446047,0.0011265578],"about_ca_topic_score_codex":0.0000017423635,"about_ca_topic_score_gemma":0.00004953876,"teacher_disagreement_score":0.17064676,"about_ca_system_score_codex":0.00038864082,"about_ca_system_score_gemma":0.00015377426,"threshold_uncertainty_score":0.99914485},"labels":[],"label_agreement":null},{"id":"W4322762923","doi":"10.35848/1882-0786/acc0d2","title":"Demonstration of electronic synapses using a sericin-based bio-memristor","year":2023,"lang":"en","type":"article","venue":"Applied Physics Express","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Neuromorphic engineering; Memristor; Sericin; Materials science; Conductance; Voltage; Synaptic plasticity; Plasticity; Spike-timing-dependent plasticity; Optoelectronics; Neuroscience; Computer science; Biological system; Electronic engineering; Artificial neural network; Electrical engineering; SILK; Chemistry; Physics; Artificial intelligence; Engineering; Biology; Composite material","score_opus":0.01865041923339488,"score_gpt":0.23690276106096997,"score_spread":0.21825234182757508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322762923","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87673473,0.000030617914,0.12209715,0.0000015640837,0.00006909607,0.000120726916,0.000004284812,0.00034307333,0.0005987246],"genre_scores_gemma":[0.9989917,0.0000044179533,0.000835757,0.000011562452,0.00009164061,0.000012214158,0.000016679578,0.00002985317,0.000006158351],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993785,0.0000074473824,0.0001426317,0.00013207951,0.00009557867,0.00024375529],"domain_scores_gemma":[0.99967724,0.00008318273,0.000044948523,0.00015288548,0.000016027025,0.000025689546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004171803,0.00011778795,0.00014659771,0.0000380832,0.00004603433,0.000006746932,0.00008961725,0.00003513148,0.0000019262889],"category_scores_gemma":[0.000001936718,0.00013062824,0.00003858469,0.0003517908,0.000025584106,0.000057171448,0.000016897047,0.00011432994,0.000008326572],"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":[0.0000053324397,0.0000050413046,0.000007035486,0.000042455704,0.0000065269414,4.8676594e-7,0.000035554945,0.4812652,0.5162917,0.0010434851,0.00001759467,0.0012796278],"study_design_scores_gemma":[0.00013733024,0.000010384928,0.000015092848,0.000018120789,0.000010272113,3.3364753e-7,0.000033401175,0.26711354,0.7315288,0.0009457566,0.000075282645,0.00011168452],"about_ca_topic_score_codex":0.00000158587,"about_ca_topic_score_gemma":3.5817362e-7,"teacher_disagreement_score":0.21523713,"about_ca_system_score_codex":0.00003601781,"about_ca_system_score_gemma":0.000018984383,"threshold_uncertainty_score":0.53268653},"labels":[],"label_agreement":null},{"id":"W4323655652","doi":"10.1088/1361-6641/acc2df","title":"Enhanced resistive switching performance of TiO<sub>2</sub> based RRAM device with graphene oxide inserting layer","year":2023,"lang":"en","type":"article","venue":"Semiconductor Science and Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Materials science; Graphene; Resistive random-access memory; Raman spectroscopy; Ohmic contact; Oxide; Optoelectronics; Transmission electron microscopy; Substrate (aquarium); Thin film; Heterojunction; Nanotechnology; Analytical Chemistry (journal); Layer (electronics); Voltage; Electrical engineering; Chemistry; Optics; Metallurgy","score_opus":0.01399905028201798,"score_gpt":0.22785602635353208,"score_spread":0.2138569760715141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323655652","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99837756,0.000076955934,0.00055842055,0.000060857135,0.00009244223,0.00011482899,0.0000010863024,0.0006383816,0.00007949229],"genre_scores_gemma":[0.9990712,0.000043428907,0.00080009055,0.000041609437,0.000012231532,0.000011490764,8.8090866e-7,0.000016869042,0.0000021890569],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988645,0.0000074249206,0.00020290649,0.00032829194,0.00018991965,0.00040700383],"domain_scores_gemma":[0.999427,0.00007050645,0.00007670046,0.00022731234,0.00014683612,0.000051643226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030306613,0.00015042542,0.00019341797,0.0005728014,0.0002430976,0.000014754828,0.00026309316,0.00007922219,6.425382e-7],"category_scores_gemma":[0.00014391773,0.0001315682,0.000014326151,0.0029836842,0.00035154622,0.00035751154,0.00008782912,0.00026449046,0.000004268822],"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":[0.000009415259,0.0000035683297,0.000953601,0.00006187507,0.00000470461,0.0000039256906,0.00007482689,0.0026601981,0.9878232,0.00010741632,0.000002069517,0.008295187],"study_design_scores_gemma":[0.00018453729,0.00008786869,0.001597892,0.00014838534,0.0000062027266,0.0000097366865,0.00025059262,0.020937202,0.9764319,0.00017351752,0.000009070696,0.00016309836],"about_ca_topic_score_codex":0.0000015670195,"about_ca_topic_score_gemma":0.000008212479,"teacher_disagreement_score":0.018277004,"about_ca_system_score_codex":0.000035740683,"about_ca_system_score_gemma":0.00006682688,"threshold_uncertainty_score":0.5365196},"labels":[],"label_agreement":null},{"id":"W4323923748","doi":"10.1021/acsmaterialslett.2c01026","title":"2D-Material-Based Volatile and Nonvolatile Memristive Devices for Neuromorphic Computing","year":2023,"lang":"en","type":"article","venue":"ACS Materials Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":47,"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":"Nanjing University of Posts and Telecommunications; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Von Neumann architecture; Computer science; Unconventional computing; Bottleneck; Reservoir computing; Memristor; Artificial neural network; Computer architecture; Artificial intelligence; Distributed computing; Electronic engineering; Embedded system; Engineering; Recurrent neural network","score_opus":0.024066383660992723,"score_gpt":0.23736756259182387,"score_spread":0.21330117893083114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323923748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934594,0.000013540515,0.0036167535,0.0002655454,0.0013314137,0.00034060763,0.00016026212,0.00080085883,0.000011644177],"genre_scores_gemma":[0.99756366,0.0000027514968,0.001106042,0.00072238065,0.00035132552,0.000025779842,0.00015657696,0.00006529534,0.000006190512],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893135,0.000041210467,0.00029410375,0.00026181675,0.000091231406,0.00038030362],"domain_scores_gemma":[0.99940556,0.0002931035,0.00007420176,0.00014645637,0.00002330465,0.00005735256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020596267,0.00022026952,0.0002895033,0.0000964632,0.00018274265,0.00011382708,0.00012217432,0.00005225627,0.000027519567],"category_scores_gemma":[0.000037515292,0.00022631393,0.000026974601,0.00014273859,0.00004090842,0.00015255847,0.000059888578,0.0000620195,0.00002530209],"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":[0.000028898477,0.0000025723741,0.00007004276,0.00026945327,0.000014411945,0.00001544237,0.00007444483,0.020004654,0.9779432,0.000013555494,0.0010279075,0.00053545856],"study_design_scores_gemma":[0.0004558534,0.000046639158,0.0025367816,0.00008969017,0.000025713802,0.0000065544764,0.00003123451,0.02333791,0.9719961,0.00006349993,0.0010713603,0.00033866052],"about_ca_topic_score_codex":0.0000049542573,"about_ca_topic_score_gemma":0.0000015466151,"teacher_disagreement_score":0.005947057,"about_ca_system_score_codex":0.000018188486,"about_ca_system_score_gemma":0.0000056073304,"threshold_uncertainty_score":0.9228814},"labels":[],"label_agreement":null},{"id":"W4324258830","doi":"10.1016/j.conb.2023.102707","title":"Spike timing-dependent plasticity and memory","year":2023,"lang":"en","type":"review","venue":"Current Opinion in Neurobiology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fondation pour la Recherche Médicale; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; Centre National de la Recherche Scientifique; Aix-Marseille Université","keywords":"Spike-timing-dependent plasticity; Neuroscience; Spike (software development); Millisecond; Metaplasticity; Synaptic plasticity; Excitatory postsynaptic potential; Memory formation; Plasticity; Computer science; Inhibitory postsynaptic potential; Psychology; Biology; Physics; Hippocampus","score_opus":0.19946241321333222,"score_gpt":0.3915284903641837,"score_spread":0.19206607715085147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324258830","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019242626,0.9848157,0.00020777665,0.0000022049564,0.013930335,0.00041894038,0.00003979306,0.00035204206,0.0000407406],"genre_scores_gemma":[0.000037910653,0.99920774,0.000012231732,0.0000030018293,0.0005218055,0.000053001928,0.000077548146,0.0000681036,0.000018638078],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841833,0.00013133866,0.0005560135,0.00046525177,0.000053231288,0.0003758354],"domain_scores_gemma":[0.9991049,0.0005438422,0.00010636969,0.00016409098,0.0000090025205,0.00007177181],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010241121,0.0003915868,0.00097535114,0.00027473283,0.00004380299,0.000012123041,0.00020705238,0.00020882965,0.0000087241215],"category_scores_gemma":[0.00008216706,0.000359788,0.00010848119,0.0002319394,0.000055970402,0.000043409775,0.00021633424,0.0009106663,0.00009254491],"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":[0.0000028544155,0.00001733565,0.000003926616,0.036982283,0.000020904117,0.000008601189,0.000026665964,0.0026149207,0.000008725002,0.0000907875,0.00049594144,0.95972705],"study_design_scores_gemma":[0.0002089155,0.000052922038,0.000013757499,0.012032645,0.00004017801,0.00009824328,0.0000034430304,0.0005966275,0.000007683158,0.00006054632,0.9863838,0.00050126977],"about_ca_topic_score_codex":4.0578988e-7,"about_ca_topic_score_gemma":7.3085084e-7,"teacher_disagreement_score":0.9858878,"about_ca_system_score_codex":0.00004978231,"about_ca_system_score_gemma":0.000022801054,"threshold_uncertainty_score":0.9998854},"labels":[],"label_agreement":null},{"id":"W4327738655","doi":"10.1142/s0218126623502596","title":"A Memristive-Based Design of a Core Digital Circuit for Elliptic Curve Cryptography","year":2023,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Carleton University; University of Windsor","funders":"","keywords":"Memristor; CMOS; Computer science; Elliptic curve cryptography; Memistor; Electronic engineering; Diode-or circuit; Circuit design; Transistor; Embedded system; Electrical engineering; Computer hardware; Engineering; Discrete circuit; Resistive random-access memory; Encryption; Public-key cryptography","score_opus":0.05688043427209109,"score_gpt":0.24601337635839154,"score_spread":0.18913294208630044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327738655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24069068,0.00066583557,0.7575674,0.000004826436,0.0007607035,0.00021166034,0.000011510088,0.000060344544,0.000027020908],"genre_scores_gemma":[0.9992694,0.000028823053,0.00046739506,0.000009408571,0.0001896501,0.0000043998425,0.0000020504092,0.000024574912,0.00000428955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989782,0.00002278027,0.0005078126,0.00010593052,0.00017661486,0.00020864922],"domain_scores_gemma":[0.99889565,0.0005266628,0.00024904613,0.00008788699,0.00013974246,0.00010100262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002933742,0.00014645737,0.0003958379,0.00028458354,0.000055123972,0.000058750727,0.00013634916,0.000048903275,3.8839877e-7],"category_scores_gemma":[0.00002790933,0.00013160634,0.00014811373,0.0003259121,0.000035152607,0.0001712055,0.000010823304,0.00011985112,7.924237e-7],"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":[0.000024754216,0.000019568992,0.00047509963,0.00074480555,0.0001445721,0.00004155957,0.00028265317,0.97413266,0.010064688,0.00056619954,0.0004510622,0.013052353],"study_design_scores_gemma":[0.0023490756,0.00104666,0.0014626872,0.0016437182,0.000085616375,0.00028627238,0.00039127353,0.98744243,0.0022352028,0.001454365,0.0011561495,0.00044656103],"about_ca_topic_score_codex":3.653428e-7,"about_ca_topic_score_gemma":3.058962e-8,"teacher_disagreement_score":0.7585787,"about_ca_system_score_codex":0.00002150157,"about_ca_system_score_gemma":0.00002532279,"threshold_uncertainty_score":0.5366751},"labels":[],"label_agreement":null},{"id":"W4328007707","doi":"10.1109/lra.2023.3259731","title":"Ev-Conv: Fast CNN Inference on Event Camera Inputs for High-Speed Robot Perception","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Artificial intelligence; Computer science; Event (particle physics); Computer vision; Leverage (statistics); Frame rate; Convolutional neural network","score_opus":0.02031087332978778,"score_gpt":0.26294062515756156,"score_spread":0.24262975182777377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328007707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66048026,0.0000034501031,0.33699784,0.001311639,0.0006011096,0.0001628367,0.000004683244,0.00042945836,0.000008736842],"genre_scores_gemma":[0.99644566,0.000022220851,0.0025316814,0.0007036152,0.00018755086,0.000010320449,0.000037090427,0.000024780189,0.00003708263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993014,0.000014689169,0.0001961785,0.00016885855,0.00010847702,0.00021043066],"domain_scores_gemma":[0.99965334,0.00011900669,0.0000552203,0.0001030352,0.00002114743,0.00004825411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081516504,0.00014068586,0.00013524924,0.000110862034,0.0001238513,0.00004449855,0.000058027723,0.000046608508,0.000004299895],"category_scores_gemma":[0.000022234552,0.00014357133,0.000035608817,0.0001496848,0.000018192046,0.00014166244,0.000009073455,0.00010896612,0.000042426083],"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":[0.0000030634376,0.0000037828474,0.000021751359,0.000051098417,0.0000071109644,0.0000017562292,0.00016069225,0.773933,0.21776225,0.00011234212,0.0003693011,0.00757385],"study_design_scores_gemma":[0.00038276546,0.00005746891,0.008412852,0.00009411696,0.000012442865,0.0000023030027,0.000028304676,0.97049123,0.020046603,0.00016535642,0.0000772524,0.00022929527],"about_ca_topic_score_codex":0.0000013780357,"about_ca_topic_score_gemma":8.9078145e-7,"teacher_disagreement_score":0.3359654,"about_ca_system_score_codex":0.000047179747,"about_ca_system_score_gemma":0.0000040703476,"threshold_uncertainty_score":0.5854669},"labels":[],"label_agreement":null},{"id":"W4353045555","doi":"10.1021/acsnano.2c12575","title":"An Atomistic Model of Field-Induced Resistive Switching in Valence Change Memory","year":2023,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"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":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Neuromorphic engineering; Conductance; Valence (chemistry); Tin; Materials science; Resistive random-access memory; Multiscale modeling; Ab initio; Chemical physics; Statistical physics; Nanotechnology; Condensed matter physics; Physics; Computer science; Chemistry; Electrode; Computational chemistry; Artificial neural network; Quantum mechanics","score_opus":0.06175962611489977,"score_gpt":0.28915703722053543,"score_spread":0.22739741110563566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353045555","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955977,0.000039456045,0.0036202196,0.000018728675,0.00012412253,0.00010891714,0.000002201007,0.00020460763,0.0002840256],"genre_scores_gemma":[0.9995325,0.00002479864,0.0002986832,0.0000443911,0.000045150526,0.00001339248,0.0000018528938,0.000017016711,0.00002222042],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942905,0.000016109214,0.00015222463,0.00013692351,0.00007823848,0.00018744715],"domain_scores_gemma":[0.99962294,0.00013167379,0.000024466248,0.00017385796,0.000012838606,0.0000342247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011837257,0.00008784379,0.00013145957,0.0001007125,0.000033718283,0.000004243635,0.0001331365,0.000049412345,0.0000019790677],"category_scores_gemma":[0.00006633398,0.00009409427,0.000020147223,0.0002710421,0.0000035410485,0.00016070003,0.00003329452,0.0001360784,0.000007877834],"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":[0.000012136428,0.000006635766,0.0001142853,0.0000855443,0.0000032058463,0.000016004167,0.0010886332,0.12696046,0.8623494,0.00009900015,0.000013419721,0.009251305],"study_design_scores_gemma":[0.00016192684,0.00003617948,0.00078527647,0.000113322705,0.0000038572216,0.0000010936236,0.00010788397,0.4671255,0.53062266,0.0009149548,0.0000030626063,0.00012427453],"about_ca_topic_score_codex":0.000011492903,"about_ca_topic_score_gemma":0.000013627375,"teacher_disagreement_score":0.34016505,"about_ca_system_score_codex":0.000021944277,"about_ca_system_score_gemma":0.000006618813,"threshold_uncertainty_score":0.38370532},"labels":[],"label_agreement":null},{"id":"W4360584657","doi":"10.1109/tcsii.2023.3260704","title":"Digital Hardware Implementations of Spiking Neural Networks With Selective Input Sparsity for Edge Inferences in Controlled Image Acquisition Environments","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Field-programmable gate array; Implementation; Baseline (sea); Inference; Enhanced Data Rates for GSM Evolution; Artificial neural network; Computer hardware; Computer architecture; Image (mathematics); Spiking neural network; Artificial intelligence; Computer engineering","score_opus":0.0188599872609858,"score_gpt":0.24263111981018462,"score_spread":0.2237711325491988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360584657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36711055,0.00003157704,0.6311184,0.0000057556645,0.00034787782,0.0009493199,0.00018289837,0.00016585978,0.000087757486],"genre_scores_gemma":[0.99933136,0.000013842397,0.000050484814,0.000008904344,0.00007179247,0.0003580069,0.000042922813,0.000039676077,0.00008300749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986323,0.000051019073,0.0004617119,0.0002915281,0.0001956894,0.000367732],"domain_scores_gemma":[0.99929553,0.0003046493,0.00012199508,0.00016911268,0.00004350733,0.000065188644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001264566,0.00023316969,0.0004272017,0.00022108442,0.0002723718,0.000060870956,0.00012816754,0.00007408754,0.00000482103],"category_scores_gemma":[0.0000054342095,0.00023301372,0.00010108482,0.00039151538,0.000045248333,0.0005844823,0.0000031541754,0.00021301507,0.0000019896124],"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":[0.00009861831,0.00004971109,0.00016063676,0.00009162513,0.00008270355,0.0000060687685,0.00071663473,0.97792137,0.017263906,0.0000074071363,0.000023438432,0.0035779031],"study_design_scores_gemma":[0.012070231,0.0008657202,0.0034280627,0.00086639373,0.00017164978,0.000038534592,0.002041436,0.89942586,0.07979987,0.00004354722,0.00026178142,0.0009869387],"about_ca_topic_score_codex":0.000015659403,"about_ca_topic_score_gemma":0.000023467279,"teacher_disagreement_score":0.6322208,"about_ca_system_score_codex":0.000103264974,"about_ca_system_score_gemma":0.000015231847,"threshold_uncertainty_score":0.9502024},"labels":[],"label_agreement":null},{"id":"W4360869638","doi":"10.1007/s11071-023-08394-x","title":"The input-dependent variable sampling (I-DEVS) energy-efficient digital neuron implementation method","year":2023,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Neuron; Computer science; Computation; Stimulus (psychology); Power consumption; Biological neuron model; Energy consumption; Artificial neuron; State variable; Power (physics); Artificial neural network; Artificial intelligence; Algorithm; Neuroscience; Electrical engineering; Engineering","score_opus":0.017021629704563817,"score_gpt":0.2961109946754569,"score_spread":0.27908936497089304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360869638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16080856,0.000034933782,0.8367907,0.0000623855,0.0008280013,0.00011564404,0.00006724293,0.0006994896,0.0005930938],"genre_scores_gemma":[0.9428589,0.0001924992,0.054032594,0.00016590275,0.000787016,0.00003975301,0.0008013604,0.00020733017,0.0009146686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998943,0.000024298808,0.0002650165,0.0001963036,0.00019468687,0.00037674865],"domain_scores_gemma":[0.99932396,0.00032209157,0.00004437483,0.00021543301,0.000035423425,0.000058693815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024553493,0.00015369196,0.0001137664,0.000063176376,0.00027448407,0.00012912309,0.0001816524,0.000045018394,0.0000050702083],"category_scores_gemma":[0.00003919685,0.00013168456,0.000048271373,0.0004007934,0.000014000426,0.000109817396,0.00011655091,0.000194478,0.000024111636],"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":[0.0000048402258,0.000005779168,0.000027159529,0.000011912905,0.000012298308,0.0000036955992,0.000044838802,0.87082136,0.0020892099,0.0017361795,0.000021304939,0.12522143],"study_design_scores_gemma":[0.00015853703,0.000020140928,0.000059014008,0.000007724593,0.000007909926,0.000009943356,0.00023238515,0.98915255,0.0013908782,0.0005965815,0.00821914,0.0001451877],"about_ca_topic_score_codex":0.000005690043,"about_ca_topic_score_gemma":0.00002669864,"teacher_disagreement_score":0.78275806,"about_ca_system_score_codex":0.00010777198,"about_ca_system_score_gemma":0.000018283074,"threshold_uncertainty_score":0.53699404},"labels":[],"label_agreement":null},{"id":"W4360991195","doi":"10.1088/1741-2552/acc7cc","title":"Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey","year":2023,"lang":"en","type":"review","venue":"Journal of Neural Engineering","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":"McGill University; York University; University of Toronto","funders":"","keywords":"Spike sorting; Computer science; Spike (software development); Sorting; Sorting algorithm; Field-programmable gate array; Field (mathematics); Neuromorphic engineering; Resource (disambiguation); Multielectrode array; Algorithm; Computer hardware; Artificial neural network; Artificial intelligence; Embedded system; Microelectrode","score_opus":0.10312042394925028,"score_gpt":0.34919864154084884,"score_spread":0.24607821759159856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360991195","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0084438035,0.98502946,0.00440061,0.0000031933596,0.0015893951,0.00027404568,0.00004716652,0.0002094482,0.0000028730938],"genre_scores_gemma":[0.002308337,0.9963038,0.0004858647,0.000007174008,0.0006759066,0.0000072165294,0.000024354451,0.00017873848,0.00000863419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804676,0.00006107611,0.0011214432,0.00019509652,0.00018966451,0.00038598603],"domain_scores_gemma":[0.9985405,0.0006545822,0.00040125754,0.00014045826,0.00010570598,0.00015749614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031416133,0.00049023866,0.0013855882,0.00034519035,0.00007164395,0.0000637482,0.00022398375,0.000109849614,0.000004154755],"category_scores_gemma":[0.000073921525,0.0003888631,0.0003345663,0.0004466008,0.000013905443,0.00012353167,0.00010558191,0.00086529786,0.0000035808541],"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":[0.0000021064322,0.0000039077922,0.0000023175005,0.007302866,0.00020824988,0.00015236113,0.0001254035,0.4052846,0.00009951067,0.0000023692555,0.000040915478,0.58677536],"study_design_scores_gemma":[0.0015774596,0.00044473397,0.00053034816,0.034397785,0.0008340096,0.0064765434,0.0005631208,0.49626637,0.0005168693,0.000013568067,0.45537317,0.0030060255],"about_ca_topic_score_codex":0.0000016421519,"about_ca_topic_score_gemma":0.0000011350787,"teacher_disagreement_score":0.5837694,"about_ca_system_score_codex":0.0001037774,"about_ca_system_score_gemma":0.00002837078,"threshold_uncertainty_score":0.99985635},"labels":[],"label_agreement":null},{"id":"W4361190416","doi":"10.1002/advs.202207661","title":"Self‐Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Samsung; Ministry of Science and ICT, South Korea; Seoul National University; National Research Foundation","keywords":"Neuromorphic engineering; Materials science; Ferroelectricity; Computer science; Convolutional neural network; Transistor; Long-term potentiation; Synapse; Artificial neural network; Electronic engineering; Optoelectronics; Artificial intelligence; Electrical engineering; Neuroscience; Engineering; Chemistry; Voltage","score_opus":0.026495162793394807,"score_gpt":0.2501657385188678,"score_spread":0.223670575725473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361190416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94976526,0.00043948868,0.043661837,0.00012872512,0.00243646,0.00058082544,0.000009614757,0.0024734945,0.0005042811],"genre_scores_gemma":[0.9921092,0.000057557478,0.0073335157,0.00012571197,0.00019934775,0.000035403536,0.00000796577,0.000031860545,0.00009948348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981097,0.000015561287,0.00022261367,0.00042124078,0.00027038294,0.0009604768],"domain_scores_gemma":[0.99913657,0.0003481467,0.00004604983,0.00023583681,0.00008555759,0.00014785765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034419112,0.00018816373,0.00018360284,0.0001475907,0.0006620384,0.000041208456,0.00041165817,0.00003438394,0.0000040736068],"category_scores_gemma":[0.00018160322,0.00019486631,0.000058896825,0.002511235,0.00014340346,0.0006144363,0.00006358921,0.00021606596,0.00003684451],"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":[0.000009741338,0.000004873787,0.00003771266,0.000033622226,0.0000045178363,0.000009264555,0.000026766016,0.9439302,0.05285547,0.0011326062,0.00024855972,0.0017066303],"study_design_scores_gemma":[0.00031303475,0.00010486239,0.000571012,0.000019627743,0.000007642299,0.000037224,0.000014197367,0.9840517,0.0087590655,0.0034580976,0.0024028872,0.00026066508],"about_ca_topic_score_codex":3.155185e-7,"about_ca_topic_score_gemma":9.679161e-7,"teacher_disagreement_score":0.044096403,"about_ca_system_score_codex":0.00009250252,"about_ca_system_score_gemma":0.000054675216,"threshold_uncertainty_score":0.7946418},"labels":[],"label_agreement":null},{"id":"W4363673606","doi":"10.1103/physrevapplied.19.044028","title":"Intrinsic and Extrinsic Factors Influencing the Dynamics of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\"><mml:msub><mml:mi>VO</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> Mott Oscillators","year":2023,"lang":"lv","type":"article","venue":"Physical Review Applied","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Division of Electrical, Communications and Cyber Systems; National Science Foundation of Sri Lanka; National Science Foundation; Stanford SystemX Alliance; Texas A and M University; Sandia National Laboratories; Laboratory Directed Research and Development; U.S. Department of Energy; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration","keywords":"Dynamics (music); Scroll; Computer science; Nanoscopic scale; Physics; Statistical physics; Materials science; Nanotechnology; Mechanical engineering; Engineering","score_opus":0.017821641474574247,"score_gpt":0.25054969397684485,"score_spread":0.2327280525022706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4363673606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913703,0.003233609,0.00044940898,0.00016094996,0.0006915698,0.00013979251,0.000112634465,0.0002557615,0.0035859253],"genre_scores_gemma":[0.99153703,0.00635254,0.0002589054,0.00040621386,0.00074261834,0.00019829026,0.00020694581,0.00027572218,0.000021755151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958042,0.0001185297,0.0011151324,0.00091375253,0.00094383495,0.0011045359],"domain_scores_gemma":[0.99621516,0.0013775597,0.0007878508,0.0011508613,0.00006563094,0.0004029343],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006942718,0.00066011894,0.00057458435,0.00014162785,0.0006596622,0.00021385825,0.00078721915,0.00040727665,0.00027007004],"category_scores_gemma":[0.0005741891,0.0008007154,0.00076439924,0.001158241,0.00047012704,0.00041961274,0.0010528516,0.0012045604,0.0004593795],"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":[0.00013642405,0.0000935899,0.000017202992,0.0047759446,0.00039593736,0.00009859154,0.0010059377,0.0068302457,0.008552099,0.964006,0.00077976903,0.013308257],"study_design_scores_gemma":[0.0012416278,0.00053586334,0.00054913963,0.005126479,0.0014654584,0.00012465185,0.0010199283,0.6411761,0.3424418,0.0019578433,0.0027011998,0.0016598891],"about_ca_topic_score_codex":0.00007126026,"about_ca_topic_score_gemma":0.000060109225,"teacher_disagreement_score":0.9620482,"about_ca_system_score_codex":0.00003108081,"about_ca_system_score_gemma":0.00019497238,"threshold_uncertainty_score":0.99944437},"labels":[],"label_agreement":null},{"id":"W4366203717","doi":"10.1002/aelm.202300010","title":"A Memristive Cell with Long Retention Time in 65 nm CMOS Technology","year":2023,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Institut de Valorisation des Données","keywords":"Memristor; CMOS; Trap (plumbing); Quantum tunnelling; Materials science; Optoelectronics; Electronic circuit; Nanotechnology; Electrical engineering; Memistor; Fabrication; Charge (physics); Non-volatile memory; Chip; Capacitor; Sensitivity (control systems); Process (computing); Voltage; Electronic engineering; Resistive random-access memory; Computer science; Engineering; Physics","score_opus":0.0033760148277340975,"score_gpt":0.19640543276829292,"score_spread":0.19302941794055883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366203717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964863,0.00021839561,0.001423792,0.00004237562,0.00012224754,0.0003188822,0.000005525584,0.0010866143,0.00029584114],"genre_scores_gemma":[0.99886173,0.0001669798,0.00024093282,0.000012463318,0.000039262955,0.00007593852,0.00003472744,0.00005268258,0.0005152824],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867886,0.000027705664,0.00024853725,0.00027716614,0.00008888321,0.0006788651],"domain_scores_gemma":[0.9996542,0.00004061901,0.000057130066,0.00019710833,0.000021121357,0.00002986039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001459164,0.0001945093,0.00027609227,0.00021888355,0.00005461589,0.000014482464,0.00013177752,0.00008885123,0.000066941255],"category_scores_gemma":[0.000018237504,0.00018678045,0.000019547148,0.0007197131,0.00002841853,0.00018052515,0.000038068843,0.00019176473,0.00019547409],"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":[0.000064087035,0.000006721073,0.00001975178,0.00006537773,0.000009145228,0.00004361705,0.0000306599,0.051013533,0.94624317,0.00015969001,0.000016759608,0.0023274925],"study_design_scores_gemma":[0.000612384,0.00016492401,0.0002181807,0.00008141737,0.00000799684,0.0000194522,0.000028919574,0.0008378269,0.9953095,0.00198854,0.0004889101,0.00024198396],"about_ca_topic_score_codex":0.0000012523509,"about_ca_topic_score_gemma":0.000010916844,"teacher_disagreement_score":0.050175704,"about_ca_system_score_codex":0.00015040253,"about_ca_system_score_gemma":0.000020212336,"threshold_uncertainty_score":0.76166856},"labels":[],"label_agreement":null},{"id":"W4366429764","doi":"10.1002/aisy.202300008","title":"Efficient Memristive Stochastic Differential Equation Solver","year":2023,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Computer science; Backward Euler method; Linearization; Memristor; Applied mathematics; Stochastic differential equation; Path (computing); Mathematical optimization; Algorithm; Mathematics; Euler equations; Nonlinear system; Mathematical analysis; Electronic engineering; Physics","score_opus":0.03153170485912957,"score_gpt":0.2594757090707506,"score_spread":0.22794400421162106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366429764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32285216,0.00025598312,0.6720875,0.000005090142,0.0030427852,0.00035998886,0.0000078244375,0.0010030773,0.00038559953],"genre_scores_gemma":[0.99912447,0.000024073155,0.00008582777,0.000006093846,0.00022955381,0.000052779404,0.000031670632,0.00004945627,0.00039606678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998677,0.000029891004,0.00036923436,0.00026932402,0.00024490044,0.00040963566],"domain_scores_gemma":[0.99937713,0.00019520835,0.000061915925,0.00021778992,0.00005393817,0.00009401216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103056314,0.00022173575,0.00024004879,0.00014868226,0.00012494175,0.00002966786,0.00013799388,0.000059545626,0.000022745391],"category_scores_gemma":[0.000059699676,0.00021363106,0.000075300944,0.00038784303,0.000025419393,0.00006373654,0.000045166405,0.00017854504,0.0006295834],"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":[0.000012650987,0.000009024882,0.000003861604,0.00007242911,0.000021525986,0.0000070201377,0.00024746585,0.97433275,0.02033142,0.0007433944,0.00007833982,0.004140129],"study_design_scores_gemma":[0.00016635215,0.00003798996,0.00005183466,0.00014556437,0.00001234348,0.000005771624,0.00039413155,0.98072857,0.017571775,0.0001355862,0.0004937055,0.0002563991],"about_ca_topic_score_codex":0.000002320059,"about_ca_topic_score_gemma":6.55652e-7,"teacher_disagreement_score":0.67627233,"about_ca_system_score_codex":0.00012630536,"about_ca_system_score_gemma":0.0000063600764,"threshold_uncertainty_score":0.87116224},"labels":[],"label_agreement":null},{"id":"W4366434773","doi":"10.2139/ssrn.4423137","title":"High-Zeta-Potential Accelerates Interface Charge Transfer in Lithium Anodes Via Mxene-Graphdiyne Heterojunction Layers","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heterojunction; Anode; Materials science; Charge (physics); Lithium (medication); Optoelectronics; Interface (matter); Zeta potential; Nanotechnology; Chemistry; Physics; Composite material; Electrode; Physical chemistry","score_opus":0.01670257382988157,"score_gpt":0.24064237571417083,"score_spread":0.22393980188428925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366434773","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7779532,0.0029673136,0.2147332,0.00016361098,0.003476358,0.00024494232,0.000009310953,0.00042693314,0.000025145806],"genre_scores_gemma":[0.9898874,0.008455659,0.0001002758,0.00002272021,0.0008359693,0.000022696704,0.00003234412,0.00015439607,0.0004885075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.995923,0.00012557766,0.0007299875,0.0005125108,0.0002682369,0.002440683],"domain_scores_gemma":[0.9993984,0.00004648374,0.00010171697,0.00027726844,0.000065631015,0.00011049638],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009257806,0.0005654934,0.00060568016,0.0005083023,0.00016374988,0.00013747394,0.00047967632,0.0003819917,0.000033990716],"category_scores_gemma":[0.00001674358,0.0005695613,0.00032248636,0.00033345557,0.000035467012,0.00031678725,0.00013196723,0.007523721,0.000050812367],"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":[0.00018696292,0.000053004398,0.00013118424,0.00020544627,0.00049726677,0.00004674304,0.00030997422,0.8234886,0.163305,0.0004711766,0.00005769374,0.011246983],"study_design_scores_gemma":[0.005967124,0.0013042382,0.0016589774,0.0018527545,0.000537099,0.0017212747,0.0013758237,0.1500158,0.60022765,0.22985822,0.0007960251,0.0046849987],"about_ca_topic_score_codex":0.000053796386,"about_ca_topic_score_gemma":0.00047865414,"teacher_disagreement_score":0.67347276,"about_ca_system_score_codex":0.0010278439,"about_ca_system_score_gemma":0.00025316398,"threshold_uncertainty_score":0.9996756},"labels":[],"label_agreement":null},{"id":"W4366827195","doi":"10.1002/aelm.202201226","title":"Trap‐Assisted Memristive Switching in HfO<sub>2</sub>‐Based Devices Studied by In Situ Soft and Hard X‐Ray Photoelectron Spectroscopy","year":2023,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Deutsches Elektronen-Synchrotron; Deutsche Forschungsgemeinschaft","keywords":"Materials science; Neuromorphic engineering; X-ray photoelectron spectroscopy; Memristor; Optoelectronics; Dielectric spectroscopy; Semiconductor; Silicon; Non-volatile memory; Capacitor; Resistive random-access memory; Spectroscopy; Electron energy loss spectroscopy; Nanotechnology; Voltage; Transmission electron microscopy; Electrical engineering; Computer science; Electrode; Physics; Artificial neural network; Electrochemistry","score_opus":0.008794185825000678,"score_gpt":0.24508958191784172,"score_spread":0.23629539609284103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366827195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99484485,0.0012173449,0.0023929204,0.00006172971,0.00023715865,0.0005788443,0.0000128576075,0.0005988359,0.000055481414],"genre_scores_gemma":[0.99865633,0.00068327016,0.00023319652,0.00007246951,0.00005333628,0.00013967862,0.00005895245,0.00009021759,0.000012561759],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972319,0.00012206941,0.0005902727,0.0005884806,0.00018382595,0.0012834428],"domain_scores_gemma":[0.999312,0.00024918618,0.00011959704,0.00021668132,0.000020379084,0.00008215122],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044984676,0.00043958434,0.0006621217,0.00028432635,0.00011815635,0.000057108784,0.00018047805,0.00011807893,0.000009062076],"category_scores_gemma":[0.00006482466,0.000472333,0.000044951663,0.0007420548,0.00003145735,0.00032618266,0.000046923993,0.00042047966,0.000015188641],"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":[0.00020028118,0.000026839703,0.00006404564,0.00015308031,0.000026114301,0.00002768957,0.00013284509,0.012920163,0.9822762,0.00003681118,0.00003564098,0.0041002766],"study_design_scores_gemma":[0.0015570205,0.0001853185,0.002932519,0.00016870939,0.000015364953,0.000005237401,0.000072233364,0.0013655822,0.9922894,0.00069520605,0.00023757375,0.00047582056],"about_ca_topic_score_codex":0.000010113581,"about_ca_topic_score_gemma":0.00034340468,"teacher_disagreement_score":0.01155458,"about_ca_system_score_codex":0.00043061466,"about_ca_system_score_gemma":0.0000534887,"threshold_uncertainty_score":0.99977285},"labels":[],"label_agreement":null},{"id":"W4366829142","doi":"10.1002/aelm.202201227","title":"Impedance Spectroscopy on Hafnium Oxide‐Based Memristive Devices","year":2023,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"McMaster University","funders":"Deutsche Forschungsgemeinschaft; Alexander von Humboldt-Stiftung","keywords":"Neuromorphic engineering; Dielectric spectroscopy; X-ray photoelectron spectroscopy; Materials science; Memristor; Electrical impedance; Optoelectronics; Oxide; Resistive random-access memory; Spectroscopy; Hafnium; Electrode; Nanotechnology; Electronic engineering; Electrical engineering; Computer science; Nuclear magnetic resonance; Chemistry; Artificial neural network; Physics; Physical chemistry","score_opus":0.009413087511633108,"score_gpt":0.2602090469236949,"score_spread":0.2507959594120618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366829142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932413,0.00023257006,0.0030171461,0.00007238942,0.0006365263,0.0003129315,0.00002855459,0.0018685141,0.00059003336],"genre_scores_gemma":[0.9984699,0.00018609798,0.0005497467,0.00021905059,0.00017964467,0.00007522062,0.000059541377,0.0000872991,0.00017350623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980451,0.000048263315,0.0003299358,0.00038967654,0.00018302148,0.0010040215],"domain_scores_gemma":[0.99929494,0.00018441505,0.00008297426,0.00033023418,0.00002891971,0.00007852407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020267832,0.00032756207,0.00036708338,0.00012536217,0.00014561918,0.00004071618,0.00023629423,0.00007357221,0.00010448756],"category_scores_gemma":[0.000046511377,0.000326077,0.000053134492,0.00039989932,0.000031087264,0.00019714235,0.000031908323,0.00022346734,0.0004885127],"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":[0.000094928706,0.0000074184945,0.00000392883,0.00007601393,0.000016788068,0.000014151274,0.000017283228,0.101202436,0.89637977,0.0010930467,0.0001937838,0.0009004252],"study_design_scores_gemma":[0.00045715485,0.00017449181,0.00023740891,0.00007272745,0.000010613716,0.00000387034,0.000017864637,0.0014414816,0.9901622,0.0016659846,0.0054054214,0.00035078157],"about_ca_topic_score_codex":0.0000022672778,"about_ca_topic_score_gemma":0.000007965858,"teacher_disagreement_score":0.09976096,"about_ca_system_score_codex":0.00020659296,"about_ca_system_score_gemma":0.00003783163,"threshold_uncertainty_score":0.9999191},"labels":[],"label_agreement":null},{"id":"W4366992580","doi":"10.36227/techrxiv.22654393","title":"An 8-Channel Ambulatory EEG Recording IC with In-Channel Fully-Analog Real-Time Motion Artifact Extraction and Removal","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Artifact (error); Computer science; Channel (broadcasting); Electronic engineering; Chip; CMOS; Computer hardware; Artificial intelligence; Engineering; Telecommunications","score_opus":0.03015132797553827,"score_gpt":0.26801717007845266,"score_spread":0.2378658421029144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366992580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92320275,0.00008591372,0.07310623,0.00003421894,0.0007655248,0.0003577933,0.000006345978,0.0013753991,0.001065792],"genre_scores_gemma":[0.99638283,0.00025158512,0.0025582463,0.000012078666,0.00023842973,0.000021931117,0.00006824387,0.00012286752,0.00034379624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982158,0.00006951159,0.00043316538,0.00068564765,0.00018820216,0.00040770808],"domain_scores_gemma":[0.9991724,0.0001083129,0.00012524548,0.00040865224,0.00004310319,0.00014231008],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003874083,0.00041597616,0.00045377095,0.00036683696,0.0001213355,0.00007251171,0.00012832903,0.0003012915,0.000022400514],"category_scores_gemma":[0.000024496829,0.00041477062,0.000065980326,0.00021007871,0.00002827244,0.00039266743,0.00011863237,0.0008745946,0.00003482543],"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":[0.00006312241,0.000021493777,0.000092460716,0.00027803847,0.000046092595,0.00021103179,0.00025709116,0.94169164,0.048144285,0.000023127608,0.00003815432,0.00913346],"study_design_scores_gemma":[0.00045887276,0.00015438146,0.011452595,0.0006789218,0.0000684651,0.00023058902,0.00035692114,0.96504587,0.014761424,0.0057450864,0.000017173908,0.0010297011],"about_ca_topic_score_codex":0.00014046511,"about_ca_topic_score_gemma":0.00015428757,"teacher_disagreement_score":0.07318004,"about_ca_system_score_codex":0.0001774248,"about_ca_system_score_gemma":0.000018777844,"threshold_uncertainty_score":0.9998304},"labels":[],"label_agreement":null},{"id":"W4367054520","doi":"10.3389/fncel.2023.1199518","title":"Editorial: Insights in non-neuronal cells: 2021","year":2023,"lang":"en","type":"editorial","venue":"Frontiers in Cellular Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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 Alberta; Université Laval; University of Victoria; McGill University; University of British Columbia; Women and Children’s Health Research Institute","funders":"","keywords":"Neuroscience; Biology; Cognitive science; Computational biology; Psychology","score_opus":0.007539849877588388,"score_gpt":0.2191436309646196,"score_spread":0.21160378108703123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367054520","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021343683,0.0002881169,0.0064144135,0.000005176118,0.9904674,0.00028316624,0.000028937879,0.00021590418,0.00016245813],"genre_scores_gemma":[0.002458699,0.0010797767,0.00041451267,0.000014496901,0.9952485,0.000032682306,0.000039022718,0.00016912552,0.00054317765],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961748,0.00010609964,0.0006400945,0.0010708127,0.0010940045,0.0009141811],"domain_scores_gemma":[0.9986832,0.00045481476,0.00011553923,0.00053297874,0.00004894173,0.00016450978],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00039280948,0.0005496818,0.0006572238,0.0007114198,0.00010968718,0.00010655705,0.0011243196,0.0006664351,0.0000024799344],"category_scores_gemma":[0.00068837265,0.0006237474,0.000118327276,0.0016614313,0.00016208318,0.0003469415,0.00026076805,0.002563985,0.000038796305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010383715,0.00001876534,0.000008750189,0.000108076194,0.000001271224,0.0002789119,0.00008440337,0.1029551,0.03706025,4.6083412e-7,0.85911965,0.00035394737],"study_design_scores_gemma":[0.00039473054,0.00005525615,0.000012617715,0.00020128688,0.000006437723,3.0416132e-7,0.000023730567,0.052418146,0.016904226,0.00014674738,0.92922246,0.000614048],"about_ca_topic_score_codex":0.000009617897,"about_ca_topic_score_gemma":0.000013605322,"teacher_disagreement_score":0.070102796,"about_ca_system_score_codex":0.00025559196,"about_ca_system_score_gemma":0.00013350892,"threshold_uncertainty_score":0.99973714},"labels":[],"label_agreement":null},{"id":"W4367173720","doi":"10.1371/journal.pcbi.1010736","title":"Selective control of synaptic plasticity in heterogeneous networks through transcranial alternating current stimulation (tACS)","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"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; Ontario Brain Institute; University of Ottawa; University Health Network","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Neuroscience; Transcranial alternating current stimulation; Neuroplasticity; Stimulation; Synaptic plasticity; Biological neural network; Brain stimulation; Transcranial direct-current stimulation; Nerve net; Computer science; Biology; Transcranial magnetic stimulation","score_opus":0.02617838114057326,"score_gpt":0.27771597469070064,"score_spread":0.2515375935501274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367173720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5983047,0.00007990584,0.40119922,0.000006273929,0.00015780251,0.00011751713,0.000011556613,0.00011200594,0.000011007675],"genre_scores_gemma":[0.99924654,0.000013437966,0.0005284726,0.00001547061,0.00011428776,0.000011964649,0.00005554758,0.0000140371785,2.199716e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991756,0.00006465312,0.0003067224,0.0001674577,0.000072539966,0.00021299193],"domain_scores_gemma":[0.998959,0.00088198815,0.00005464982,0.00003321018,0.00004922124,0.00002190482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048475453,0.000115765004,0.00021643493,0.00008814014,0.000040745184,0.0000036412464,0.00007193105,0.000055964436,0.0000046734417],"category_scores_gemma":[0.00004956168,0.000121505895,0.00004118501,0.00025800444,0.000033360047,0.000057468398,0.0000133251015,0.00018334136,0.0000056820804],"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":[0.000042490796,0.000023627512,0.0018678355,0.000036871046,0.000047453737,0.0000039473516,0.00013165477,0.9862368,0.008817518,0.00038224828,0.0000011371966,0.0024083909],"study_design_scores_gemma":[0.0007052634,0.00008235591,0.004134796,0.000040686897,0.000009605332,0.0000037818986,0.0000037112693,0.98862505,0.0016034336,0.0046853027,0.000002802237,0.00010320771],"about_ca_topic_score_codex":0.0000020883444,"about_ca_topic_score_gemma":0.0000033592405,"teacher_disagreement_score":0.40094185,"about_ca_system_score_codex":0.000041642656,"about_ca_system_score_gemma":0.000009893866,"threshold_uncertainty_score":0.49548668},"labels":[],"label_agreement":null},{"id":"W4367359592","doi":"10.1109/tnano.2023.3271308","title":"A Novel Cascadable TCAM Using RRAM and Current Race Scheme for High-Speed Energy-Efficient Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Resistive random-access memory; Electronic engineering; Computer science; Static random-access memory; Robustness (evolution); Efficient energy use; CMOS; Energy consumption; Speedup; Memristor; Engineering; Voltage; Electrical engineering; Parallel computing","score_opus":0.028126516143906457,"score_gpt":0.26774466243849204,"score_spread":0.23961814629458558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367359592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23101096,0.00016197203,0.7669513,0.00008968364,0.0004122512,0.0002657867,0.000034923836,0.0010689615,0.0000041569087],"genre_scores_gemma":[0.9915919,0.00014845253,0.007927332,0.000016443291,0.000040409795,0.0001637508,0.0000044700337,0.000042725966,0.000064537875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991118,0.00000559871,0.00019531437,0.00028642156,0.00007081927,0.0003300972],"domain_scores_gemma":[0.99954283,0.000113963106,0.000031926436,0.00023129187,0.000032603057,0.0000473738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005857617,0.00016624552,0.000184285,0.00031759168,0.00029872794,0.000011947334,0.00011263486,0.00016112636,0.0000030109704],"category_scores_gemma":[0.0000040840678,0.00018207428,0.00005031379,0.0006675552,0.00006338794,0.00004873428,0.0000032421203,0.00026057588,0.0000081075095],"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":[0.0000076121364,0.000033513923,8.391664e-8,0.00003198118,0.00001228914,5.675533e-7,0.000016002447,0.5181112,0.4373086,0.00066813396,0.000010675042,0.043799303],"study_design_scores_gemma":[0.00037448076,0.000032847965,8.535677e-7,0.000022491362,0.000017822147,0.000021443513,0.00002769915,0.49251717,0.50285035,0.00026224533,0.0037229438,0.0001496694],"about_ca_topic_score_codex":0.000006075615,"about_ca_topic_score_gemma":0.000008095051,"teacher_disagreement_score":0.7605809,"about_ca_system_score_codex":0.00007247257,"about_ca_system_score_gemma":0.000014164287,"threshold_uncertainty_score":0.74247736},"labels":[],"label_agreement":null},{"id":"W4376464560","doi":"10.1039/d3tc00161j","title":"On the factors affecting the response time of synaptic ion-gated transistors","year":2023,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Materials science; Biasing; Response time; Long-term potentiation; Transistor; Function (biology); Synaptic plasticity; Optoelectronics; Ion; Neuroscience; Voltage; Nanotechnology; Computer science; Electrical engineering; Artificial neural network; Artificial intelligence; Psychology; Physics; Engineering","score_opus":0.014551208009829085,"score_gpt":0.22296053361179605,"score_spread":0.20840932560196695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376464560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9993689,0.000024087587,0.000019846846,0.00012043026,0.0003179302,0.00004380669,0.0000062409567,0.000052622356,0.000046135443],"genre_scores_gemma":[0.99980235,0.000008915679,0.000007207881,0.0000075391135,0.00010015146,3.430505e-7,9.448673e-7,0.00001768602,0.000054866872],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992578,0.00009159454,0.0003245502,0.000052091375,0.00014231182,0.00013162271],"domain_scores_gemma":[0.9984492,0.0011989933,0.00016796256,0.00012384952,0.000030865336,0.000029111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008538138,0.000105140985,0.00019400084,0.000023932398,0.000075585864,0.000016355956,0.00018643012,0.000045588276,0.00012371123],"category_scores_gemma":[0.00042940193,0.000057423138,0.000075990596,0.00014008814,0.00003180414,0.000039476254,0.000014026782,0.0001656901,0.000008525996],"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":[0.0001672911,0.0000042004267,0.000002194984,0.00006560714,0.00004056602,0.000013930147,0.00035385083,0.01726457,0.98185253,6.7005055e-7,0.00021616979,0.00001843744],"study_design_scores_gemma":[0.000113686765,0.000031156145,0.00023750468,0.00015788653,0.00001574605,0.000034608536,0.00027830436,0.00013556029,0.99884254,0.000039493458,0.00005512193,0.000058394773],"about_ca_topic_score_codex":2.2337254e-7,"about_ca_topic_score_gemma":1.595534e-8,"teacher_disagreement_score":0.01712901,"about_ca_system_score_codex":0.00003266401,"about_ca_system_score_gemma":0.0000121056355,"threshold_uncertainty_score":0.23416476},"labels":[],"label_agreement":null},{"id":"W4377089374","doi":"10.1109/ner52421.2023.10123854","title":"Optimizing Neuromorphic Spike Encoding of Dynamic Stimulus Signals Using Information Theory","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Computer science; ENCODE; Spike (software development); Spike train; Encoding (memory); Decoding methods; Stimulus (psychology); Artificial intelligence; Speech recognition; Algorithm; Artificial neural network","score_opus":0.033877982833422346,"score_gpt":0.25835581342872627,"score_spread":0.2244778305953039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377089374","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7600849,0.00002894613,0.23782599,0.0000044904555,0.00021359076,0.000080964484,0.00000230212,0.00051201123,0.0012467521],"genre_scores_gemma":[0.9962843,0.0000196995,0.0036022044,0.000028559865,0.000016868938,0.0000012918991,0.0000061533374,0.000015419962,0.000025523901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935246,0.00002119801,0.00025987814,0.000069951835,0.00009926897,0.00019726984],"domain_scores_gemma":[0.999618,0.00016859049,0.00004698482,0.00010753871,0.00002502168,0.00003386638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021681863,0.00010171838,0.00013150713,0.00017428858,0.00006626508,0.000015997732,0.0000876497,0.000033859087,0.00003139165],"category_scores_gemma":[0.00005137721,0.00010153276,0.000040743063,0.00037821088,0.000016683585,0.0005015742,0.000048354035,0.00011318171,0.000025639752],"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":[0.0000038718263,0.0000010214542,0.0000043787986,0.00006040821,0.0000049545483,0.0000034534205,0.00025035135,0.7863884,0.20740204,0.00020161454,0.0000036959343,0.0056758453],"study_design_scores_gemma":[0.00011950847,0.000014552876,0.00007180631,0.00005315602,0.0000072197486,0.0000075340104,0.00025135645,0.92600673,0.07286276,0.00047923689,0.000018224024,0.00010793644],"about_ca_topic_score_codex":9.279317e-7,"about_ca_topic_score_gemma":2.7968179e-7,"teacher_disagreement_score":0.23619932,"about_ca_system_score_codex":0.000025724734,"about_ca_system_score_gemma":0.000006667077,"threshold_uncertainty_score":0.41403857},"labels":[],"label_agreement":null},{"id":"W4377099300","doi":"10.1149/11101.0123ecst","title":"(Invited) Potential of Silicon Oxide Films for Low-Cost and High-Performance Resistive Switching Devices","year":2023,"lang":"en","type":"article","venue":"ECS Transactions","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Association of Canadian Archivists","funders":"","keywords":"Neuromorphic engineering; Materials science; Resistive touchscreen; Optoelectronics; Silicon; Oxide; Silicon oxide; Electronic engineering; Nanotechnology; Computer science; Electrical engineering; Engineering; Artificial neural network; Artificial intelligence","score_opus":0.012095960793431147,"score_gpt":0.22874158353656,"score_spread":0.21664562274312885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377099300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84669185,0.000040075985,0.15263642,0.00005654372,0.00018913721,0.00014427037,0.000028275048,0.00019632406,0.000017081882],"genre_scores_gemma":[0.99846107,0.000076790784,0.0013326481,0.000022708582,0.000025566942,0.000023432536,0.000008155257,0.000019467074,0.000030185958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950755,0.000005936394,0.00015017673,0.000117108495,0.000054119497,0.00016509047],"domain_scores_gemma":[0.99973494,0.000109978406,0.00002328009,0.00007509132,0.000019735067,0.000036964037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059719405,0.00008969804,0.00011602686,0.00008222349,0.0001646192,0.000009366803,0.000047741287,0.00003856501,0.000005029436],"category_scores_gemma":[0.000006232737,0.000094708965,0.000035509944,0.00018635619,0.000015508296,0.00017808791,0.0000030301483,0.00011191051,0.0000026416933],"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":[0.000021163643,0.0000058019077,0.000028846509,0.00021218711,0.000020969786,0.000001324671,0.00012268273,0.69968706,0.29142204,0.0000048632014,0.000033566444,0.008439458],"study_design_scores_gemma":[0.0004099972,0.00003058902,0.011884794,0.00011737538,0.000038841874,0.0000050976464,0.00018795794,0.39670208,0.5901596,0.00005907564,0.00024581075,0.00015880632],"about_ca_topic_score_codex":0.0000044886638,"about_ca_topic_score_gemma":0.000011844491,"teacher_disagreement_score":0.302985,"about_ca_system_score_codex":0.000009551951,"about_ca_system_score_gemma":0.0000029698608,"threshold_uncertainty_score":0.38621196},"labels":[],"label_agreement":null},{"id":"W4377564358","doi":"10.3389/fnins.2023.1197918","title":"Editorial: Powering the next-generation IoT applications: new tools and emerging technologies for the development of Neuromorphic System of Systems","year":2023,"lang":"en","type":"editorial","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"Agencia Estatal de Investigación; European Regional Development Fund","keywords":"Neuromorphic engineering; Internet of Things; Computer science; Computer architecture; Embedded system; Artificial intelligence; Artificial neural network","score_opus":0.05495597165087737,"score_gpt":0.26394182522225235,"score_spread":0.20898585357137497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377564358","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043743337,0.0015344606,0.20086709,0.000012647153,0.7961503,0.00077944464,0.000021036089,0.00019535195,0.0000022002118],"genre_scores_gemma":[0.0447488,0.0014900728,0.006648213,0.000003591101,0.94601166,0.0008767949,0.000022137205,0.00013082592,0.00006788092],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846506,0.000027995831,0.0005423169,0.00033034617,0.00039177635,0.00024252603],"domain_scores_gemma":[0.9984647,0.0008688787,0.00023965658,0.00033372818,0.000071679584,0.000021366366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005715892,0.00019346383,0.0003120173,0.0001510369,0.00022925029,0.00009702882,0.0006764265,0.00017099461,1.7577237e-8],"category_scores_gemma":[0.0008042841,0.00013974165,0.000036183745,0.0005671934,0.00012593082,0.00014594692,0.00014177803,0.00037863918,1.11221276e-7],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014683933,0.0000064363585,0.000008790228,0.0016772845,0.000013799652,9.3135327e-7,0.00051229476,0.24671032,0.067211084,0.00010528693,0.657297,0.026442064],"study_design_scores_gemma":[0.00022879275,0.000045899564,0.000009666501,0.0005191281,0.000029355742,0.0000014743836,0.001805671,0.20118873,0.010080959,0.000031476484,0.7857739,0.00028495016],"about_ca_topic_score_codex":0.0000031079378,"about_ca_topic_score_gemma":0.0000029630953,"teacher_disagreement_score":0.19421887,"about_ca_system_score_codex":0.00006952604,"about_ca_system_score_gemma":0.000108277825,"threshold_uncertainty_score":0.5698499},"labels":[],"label_agreement":null},{"id":"W4377965434","doi":"10.1007/s41870-023-01287-7","title":"Multiobjective piecewise regressive elitism spotted hyena optimized mapping for 3D NoC architecture design","year":2023,"lang":"en","type":"article","venue":"International Journal of Information Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Hyena; Latency (audio); Piecewise; Benchmark (surveying); Network topology; Network on a chip; Bat algorithm; Throughput; Parallel computing; Computer architecture; Distributed computing; Embedded system; Algorithm; Computer network; Mathematics","score_opus":0.013704093764009273,"score_gpt":0.2546211613505222,"score_spread":0.24091706758651293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377965434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044257615,0.000053616095,0.9530731,0.0007774104,0.0010728355,0.00021599933,0.000018209443,0.00036745964,0.0001637603],"genre_scores_gemma":[0.627501,0.00018730451,0.37161696,0.00023627523,0.00031019183,0.000041240426,0.000043455297,0.000026235806,0.000037324942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900126,0.000014111199,0.00054791913,0.000058150417,0.00019710582,0.00018142712],"domain_scores_gemma":[0.99879414,0.00022676913,0.00034227927,0.000076202814,0.0005261349,0.000034478333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022881238,0.000121864345,0.00018379661,0.0012808021,0.000061190825,0.000033584063,0.00034451796,0.00012235566,0.000008696812],"category_scores_gemma":[0.00054063054,0.000113236936,0.000081830374,0.00036428397,0.00003787404,0.00062603655,0.00004848239,0.00033215355,0.000020870573],"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":[0.00017191515,0.0000064774977,0.0000122655365,0.000024692336,0.00015872992,0.000030759227,0.0007659669,0.72756827,0.0072283666,0.0006954743,0.00071207853,0.26262504],"study_design_scores_gemma":[0.007601658,0.00035675862,0.0007113602,0.0007027622,0.000038615344,0.0013052964,0.0021844613,0.79108375,0.12860782,0.036880538,0.029922668,0.000604288],"about_ca_topic_score_codex":3.025224e-7,"about_ca_topic_score_gemma":1.5181884e-7,"teacher_disagreement_score":0.5832434,"about_ca_system_score_codex":0.00011713129,"about_ca_system_score_gemma":0.000029064351,"threshold_uncertainty_score":0.46176684},"labels":[],"label_agreement":null},{"id":"W4378191208","doi":"10.1109/syscon53073.2023.10131076","title":"Spiking Neural Network Implementation on FPGA for Multiclass Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Spiking neural network; Computer science; Softmax function; Artificial neural network; Field-programmable gate array; Benchmark (surveying); Artificial intelligence; Computer hardware","score_opus":0.07478725545374933,"score_gpt":0.3414630457453096,"score_spread":0.2666757902915603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378191208","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9264191,0.000012480419,0.06966832,0.00019365699,0.00088888133,0.00041879556,0.0000035575467,0.0014230271,0.0009721694],"genre_scores_gemma":[0.99776316,0.000004620873,0.0016131416,0.000103096514,0.000310726,0.000037915255,0.000041862793,0.000021789267,0.00010367553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994608,0.000007784799,0.00013320569,0.00011552556,0.000057170073,0.0002254708],"domain_scores_gemma":[0.9997203,0.00013921052,0.000018744162,0.000081621765,0.0000131580355,0.000027009113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009173801,0.000076481396,0.000062412284,0.000037803635,0.00010529269,0.000016613412,0.00004751987,0.000022359643,0.000007202894],"category_scores_gemma":[0.000010489425,0.000074698306,0.000031774503,0.00018544805,0.000004329912,0.00008721856,0.000009919534,0.000057605754,0.000023895638],"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":[0.000008679496,0.0000020655987,0.00014734517,0.000022789422,0.000006534283,8.1330705e-7,0.000052474603,0.85644484,0.02551907,0.0014278238,0.0025839845,0.11378359],"study_design_scores_gemma":[0.0003065242,0.000041581232,0.0058541084,0.000009320324,0.0000054428488,6.421675e-7,0.00015360588,0.9590064,0.030925643,0.0005074874,0.0030737678,0.000115436385],"about_ca_topic_score_codex":5.755956e-7,"about_ca_topic_score_gemma":0.0000072737776,"teacher_disagreement_score":0.11366816,"about_ca_system_score_codex":0.000028595314,"about_ca_system_score_gemma":0.0000017495099,"threshold_uncertainty_score":0.30461085},"labels":[],"label_agreement":null},{"id":"W4378194738","doi":"10.1109/tfuzz.2023.3279786","title":"Coding Method Based on Fuzzy C-Means Clustering for Spiking Neural Network With Triangular Spike Response Function","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Artificial neural network; Coding (social sciences); Spike (software development); Pattern recognition (psychology); Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.031630790933444924,"score_gpt":0.2680999624925335,"score_spread":0.23646917155908856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378194738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04834876,0.00003846131,0.9441863,0.00006976855,0.0044840504,0.0010022848,0.000032862292,0.0015913886,0.00024617682],"genre_scores_gemma":[0.99538726,0.000005097847,0.00326258,0.000089031615,0.0005340746,0.00026940796,0.000010059954,0.0001438225,0.00029869223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978473,0.00027117698,0.00045651008,0.00046893975,0.00030465165,0.0006513793],"domain_scores_gemma":[0.99801654,0.0012933785,0.000093386814,0.0004016591,0.000056060613,0.0001389625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011066654,0.00038003727,0.00042908103,0.00038215538,0.00057905907,0.00009685294,0.00015096193,0.0001441364,0.0000040665755],"category_scores_gemma":[0.000016979171,0.00036271388,0.0001945123,0.00091366743,0.000021749749,0.00020462234,0.0000015475536,0.00043218912,0.000025855179],"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":[0.0025051462,0.000016446771,0.0000029292817,0.00025752076,0.00005665869,0.000027032467,0.000099058256,0.97744644,0.0124805225,0.00002142587,0.00010646254,0.0069803763],"study_design_scores_gemma":[0.0015630159,0.00069829787,0.00004506498,0.0005569765,0.00008667512,0.000027750555,0.00022357402,0.98938686,0.005806878,0.000024148203,0.0011668075,0.00041394818],"about_ca_topic_score_codex":0.000006979687,"about_ca_topic_score_gemma":0.000017625485,"teacher_disagreement_score":0.9470385,"about_ca_system_score_codex":0.00018307535,"about_ca_system_score_gemma":0.000023147488,"threshold_uncertainty_score":0.99988246},"labels":[],"label_agreement":null},{"id":"W4378800804","doi":"10.1145/3583781.3590230","title":"Digital LIF Neuron for CTT-Based Neuromorphic Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Neuromorphic engineering; Comparator; Computer science; Voltage; Capacitor; Digital electronics; Electronic engineering; Electrical engineering; Artificial neural network; Electronic circuit; Engineering; Artificial intelligence","score_opus":0.04280326049901802,"score_gpt":0.23140974892736144,"score_spread":0.18860648842834343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378800804","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8934841,0.000059276234,0.09418883,0.00011796905,0.0020934418,0.0004997178,0.00003455808,0.004471953,0.005050146],"genre_scores_gemma":[0.99845195,0.0000020661066,0.00006577912,0.000055484415,0.00014068707,0.000020225654,0.000021406428,0.000038499285,0.001203917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994542,0.0000049170926,0.00012395378,0.00012974803,0.00006469515,0.0002224875],"domain_scores_gemma":[0.9996052,0.000197694,0.000011556158,0.00012038404,0.000013580379,0.000051628216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037866706,0.00009896358,0.000101080186,0.00005570826,0.00005260505,0.000053457265,0.00007922269,0.000025192721,0.0000032544515],"category_scores_gemma":[0.000037651746,0.00009375577,0.000046016743,0.0001862787,0.00000770641,0.00011528128,0.000015054521,0.00006962421,0.000092212176],"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":[0.000007966493,0.0000045869474,0.000052760297,0.0001681341,0.0000045910856,0.000023934947,0.0000066920147,0.97464913,0.017677942,0.00022119112,0.0024893924,0.0046936544],"study_design_scores_gemma":[0.00027462177,0.00006495159,0.00017470437,0.000020090662,0.000003578535,0.0000064515148,0.000014753362,0.97007275,0.009098923,0.00004168743,0.020071723,0.00015576919],"about_ca_topic_score_codex":2.4890238e-7,"about_ca_topic_score_gemma":1.9410268e-7,"teacher_disagreement_score":0.10496783,"about_ca_system_score_codex":0.000008684851,"about_ca_system_score_gemma":0.0000041860108,"threshold_uncertainty_score":0.38232493},"labels":[],"label_agreement":null},{"id":"W4378800877","doi":"10.1145/3583781.3590325","title":"Statistical Weight Refresh System for CTT-Based Synaptic Arrays","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Neuromorphic engineering; Computer science; Threshold voltage; Trap (plumbing); Voltage; Capacitor; Electrical engineering; Transistor; Physics; Artificial neural network; Engineering; Artificial intelligence","score_opus":0.020137494553581613,"score_gpt":0.2496767175129701,"score_spread":0.2295392229593885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378800877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05495348,0.000022504391,0.93944174,0.000040986502,0.0006153744,0.00020230097,0.000029501127,0.0023084127,0.002385729],"genre_scores_gemma":[0.9854725,0.000001269149,0.01402624,0.000029229823,0.00012655684,0.000031602623,0.000027752505,0.00003182312,0.00025300635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993767,0.000009808053,0.00014823333,0.00013451782,0.00007225646,0.00025845427],"domain_scores_gemma":[0.9993898,0.00039553037,0.000010392945,0.0001246156,0.000015961474,0.00006365415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009158705,0.00009733099,0.0001276138,0.000050048453,0.00007081585,0.00001222945,0.00007132089,0.00003586772,0.000016569187],"category_scores_gemma":[0.0000384577,0.00008676693,0.00003343301,0.00014175597,0.000011711753,0.000037123216,0.000010648095,0.00007300722,0.00013515781],"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":[0.00007009858,0.000016426486,0.000061028473,0.0025049287,0.00008919823,0.00016579623,0.000063898,0.81105876,0.09065591,0.06251001,0.023041567,0.009762403],"study_design_scores_gemma":[0.00037281692,0.000046266923,0.000053493866,0.00005939993,0.000012918034,0.0000049549235,0.00006015698,0.93478304,0.06061189,0.00036563416,0.0034547292,0.00017468064],"about_ca_topic_score_codex":4.9696047e-7,"about_ca_topic_score_gemma":0.000001121606,"teacher_disagreement_score":0.93051904,"about_ca_system_score_codex":0.00004051483,"about_ca_system_score_gemma":0.000006632265,"threshold_uncertainty_score":0.35382527},"labels":[],"label_agreement":null},{"id":"W4379115853","doi":"10.23919/date56975.2023.10136955","title":"Hardware Efficient Weight-Binarized Spiking Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Spiking neural network; MNIST database; Computer science; Artificial neural network; Bottleneck; Perceptron; Spike (software development); Encoder; Artificial intelligence; Layer (electronics); Multilayer perceptron; Computer hardware; Embedded system","score_opus":0.01513666082765532,"score_gpt":0.2280802787711019,"score_spread":0.21294361794344657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379115853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8996351,0.00013432163,0.091220096,0.00006806192,0.001600973,0.00013319889,8.1613535e-7,0.0035136396,0.0036938386],"genre_scores_gemma":[0.99885786,0.0000128112215,0.0003248165,0.000066110246,0.00024571284,0.0000056811673,0.000007307856,0.00003242125,0.00044726417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992171,0.000011745114,0.00015038521,0.00015918583,0.00009488343,0.0003666826],"domain_scores_gemma":[0.9996802,0.00007865955,0.000013257244,0.00014851465,0.000012580724,0.000066738074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007815618,0.00013254382,0.00013024153,0.00006651604,0.00011489544,0.000023911078,0.000111052046,0.00004343456,0.000053372154],"category_scores_gemma":[0.000012604429,0.000119150805,0.000059113183,0.00041228297,0.0000114373,0.00005449435,0.00006542356,0.00019121495,0.00008808106],"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":[0.0000038034548,0.0000023044706,0.00005648827,0.0000135792525,0.000005136595,0.00003803821,0.00003655859,0.9873559,0.004174706,0.00015116869,0.00057127915,0.007591048],"study_design_scores_gemma":[0.00019887552,0.000011019732,0.0005126206,0.000015770955,0.0000040032064,0.000007724821,0.00002402346,0.99365,0.0041411263,0.000037691596,0.0012412419,0.00015587885],"about_ca_topic_score_codex":6.6590223e-7,"about_ca_topic_score_gemma":6.935176e-7,"teacher_disagreement_score":0.09922282,"about_ca_system_score_codex":0.000021797685,"about_ca_system_score_gemma":0.0000016899763,"threshold_uncertainty_score":0.48588288},"labels":[],"label_agreement":null},{"id":"W4379382636","doi":"10.1109/icjece.2023.3261886","title":"A New Electronic Tunable High-Frequency Meminductor Emulator Based on a Single VDTA","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Capacitor; CMOS; Notation; Transconductance; Cadence; Computer science; Operational transconductance amplifier; Transistor; Amplifier; Electrical engineering; Electronic engineering; Mathematics; Arithmetic; Operational amplifier; Engineering; Voltage","score_opus":0.007515698600935552,"score_gpt":0.1695982229674889,"score_spread":0.16208252436655335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379382636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74256265,0.00090191525,0.25518453,0.0001498919,0.000861986,0.00009502914,0.000002243278,0.0001747864,0.00006698082],"genre_scores_gemma":[0.99590236,0.000011813987,0.0033497561,0.00008046776,0.00059013255,9.173664e-7,0.0000015813553,0.000036390073,0.000026561196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988838,0.000009673106,0.00024854927,0.00013286402,0.00011566792,0.00060947763],"domain_scores_gemma":[0.9990931,0.00013648072,0.00003378343,0.00009933871,0.000032829044,0.0006044432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008847821,0.0001801319,0.00023809823,0.00046902182,0.00006358101,0.000054475913,0.0001603973,0.00006693464,0.000014654164],"category_scores_gemma":[0.000040400777,0.0001768039,0.000067421046,0.00066264527,0.000006931663,0.00011663901,0.000007295906,0.0004519816,0.000006405189],"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":[0.0000075776375,0.0000058198084,0.00011368873,0.000035574176,0.000041305837,0.00029887186,0.00005126828,0.9543856,0.021442495,0.0010587852,0.00113169,0.021427337],"study_design_scores_gemma":[0.0005996236,0.00055814435,0.0013306312,0.00014191304,0.000019788686,0.00015838974,0.0000020218183,0.97806436,0.015277232,0.00059165445,0.0028989394,0.00035731308],"about_ca_topic_score_codex":0.000043859585,"about_ca_topic_score_gemma":0.000034471155,"teacher_disagreement_score":0.25333974,"about_ca_system_score_codex":0.00021246448,"about_ca_system_score_gemma":0.00020717389,"threshold_uncertainty_score":0.72098535},"labels":[],"label_agreement":null},{"id":"W4379933137","doi":"10.48550/arxiv.2306.03922","title":"Electronic ratchet effect in a moiré system: signatures of excitonic ferroelectricity","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"Lawrence Berkeley National Laboratory; Air Force Office of Scientific Research; Canadian Institute for Advanced Research; Japan Society for the Promotion of Science; Office of Science; Basic Energy Sciences; U.S. Department of Energy; Division of Electrical, Communications and Cyber Systems; National Energy Research Scientific Computing Center; Gordon and Betty Moore Foundation; Harvard University; National Science Foundation","keywords":"Ferroelectricity; Condensed matter physics; Dipole; Materials science; Exciton; Polarization (electrochemistry); Electronic structure; Heterojunction; Optoelectronics; Physics; Dielectric; Quantum mechanics; Chemistry","score_opus":0.03598883248853465,"score_gpt":0.17964835470518276,"score_spread":0.1436595222166481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379933137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98418844,0.00040891598,0.0133676585,0.0000028110496,0.0004214996,0.00043207975,0.000009017004,0.00070320047,0.0004664002],"genre_scores_gemma":[0.9994729,0.00023498914,0.000011580355,0.0000037726354,0.00005878766,0.0000014356268,0.00001602098,0.000057421606,0.0001430684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838275,0.0001563793,0.00026161462,0.0005718468,0.00007142822,0.0005559881],"domain_scores_gemma":[0.99901336,0.0002995086,0.00012299758,0.00045924596,0.000034375873,0.00007049523],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028482112,0.00036206946,0.000592047,0.00041452335,0.000048143866,0.000011453246,0.0004950718,0.00034878342,0.000005262145],"category_scores_gemma":[0.000031312607,0.0004243543,0.00020848791,0.0008965348,0.000036842346,0.00009074495,0.0002928312,0.0014008617,0.000016238831],"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":[0.000068719404,0.000012670584,0.0005856733,0.001139867,0.00007493433,0.00024320299,0.00003936714,0.992937,0.003284491,0.001473248,0.000033127213,0.00010764779],"study_design_scores_gemma":[0.0008226476,0.00016607548,0.001094975,0.00073002616,0.000115727766,0.000004080131,0.00006428552,0.9649395,0.026425349,0.004986319,0.000019848183,0.0006311737],"about_ca_topic_score_codex":0.000055186953,"about_ca_topic_score_gemma":0.00009342928,"teacher_disagreement_score":0.027997559,"about_ca_system_score_codex":0.0006478932,"about_ca_system_score_gemma":0.00007302491,"threshold_uncertainty_score":0.9998208},"labels":[],"label_agreement":null},{"id":"W4380028494","doi":"10.21203/rs.3.rs-3027417/v1","title":"Probabilistic Computing with NbOx Mott Memristor-based Self-oscillatory pbit","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National NanoFab Center; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Memristor; Probabilistic logic; Computer science; Artificial intelligence; Electrical engineering; Engineering","score_opus":0.08124820773869065,"score_gpt":0.35262791438363245,"score_spread":0.2713797066449418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380028494","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9638288,0.0012782823,0.019581024,0.00021240038,0.0013491104,0.0031955366,0.000090919246,0.0076592937,0.0028046046],"genre_scores_gemma":[0.99459106,0.000042110172,0.0041554607,0.000014299697,0.000583749,0.000098562065,0.00007191652,0.0002518168,0.00019102772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959377,0.00030857927,0.00045141287,0.0009018789,0.0012252049,0.0011752222],"domain_scores_gemma":[0.9970003,0.0011945756,0.00008291691,0.000984878,0.00042532908,0.0003120083],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013906849,0.0004980574,0.00056212395,0.00052674033,0.00043809344,0.00015188452,0.0006482014,0.0003406769,0.000022958546],"category_scores_gemma":[0.00031552298,0.0004692375,0.00015508362,0.00077948056,0.00016126962,0.00007629808,0.0007421534,0.0032518452,0.00015920696],"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":[0.00003402104,0.000037403497,0.00044983806,0.005617876,0.000069126145,0.00022634455,0.0003304445,0.99114674,0.00031243498,0.00009695416,0.0007967511,0.0008820815],"study_design_scores_gemma":[0.00083363405,0.00031919993,0.002249341,0.004279832,0.000040481325,0.00001500308,0.00025463488,0.98231626,0.0030630725,0.0016773153,0.0037918044,0.0011594058],"about_ca_topic_score_codex":0.000022112581,"about_ca_topic_score_gemma":0.00002403475,"teacher_disagreement_score":0.030762227,"about_ca_system_score_codex":0.0008587354,"about_ca_system_score_gemma":0.0003519479,"threshold_uncertainty_score":0.99977595},"labels":[],"label_agreement":null},{"id":"W4380272255","doi":"10.23977/jeis.2023.080206","title":"Hardware Implementation of Artificial Synapses","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crossbar switch; Computer science; Line (geometry); Path (computing); Array data structure; Limiting; Computer hardware; Perpendicular; Word (group theory); Process (computing); Electrical engineering; Electronic engineering; Telecommunications; Engineering; Mathematics","score_opus":0.014165847979751362,"score_gpt":0.2913609741101115,"score_spread":0.2771951261303602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380272255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944086,0.00006277184,0.005230928,0.000034903584,0.00011009203,0.000021821486,9.896809e-7,0.00001221453,0.000117643125],"genre_scores_gemma":[0.99948275,0.00032112023,0.00016156046,0.000015617756,0.000016406702,1.729259e-7,5.6446015e-7,9.974983e-7,7.8109696e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948543,0.000002273661,0.00024442814,0.000016690587,0.0001512992,0.0000999],"domain_scores_gemma":[0.999711,0.000016917507,0.0001101639,0.000024755233,0.00011325959,0.000023909335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037474398,0.000026969707,0.00005249635,0.00018898812,0.000058731275,0.000025199699,0.000062748295,0.000007630546,0.0000033876609],"category_scores_gemma":[0.000031681095,0.000022846101,0.000013146381,0.00043807158,0.000026059899,0.0022026761,0.000011133424,0.000059559447,0.0000014171873],"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":[0.000017001386,0.00000398973,0.0003135688,0.000089314315,0.000010911343,0.0000011700821,0.0021857733,0.13343713,0.40514046,0.018465942,0.00031683216,0.4400179],"study_design_scores_gemma":[0.00030014207,0.0003684536,0.0097819045,0.000036957234,0.000008942984,0.0000622107,0.0021015662,0.10007843,0.87624997,0.00191679,0.008969053,0.00012556021],"about_ca_topic_score_codex":1.7243369e-7,"about_ca_topic_score_gemma":4.145125e-7,"teacher_disagreement_score":0.47110954,"about_ca_system_score_codex":0.00002166912,"about_ca_system_score_gemma":0.000054513253,"threshold_uncertainty_score":0.1596887},"labels":[],"label_agreement":null},{"id":"W4380366610","doi":"10.48550/arxiv.2306.05991","title":"Approximate information state based convergence analysis of recurrent Q-learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Alliance de recherche numérique du Canada","keywords":"Convergence (economics); Reinforcement learning; Computer science; Markov decision process; Representation (politics); Observable; Partially observable Markov decision process; Recurrent neural network; Markov chain; Markov process; Artificial intelligence; Machine learning; Mathematical optimization; Algorithm; Markov model; Artificial neural network; Mathematics; Statistics","score_opus":0.06678416009321476,"score_gpt":0.18960283349016846,"score_spread":0.1228186733969537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380366610","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6019627,0.000011813849,0.39711598,0.0000018204945,0.0002865867,0.000098043965,0.000032963242,0.00035537395,0.00013472675],"genre_scores_gemma":[0.9993663,0.00016609537,0.0001932287,0.000005291291,0.0000092246955,5.6571827e-7,0.00015896844,0.000015190885,0.00008517562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918586,0.000047448488,0.00025769608,0.00024264146,0.00006403127,0.00020231713],"domain_scores_gemma":[0.99925727,0.000096716285,0.00020342258,0.00028665253,0.00009036847,0.0000655684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015464345,0.00019171817,0.00033192855,0.0005828319,0.000057679736,0.000015640326,0.00025008348,0.00009453888,0.000025408379],"category_scores_gemma":[0.000036137728,0.0002398652,0.00020010029,0.0011967412,0.000032744498,0.00022339018,0.00020851906,0.00046361564,0.00002500307],"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":[0.000021663878,0.000005259197,0.0015661565,0.00031271824,0.0002557175,0.000012334902,0.000149054,0.9968373,0.000102802886,0.00019843939,0.00000961211,0.0005289438],"study_design_scores_gemma":[0.00013468656,0.000015927342,0.0015362052,0.00008268427,0.000317645,8.358493e-8,0.00007231439,0.9954756,0.0015884214,0.00048010092,0.000075029144,0.00022133254],"about_ca_topic_score_codex":0.00001406846,"about_ca_topic_score_gemma":0.000009713828,"teacher_disagreement_score":0.39740357,"about_ca_system_score_codex":0.000094361276,"about_ca_system_score_gemma":0.00002446707,"threshold_uncertainty_score":0.9781419},"labels":[],"label_agreement":null},{"id":"W4381085527","doi":"10.1145/3605153","title":"Offloading Machine Learning to Programmable Data Planes: A Systematic Survey","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Machine learning; Artificial intelligence; Deep learning; Computer architecture; Embedded system","score_opus":0.25495857673590755,"score_gpt":0.3810642036258785,"score_spread":0.12610562688997096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381085527","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022501806,0.97654164,0.015381224,0.00000197649,0.0016563251,0.0021827123,0.00026632167,0.0037002042,0.00004459464],"genre_scores_gemma":[0.0012288921,0.9873799,0.003156124,0.000014837682,0.00067556754,0.000069705566,0.006205271,0.0007979713,0.00047169978],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9920464,0.003586744,0.0016636666,0.0011444535,0.00043820706,0.001120506],"domain_scores_gemma":[0.9879203,0.0087743895,0.00042942207,0.0025216918,0.00008205051,0.0002721747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.012118873,0.0009671295,0.0034424295,0.00046669066,0.00038527968,0.00027950175,0.0032391949,0.0002929444,0.0000055677265],"category_scores_gemma":[0.0072371457,0.00088846224,0.00023261411,0.0020389673,0.000024604366,0.00017309785,0.0026906002,0.0014552171,0.00076652656],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018486527,0.000016970273,0.00010755232,0.44915923,0.0005533244,0.000162958,0.00014430244,0.04576442,0.0000010593848,0.0000030134008,0.00028658076,0.5037987],"study_design_scores_gemma":[0.0004884955,0.00019419641,0.00012013825,0.5431491,0.0012313232,0.0005539312,0.00007828439,0.26263747,0.0000049657797,0.000028999748,0.18680085,0.004712217],"about_ca_topic_score_codex":0.000108027016,"about_ca_topic_score_gemma":0.00021060788,"teacher_disagreement_score":0.49908653,"about_ca_system_score_codex":0.00017050527,"about_ca_system_score_gemma":0.00007583422,"threshold_uncertainty_score":0.9993566},"labels":[],"label_agreement":null},{"id":"W4381334434","doi":"10.1063/5.0151967","title":"The influence of thermal cycling on the activation energy of conduction electrons and filament temperature in Pt/NiO<i>x</i>/Pt ReRAMs","year":2023,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"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 Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal conduction; Activation energy; Temperature cycling; Materials science; Non-blocking I/O; Protein filament; Temperature coefficient; Thermal resistance; Electron; Cycling; Thermal; Chemistry; Composite material; Thermodynamics","score_opus":0.00880133492704012,"score_gpt":0.2024974397783407,"score_spread":0.19369610485130057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381334434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99928945,0.000006806329,0.00018193177,0.0003013028,0.00003171335,0.00008864447,7.8419384e-7,0.000034239703,0.00006514116],"genre_scores_gemma":[0.9995805,0.0000241141,0.000012682314,0.000296087,0.000052273845,0.000017118766,0.0000038742364,0.000011220355,0.0000021465812],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995703,0.000016194053,0.000118720825,0.00008745563,0.00008381688,0.00012352792],"domain_scores_gemma":[0.9995752,0.00024638267,0.000047916885,0.000115200506,0.0000072206617,0.000008105723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007819121,0.000079478246,0.00008082232,0.000018124128,0.000073749274,0.000007779433,0.00006632594,0.000021757643,1.881826e-7],"category_scores_gemma":[0.0000051209754,0.00005421377,0.000016584334,0.00022623186,0.00003973907,0.000050231145,0.000015984733,0.00017503003,3.6256267e-7],"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":[0.000008065222,0.0000020881876,0.000006562112,0.000006515851,0.000005731861,9.249833e-8,0.00014794692,0.40912452,0.58561957,0.003622563,0.000018659814,0.0014376433],"study_design_scores_gemma":[0.00009883351,0.000010490214,0.0012695406,0.000031496515,0.000002618362,1.5574741e-7,0.000086860004,0.0014054327,0.9965024,0.00048943417,0.000044346485,0.000058420075],"about_ca_topic_score_codex":0.000003554223,"about_ca_topic_score_gemma":7.324132e-7,"teacher_disagreement_score":0.41088277,"about_ca_system_score_codex":0.000016659611,"about_ca_system_score_gemma":0.0000029288121,"threshold_uncertainty_score":0.22107734},"labels":[],"label_agreement":null},{"id":"W4381488897","doi":"10.1039/d3nr01311a","title":"Organic multilevel (opto)electronic memories towards neuromorphic applications","year":2023,"lang":"en","type":"review","venue":"Nanoscale","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Key Research and Development Program of China; Fonds de recherche du Québec – Nature et technologies; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Ministry of Science and Technology of the People's Republic of China; Centre québécois sur les matériaux fonctionnels","keywords":"Neuromorphic engineering; Bottleneck; Von Neumann architecture; Computer science; Computer architecture; Computational science; Nanotechnology; Materials science; Artificial intelligence; Parallel computing; Artificial neural network; Embedded system","score_opus":0.0726380088712965,"score_gpt":0.3102260979932991,"score_spread":0.23758808912200258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381488897","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022443686,0.9904757,0.0058573876,0.0000069325097,0.0006038154,0.0008996475,0.00006242805,0.001700062,0.0003715872],"genre_scores_gemma":[0.00017269103,0.996911,0.00012885017,0.000014924959,0.00048130692,0.00037143077,0.00010999332,0.00024365606,0.001566156],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822366,0.000045547764,0.0005025585,0.00044366595,0.00018067623,0.0006039036],"domain_scores_gemma":[0.9990647,0.00019474163,0.0001021252,0.0005003816,0.000032962038,0.00010513115],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009848394,0.00048151327,0.0010073591,0.00015437265,0.00015836755,0.000036061716,0.0004627664,0.0002766236,0.000058743215],"category_scores_gemma":[0.000041861003,0.00045710482,0.00028541128,0.00074367865,0.000041545278,0.00008160393,0.00012797213,0.0008474745,0.0008623692],"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":[4.6859435e-7,0.000010092504,6.561247e-8,0.0072088167,0.000059239082,0.000010340201,0.00001715412,0.000109687884,0.00011887705,0.000102057216,0.0002975283,0.99206567],"study_design_scores_gemma":[0.00007413036,0.000019022013,8.2923714e-7,0.0011351206,0.00018954628,0.00004902781,0.0000035498297,0.00024107352,0.00046131134,0.00020135626,0.9971884,0.0004366212],"about_ca_topic_score_codex":8.099101e-7,"about_ca_topic_score_gemma":0.000005517723,"teacher_disagreement_score":0.9968909,"about_ca_system_score_codex":0.00017525897,"about_ca_system_score_gemma":0.00013779887,"threshold_uncertainty_score":0.9999156},"labels":[],"label_agreement":null},{"id":"W4382119027","doi":"10.1109/tbcas.2023.3289159","title":"An 8-Channel Ambulatory EEG Recording IC With In-Channel Fully-Analog Real-Time Motion Artifact Extraction and Removal","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Artifact (error); Computer science; Electronic engineering; CMOS; Channel (broadcasting); Chip; Artificial intelligence; Computer hardware; Engineering; Telecommunications","score_opus":0.02378564895239377,"score_gpt":0.25075440379705594,"score_spread":0.22696875484466217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382119027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7508667,0.00008945767,0.24746053,0.000027582528,0.0007703233,0.00022400571,0.000014820742,0.00044249315,0.00010407033],"genre_scores_gemma":[0.9994816,0.0002154501,0.000020801433,0.000009130367,0.0001166943,0.000024613144,0.000011496614,0.00003989358,0.000080331505],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986067,0.00008127672,0.0003540537,0.0003737852,0.00024408975,0.0003400836],"domain_scores_gemma":[0.9993899,0.00013069811,0.00005101716,0.0001654843,0.000024492743,0.0002384398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040049976,0.0002147694,0.0003010511,0.00041594697,0.00023591476,0.000055243338,0.00005735015,0.00016743362,0.000007895327],"category_scores_gemma":[0.0000043367572,0.0001922131,0.000036061,0.0005184155,0.00007034368,0.00031586585,0.000001000142,0.00032838644,0.000017924247],"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":[0.00010630409,0.00018153514,0.00004343808,0.0007205648,0.00013023958,0.0004913144,0.0014422958,0.34522605,0.47439006,0.000037231483,0.0000781817,0.1771528],"study_design_scores_gemma":[0.0013101926,0.00064623734,0.0027548173,0.00063151686,0.00006441526,0.00088218856,0.0011187003,0.98560005,0.005997463,0.00016800707,0.0001617191,0.00066469784],"about_ca_topic_score_codex":0.00006170118,"about_ca_topic_score_gemma":0.000020835643,"teacher_disagreement_score":0.640374,"about_ca_system_score_codex":0.000072444374,"about_ca_system_score_gemma":0.000011640714,"threshold_uncertainty_score":0.7838223},"labels":[],"label_agreement":null},{"id":"W4382464228","doi":"10.1609/aaai.v37i10.26363","title":"Emergent Quantized Communication","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"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":"Azrieli Foundation; Israel Science Foundation","keywords":"Quantization (signal processing); Computer science; Communications system; Softmax function; Reinforcement learning; Telecommunications network; Models of communication; Artificial intelligence; Theoretical computer science; Distributed computing; Algorithm; Deep learning; Computer network","score_opus":0.11802550640098856,"score_gpt":0.31385917545410114,"score_spread":0.19583366905311256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382464228","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98822093,0.00003490853,0.0009195262,0.0008178372,0.0004181472,0.00023604515,0.0000028780134,0.00045677548,0.008892937],"genre_scores_gemma":[0.99928373,0.00022318776,0.0002333759,0.0000316091,0.000033964945,0.000013373286,9.976294e-7,0.000016145861,0.00016361204],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991216,0.000007209785,0.00032494002,0.00014827627,0.00018541272,0.00021256552],"domain_scores_gemma":[0.9994853,0.000065964625,0.00009007228,0.00017123991,0.0001479733,0.000039455834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023630768,0.00012623967,0.00014546872,0.00007795785,0.00014551534,0.000033276636,0.00058646617,0.000044098157,0.000056489942],"category_scores_gemma":[0.00015186644,0.00010124174,0.00006854258,0.0005949671,0.00009119759,0.00012219549,0.00012504036,0.00022044765,0.0001797762],"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":[0.000047212143,0.000033042204,0.00008866265,0.00009978487,0.000019029612,3.4073466e-7,0.0010687647,0.012265671,0.61231005,0.31904292,0.0007979294,0.054226577],"study_design_scores_gemma":[0.000014047388,0.000028364246,0.00012557053,0.00013838874,0.000006105244,8.9397275e-7,0.00052899827,0.112075366,0.83556736,0.051179007,0.0002120568,0.00012382743],"about_ca_topic_score_codex":0.0000030171923,"about_ca_topic_score_gemma":0.000002639633,"teacher_disagreement_score":0.2678639,"about_ca_system_score_codex":0.000018921619,"about_ca_system_score_gemma":0.000008265201,"threshold_uncertainty_score":0.41285184},"labels":[],"label_agreement":null},{"id":"W4382468787","doi":"10.1609/aaai.v37i1.25197","title":"Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack against Multispectral Object Detectors Using Transparent Low-E Films","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration; Agency for Defense Development","keywords":"Multispectral image; Computer science; Detector; Multispectral pattern recognition; Computer vision; Artificial intelligence; Remote sensing; Computer security; Telecommunications; Geology","score_opus":0.10752814953188385,"score_gpt":0.32277190178064863,"score_spread":0.2152437522487648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382468787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99674195,0.000011983272,0.00092446373,0.000053298187,0.00047090356,0.00044102193,0.000015453435,0.0005751784,0.0007657429],"genre_scores_gemma":[0.9991442,0.000027929493,0.00051744963,0.000028137394,0.00015475063,0.00001774716,0.0000032544417,0.000065309774,0.000041245028],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977443,0.000021070298,0.0005954199,0.00048265,0.0004286333,0.0007278956],"domain_scores_gemma":[0.9991426,0.00015049388,0.00018869944,0.00019884323,0.00018939721,0.00013000765],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022790766,0.00041740853,0.0004393119,0.00019580834,0.00028705763,0.00010558385,0.0006985944,0.00011588546,0.000033739718],"category_scores_gemma":[0.00017210733,0.00035642122,0.00021723201,0.0011589496,0.00022263911,0.00030366483,0.00011263567,0.0006241763,0.000064589185],"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":[0.000054768916,0.000059134258,0.00009906522,0.00012311147,0.000021578673,0.0000034030277,0.0012987249,0.23376703,0.75342613,0.00097288453,0.000012278786,0.010161881],"study_design_scores_gemma":[0.000043519234,0.00006842659,0.00022408967,0.00025581886,0.000012158162,0.0000017188064,0.000398498,0.47048151,0.52790445,0.00039210482,0.0000023112611,0.00021541958],"about_ca_topic_score_codex":0.000012482299,"about_ca_topic_score_gemma":0.000011934076,"teacher_disagreement_score":0.23671448,"about_ca_system_score_codex":0.00008747515,"about_ca_system_score_gemma":0.000036594898,"threshold_uncertainty_score":0.9998888},"labels":[],"label_agreement":null},{"id":"W4382468866","doi":"10.1609/aaai.v37i7.26061","title":"Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Discretization; Vector quantization; Codebook; Computer science; Bottleneck; Reinforcement learning; Quantization (signal processing); Hyperparameter; Artificial intelligence; Learning vector quantization; Robustness (evolution); Generalization; Theoretical computer science; Machine learning; Algorithm; Mathematics","score_opus":0.07642449509526475,"score_gpt":0.3147651972544703,"score_spread":0.23834070215920553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382468866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91873604,0.000027969518,0.07879762,0.00049108826,0.00023437593,0.0007339919,0.000030384777,0.00031461843,0.0006339081],"genre_scores_gemma":[0.9985738,0.000048235393,0.0011780398,0.000016960708,0.000024184134,0.00006130743,0.000010965322,0.00002444562,0.00006210489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913996,0.000008724915,0.00027000226,0.0002054463,0.00018607019,0.00018980545],"domain_scores_gemma":[0.9992557,0.00015280396,0.00013303121,0.00014365389,0.00028248504,0.00003228485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001447,0.00013929837,0.00014660548,0.000069637965,0.00018395578,0.000045536442,0.00035503114,0.00004396939,0.000007686367],"category_scores_gemma":[0.00010120903,0.00010909892,0.000052199965,0.0003915016,0.00012247164,0.00016933959,0.000060848564,0.00012875911,0.000011575315],"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":[0.0005672399,0.000047682362,0.00015516042,0.00020041384,0.000065893626,7.266844e-7,0.0015554026,0.5114432,0.2739845,0.18148668,0.00006099869,0.030432131],"study_design_scores_gemma":[0.000027158645,0.00008949541,0.0001325796,0.00015159133,0.000010562881,0.0000022986724,0.0004604111,0.5523833,0.42728248,0.01933798,0.000008836883,0.00011332173],"about_ca_topic_score_codex":0.0000033826839,"about_ca_topic_score_gemma":0.000014146858,"teacher_disagreement_score":0.1621487,"about_ca_system_score_codex":0.000033957906,"about_ca_system_score_gemma":0.00001704883,"threshold_uncertainty_score":0.44489247},"labels":[],"label_agreement":null},{"id":"W4383223592","doi":"10.3389/fnins.2023.1190515","title":"Exploiting semantic information in a spiking neural SLAM system","year":2023,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Simultaneous localization and mapping; Computer vision; Cognitive map; Pattern recognition (psychology); Robot; Mobile robot; Cognition","score_opus":0.01474928068527864,"score_gpt":0.21808633828784227,"score_spread":0.20333705760256363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383223592","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9622826,0.000027330596,0.03343198,0.000020556146,0.003101271,0.00014768713,9.2654e-7,0.0005809235,0.00040674172],"genre_scores_gemma":[0.9993431,0.000014908195,0.0005190702,0.000060047216,0.000029679399,0.000012498371,0.0000012004709,0.000010777072,0.000008715448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989812,0.000025022859,0.00027809737,0.00016284524,0.00016032984,0.00039250078],"domain_scores_gemma":[0.9997681,0.000030000365,0.00003588395,0.00012036814,0.0000075442795,0.000038153506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023143842,0.00010801965,0.00013643087,0.0004378508,0.000076747456,0.000050674647,0.00021036148,0.000028952329,1.8568105e-7],"category_scores_gemma":[0.00011307353,0.00011893121,0.000021581405,0.0014689644,0.000029701356,0.0010218365,0.000062146726,0.00021640134,0.000007675863],"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":[0.0000043986956,0.0000021982285,0.009965518,0.00017755861,2.5078822e-7,0.000089838264,0.0006334946,0.9675698,0.010237574,0.000066550594,0.000110702545,0.011142098],"study_design_scores_gemma":[0.00014586581,0.000013317004,0.011495225,0.00012266908,6.4204994e-7,0.000014174999,0.00076694327,0.9851802,0.0019072047,0.000040227358,0.00019106355,0.0001224517],"about_ca_topic_score_codex":0.0000024288556,"about_ca_topic_score_gemma":0.000001502955,"teacher_disagreement_score":0.03706052,"about_ca_system_score_codex":0.00008363912,"about_ca_system_score_gemma":0.000006628489,"threshold_uncertainty_score":0.48498738},"labels":[],"label_agreement":null},{"id":"W4383822492","doi":"10.3389/fnano.2023.1233885","title":"Editorial: Emerging memories, circuits, and systems for post-Moore computing applications in nanotechnology","year":2023,"lang":"en","type":"editorial","venue":"Frontiers in Nanotechnology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Nanotechnology; Computer science; Electronic circuit; Engineering; Materials science; Electrical engineering","score_opus":0.006967953494953521,"score_gpt":0.24095659680383658,"score_spread":0.23398864330888305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383822492","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017997617,0.0067293453,0.068800844,0.00009926704,0.918785,0.00143134,0.00018965702,0.0021569566,0.0000078576395],"genre_scores_gemma":[0.0063537806,0.003130831,0.0033492376,0.0000074572754,0.9850772,0.0009041719,0.0006630237,0.00041301353,0.00010129294],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965888,0.000053401505,0.0009855137,0.0009813225,0.00029259062,0.001098323],"domain_scores_gemma":[0.9977226,0.0011569203,0.00025908655,0.00062536646,0.00016915466,0.000066873705],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00056578993,0.00063582684,0.0013069317,0.0017368774,0.0002027291,0.000046641435,0.0008097749,0.00400407,3.222764e-7],"category_scores_gemma":[0.0014885245,0.0007585879,0.00009468661,0.0013524421,0.00023368454,0.00012772241,0.00030687454,0.0026389498,0.0000028395166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021189588,0.000018959592,0.00007409197,0.0012151502,0.000067703324,0.000022229045,0.00014575862,0.01637878,0.0011980683,0.0002911059,0.9641821,0.016384877],"study_design_scores_gemma":[0.0010581294,0.00010152355,0.0000024012402,0.00044457876,0.00003814821,0.000004659643,0.0010459812,0.016718691,0.0008513647,0.0020888948,0.97687,0.000775624],"about_ca_topic_score_codex":0.000040534338,"about_ca_topic_score_gemma":0.000077974066,"teacher_disagreement_score":0.06629222,"about_ca_system_score_codex":0.0005248334,"about_ca_system_score_gemma":0.00011173735,"threshold_uncertainty_score":0.999662},"labels":[],"label_agreement":null},{"id":"W4384698728","doi":"10.21203/rs.3.rs-3134569/v1","title":"Computing with Heat Using Biocompatible Mott Neurons","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National NanoFab Center; National Nuclear Security Administration; Korea Advanced Institute of Science and Technology; National Research Foundation; Sandia National Laboratories; National Research Foundation of Korea; U.S. Department of Energy","keywords":"Dissipation; Electronics; Thermal management of electronic devices and systems; Computer science; Biocompatible material; Electronic component; Molecular dynamics; Nanotechnology; Materials science; Chemistry; Physics; Mechanical engineering; Engineering; Thermodynamics; Computational chemistry; Biomedical engineering","score_opus":0.183031785947372,"score_gpt":0.40859788104692363,"score_spread":0.22556609509955164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384698728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9660429,0.0005589324,0.029574618,0.00007584748,0.0005874222,0.0009078965,0.00003846909,0.0016114464,0.000602462],"genre_scores_gemma":[0.9953933,0.00009875396,0.0036112692,0.000009490479,0.0005166753,0.00001943961,0.000047841622,0.00020025889,0.000102981045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705625,0.00020854904,0.0003208096,0.0006330521,0.00074501446,0.0010363224],"domain_scores_gemma":[0.99835193,0.0005116336,0.00003181363,0.00067687436,0.00021135506,0.00021640114],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006174568,0.0003642268,0.00042137597,0.00053876673,0.00044343065,0.00017849202,0.00048008448,0.00019078965,0.000014984723],"category_scores_gemma":[0.00008399087,0.00034567,0.00010309486,0.00086109224,0.00011267385,0.00009587511,0.0011413038,0.002671164,0.00007316692],"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":[0.000012936159,0.00001432694,0.00060658756,0.0014675547,0.00003503171,0.00017094538,0.00021701635,0.9901771,0.005662507,0.000029616314,0.0002176105,0.0013887621],"study_design_scores_gemma":[0.00026034107,0.00011966083,0.0015058839,0.0029947306,0.0000127592875,0.000026652522,0.00026934326,0.98212546,0.011315857,0.0004646664,0.0003933466,0.000511322],"about_ca_topic_score_codex":0.00010221476,"about_ca_topic_score_gemma":0.00002579085,"teacher_disagreement_score":0.029350385,"about_ca_system_score_codex":0.00025111242,"about_ca_system_score_gemma":0.00011551756,"threshold_uncertainty_score":0.9998995},"labels":[],"label_agreement":null},{"id":"W4384823606","doi":"10.1021/acsmaterialslett.3c00088","title":"From Spintronic Memristors to Quantum Computing","year":2023,"lang":"en","type":"article","venue":"ACS Materials Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":47,"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":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Fujian Normal University","keywords":"Memristor; Spintronics; Scalability; Computer science; Quantum computer; Electrical engineering; Quantum; Engineering; Physics","score_opus":0.014179005458345731,"score_gpt":0.23764763930190438,"score_spread":0.22346863384355864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384823606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899852,0.000017444328,0.0047716144,0.0007593217,0.0030655279,0.00012292051,0.000017746093,0.0012403299,0.00001986119],"genre_scores_gemma":[0.99743724,0.0000050324743,0.00048086615,0.0010391123,0.00093821203,0.00000518086,0.000034332606,0.000050851635,0.000009193963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900305,0.000029631074,0.00024212003,0.00021874935,0.00010668908,0.00039978162],"domain_scores_gemma":[0.99960023,0.000084344305,0.000029968824,0.00020667963,0.000006946048,0.00007181019],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013141138,0.00016569202,0.00021518866,0.000089598194,0.00008468729,0.000052670508,0.00018702076,0.00003687107,0.00004846395],"category_scores_gemma":[0.000028350665,0.00017560956,0.000029164488,0.00021344177,0.00001368867,0.000090033136,0.00009545899,0.000082543236,0.00086842565],"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":[0.000005589356,0.0000012086019,0.00000968685,0.000020960812,0.000012632376,0.000027681186,0.0002470134,0.070387065,0.92285854,0.00004054727,0.005742757,0.00064630946],"study_design_scores_gemma":[0.00018145371,0.00001674591,0.0011567797,0.000068649206,0.00000931249,0.0000028573875,0.00006808467,0.0017088167,0.9920548,0.00023961648,0.0041736327,0.00031923444],"about_ca_topic_score_codex":0.000023089993,"about_ca_topic_score_gemma":0.000001033551,"teacher_disagreement_score":0.06919627,"about_ca_system_score_codex":0.000059660575,"about_ca_system_score_gemma":0.0000028896773,"threshold_uncertainty_score":0.9999095},"labels":[],"label_agreement":null},{"id":"W4384858822","doi":"10.48550/arxiv.2307.09463","title":"A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"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":"Ministry of Colleges and Universities; Institut National des Sciences Appliquées de Lyon; École Centrale de Lyon; National Science Foundation; Government of Canada; Indian National Science Academy; Institut Périmètre de physique théorique; Centre National de la Recherche Scientifique; Innovation, Science and Economic Development Canada; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Decoding methods; Computer science; Scalability; Error detection and correction; Artificial neural network; Bottleneck; Crossbar switch; Computer engineering; Algorithm; Embedded system; Artificial intelligence","score_opus":0.12630453379327786,"score_gpt":0.2236082396145084,"score_spread":0.09730370582123055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384858822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63732064,0.00006474603,0.3563668,0.000016848322,0.0046129012,0.00045676957,0.000047259404,0.0010272566,0.00008676151],"genre_scores_gemma":[0.9975246,0.00008560401,0.0001715766,0.000025748004,0.00025089236,0.000006284532,0.000066107605,0.00009664661,0.0017725072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848497,0.000047429134,0.00023865001,0.0007222073,0.000056639197,0.00045012988],"domain_scores_gemma":[0.9989779,0.00026485004,0.00012072078,0.00040286087,0.00010540094,0.00012829046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000112441514,0.00039122248,0.00039595604,0.00020856256,0.00020273351,0.000032956963,0.00035911816,0.000273222,0.000014035],"category_scores_gemma":[0.000059602397,0.00047782372,0.00032114057,0.00031898078,0.000051240637,0.00015418816,0.00028113078,0.0006917772,0.00005481755],"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":[0.00010157527,0.000014776936,0.000080787075,0.0001878132,0.000097370095,0.00010224872,0.000116389274,0.9971366,0.00049116893,0.00039785783,0.0007796556,0.0004937497],"study_design_scores_gemma":[0.00046324765,0.000051841394,0.00029746236,0.00011129431,0.00012639811,0.000007700397,0.00023571437,0.99190557,0.0014224577,0.004493839,0.00037966575,0.00050482614],"about_ca_topic_score_codex":0.000025521847,"about_ca_topic_score_gemma":0.00009185022,"teacher_disagreement_score":0.36020398,"about_ca_system_score_codex":0.000237854,"about_ca_system_score_gemma":0.000036180623,"threshold_uncertainty_score":0.99976736},"labels":[],"label_agreement":null},{"id":"W4384947937","doi":"10.1109/iscas46773.2023.10181383","title":"FinFET 6T-SRAM Compute-in-Memory Targeting Low Power Neural Networks Operations","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Static random-access memory; Bottleneck; Artificial neural network; Computer science; Latency (audio); Power (physics); Parallel computing; Computation; Key (lock); Computer engineering; Computer hardware; Artificial intelligence; Embedded system; Algorithm","score_opus":0.010170939172479548,"score_gpt":0.23287335412529556,"score_spread":0.222702414952816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384947937","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93521047,0.0001334393,0.058384247,0.00012001891,0.0012962996,0.00019198864,0.0000018493165,0.0016993204,0.0029623539],"genre_scores_gemma":[0.9979257,0.000015708118,0.0011504825,0.00022240657,0.00020944983,0.000008709022,0.000027275542,0.000037126913,0.00040314725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989813,0.000023930268,0.0002698851,0.00020264003,0.00008776464,0.00043450628],"domain_scores_gemma":[0.9996324,0.00010865034,0.0000113259,0.0001546192,0.000019903051,0.00007307203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013308348,0.00016477342,0.00016782,0.00010282404,0.00012866834,0.000042993837,0.00013633734,0.00006306246,0.00012042506],"category_scores_gemma":[0.000028597371,0.0001648371,0.000052761832,0.00050502684,0.000015980233,0.0002167123,0.00008172999,0.00032269827,0.00012369738],"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":[0.0000013452081,0.000005920934,0.00009446251,0.0000117573145,0.0000042217016,0.00004376255,0.00009207296,0.99361664,0.0016136875,0.00008679809,0.001988419,0.0024409357],"study_design_scores_gemma":[0.00019766473,0.00001508903,0.0013568165,0.00002000678,0.0000016071231,0.000005519588,0.000076143486,0.996501,0.0012966781,0.000022177086,0.0003061841,0.00020110935],"about_ca_topic_score_codex":0.0000033082702,"about_ca_topic_score_gemma":0.000019783613,"teacher_disagreement_score":0.06271521,"about_ca_system_score_codex":0.000028273713,"about_ca_system_score_gemma":0.0000052359032,"threshold_uncertainty_score":0.67218614},"labels":[],"label_agreement":null},{"id":"W4385079225","doi":"10.23919/snw57900.2023.10183973","title":"Scalability of h-BN Based Memristors: Yield and Variability Considerations","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Memristor; Scalability; Probabilistic logic; Voltage; Computer science; Electronic engineering; Yield (engineering); Materials science; Set (abstract data type); Optoelectronics; Biological system; Electrical engineering; Engineering; Artificial intelligence","score_opus":0.031003505182763822,"score_gpt":0.2431373191062419,"score_spread":0.21213381392347808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385079225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97396964,0.000011242971,0.022520352,0.00018473729,0.00011919169,0.00010010571,0.000005669291,0.00048408093,0.002604979],"genre_scores_gemma":[0.9979878,0.0000017336786,0.0019303147,0.00003124628,0.00001455159,0.000003170223,0.0000014311817,0.0000054632983,0.000024298415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995995,0.000016781061,0.00014445724,0.00010418754,0.000045717883,0.00008933772],"domain_scores_gemma":[0.99913883,0.00065385445,0.000010301964,0.00013999638,0.000022456834,0.000034551016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023011758,0.000056358076,0.00009844776,0.000029427387,0.00004390574,0.000004788922,0.000020871623,0.000029040046,0.00009047959],"category_scores_gemma":[0.00040712729,0.000053406282,0.000020403419,0.0001293629,0.000044066168,0.000055062163,0.00001689039,0.00006631402,0.0000041945436],"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":[0.00002030252,0.00007880658,0.019577662,0.0009968657,0.000041749827,0.000011943377,0.000504178,0.50281936,0.4564815,0.008774858,0.0042365426,0.006456242],"study_design_scores_gemma":[0.0003399918,0.0000514084,0.028709508,0.00004366142,0.000017280141,0.00000418983,0.00011417626,0.4165175,0.527624,0.025964953,0.00032286023,0.00029042806],"about_ca_topic_score_codex":0.0000038831226,"about_ca_topic_score_gemma":0.00000696264,"teacher_disagreement_score":0.08630185,"about_ca_system_score_codex":0.000011200319,"about_ca_system_score_gemma":0.000008352991,"threshold_uncertainty_score":0.2177845},"labels":[],"label_agreement":null},{"id":"W4385080043","doi":"10.1109/iscas46773.2023.10181572","title":"High-Performance FPGA Implementation of Fully Connected Networks of SAM Neurons","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Neuromorphic engineering; Computer science; Field-programmable gate array; Von Neumann architecture; Artificial neural network; Computer architecture; Neuron; Spiking neural network; Computation; Artificial neuron; Parallel computing; Embedded system; Artificial intelligence; Neuroscience; Algorithm","score_opus":0.014616828039583012,"score_gpt":0.25102668983990833,"score_spread":0.23640986180032533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385080043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895168,0.000011979053,0.00972133,0.000010967239,0.00022392589,0.00007469545,0.0000029734476,0.0002580217,0.00017929316],"genre_scores_gemma":[0.9995266,0.000044778546,0.0003225179,0.000012406688,0.000035022586,0.0000025148058,0.000014089122,0.000011553582,0.00003050302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995488,0.000007582808,0.00019310773,0.00006396788,0.00005492567,0.00013163408],"domain_scores_gemma":[0.99977595,0.000066303284,0.00003346945,0.00008447459,0.000021960876,0.000017872646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047164718,0.000060239625,0.00010318264,0.00004943901,0.000021589316,0.0000018181601,0.000054322925,0.00001861202,0.00005233778],"category_scores_gemma":[0.000005079017,0.000059049446,0.000017920922,0.0003066154,0.000010560331,0.00007547597,0.000021793341,0.00005742252,0.000004357814],"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":[0.0000045352044,0.000002069133,0.00043030974,0.000052171505,0.000008185032,8.0170526e-7,0.00004112596,0.9037074,0.07089595,0.00015111166,0.00016538649,0.024540966],"study_design_scores_gemma":[0.00029218814,0.000079971156,0.04731372,0.000017231716,0.000006799518,0.0000012833519,0.000111403235,0.21321444,0.7387833,0.00003692953,0.000058122852,0.000084602405],"about_ca_topic_score_codex":0.0000058066303,"about_ca_topic_score_gemma":0.0000070231526,"teacher_disagreement_score":0.6904929,"about_ca_system_score_codex":0.0000057436205,"about_ca_system_score_gemma":0.000003408398,"threshold_uncertainty_score":0.24079664},"labels":[],"label_agreement":null},{"id":"W4385080174","doi":"10.1109/iscas46773.2023.10182097","title":"SEVDA: Singular Value Decomposition Based Parallel Write Scheme for Memristive CNN Accelerators","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Computer science; Singular value decomposition; Parallel computing; Convolutional neural network; FLOPS; Artificial neural network; Von Neumann architecture; Multiplication (music); Matrix multiplication; Algorithm; Artificial intelligence; Mathematics; Quantum","score_opus":0.026458111508624354,"score_gpt":0.2923820822869485,"score_spread":0.26592397077832414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385080174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38668382,0.000045302168,0.6104841,0.000078077464,0.00028931178,0.0002716412,0.000007932547,0.0013028579,0.00083690445],"genre_scores_gemma":[0.9414911,0.0000059595286,0.05783293,0.00019418656,0.00015628425,0.000036264264,0.00008179282,0.00004532401,0.00015611053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924445,0.000013355594,0.00017677086,0.00019184635,0.00008740791,0.0002861815],"domain_scores_gemma":[0.99957687,0.00017673912,0.000021340358,0.00012167551,0.00003671695,0.0000666878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011429396,0.00014828349,0.00015137104,0.00008575134,0.00014701784,0.000027932723,0.00008488816,0.000057950096,0.000023999726],"category_scores_gemma":[0.000027293483,0.0001504374,0.00007802302,0.00026411703,0.000012682265,0.00014089905,0.000021152835,0.00010510314,0.000053017484],"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":[0.000036610687,0.000013504269,0.00007230645,0.00010831726,0.00003206192,0.000015604728,0.00006933983,0.8638889,0.12950735,0.0012581022,0.0017119398,0.0032859242],"study_design_scores_gemma":[0.0005083613,0.0000394753,0.00036309013,0.000034058474,0.000010063892,0.0000014892445,0.000035726578,0.83787084,0.158534,0.000914877,0.0014633393,0.00022468527],"about_ca_topic_score_codex":9.0713337e-7,"about_ca_topic_score_gemma":7.017496e-7,"teacher_disagreement_score":0.5548073,"about_ca_system_score_codex":0.000045080757,"about_ca_system_score_gemma":0.000009004171,"threshold_uncertainty_score":0.6134659},"labels":[],"label_agreement":null},{"id":"W4385245081","doi":"10.1109/iscas46773.2023.10181753","title":"Observation of a Pinched-Loop in a Current-Excited Inductive Circuit","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Voltage; Inductor; Context (archaeology); Loop (graph theory); Current (fluid); Equivalent circuit; Equivalent series resistance; Mesh analysis; Series (stratigraphy); Nonlinear system; Physics; Topology (electrical circuits); Electrical engineering; Control theory (sociology); Voltage source; Engineering; Computer science; Mathematics","score_opus":0.09209069528872768,"score_gpt":0.2882123803784213,"score_spread":0.1961216850896936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385245081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939076,0.00004317196,0.0049644215,0.000010907224,0.00017612142,0.0000825547,0.0000010185514,0.00027529916,0.0005389121],"genre_scores_gemma":[0.9997596,0.000020846259,0.00009975452,0.000007323752,0.000027684137,0.000005781145,0.00000706357,0.0000093448325,0.000062562984],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995867,0.000009142713,0.0001449209,0.00008192224,0.000057589437,0.00011973029],"domain_scores_gemma":[0.9998281,0.00004947136,0.000016806234,0.000071196286,0.000017612592,0.00001680275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060654813,0.000058377063,0.000091203685,0.00012060212,0.000010644639,0.000002540181,0.00005083237,0.000024642499,0.000010473018],"category_scores_gemma":[0.000039333656,0.00005996687,0.000018055165,0.0007687768,0.000007692367,0.00012040668,0.00001862424,0.00011087621,0.000015605867],"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":[0.000013626643,0.000044758595,0.017510233,0.0003897335,0.00002245062,0.000013642535,0.0019115431,0.24808678,0.50007784,0.004128683,0.00026165583,0.22753906],"study_design_scores_gemma":[0.0013024505,0.0000662813,0.26124313,0.0003557139,0.000010553005,0.0000032815299,0.0007120137,0.47191063,0.25186303,0.010844491,0.001137961,0.0005504675],"about_ca_topic_score_codex":0.0000038906237,"about_ca_topic_score_gemma":0.000005850702,"teacher_disagreement_score":0.24821481,"about_ca_system_score_codex":0.000022114458,"about_ca_system_score_gemma":0.000004450564,"threshold_uncertainty_score":0.2445378},"labels":[],"label_agreement":null},{"id":"W4385290324","doi":"10.1109/iscas46773.2023.10181445","title":"HESSPROP: Mitigating Memristive DNN Weight Mapping Errors with Hessian Backpropagation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Backpropagation; Hessian matrix; Computer science; Artificial intelligence; Artificial neural network; Mathematics; Applied mathematics","score_opus":0.016028544039350136,"score_gpt":0.21627583005496234,"score_spread":0.2002472860156122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385290324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92194164,0.000043105538,0.061160598,0.00015733908,0.00026439366,0.00020809684,0.0000014690802,0.0021636493,0.014059737],"genre_scores_gemma":[0.99335563,0.000010457295,0.005833162,0.00004377057,0.00014491942,0.000015637797,0.000015273139,0.000043165357,0.0005379828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912643,0.000017989769,0.00018281394,0.00021041978,0.00014191798,0.0003204324],"domain_scores_gemma":[0.9996549,0.0000793031,0.000035584122,0.00013119706,0.000028180137,0.00007081701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101820784,0.00016401988,0.0001466793,0.00010038633,0.00016993974,0.000024512741,0.00008679334,0.00004224187,0.0000392625],"category_scores_gemma":[0.000018557379,0.00013286751,0.000030176725,0.0005843948,0.000029753426,0.00024176142,0.000029698036,0.00019207092,0.00013244706],"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":[0.000035663,0.000022064074,0.003751152,0.0007403778,0.00015700441,0.0003199518,0.0035119369,0.6306857,0.26902464,0.0023496456,0.0017754412,0.08762643],"study_design_scores_gemma":[0.00066976267,0.0000805102,0.005892952,0.0005402677,0.00001968475,0.000037910093,0.0026609562,0.4688852,0.5165533,0.0012045095,0.002632202,0.00082278193],"about_ca_topic_score_codex":0.000002252139,"about_ca_topic_score_gemma":0.0000067426326,"teacher_disagreement_score":0.24752863,"about_ca_system_score_codex":0.000048201782,"about_ca_system_score_gemma":0.000009375236,"threshold_uncertainty_score":0.54181796},"labels":[],"label_agreement":null},{"id":"W4385334085","doi":"10.1109/isscs58449.2023.10190874","title":"A Low-Complexity Analog Implementation of Persistent Sodium I<sub>Na,p</sub>-Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"CMOS; Realization (probability); Circuit complexity; Computer science; Topology (electrical circuits); Electronic engineering; Computational science; Computer engineering; Mathematics; Engineering; Electronic circuit; Electrical engineering","score_opus":0.04817344483538373,"score_gpt":0.28781675930812706,"score_spread":0.23964331447274334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385334085","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9815254,0.000015553662,0.016982337,0.00003344902,0.000115370516,0.00010979997,0.000010121117,0.00042191576,0.00078605267],"genre_scores_gemma":[0.9992536,0.000017463868,0.0005826095,0.000036229867,0.000031133975,0.0000055978326,0.000023493381,0.000016618222,0.000033304736],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993208,0.000009196159,0.0002051674,0.0001305252,0.000112207126,0.0002220763],"domain_scores_gemma":[0.99974763,0.000027304177,0.00002764923,0.00012359797,0.000026682614,0.000047132755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084800275,0.000101880294,0.00012807675,0.000085665335,0.000056343044,0.0000070374813,0.00007864535,0.00002594863,0.000026594378],"category_scores_gemma":[0.0000047250273,0.000102738406,0.00011754465,0.0002469729,0.000021225915,0.0001002931,0.000041765656,0.00007808071,0.000034330533],"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":[0.000004829006,0.0000067884935,0.00009558097,0.000108722386,0.000022364684,0.0000027737196,0.00025393307,0.4720329,0.51922053,0.00033236068,0.00036853726,0.0075506554],"study_design_scores_gemma":[0.00017584684,0.000021511003,0.00076564343,0.000008776026,0.00001011325,0.0000015547514,0.000401161,0.40497398,0.5930985,0.00044307078,0.0000060928523,0.00009371591],"about_ca_topic_score_codex":0.000004538085,"about_ca_topic_score_gemma":0.000024791634,"teacher_disagreement_score":0.07387796,"about_ca_system_score_codex":0.000035271863,"about_ca_system_score_gemma":0.000009308582,"threshold_uncertainty_score":0.41895506},"labels":[],"label_agreement":null},{"id":"W4385334613","doi":"10.1101/2023.07.25.550525","title":"Burstprop for Learning in Spiking Neuromorphic Hardware","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Vector Institute","keywords":"Neuromorphic engineering; MNIST database; Spiking neural network; Computer science; Spike (software development); Artificial neural network; Adaptation (eye); Artificial intelligence; Backpropagation; Spike-timing-dependent plasticity; Computer architecture; Energy (signal processing); Machine learning; Synaptic plasticity; Neuroscience","score_opus":0.03689002856491846,"score_gpt":0.23202718784164728,"score_spread":0.1951371592767288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385334613","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9718829,0.00076933636,0.019241327,0.00009411303,0.0031177169,0.0010906117,0.00007079187,0.0037267404,0.0000064922183],"genre_scores_gemma":[0.9946471,0.00019101865,0.0036941585,0.00005076034,0.0006879828,0.00031105158,8.114768e-7,0.00040334414,0.000013778677],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99730396,0.00007400619,0.000611931,0.00091666466,0.000244409,0.00084903184],"domain_scores_gemma":[0.9985973,0.00022156716,0.00018403168,0.00066861016,0.0001534513,0.00017498955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052624027,0.00062320085,0.00066071376,0.00043542587,0.00017978589,0.00015144049,0.00050604757,0.00043147433,0.000006440518],"category_scores_gemma":[0.0004305516,0.0007701699,0.00018321675,0.0006104272,0.000044230303,0.000167713,0.00037748602,0.0017458312,0.000047725425],"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":[0.000026563477,0.000027181875,0.0030545732,0.0019607546,0.00006708836,0.00024766912,0.000020425277,0.40151748,0.59277993,0.00014640008,0.00012943694,0.00002248555],"study_design_scores_gemma":[0.0020678549,0.00020047158,0.054516412,0.004516504,0.00017097277,1.1095595e-7,0.000015744345,0.23608975,0.68080837,0.000046555775,0.017651457,0.0039157835],"about_ca_topic_score_codex":0.0000066021657,"about_ca_topic_score_gemma":0.0000026769671,"teacher_disagreement_score":0.16542773,"about_ca_system_score_codex":0.00025014722,"about_ca_system_score_gemma":0.000110516674,"threshold_uncertainty_score":0.99947494},"labels":[],"label_agreement":null},{"id":"W4385381403","doi":"10.1002/aelm.202300135","title":"In‐Depth Physical Mechanism Analysis of Polymer Artificial Optoelectronic Synapse with High Endurance and Applications of Visual System and Operant Conditioning","year":2023,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"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":"Institute of Nutrition, Metabolism and Diabetes; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Synapse; Materials science; Optoelectronics; SIGNAL (programming language); Computer science; Neuroscience; Artificial neural network; Artificial intelligence","score_opus":0.004020032903283486,"score_gpt":0.2401246927298935,"score_spread":0.23610465982661002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385381403","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928474,0.00025987852,0.0064443424,0.0000072845887,0.0000256844,0.00026730372,0.000029385483,0.00011132444,0.000007385743],"genre_scores_gemma":[0.99958813,0.00011813882,0.0000530895,0.000004394252,0.000035194782,0.00013764498,0.000032719356,0.000023561726,0.000007126566],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895424,0.00003604121,0.00031316368,0.0002396972,0.00010000246,0.0003568833],"domain_scores_gemma":[0.9996293,0.00008806012,0.000100109195,0.00012546149,0.000024452775,0.00003261206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011739071,0.00015658066,0.0005053374,0.00023347813,0.00005421814,0.000010431451,0.000063143016,0.000038676164,0.0000072217754],"category_scores_gemma":[0.000004463139,0.00014685674,0.00002580632,0.0007943909,0.00004876783,0.00015450743,0.000019919184,0.000091275826,8.930571e-7],"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":[0.00005492612,0.0000127691255,0.0000081860635,0.00016325431,0.00013738705,0.0000022948682,0.0000811008,0.031277336,0.9230446,0.044686846,9.611161e-8,0.00053117814],"study_design_scores_gemma":[0.00030598987,0.00014228844,0.00034305322,0.000055960336,0.00014077307,0.000007900791,0.000107696585,0.00489133,0.9927266,0.0011319065,0.000001916765,0.00014454497],"about_ca_topic_score_codex":0.0000127081,"about_ca_topic_score_gemma":0.000022215414,"teacher_disagreement_score":0.06968202,"about_ca_system_score_codex":0.00005394042,"about_ca_system_score_gemma":0.000019429746,"threshold_uncertainty_score":0.59886444},"labels":[],"label_agreement":null},{"id":"W4385412027","doi":"10.1109/smacd58065.2023.10192103","title":"A Programmable Circuit Based on the Combination of VTM Cellular Crossbars","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Computer science; Crossbar switch; NAND gate; Pipeline (software); Logic gate; Logic synthesis; Novelty; Parallel computing; Digital electronics; Memristor; Programmable logic array; Electronic circuit; Computer hardware; Electronic engineering; Algorithm; Electrical engineering; Engineering","score_opus":0.03025708271850923,"score_gpt":0.2252444865844353,"score_spread":0.19498740386592606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385412027","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97010225,0.000010037606,0.019919813,0.000099312296,0.0001441181,0.0001984227,0.0000010742893,0.00058972894,0.008935231],"genre_scores_gemma":[0.9994887,0.0000011078914,0.00007253475,0.00003991755,0.000011591866,0.00000952697,0.000004399611,0.000011391459,0.00036079725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962336,0.0000108982085,0.00008847214,0.00006436322,0.0000889386,0.00012394784],"domain_scores_gemma":[0.9997149,0.000119278375,0.000013200438,0.0001222224,0.000015041035,0.000015368814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012757156,0.000054498112,0.00005783813,0.00003662086,0.000054225537,0.000010824381,0.00007184023,0.00001892306,0.000031345142],"category_scores_gemma":[0.000022197222,0.000039554674,0.000029341916,0.0002879065,0.00001573791,0.000032335945,0.00000939286,0.00007087514,0.00003745737],"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":[0.000006721043,0.000031424017,0.00016632682,0.00011336465,0.000009459975,0.000008623069,0.00012908925,0.7756932,0.19975454,0.011137268,0.00069666706,0.012253327],"study_design_scores_gemma":[0.00015796648,0.000040156443,0.0002544871,0.000019385318,0.0000021696537,2.0665887e-7,0.000033906243,0.5477035,0.44958583,0.0013364926,0.00080668594,0.00005919752],"about_ca_topic_score_codex":9.1445503e-7,"about_ca_topic_score_gemma":3.4950392e-7,"teacher_disagreement_score":0.24983129,"about_ca_system_score_codex":0.0000101383575,"about_ca_system_score_gemma":0.0000034678505,"threshold_uncertainty_score":0.16129927},"labels":[],"label_agreement":null},{"id":"W4385431395","doi":"10.1016/j.neunet.2023.07.031","title":"Memristor-based spiking neural network with online reinforcement learning","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Science Foundation","keywords":"Computer science; Memristor; Spiking neural network; Reinforcement learning; Initialization; Neuromorphic engineering; Artificial neural network; Artificial intelligence; Benchmark (surveying); Spike (software development); Machine learning","score_opus":0.01675492486756653,"score_gpt":0.23059277423016225,"score_spread":0.21383784936259573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385431395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93319595,0.00043593234,0.05976877,0.00015360817,0.0016197719,0.00035183964,0.0000010875087,0.0038161678,0.0006568909],"genre_scores_gemma":[0.99737763,0.000035757774,0.0004538673,0.00035712039,0.0013346381,0.00001538404,0.00010858053,0.00009999102,0.00021702667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817985,0.000051297236,0.00032915646,0.00031986096,0.00024551468,0.0008743121],"domain_scores_gemma":[0.9992836,0.0002039916,0.00007999676,0.00024958054,0.00003343651,0.00014937793],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014533616,0.0003342331,0.00029425582,0.00008644016,0.00033053788,0.00005003072,0.00020815061,0.000097287906,0.000027528635],"category_scores_gemma":[0.000016856775,0.00030084647,0.000091368136,0.00087632256,0.000042853713,0.00017284394,0.00007117116,0.0008885938,0.000013552786],"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":[0.000051590156,0.000004218567,0.00089140306,0.00002981186,0.000015532934,0.00011413488,0.000025848685,0.9838863,0.0002366056,0.000014972707,0.00079365267,0.013935933],"study_design_scores_gemma":[0.00047026225,0.0001793167,0.0009680791,0.00008869409,0.000018868941,0.00001531906,0.000025586454,0.9947541,0.00019133066,0.000009690788,0.0029205126,0.00035827485],"about_ca_topic_score_codex":0.000002743186,"about_ca_topic_score_gemma":0.000014637469,"teacher_disagreement_score":0.0641817,"about_ca_system_score_codex":0.00006232109,"about_ca_system_score_gemma":0.0000074054947,"threshold_uncertainty_score":0.9999444},"labels":[],"label_agreement":null},{"id":"W4385438822","doi":"10.48550/arxiv.2307.15538","title":"Analog programming of CMOS-compatible Al$_2$O$_3$/TiO$_\\textrm{2-x}$ memristor at 4.2 K after metal-insulator transition suppression by cryogenic reforming","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Fonds de recherche du Québec – Nature et technologies; Centre National de la Recherche Scientifique; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; École Centrale de Lyon; Indian National Science Academy","keywords":"Memristor; Materials science; Nanotechnology; Optoelectronics; CMOS; Electrical engineering; Thermal conduction; Engineering; Composite material","score_opus":0.047675508201032286,"score_gpt":0.19087564559657635,"score_spread":0.14320013739554407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385438822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95721656,0.0004996314,0.040104076,0.000015466958,0.00072612957,0.00046996592,0.000115275114,0.0007270978,0.00012581114],"genre_scores_gemma":[0.99834955,0.00023045125,0.00042863138,0.000024418032,0.00007478508,0.000006577935,0.00020440266,0.00010821433,0.00057295256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789417,0.000088732326,0.00047443906,0.00084494177,0.0001594792,0.00053821894],"domain_scores_gemma":[0.998721,0.00009374582,0.00024580827,0.00065955287,0.0000955257,0.00018438356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021006081,0.0005072454,0.0006373672,0.0003280272,0.00018822044,0.000021818318,0.0004270333,0.0003418654,0.000060785827],"category_scores_gemma":[0.000018702556,0.00057865016,0.00040295091,0.00057859125,0.00009663734,0.00029037363,0.0004309876,0.0006874937,0.000039647253],"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":[0.00026817378,0.00006870316,0.0006254979,0.0010507338,0.00031192615,0.0002752295,0.00068829104,0.9110578,0.08404062,0.000094485746,0.00039259144,0.0011259709],"study_design_scores_gemma":[0.002583333,0.00029949658,0.0014866513,0.0021596036,0.0011686984,0.000032674507,0.0007742945,0.6545315,0.32597715,0.002240539,0.0057300166,0.0030160376],"about_ca_topic_score_codex":0.000048205275,"about_ca_topic_score_gemma":0.000048682115,"teacher_disagreement_score":0.25652626,"about_ca_system_score_codex":0.00053547544,"about_ca_system_score_gemma":0.000031531086,"threshold_uncertainty_score":0.9996665},"labels":[],"label_agreement":null},{"id":"W4385453110","doi":"10.1109/tvlsi.2023.3296057","title":"A Resource-Efficient and High-Accuracy CORDIC-Based Digital Implementation of the Hodgkin–Huxley Neuron","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"CORDIC; Field-programmable gate array; Computer science; Multiplication (music); Throughput; Computer hardware; Gate array; Mathematics","score_opus":0.01260447477533292,"score_gpt":0.24695094515054863,"score_spread":0.23434647037521572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385453110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6304323,0.000017066306,0.3676711,0.000045511675,0.0009945892,0.00036266493,0.00017588874,0.0002479106,0.00005295896],"genre_scores_gemma":[0.9996634,0.000008747122,0.000024542165,0.0000271814,0.00005562739,0.000054314085,0.00002503729,0.000034107456,0.00010703629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874574,0.000083445855,0.00041943387,0.00023196709,0.00027754327,0.00024185285],"domain_scores_gemma":[0.9992946,0.00022945125,0.00010258225,0.00025845133,0.00005522575,0.000059737355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017155,0.00019091516,0.00019944139,0.00017188772,0.00024853184,0.00008208267,0.000118929005,0.000067823035,0.000014074594],"category_scores_gemma":[0.000010907862,0.00015430756,0.0001044158,0.00052647607,0.000039982013,0.00019521663,0.0000026677235,0.00024382077,0.000017627743],"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":[0.000037647576,0.0000444801,0.00007138036,0.00010895818,0.00002206739,0.0000026144169,0.0005362848,0.9529819,0.031286735,0.00004441412,0.00019206926,0.014671442],"study_design_scores_gemma":[0.0010391243,0.00016886755,0.0017455699,0.00028959525,0.00004200898,0.000015861082,0.0031670318,0.75543773,0.23638488,0.000012654399,0.001405916,0.0002907725],"about_ca_topic_score_codex":0.000028625724,"about_ca_topic_score_gemma":0.000046569818,"teacher_disagreement_score":0.3692311,"about_ca_system_score_codex":0.00007293751,"about_ca_system_score_gemma":0.000020017053,"threshold_uncertainty_score":0.62924796},"labels":[],"label_agreement":null},{"id":"W4385456901","doi":"10.1002/adfm.202303879","title":"Broadband Optoelectronic Synapse Enables Compact Monolithic Neuromorphic Machine Vision for Information Processing","year":2023,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Neuromorphic engineering; Computer science; Convolutional neural network; Broadband; Photonics; Machine vision; Artificial intelligence; Materials science; Optoelectronics; Computer architecture; Artificial neural network; Telecommunications","score_opus":0.01905387227985376,"score_gpt":0.24364682929486703,"score_spread":0.22459295701501328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385456901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9690908,0.0002311158,0.027812792,0.000096398304,0.0010039607,0.00046002207,0.00009641081,0.0011092057,0.000099253004],"genre_scores_gemma":[0.9986668,0.00008345926,0.00028609994,0.000094557414,0.00017430345,0.000059339058,0.00051973504,0.00003865673,0.000076997916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998887,0.000023881294,0.00036603262,0.00017768802,0.00015301263,0.00039235764],"domain_scores_gemma":[0.99950707,0.00014034075,0.00009168732,0.00012553438,0.00007593468,0.000059408212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020145897,0.00020830055,0.00023567856,0.00016242401,0.00025120602,0.00009050154,0.00008962876,0.00006618653,0.000042647524],"category_scores_gemma":[0.0000791881,0.00019998474,0.00004524584,0.00029434636,0.000022741357,0.0011683335,0.000018538745,0.00011243754,0.00007749772],"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":[0.00016126035,0.0000057869515,0.000005507886,0.00024778518,0.000010464866,0.0000012617135,0.00002695517,0.28430414,0.70772004,0.00025027632,0.00018781525,0.0070786723],"study_design_scores_gemma":[0.0012089867,0.0002357382,0.0023156928,0.00014384287,0.000021428243,0.000040136692,0.000031618743,0.032039847,0.9538628,0.0029632978,0.0067398525,0.00039679918],"about_ca_topic_score_codex":0.0000012575501,"about_ca_topic_score_gemma":6.963823e-7,"teacher_disagreement_score":0.2522643,"about_ca_system_score_codex":0.000068304544,"about_ca_system_score_gemma":0.000022669417,"threshold_uncertainty_score":0.8155141},"labels":[],"label_agreement":null},{"id":"W4385484718","doi":"10.1109/ijcnn54540.2023.10191884","title":"Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","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":"Université de Sherbrooke","funders":"","keywords":"Parallelizable manifold; Computer science; Spiking neural network; Training (meteorology); Artificial intelligence; Algorithm; Artificial neural network","score_opus":0.06851446829276148,"score_gpt":0.25017088781321056,"score_spread":0.1816564195204491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385484718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8029668,0.000026289144,0.18436477,0.00004601592,0.00022097396,0.00012769295,7.4066406e-7,0.0025807181,0.009665997],"genre_scores_gemma":[0.99591595,0.000003226599,0.0034369268,0.00006757925,0.000121317105,0.000010671544,0.0000038598528,0.000044532517,0.00039591364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919355,0.000008399588,0.00013813858,0.00016975653,0.000100833546,0.0003893132],"domain_scores_gemma":[0.9996691,0.00011905484,0.00001648053,0.00011892899,0.000011463055,0.00006497355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073067014,0.00013869649,0.00013391922,0.000072111114,0.00016791925,0.00004155216,0.00009302067,0.00002535091,0.000037478363],"category_scores_gemma":[0.000023510227,0.000120340344,0.000024068384,0.000384371,0.000013272079,0.00017969028,0.000031939628,0.00018489301,0.000053237556],"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":[0.0000027096883,0.000001327897,0.00001621935,0.00001802905,0.0000069087973,0.000029783123,0.00034804337,0.98270434,0.009039409,0.00028314698,0.000090973816,0.0074591297],"study_design_scores_gemma":[0.00028665108,0.00005414489,0.00023929859,0.000076000055,0.000008088541,0.00003949521,0.00066479493,0.9937688,0.0039633913,0.000211622,0.00040189092,0.00028582886],"about_ca_topic_score_codex":0.0000011529786,"about_ca_topic_score_gemma":0.0000058252485,"teacher_disagreement_score":0.19294918,"about_ca_system_score_codex":0.0000133858875,"about_ca_system_score_gemma":0.000008477506,"threshold_uncertainty_score":0.49073368},"labels":[],"label_agreement":null},{"id":"W4385496344","doi":"10.1002/aisy.202300136","title":"A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits","year":2023,"lang":"en","type":"review","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University","funders":"","keywords":"Neuromorphic engineering; Memristor; Graphene; Computer science; Computer architecture; Electronic circuit; Nanotechnology; Fabrication; Electronic engineering; Materials science; Artificial neural network; Artificial intelligence; Electrical engineering; Engineering","score_opus":0.12243179940503678,"score_gpt":0.3364787428254537,"score_spread":0.2140469434204169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385496344","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005373147,0.99053746,0.0058264267,0.0000018154694,0.0012477875,0.0016883991,0.0000799352,0.00045424912,0.00015856275],"genre_scores_gemma":[0.00015794084,0.99917513,0.000057404228,0.000039206087,0.000099340614,0.00018222709,0.00007535431,0.0001662636,0.00004710987],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972404,0.00018001247,0.0014275407,0.00052396837,0.00024115662,0.00038687777],"domain_scores_gemma":[0.9979283,0.0007673515,0.00056564034,0.0004908955,0.00010909611,0.00013871999],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031871675,0.0006409664,0.0025169905,0.00028282133,0.000064587446,0.000019481779,0.000341073,0.00016504434,0.0000063270527],"category_scores_gemma":[0.00015700521,0.00056214916,0.00038019769,0.000883155,0.00006423808,0.00011220164,0.00006558429,0.0004572853,0.000049890958],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.659363e-7,0.000007139515,4.6354072e-7,0.6066885,0.00012294023,0.00002473512,0.000010166806,0.0032414084,0.000008607119,0.0000803799,0.00009037177,0.38972443],"study_design_scores_gemma":[0.000049524235,0.000047340047,2.3228677e-7,0.40052593,0.00033033016,0.000046822337,0.000014240897,0.0007013809,0.00006753063,0.000014003459,0.59778214,0.00042054703],"about_ca_topic_score_codex":0.0000030571807,"about_ca_topic_score_gemma":0.0000015108004,"teacher_disagreement_score":0.5976917,"about_ca_system_score_codex":0.000073126714,"about_ca_system_score_gemma":0.000045731525,"threshold_uncertainty_score":0.999683},"labels":[],"label_agreement":null},{"id":"W4385525205","doi":"10.1109/tetci.2023.3300176","title":"Multiplierless Implementation of Fitz-Hugh Nagumo (FHN) Modeling Using CORDIC Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"CORDIC; Field-programmable gate array; Computer science; Neuromorphic engineering; Virtex; Computer hardware; Hardware description language; Artificial neural network; Computer architecture; Artificial intelligence","score_opus":0.08322204347701156,"score_gpt":0.35325374007595906,"score_spread":0.2700316965989475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385525205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35716242,0.00001784092,0.6421535,0.000016358737,0.00036859026,0.00011110001,0.000007854629,0.00012911577,0.000033228782],"genre_scores_gemma":[0.9782747,0.00003982956,0.021564487,0.000015650672,0.00004425922,0.000014619736,0.000010149684,0.000024180412,0.000012134659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988277,0.000029202343,0.00046320594,0.00022496117,0.00022515685,0.00022980265],"domain_scores_gemma":[0.9995877,0.00014573001,0.000048173068,0.00011801594,0.00006338455,0.000037003458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015336084,0.00015235608,0.00017020183,0.00034501788,0.00012300961,0.000013092296,0.00014307527,0.00005170227,0.000015736337],"category_scores_gemma":[0.0000036527529,0.00018357599,0.00006877778,0.000743398,0.000029780633,0.00015947927,0.0000031878976,0.00026020422,0.000006207958],"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":[0.0000060490343,0.000021581684,0.000029086212,0.00007405231,0.000015035695,0.0000026057473,0.0005889049,0.9609174,0.0008336697,0.00027783916,0.0000013657738,0.037232403],"study_design_scores_gemma":[0.00011094689,0.000016935288,0.00005054488,0.00006204743,0.00000779557,0.000004289355,0.000796077,0.97725636,0.019134503,0.002397607,0.000004657101,0.00015821964],"about_ca_topic_score_codex":0.000035836707,"about_ca_topic_score_gemma":0.000015305905,"teacher_disagreement_score":0.6211123,"about_ca_system_score_codex":0.00009084598,"about_ca_system_score_gemma":0.000023594905,"threshold_uncertainty_score":0.74860114},"labels":[],"label_agreement":null},{"id":"W4385625232","doi":"10.1101/2023.08.07.552264","title":"Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Board of the Swiss Federal Institutes of Technology; École Polytechnique Fédérale de Lausanne","keywords":"Neuroscience; Synaptic plasticity; Plasticity; Metaplasticity; Homosynaptic plasticity; Neuroplasticity; Connectome; Homeostatic plasticity; Computer science; Synaptic scaling; Synapse; Excitatory postsynaptic potential; Biology; Functional connectivity; Inhibitory postsynaptic potential; Synaptic augmentation; Physics","score_opus":0.021454469409759085,"score_gpt":0.21994389399160127,"score_spread":0.19848942458184218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385625232","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9174571,0.00009132985,0.080242515,0.000052500734,0.0013261467,0.0004085962,0.000044973087,0.0003749798,0.0000018279455],"genre_scores_gemma":[0.9970694,0.000014421343,0.002469095,0.000047414782,0.00027866842,0.000032845237,2.2642529e-7,0.00008656543,0.0000013306133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984421,0.00015420848,0.0004045419,0.0004570795,0.00016618734,0.00037589626],"domain_scores_gemma":[0.9985741,0.0005817759,0.00015561013,0.00044177403,0.0001079753,0.00013879722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024861202,0.0003408169,0.0004275249,0.00012422513,0.0002189886,0.00005324836,0.00023565427,0.00026574582,0.000006520303],"category_scores_gemma":[0.00044488866,0.00028660742,0.00004664836,0.00044930898,0.00008545366,0.00009602834,0.00045448865,0.0012049723,0.0000033014508],"study_design_candidate":"observational","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":[0.00012480932,0.000010830229,0.014286233,0.0004444908,0.00015548011,0.000019051107,0.000031710326,0.6208377,0.36322984,0.0007898524,0.00006792933,0.0000020981688],"study_design_scores_gemma":[0.00065793574,0.00023145108,0.76859564,0.0019147795,0.0002601481,5.1029946e-7,0.000026875021,0.060104672,0.1667813,0.000050057533,0.0002580103,0.0011186],"about_ca_topic_score_codex":0.000006576585,"about_ca_topic_score_gemma":0.000040280906,"teacher_disagreement_score":0.7543094,"about_ca_system_score_codex":0.000114149545,"about_ca_system_score_gemma":0.00005761076,"threshold_uncertainty_score":0.99995863},"labels":[],"label_agreement":null},{"id":"W4386227043","doi":"10.1145/3589737.3605968","title":"Burstprop for Learning in Spiking Neuromorphic Hardware","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Vector Institute","keywords":"Neuromorphic engineering; MNIST database; Spiking neural network; Computer science; Spike (software development); Adaptation (eye); Artificial neural network; Computer architecture; Artificial intelligence; Efficient energy use; Energy (signal processing); Spike-timing-dependent plasticity; Machine learning; Computer engineering; Engineering; Synaptic plasticity","score_opus":0.04887811495617326,"score_gpt":0.25577125332393336,"score_spread":0.2068931383677601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386227043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97730964,0.0000365579,0.018944424,0.000074371754,0.0003047894,0.00015671246,6.3996686e-7,0.001612734,0.001560102],"genre_scores_gemma":[0.99847,0.000012436446,0.00052945205,0.000031702813,0.000076772965,0.000014950074,0.0000060234606,0.000028061595,0.0008306096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999502,0.000007247764,0.00010556155,0.00011348783,0.000043505643,0.00022820431],"domain_scores_gemma":[0.9997971,0.00010362807,0.000008451462,0.000058016503,0.000008188635,0.000024599914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000907295,0.00007353312,0.00008768207,0.00008778993,0.00005320718,0.000012084973,0.000057737914,0.000024722012,0.000010323518],"category_scores_gemma":[0.000063180334,0.00007508843,0.000027515489,0.00028310268,0.0000048590855,0.00007408466,0.000022580456,0.0001542762,0.000035387788],"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":[0.000004623111,0.0000018295282,0.00077541126,0.00008311129,0.0000024406468,0.000031687625,0.00010248521,0.93562603,0.041754298,0.0002135457,0.00019067677,0.021213861],"study_design_scores_gemma":[0.0004927583,0.0000635917,0.003286124,0.000081487735,0.0000034280965,0.000009271643,0.00016837596,0.93065447,0.04823763,0.00075739436,0.015965959,0.0002794883],"about_ca_topic_score_codex":7.464323e-7,"about_ca_topic_score_gemma":0.0000032023756,"teacher_disagreement_score":0.021160323,"about_ca_system_score_codex":0.000013290142,"about_ca_system_score_gemma":0.0000026332225,"threshold_uncertainty_score":0.30620173},"labels":[],"label_agreement":null},{"id":"W4386307567","doi":"10.1557/s43579-023-00437-z","title":"Metal oxide ion-gated transistors: A perspective on in operando characterizations and emerging Li-ion-based applications","year":2023,"lang":"en","type":"article","venue":"MRS Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Global Affairs Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Materials science; Oxide; Ionic bonding; Nanoscopic scale; Ionic conductivity; Nanotechnology; Ion; Transistor; Thin-film transistor; Metal; Optoelectronics; Voltage; Physical chemistry; Electrode; Electrical engineering; Metallurgy","score_opus":0.025458707720685137,"score_gpt":0.2840731318894397,"score_spread":0.2586144241687546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386307567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9491682,0.0008555101,0.0362231,0.00715726,0.00012404466,0.0012276368,0.00008814239,0.0017334525,0.003422676],"genre_scores_gemma":[0.9978287,0.0003642175,0.0012270382,0.00009137338,0.000019877387,0.00025254674,0.0001412896,0.000031153904,0.000043778648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928266,0.00007241,0.00022491063,0.00017162222,0.000072227536,0.00017615543],"domain_scores_gemma":[0.9989445,0.00031566314,0.000028897266,0.00060728256,0.000048107417,0.000055522563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119790144,0.00013056245,0.00015214189,0.0002873556,0.00036373764,0.000023830004,0.0002814151,0.00004335788,0.0000074180825],"category_scores_gemma":[0.00003926173,0.00014649013,0.00004326616,0.0009800959,0.000067714616,0.00012591301,0.000047819092,0.00027883105,0.000027633621],"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":[0.00003903645,0.00030682868,0.0005333969,0.00010126082,0.00010725899,0.0000071326385,0.0071197334,0.5850914,0.36343333,0.03347186,0.000108392596,0.009680376],"study_design_scores_gemma":[0.0028081702,0.000081617756,0.025268268,0.00043046023,0.00009223383,0.000010661787,0.0044671847,0.891423,0.040774446,0.0020463176,0.031460658,0.0011369712],"about_ca_topic_score_codex":0.000020217063,"about_ca_topic_score_gemma":0.00006570201,"teacher_disagreement_score":0.32265887,"about_ca_system_score_codex":0.00010059481,"about_ca_system_score_gemma":0.000018286833,"threshold_uncertainty_score":0.59736943},"labels":[],"label_agreement":null},{"id":"W4386510837","doi":"10.1145/3605731.3608932","title":"Polar Representation of 2D Image Using Complex Exponential Spiking Neuron Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Pixel; Representation (politics); Encoding (memory); Artificial intelligence; Exponential function; Image (mathematics); Polar coordinate system; Cartesian coordinate system; Algorithm; Spiking neural network; Pattern recognition (psychology); Computer vision; Artificial neural network; Mathematics; Geometry","score_opus":0.07124379986675287,"score_gpt":0.3086236140329124,"score_spread":0.2373798141661595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386510837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8873703,0.00001836383,0.11092286,0.000007749865,0.00037696294,0.00007358164,0.0000011618506,0.0004335142,0.0007955428],"genre_scores_gemma":[0.989797,0.000008101633,0.009859378,0.000015603493,0.00025726247,7.1236974e-7,0.000012306855,0.000023496701,0.000026120777],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993867,0.000022324772,0.0001894748,0.00011705625,0.00007892934,0.00020550178],"domain_scores_gemma":[0.9997368,0.00006281864,0.00003323791,0.000123448,0.00001586189,0.000027859644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006558568,0.00008068733,0.00012166001,0.00005446448,0.000070861635,0.000012322802,0.00006579034,0.000021862963,0.000033629043],"category_scores_gemma":[0.000015994132,0.00008756886,0.000045348504,0.0004053482,0.000014917817,0.00012700725,0.00005338686,0.000078345474,0.000010961102],"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":[0.0000033465524,0.0000013072053,0.00018314418,0.000020589083,0.0000039295246,0.000007619939,0.000032652028,0.46297833,0.5352236,0.00004459542,0.00012647534,0.0013744335],"study_design_scores_gemma":[0.00017078701,0.000013582301,0.0059961057,0.000030634816,0.000010140359,0.000007811332,0.000073959876,0.8167184,0.17637788,0.00030727815,0.00016620848,0.00012721178],"about_ca_topic_score_codex":0.000012301116,"about_ca_topic_score_gemma":0.000002457904,"teacher_disagreement_score":0.3588457,"about_ca_system_score_codex":0.000010983554,"about_ca_system_score_gemma":0.0000022747338,"threshold_uncertainty_score":0.35709542},"labels":[],"label_agreement":null},{"id":"W4386595136","doi":"10.22541/au.169446657.78476963/v1","title":"MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Successive approximation ADC; Electronic engineering; Comparator; Delta-sigma modulation; Computer science; Effective number of bits; Memristor; Integrator; Artificial neural network; Engineering; CMOS; Artificial intelligence; Voltage; Electrical engineering","score_opus":0.08061505663793028,"score_gpt":0.29342743901340274,"score_spread":0.21281238237547245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386595136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14679614,0.00045441222,0.8422939,0.00011517166,0.003550574,0.0015288844,0.00012886636,0.0040456974,0.0010863177],"genre_scores_gemma":[0.99432206,0.000034226832,0.0033526549,0.00010202188,0.000927229,0.00039195982,0.00007041501,0.00022546804,0.00057394506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976401,0.00004614913,0.0006139229,0.00073736825,0.00022861597,0.000733876],"domain_scores_gemma":[0.9980367,0.0010547076,0.00016989447,0.0004370128,0.00013609673,0.00016560029],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025745344,0.0005782218,0.000674779,0.00020430847,0.00022793404,0.00009480448,0.00044681746,0.00034589725,0.00004660768],"category_scores_gemma":[0.00040864974,0.0005920966,0.00036964574,0.00021398257,0.00003374864,0.00012030596,0.00046983093,0.0010511773,0.000029203276],"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":[0.00002079781,0.000008273863,0.0000191382,0.00081078114,0.0001840029,0.00006182733,0.0002923523,0.9406863,0.015904449,0.00012783785,0.0007968022,0.041087423],"study_design_scores_gemma":[0.0006290482,0.000105940904,0.00018993525,0.00051140337,0.00015981677,0.00003071286,0.0003052687,0.7922583,0.18903187,0.014530813,0.0008268159,0.0014200368],"about_ca_topic_score_codex":0.000014408088,"about_ca_topic_score_gemma":0.00003434279,"teacher_disagreement_score":0.84752595,"about_ca_system_score_codex":0.00015624975,"about_ca_system_score_gemma":0.0000369645,"threshold_uncertainty_score":0.99965304},"labels":[],"label_agreement":null},{"id":"W4386617144","doi":"10.22541/au.169446658.81099759/v1","title":"STDG: Fast and Lightweight SNN Training Technique Using Spike Temporal Locality","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Spiking neural network; Spike (software development); MNIST database; Computer science; Asynchronous communication; Neuromorphic engineering; Locality; Efficient energy use; Artificial neural network; Artificial intelligence; Energy (signal processing); Differentiable function; Pattern recognition (psychology); Machine learning; Algorithm; Mathematics","score_opus":0.10174650954394872,"score_gpt":0.29918151958378153,"score_spread":0.19743501003983283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386617144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27302834,0.00020221606,0.72115195,0.000039269813,0.0008807259,0.00052921154,0.000020831056,0.002540058,0.0016074184],"genre_scores_gemma":[0.95685863,0.000037689246,0.04230783,0.000027709446,0.00036115292,0.000028944598,0.000025602169,0.00011266925,0.0002397963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984593,0.000037150417,0.0004248029,0.0005182843,0.00015421625,0.000406241],"domain_scores_gemma":[0.9993254,0.0000795654,0.000075045675,0.00035938408,0.000029736275,0.00013085804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030667274,0.00040868405,0.00048030334,0.00015168678,0.00012228593,0.00007207304,0.0002012934,0.00033701173,0.000019131121],"category_scores_gemma":[0.00002444203,0.00040258246,0.00009830426,0.00016283958,0.000056731653,0.00010926916,0.0005472114,0.0010156701,0.00000713727],"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":[0.000025832003,0.00003090213,0.001095194,0.0032592574,0.00020446721,0.00052294997,0.0017305287,0.86080897,0.09104691,0.0008081524,0.0003379275,0.040128894],"study_design_scores_gemma":[0.00044406243,0.00006458996,0.00057459023,0.001993163,0.000099871424,0.00021934976,0.0006707685,0.7637341,0.21195388,0.01583385,0.002043977,0.0023678085],"about_ca_topic_score_codex":0.000029968933,"about_ca_topic_score_gemma":0.000016311817,"teacher_disagreement_score":0.68383026,"about_ca_system_score_codex":0.00008924961,"about_ca_system_score_gemma":0.00003572654,"threshold_uncertainty_score":0.9998426},"labels":[],"label_agreement":null},{"id":"W4386701110","doi":"10.1016/j.sse.2023.108779","title":"A tunable and versatile 28 nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses","year":2023,"lang":"en","type":"article","venue":"Solid-State Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Agence Nationale de la Recherche","keywords":"Silicon on insulator; Crossbar switch; Spin-transfer torque; Electrical engineering; Computer science; CMOS; Capacitance; Spice; Electronic engineering; Magnetoresistive random-access memory; Materials science; Engineering; Optoelectronics; Computer hardware; Physics; Silicon; Random access memory","score_opus":0.013545186228376362,"score_gpt":0.2519372507036726,"score_spread":0.23839206447529626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386701110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97824824,0.00072980876,0.019418452,0.000035372123,0.00012768139,0.00035493297,0.000044547356,0.0006455747,0.00039539702],"genre_scores_gemma":[0.99850595,0.0003537665,0.00020269092,0.00006087475,0.000043134736,0.000041091218,0.000041888547,0.0000697645,0.00068083615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983656,0.000016252015,0.00022108498,0.00034263547,0.000139836,0.0009145952],"domain_scores_gemma":[0.9993145,0.00023177074,0.000050731902,0.0002259518,0.000062837076,0.00011419932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015199423,0.00026508368,0.00028800932,0.000108260574,0.00022412503,0.00007788166,0.0001541365,0.000072380244,0.000011219486],"category_scores_gemma":[0.00005533647,0.00025457458,0.00005092614,0.0003739003,0.00006276826,0.0002446851,0.000051464707,0.0003070334,0.000024123708],"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":[0.00025985864,0.00005763783,0.0006606943,0.000802526,0.00035397534,0.00016900027,0.0034058243,0.91585463,0.057778623,0.0027055896,0.0020232555,0.015928403],"study_design_scores_gemma":[0.0064877523,0.0040078503,0.0026044797,0.00058873993,0.00021752826,0.00019808243,0.0010660664,0.489673,0.36195463,0.037486352,0.09197882,0.0037367344],"about_ca_topic_score_codex":0.000003164895,"about_ca_topic_score_gemma":0.00003845452,"teacher_disagreement_score":0.42618164,"about_ca_system_score_codex":0.000104387254,"about_ca_system_score_gemma":0.00008695149,"threshold_uncertainty_score":0.99999064},"labels":[],"label_agreement":null},{"id":"W4386743108","doi":"10.3390/brainsci13091316","title":"From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?","year":2023,"lang":"en","type":"review","venue":"Brain Sciences","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Spiking neural network; Computer science; Artificial intelligence; Computational neuroscience; Robotics; Cognitive robotics; Artificial neural network; Cognition; Embodied cognition; Focus (optics); Human–computer interaction; Robot; Neuroscience; Psychology","score_opus":0.32701407146230543,"score_gpt":0.35412985575818473,"score_spread":0.027115784295879297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386743108","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017834656,0.9742596,0.011415246,0.0005794072,0.004967322,0.002755571,0.00038928667,0.002240436,0.0016096506],"genre_scores_gemma":[0.011951324,0.98273945,0.0015082967,0.00060444896,0.0018244759,0.00035953356,0.00023923264,0.00012993324,0.00064329675],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99741906,0.00019624324,0.0006078989,0.0008039362,0.00040339358,0.00056944694],"domain_scores_gemma":[0.99641323,0.0028242045,0.0001581091,0.00035839845,0.00004161549,0.00020443572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009180176,0.00049005455,0.0012722616,0.00023239804,0.00030364475,0.000310929,0.00080766046,0.0002411824,0.000012688652],"category_scores_gemma":[0.00067982636,0.00030928312,0.00022551142,0.0011647217,0.00022795185,0.0003961351,0.00026012803,0.00043116338,0.00011601912],"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":[0.000010666156,0.000041757685,0.000002982349,0.017004477,0.00019007985,0.00024468373,0.00077191147,0.42413706,0.00009578272,0.0030244382,0.0048474437,0.54962873],"study_design_scores_gemma":[0.00059323217,0.0006924068,0.000018851848,0.041757885,0.0003728643,0.00022143443,0.0014953867,0.17764035,0.000056034864,0.02797233,0.7443903,0.00478894],"about_ca_topic_score_codex":0.000011872767,"about_ca_topic_score_gemma":0.000017822278,"teacher_disagreement_score":0.73954284,"about_ca_system_score_codex":0.00006781933,"about_ca_system_score_gemma":0.00008250445,"threshold_uncertainty_score":0.9999359},"labels":[],"label_agreement":null},{"id":"W4386854053","doi":"10.1149/ma2023-01331854mtgabs","title":"(Invited) Potential of Silicon Oxide Films for Low-Cost and High-Performance Resistive Switching Devices","year":2023,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Association of Canadian Archivists","funders":"","keywords":"Oxide; Materials science; Silicon; Silicon oxide; Optoelectronics; Sputtering; Substrate (aquarium); Nanotechnology; Electric field; Thin film; Engineering physics; Metallurgy; Physics","score_opus":0.0131498021014908,"score_gpt":0.23401250292955347,"score_spread":0.22086270082806267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386854053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985878,0.000081708466,0.00013292588,0.000046468107,0.00027307565,0.00019349219,0.000010958674,0.0003107592,0.00036281632],"genre_scores_gemma":[0.9980106,0.00003377257,0.0017527902,0.000038481718,0.00008880857,0.000011142101,0.0000140082,0.00003457366,0.000015829113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999049,0.000010472311,0.0003172896,0.00019880339,0.000110948706,0.0003134981],"domain_scores_gemma":[0.99931556,0.00035949802,0.00011143579,0.000111423775,0.000038525774,0.00006354559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031550875,0.000151412,0.00019421398,0.00009315923,0.00015612676,0.000023106175,0.000094453404,0.000064129046,8.7804455e-7],"category_scores_gemma":[0.00018565066,0.00015739111,0.000033663375,0.00016240797,0.000019217774,0.0001766348,0.00004146946,0.00016530916,0.00000522393],"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":[0.00001808011,0.0000032993528,0.00022054136,0.00030972282,0.000009645333,0.000005455837,0.00007890219,0.57770544,0.4200054,7.3845484e-7,0.000075525735,0.0015672066],"study_design_scores_gemma":[0.00029626794,0.000023554574,0.06555657,0.00045396062,0.000016052567,0.0000046002992,0.00013293672,0.12994175,0.8032591,0.00005044933,0.00008699472,0.00017776049],"about_ca_topic_score_codex":0.000012278681,"about_ca_topic_score_gemma":0.000004595385,"teacher_disagreement_score":0.4477637,"about_ca_system_score_codex":0.000013693953,"about_ca_system_score_gemma":0.0000046819455,"threshold_uncertainty_score":0.64182234},"labels":[],"label_agreement":null},{"id":"W4386896055","doi":"10.20944/preprints202309.1149.v1","title":"Critical Review of Neural Network Generations and Models Design","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Interpretability; Artificial intelligence; Artificial neural network; Overfitting; Machine learning; Deep learning; Recurrent neural network; Nervous system network models; Convolutional neural network; Scalability; Types of artificial neural networks","score_opus":0.35302822704953507,"score_gpt":0.37955362484045546,"score_spread":0.02652539779092039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386896055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35958052,0.0599615,0.5690592,0.0009282637,0.00371382,0.0022034652,0.000047843932,0.0022221967,0.0022831855],"genre_scores_gemma":[0.9729664,0.019441398,0.0067703356,0.00016338234,0.00036864058,0.000100084384,0.000019499692,0.0000747275,0.00009554753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984637,0.00015512876,0.0005026311,0.00044154123,0.00015065068,0.00028637904],"domain_scores_gemma":[0.99870145,0.00047705334,0.00006791894,0.0005614965,0.00009168158,0.000100420206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063865876,0.0002517084,0.0004556055,0.000046995145,0.000076726676,0.000009826933,0.00023640295,0.00014192,0.00004459209],"category_scores_gemma":[0.0004737858,0.0002749171,0.000106839754,0.00012729873,0.00006066804,0.00011126995,0.0007470873,0.00064637914,0.00003990876],"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":[0.0000045922707,0.00000650486,0.00034910964,0.0047638514,0.000029716319,0.000008760047,0.000056788536,0.99185616,0.0017769731,0.0005348767,0.00016372475,0.00044893043],"study_design_scores_gemma":[0.00011955903,0.000015128593,0.0026954955,0.008699223,0.00014299473,0.00002158912,0.000008419566,0.9429319,0.015989438,0.028605642,0.00018756148,0.0005830418],"about_ca_topic_score_codex":0.0000026751713,"about_ca_topic_score_gemma":7.447855e-7,"teacher_disagreement_score":0.61338586,"about_ca_system_score_codex":0.00002737055,"about_ca_system_score_gemma":0.000024406558,"threshold_uncertainty_score":0.9999703},"labels":[],"label_agreement":null},{"id":"W4386931170","doi":"10.1002/aelm.202300467","title":"Artificial Neurons Using Ag−In−Zn−S/Sericin Peptide‐Based Threshold Switching Memristors for Spiking Neural Networks","year":2023,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"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":"China Postdoctoral Science Foundation; Ministry of Science and Technology","keywords":"Neuromorphic engineering; Spiking neural network; MNIST database; Emulation; Memristor; Materials science; Artificial neural network; Sericin; Threshold voltage; Artificial neuron; Biological system; Computer science; Transistor; Voltage; Electronic engineering; Nanotechnology; Electrical engineering; Artificial intelligence; SILK; Engineering","score_opus":0.027548914299627567,"score_gpt":0.27985227885618597,"score_spread":0.2523033645565584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386931170","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9431239,0.0001843724,0.053509176,0.00003957409,0.0015671073,0.0006441302,0.000008568875,0.00090039894,0.00002282207],"genre_scores_gemma":[0.9985008,0.00003647689,0.0005834256,0.000110205016,0.00046926524,0.000086068154,0.000045348028,0.00015124223,0.000017174301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971408,0.000054271288,0.00067194033,0.0004948237,0.00015484571,0.001483281],"domain_scores_gemma":[0.9992076,0.00023798078,0.00013041694,0.0003117516,0.000026893256,0.00008532177],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042355777,0.0004001087,0.0005208296,0.00024277494,0.00026202248,0.00007215788,0.0002487413,0.000102042744,0.000017280092],"category_scores_gemma":[0.000066268825,0.00044478843,0.000111779846,0.0006291914,0.000022475497,0.00035626144,0.000055456607,0.00034418437,0.0000056162194],"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":[0.00006678889,0.000005219834,0.000013527051,0.00005424469,0.0000059506024,0.000011818797,0.000022706708,0.54420143,0.45291653,0.0001671946,0.000008374018,0.0025261994],"study_design_scores_gemma":[0.0004131231,0.00009364305,0.00009987878,0.0000801357,0.000019525247,0.000008682742,0.000024689434,0.6194245,0.3776944,0.0014054183,0.0003135309,0.00042243232],"about_ca_topic_score_codex":0.0000077752475,"about_ca_topic_score_gemma":0.000047383386,"teacher_disagreement_score":0.07522312,"about_ca_system_score_codex":0.00028234205,"about_ca_system_score_gemma":0.00003679231,"threshold_uncertainty_score":0.9998004},"labels":[],"label_agreement":null},{"id":"W4387031818","doi":"","title":"VO2 stabilization on Si for memristor in neuromorphic computing applications","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Neuromorphic engineering; Memristor; Computer science; Computer architecture; Artificial neural network; Electronic engineering; Artificial intelligence; Engineering","score_opus":0.04413001951700951,"score_gpt":0.24948313244161546,"score_spread":0.20535311292460595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387031818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12972496,0.00027960562,0.86279505,0.0013335816,0.0005158313,0.0014244649,0.00008073106,0.0011603781,0.0026854174],"genre_scores_gemma":[0.9795408,0.00019024735,0.018170152,0.00006133334,0.00007050665,0.00028557825,0.0006316358,0.000128278,0.0009214447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754006,0.00079390104,0.00052213867,0.00061103754,0.00020027229,0.00033261967],"domain_scores_gemma":[0.9961288,0.0020462952,0.00020889642,0.0010285049,0.0004947682,0.000092708455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018831937,0.00029217234,0.00032064132,0.00025015796,0.00023837341,0.00011456103,0.0006064976,0.00019288472,0.000007329683],"category_scores_gemma":[0.00071660127,0.00036018775,0.0001268302,0.0004471432,0.0000611321,0.00006975964,0.00038170224,0.0006289625,0.000023340632],"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":[0.00003183516,0.0005639909,0.0009742574,0.002263994,0.00008281853,0.000009980634,0.0074161594,0.7868796,0.02330489,0.054692786,0.0012866036,0.12249311],"study_design_scores_gemma":[0.0006741167,0.000001017382,0.0019221616,0.0021752296,0.000030455867,0.0000033669166,0.0000915102,0.9147709,0.05910517,0.008525863,0.01196211,0.00073806656],"about_ca_topic_score_codex":0.000035768713,"about_ca_topic_score_gemma":0.0003731695,"teacher_disagreement_score":0.84981585,"about_ca_system_score_codex":0.00017958318,"about_ca_system_score_gemma":0.000058418267,"threshold_uncertainty_score":0.999885},"labels":[],"label_agreement":null},{"id":"W4387244229","doi":"10.1101/2023.10.01.560360","title":"Inferring plasticity rules from single-neuron spike trains using deep learning methods","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Learning rule; Classifier (UML); Machine learning; Convolutional neural network; Synaptic plasticity; Spike-timing-dependent plasticity; Transformer; Deep learning; Artificial neural network; Pattern recognition (psychology)","score_opus":0.05328873694300106,"score_gpt":0.274855518923678,"score_spread":0.22156678198067692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387244229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6654947,0.0005049489,0.3284521,0.000007757213,0.002244406,0.00029435128,0.000071509785,0.002922888,0.000007340487],"genre_scores_gemma":[0.873908,0.00011878256,0.124364845,0.000035132663,0.0010604837,0.000037679252,0.0000010523445,0.0004719437,0.000002106302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960624,0.00034086316,0.0008861912,0.0012683719,0.00039174635,0.0010504571],"domain_scores_gemma":[0.99760395,0.0007286883,0.00035738395,0.0007735411,0.00017310059,0.0003633532],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00055658905,0.00100271,0.0010001302,0.00043804283,0.00038856617,0.0003111249,0.0006670609,0.00066086213,0.00002603614],"category_scores_gemma":[0.0008624003,0.0012303527,0.00027333712,0.0005567882,0.00010419251,0.00031517824,0.0008204962,0.0025559769,0.000067315945],"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":[0.000008957917,0.000021551377,0.0004898471,0.00027806152,0.00010304277,0.00009089991,0.00002663877,0.33412242,0.6647814,0.000022179456,0.0000028026584,0.000052188974],"study_design_scores_gemma":[0.0003711659,0.0000425172,0.016357208,0.0009612,0.00019895815,6.007168e-8,0.00001035313,0.38206068,0.59777045,0.000019550582,0.00071402214,0.0014938139],"about_ca_topic_score_codex":0.00004429344,"about_ca_topic_score_gemma":0.0000050628328,"teacher_disagreement_score":0.20841327,"about_ca_system_score_codex":0.0005052907,"about_ca_system_score_gemma":0.00009890665,"threshold_uncertainty_score":0.9997452},"labels":[],"label_agreement":null},{"id":"W4387322640","doi":"10.48550/arxiv.2310.01022","title":"Subtractor-Based CNN Inference Accelerator","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Multiplication (music); Rounding; Adder; Sorting; Inference; Subtractor; Subtraction; Reduction (mathematics); Power (physics); Convolution (computer science); Preprocessor; Algorithm; Computer engineering; Parallel computing; Artificial intelligence; Electronic engineering; Deep learning; Arithmetic; Mathematics; Artificial neural network","score_opus":0.18169025507409114,"score_gpt":0.21266071366745534,"score_spread":0.030970458593364197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387322640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88564265,0.000031900316,0.11063368,0.000014844389,0.0010793012,0.00017967664,0.000026745302,0.0016372212,0.00075399585],"genre_scores_gemma":[0.998909,0.00008092758,0.00016039524,0.000043790882,0.00014081218,8.87647e-7,0.000034976154,0.00006238572,0.0005668243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988324,0.000035437963,0.00016761685,0.0005522159,0.00005663984,0.00035565754],"domain_scores_gemma":[0.99897134,0.00021110744,0.0000749183,0.0005395954,0.000059881506,0.00014316692],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007810999,0.00032624556,0.0002879026,0.00018377122,0.00009865559,0.000044247397,0.00051847636,0.00026204303,0.000056223078],"category_scores_gemma":[0.00004163611,0.0004090665,0.00014971205,0.00037535458,0.000046539797,0.00015093417,0.00031708006,0.00086292974,0.00024744475],"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":[0.000012685439,0.000011860449,0.00097455934,0.00017422457,0.000031066735,0.00026712054,0.000025175252,0.99623346,0.0009905004,0.00095396605,0.00014783433,0.00017751791],"study_design_scores_gemma":[0.00052806985,0.00003704832,0.0030443526,0.000308619,0.00007992993,0.0000011289886,0.0000554601,0.97162175,0.014321679,0.00788853,0.0010790532,0.0010343926],"about_ca_topic_score_codex":0.000011923919,"about_ca_topic_score_gemma":0.000014932298,"teacher_disagreement_score":0.113266364,"about_ca_system_score_codex":0.00015623463,"about_ca_system_score_gemma":0.000067108194,"threshold_uncertainty_score":0.99983615},"labels":[],"label_agreement":null},{"id":"W4387411157","doi":"10.1109/esscirc59616.2023.10268776","title":"A 22nm 56TOPS/W 6/8-bit Linearly-scalable R-2R Multiply-and-Accumulate Architecture with 2.2ns Latency","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scalability; Latency (audio); Resistor; CMOS; Computer science; Binary number; Electronic engineering; Electrical engineering; Engineering; Mathematics; Arithmetic; Voltage; Telecommunications","score_opus":0.011860017623590454,"score_gpt":0.21310876422169134,"score_spread":0.20124874659810088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387411157","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9761277,0.00010107844,0.018092126,0.00014107274,0.00014748152,0.00019422609,0.000004593886,0.0019012104,0.003290545],"genre_scores_gemma":[0.99089915,0.000050520608,0.005754283,0.00009188503,0.00012336629,0.000011076156,0.000009223428,0.000059688504,0.0030008077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990181,0.000012818852,0.00016930039,0.00025955783,0.0001199097,0.0004203188],"domain_scores_gemma":[0.99952483,0.0001100946,0.000019180761,0.00020410046,0.000021907827,0.00011989904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075924116,0.00021348553,0.00019960421,0.00009805901,0.00011544229,0.000038267957,0.000105924664,0.000068084715,0.00004824448],"category_scores_gemma":[0.000020512793,0.000163187,0.000038292364,0.00043859583,0.0000309948,0.00013227703,0.000070921815,0.0003042792,0.00011101597],"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":[0.000038877417,0.00001130564,0.0014165677,0.00012617391,0.000048042253,0.00012913006,0.00049730117,0.9333922,0.036894754,0.00014695906,0.0002642366,0.027034432],"study_design_scores_gemma":[0.0020459297,0.00029188822,0.012047645,0.0002490008,0.000046524205,0.00017404683,0.00022301055,0.8838593,0.08419186,0.001288627,0.014301493,0.0012806959],"about_ca_topic_score_codex":0.000010516066,"about_ca_topic_score_gemma":0.00002090511,"teacher_disagreement_score":0.049532942,"about_ca_system_score_codex":0.000013160544,"about_ca_system_score_gemma":0.000006464546,"threshold_uncertainty_score":0.66545725},"labels":[],"label_agreement":null},{"id":"W4387453012","doi":"10.5772/intechopen.113050","title":"Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing","year":2023,"lang":"en","type":"book-chapter","venue":"Artificial intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neuromorphic engineering; MNIST database; Spiking neural network; Computer science; Robustness (evolution); Computer architecture; Von Neumann architecture; Encoder; Multiplexing; Implementation; Computer engineering; Encoding (memory); Spike (software development); Artificial intelligence; Artificial neural network; Computer hardware; Programming language","score_opus":0.1831538013492192,"score_gpt":0.3262542713948658,"score_spread":0.1431004700456466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387453012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025515975,0.00093950366,0.92058414,0.00047265156,0.010012088,0.0028242436,0.00034556453,0.00397031,0.035335533],"genre_scores_gemma":[0.9846056,0.00020799233,0.0024462275,0.00016138483,0.0013761899,0.000023274475,0.00010513435,0.0002996221,0.010774571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864894,0.0000068873846,0.00049942883,0.0003731171,0.00012322962,0.00034838816],"domain_scores_gemma":[0.9991612,0.00043793197,0.00009840287,0.00016428964,0.000058434594,0.000079744765],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013925586,0.00030005153,0.0002793779,0.00013870206,0.00032815433,0.00008576909,0.00015699513,0.000102803126,0.000038970167],"category_scores_gemma":[0.00006262238,0.00035393148,0.000101102545,0.00006341877,0.00007176896,0.00013407349,0.00010186493,0.00035281386,0.000052129115],"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":[0.000018057626,0.000005526143,0.000007129908,0.00047271693,0.00007602584,0.00008733517,0.000785723,0.20570321,0.013186198,0.289863,0.00027763358,0.48951742],"study_design_scores_gemma":[0.0000676065,0.00018663886,0.000016917762,0.0006603194,0.00013621032,0.00006394022,0.0003576046,0.7743516,0.03937637,0.17249535,0.010919802,0.0013676087],"about_ca_topic_score_codex":0.0000024195974,"about_ca_topic_score_gemma":0.000034536733,"teacher_disagreement_score":0.95908964,"about_ca_system_score_codex":0.000033761637,"about_ca_system_score_gemma":0.000009803234,"threshold_uncertainty_score":0.9998913},"labels":[],"label_agreement":null},{"id":"W4387459935","doi":"10.1039/bk9781839169946-00680","title":"Memristive Devices for Neuromorphic and Deep Learning Applications","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Neuromorphic engineering; Memristor; Computer architecture; Implementation; Computer science; Computer engineering; Electronic engineering; Artificial intelligence; Artificial neural network; Engineering","score_opus":0.03798666797974482,"score_gpt":0.23841131701995247,"score_spread":0.20042464904020765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387459935","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003299125,0.0025782832,0.29567957,0.00007294413,0.00048648802,0.0018932375,0.000052037583,0.004009409,0.6948981],"genre_scores_gemma":[0.040805504,0.0018573444,0.0046908376,0.00015626661,0.0009177777,0.00029232082,0.00024723902,0.00048309358,0.9505496],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994744,0.000002175138,0.00013824114,0.00020592881,0.00004919044,0.00013007525],"domain_scores_gemma":[0.9995158,0.00028775667,0.000037834587,0.00008429427,0.000028618797,0.00004572562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003701534,0.00017556037,0.00017627834,0.00006017735,0.0001225283,0.000017552567,0.00006218073,0.00009373818,0.000020734062],"category_scores_gemma":[0.000009173104,0.00018166208,0.00003878134,0.000019099201,0.000022133467,0.00003773916,0.00003733285,0.00026567723,0.00005226435],"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":[0.000023935709,0.0000059873214,0.000015137222,0.0022201254,0.00031549763,0.000037311092,0.00023122835,0.16258585,0.0026609385,0.3675117,0.0006171916,0.4637751],"study_design_scores_gemma":[0.0002487011,0.00008424883,0.000044580804,0.00014112414,0.00011842496,0.000023972547,0.000050270526,0.058510713,0.00050322263,0.021780672,0.9177303,0.0007637725],"about_ca_topic_score_codex":2.432717e-7,"about_ca_topic_score_gemma":0.00000894397,"teacher_disagreement_score":0.9171131,"about_ca_system_score_codex":0.000011011413,"about_ca_system_score_gemma":0.0000027077115,"threshold_uncertainty_score":0.74079645},"labels":[],"label_agreement":null},{"id":"W4387610460","doi":"10.1063/5.0169682","title":"Low-temperature enhanced OFF-state telegraph noise in defect engineered ReRAMs","year":2023,"lang":"en","type":"article","venue":"APL Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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 Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; American University of Sharjah","keywords":"Materials science; Noise (video); Trapping; Stacking; Condensed matter physics; Amplitude; Resistive touchscreen; Optoelectronics; Resistive random-access memory; Voltage; Nuclear magnetic resonance; Electrical engineering; Physics","score_opus":0.007477818467592674,"score_gpt":0.21785856011935795,"score_spread":0.21038074165176526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387610460","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99727297,0.00010977042,0.00009215003,0.00001557704,0.0011914578,0.0002296674,0.000028479302,0.0009531378,0.00010681702],"genre_scores_gemma":[0.9991779,0.00020466992,0.00011459799,0.000042434785,0.00015792804,0.00003775434,0.000046655234,0.000058700476,0.00015940124],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988908,0.00004286415,0.0003130997,0.0002318222,0.00009869023,0.00042269766],"domain_scores_gemma":[0.999588,0.00008302571,0.000028724256,0.0002234989,0.00001749365,0.000059236238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020182476,0.00021878978,0.00030357606,0.00016096144,0.000043397074,0.00005189116,0.00013673159,0.00009014261,0.000053836415],"category_scores_gemma":[0.000054608066,0.00021312182,0.00005830852,0.0005258124,0.000013744775,0.00015214308,0.000040294723,0.00014493895,0.00019155313],"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":[0.000021034886,0.0000055206065,0.000004918614,0.00012843394,0.000011441665,0.000055478995,0.00020417577,0.022800252,0.9753449,0.000024338828,0.00017312946,0.001226395],"study_design_scores_gemma":[0.00033765624,0.000020830816,0.0014015333,0.00013125529,0.0000038913568,0.0000046752966,0.000015851212,0.0001547351,0.9970549,0.00037331137,0.00025662887,0.00024475905],"about_ca_topic_score_codex":0.0000025609147,"about_ca_topic_score_gemma":0.000005223337,"teacher_disagreement_score":0.022645516,"about_ca_system_score_codex":0.000028866725,"about_ca_system_score_gemma":0.000006363732,"threshold_uncertainty_score":0.86908555},"labels":[],"label_agreement":null},{"id":"W4387742506","doi":"10.1063/5.0170058","title":"Analog programming of CMOS-compatible Al2O3/TiO2−x memristor at 4.2 K after metal-insulator transition suppression by cryogenic reforming","year":2023,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Institut quantique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Canada First Research Excellence Fund; Centre National de la Recherche Scientifique; Natural Sciences and Engineering Research Council of Canada; École Centrale de Lyon; Indian National Science Academy","keywords":"Memristor; Materials science; Optoelectronics; CMOS; Nanotechnology; Electrical engineering; Thermal conduction; Voltage; Resistive random-access memory; Engineering; Composite material","score_opus":0.009062924455002147,"score_gpt":0.20980585445680705,"score_spread":0.2007429300018049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387742506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98670644,0.000066204644,0.012171894,0.000041202045,0.0001713054,0.00026174332,0.000021450407,0.00044498398,0.00011478536],"genre_scores_gemma":[0.9987132,0.000008947497,0.0006673344,0.0001788184,0.00013199003,0.000077306286,0.00014562014,0.00006246873,0.000014326737],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988392,0.0000148370555,0.00028905776,0.0002656412,0.00021829772,0.00037296608],"domain_scores_gemma":[0.99955976,0.000049204653,0.00007451951,0.00023723376,0.000013846363,0.00006542316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000861076,0.00022724585,0.000278538,0.00006594051,0.00011329171,0.00001101676,0.000121362486,0.000055609038,0.000009374328],"category_scores_gemma":[0.0000016985023,0.00023050311,0.0001193525,0.00044462664,0.00004415515,0.00014092347,0.000044906355,0.00018061833,0.000032688193],"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":[0.000036898233,0.000011971841,0.000018359808,0.0001420518,0.000044342712,0.0000052360065,0.0006428697,0.06135102,0.9312768,0.000030596006,0.0005764926,0.0058633815],"study_design_scores_gemma":[0.00039839128,0.000018088364,0.000114136696,0.00005113121,0.000050298182,0.0000018373615,0.00007483394,0.0067737736,0.99073225,0.00006961866,0.00140309,0.00031252616],"about_ca_topic_score_codex":0.0000033597546,"about_ca_topic_score_gemma":9.4804545e-7,"teacher_disagreement_score":0.059455495,"about_ca_system_score_codex":0.0001038041,"about_ca_system_score_gemma":0.0000036477988,"threshold_uncertainty_score":0.9399644},"labels":[],"label_agreement":null},{"id":"W4387765997","doi":"10.1016/j.neucom.2023.126933","title":"WALLAX: A memristor-based Gaussian random number generator","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Adelaide","keywords":"Random number generation; Memristor; Computer science; Randomness tests; Randomness; Matrix multiplication; Pseudorandom number generator; Gaussian; NIST; Algorithm; Computer engineering; Parallel computing; Electronic engineering; Mathematics; Engineering","score_opus":0.01658810450135522,"score_gpt":0.2429895359164895,"score_spread":0.22640143141513427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387765997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9692609,0.000042901174,0.021922965,0.00011066677,0.0012559878,0.00022169408,0.0000024359251,0.0035991815,0.0035832904],"genre_scores_gemma":[0.99709374,0.0000053358403,0.0017586049,0.00031183727,0.00060348277,0.00001071567,0.000009219051,0.00008745453,0.000119615244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998606,0.000055102515,0.00030110727,0.0003189509,0.00018846427,0.00053037226],"domain_scores_gemma":[0.9993064,0.00025500587,0.000044585933,0.00024037583,0.000023943785,0.00012969835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017653534,0.00024000484,0.00024742755,0.000100475714,0.00024102605,0.00004826949,0.0001962707,0.00006455557,0.00003722914],"category_scores_gemma":[0.00004919462,0.00024915623,0.000111019355,0.00060104136,0.000020573509,0.00008429117,0.000064980944,0.00031439378,0.00043335962],"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":[0.000019435865,0.000010056499,0.0005646813,0.00011134269,0.000013955069,0.00019430752,0.0001297321,0.91616154,0.06281469,0.00006501768,0.0031004052,0.016814826],"study_design_scores_gemma":[0.0014337887,0.000022519362,0.00092593336,0.00006311642,0.000011702725,0.000031481275,0.000019395417,0.9243593,0.05311969,0.00008776597,0.019504905,0.00042037806],"about_ca_topic_score_codex":0.0000012119666,"about_ca_topic_score_gemma":9.928347e-7,"teacher_disagreement_score":0.027832856,"about_ca_system_score_codex":0.000041994204,"about_ca_system_score_gemma":0.00001554353,"threshold_uncertainty_score":0.99999607},"labels":[],"label_agreement":null},{"id":"W4387914431","doi":"10.1109/codit58514.2023.10284125","title":"SMA-Based Tuning of PI Controller Using Takagi-Sugeno Fuzzy Observers for an Electromechanical System with Variable Parameters","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Control theory (sociology); Moment of inertia; Fuzzy logic; Nonlinear system; Controller (irrigation); Fuzzy control system; Mathematics; Moment (physics); Convergence (economics); Stability (learning theory); Observer (physics); Computer science; Control (management); Physics","score_opus":0.05231346171963315,"score_gpt":0.25303836440307115,"score_spread":0.200724902683438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387914431","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53796405,0.0000104598685,0.46102452,0.0000032548805,0.00008953168,0.00026053248,0.000004259143,0.0005046023,0.00013878042],"genre_scores_gemma":[0.9407253,5.48123e-7,0.059110727,0.000027595986,0.00003193665,0.000019406027,0.0000106005455,0.000050342267,0.000023543575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989763,0.00003135967,0.00025083634,0.00021611224,0.00012104154,0.00040436094],"domain_scores_gemma":[0.9993596,0.00027158193,0.00005668266,0.00017000425,0.000053736818,0.000088408065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002511871,0.00016840262,0.00031691024,0.0000930174,0.00009728097,0.000018795861,0.00012076808,0.00006370572,0.0000023718721],"category_scores_gemma":[0.000027740756,0.00014671672,0.00005903836,0.00041259665,0.000015024447,0.00013795495,0.000013001274,0.00011346397,0.0000015787886],"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":[0.00008680446,0.0000054892453,0.000016621181,0.00018594624,0.000033172146,0.0000034544935,0.000016198035,0.69451094,0.30345917,0.0014967002,0.00000679108,0.00017872913],"study_design_scores_gemma":[0.0011145876,0.00030520943,0.000009521872,0.00011381805,0.00003652051,0.0000070765163,0.00018024574,0.9056848,0.09223721,0.000108772205,0.00002161417,0.00018059587],"about_ca_topic_score_codex":0.000016048387,"about_ca_topic_score_gemma":0.0000044365333,"teacher_disagreement_score":0.40276122,"about_ca_system_score_codex":0.00007828029,"about_ca_system_score_gemma":0.000029308847,"threshold_uncertainty_score":0.5982934},"labels":[],"label_agreement":null},{"id":"W4388052734","doi":"10.1109/jetcas.2023.3328926","title":"Spike Timing Dependent Gradient for Direct Training of Fast and Efficient Binarized Spiking Neural Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Computer science; Spiking neural network; Neuromorphic engineering; Speedup; Backpropagation; Spike (software development); Artificial neural network; Artificial intelligence; Efficient energy use; Pattern recognition (psychology); Parallel computing","score_opus":0.04430999213860597,"score_gpt":0.26743055858459547,"score_spread":0.2231205664459895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388052734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899174,0.0011527484,0.007252329,0.000023950577,0.0013349241,0.00017383354,0.0000021607764,0.0000685601,0.00007411579],"genre_scores_gemma":[0.99923563,0.0003292752,0.00002456373,0.000009011679,0.00033802158,0.000005973195,0.0000015664566,0.00002115335,0.000034813205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989007,0.00004991999,0.00040541033,0.00017966183,0.00012707617,0.0003371917],"domain_scores_gemma":[0.9995076,0.00019608698,0.00009947694,0.000057555237,0.000049284135,0.000089968984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004615295,0.00015900946,0.00034377287,0.00023449275,0.0002106316,0.000063507534,0.000050996532,0.00006480848,2.3933697e-7],"category_scores_gemma":[0.00004941495,0.00013923612,0.0000331909,0.00028229345,0.000017826304,0.000049209582,0.0000115165585,0.00031246562,3.8934164e-8],"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":[0.000011593419,0.0000073985825,0.0005273508,0.0001827137,0.000027170163,0.000030978816,0.002070306,0.9387085,0.0072083264,0.0001059929,0.000007916554,0.051111765],"study_design_scores_gemma":[0.00069498725,0.00012690263,0.0011778071,0.00043904834,0.000015494506,0.00016130351,0.0005131737,0.99616015,0.0003882421,0.000024802308,0.00013485287,0.00016324264],"about_ca_topic_score_codex":0.0000042938823,"about_ca_topic_score_gemma":0.0000042714128,"teacher_disagreement_score":0.057451654,"about_ca_system_score_codex":0.00003349287,"about_ca_system_score_gemma":0.0000065466465,"threshold_uncertainty_score":0.56778836},"labels":[],"label_agreement":null},{"id":"W4388442813","doi":"10.1007/978-3-031-47448-4_3","title":"Third Generation Neural Nets and Their Applications in Multi-modal Deep Learning: A Survey","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Spiking neural network; Machine learning; Artificial neural network; Deep neural networks","score_opus":0.042139950952652265,"score_gpt":0.24528910922727473,"score_spread":0.20314915827462246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388442813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027781267,0.031387962,0.93724054,0.00001697532,0.0011779988,0.0012808468,0.000016366748,0.00039440862,0.0007036451],"genre_scores_gemma":[0.99785054,0.0008334731,0.000046689867,0.000017093542,0.00042596902,0.000037151753,0.00015478404,0.00007680829,0.0005575061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989445,0.000068973175,0.0003412317,0.0003380486,0.000063203566,0.00024399714],"domain_scores_gemma":[0.99916357,0.0005474844,0.00007511357,0.00013980463,0.000021658321,0.000052367533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026358312,0.00031789296,0.0004189035,0.0001185599,0.000094727264,0.00006725953,0.00007147559,0.00038450366,7.2449865e-7],"category_scores_gemma":[0.000029875584,0.00027404493,0.000033171484,0.000093625626,0.00002865133,0.00004543124,0.000044537745,0.0008741047,8.288849e-7],"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":[0.0000039418996,0.0000016773249,0.0010618521,0.000070389615,0.000011545133,0.000005988164,0.00018276597,0.98572505,0.00009274052,0.00012935275,0.000003934914,0.012710767],"study_design_scores_gemma":[0.00017024318,0.000018976567,0.0011661737,0.00014842293,0.0000046204605,0.000012924494,0.0000063357297,0.99753046,0.000008184885,0.00016711678,0.0004981035,0.0002684078],"about_ca_topic_score_codex":0.00004464985,"about_ca_topic_score_gemma":0.0021711085,"teacher_disagreement_score":0.9700692,"about_ca_system_score_codex":0.00003486749,"about_ca_system_score_gemma":0.000003974491,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W4388463335","doi":"10.1088/2634-4386/ad06ca","title":"Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications","year":2023,"lang":"en","type":"editorial","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","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":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Neuromorphic engineering; Interfacing; Focus (optics); Computer science; Computer architecture; Sensory system; Nanotechnology; Neuroscience; Human–computer interaction; Artificial intelligence; Materials science; Artificial neural network; Computer hardware; Biology; Physics","score_opus":0.021646349049924567,"score_gpt":0.2356136515851405,"score_spread":0.21396730253521593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388463335","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37802222,0.000604735,0.003021727,0.000042680153,0.6162347,0.00046263778,0.000048096485,0.0015511202,0.000012099569],"genre_scores_gemma":[0.5727289,0.00023137774,0.00022329224,0.000008468606,0.42648062,0.000012034435,0.000035886987,0.00027302577,0.000006434036],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667275,0.00008499843,0.0008857054,0.0010654402,0.0004390589,0.0008520683],"domain_scores_gemma":[0.9976324,0.0015491908,0.00019593822,0.0003147202,0.00008174753,0.00022600224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005695521,0.00083855615,0.0009411053,0.000539738,0.00035432834,0.0003743809,0.0002280851,0.0005907239,0.0000015627178],"category_scores_gemma":[0.00046394338,0.0009869073,0.0000497477,0.0005497697,0.00008553254,0.00011327845,0.00047726263,0.0022232726,0.000007464757],"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":[0.00013454401,0.000096015625,0.000045864865,0.008961071,0.00011456321,0.0004887959,0.0015453934,0.27827153,0.59157014,0.00012841178,0.040127054,0.07851661],"study_design_scores_gemma":[0.0028736054,0.0006605403,0.00057326653,0.008930798,0.0002943787,0.00039952958,0.00023499526,0.90332764,0.01994379,0.00059353776,0.05707964,0.0050882897],"about_ca_topic_score_codex":0.000018422541,"about_ca_topic_score_gemma":0.000008467799,"teacher_disagreement_score":0.6250561,"about_ca_system_score_codex":0.000092704926,"about_ca_system_score_gemma":0.000044411394,"threshold_uncertainty_score":0.99925816},"labels":[],"label_agreement":null},{"id":"W4388478030","doi":"10.20944/preprints202311.0309.v1","title":"Complex Exponential Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado Design Suite","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Field-programmable gate array; Artificial neural network; Lookup table; Implementation; Suite; Embedded system; Computer engineering; Artificial intelligence","score_opus":0.30491386969154605,"score_gpt":0.3695375112815756,"score_spread":0.06462364159002953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388478030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8036538,0.000043637712,0.19398898,0.000014331516,0.0005850073,0.00089809246,0.000039391314,0.0007584837,0.000018274985],"genre_scores_gemma":[0.99264234,0.000024873052,0.0068114647,0.000029129205,0.00016248638,0.00009658116,0.00009077358,0.00013378476,0.000008544922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973755,0.00021000828,0.0007636997,0.0009015118,0.00025808028,0.00049122353],"domain_scores_gemma":[0.99891937,0.000103436614,0.00018937698,0.0006026891,0.000052887775,0.00013227024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055987766,0.0004712301,0.0005296242,0.00032870902,0.000139761,0.000046812274,0.00029911113,0.00021611755,0.000020219317],"category_scores_gemma":[0.00003301791,0.0005782424,0.0001065179,0.00020704692,0.00003662082,0.0001436392,0.00070560473,0.00058638566,0.0000329505],"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":[0.000026512964,0.00000961122,0.005895033,0.00049989356,0.00001962388,0.000025976073,0.00016043811,0.65247905,0.34062296,0.000011725738,0.000002099044,0.00024710136],"study_design_scores_gemma":[0.00052332075,0.000007994476,0.011870867,0.00021628595,0.00003578516,0.0000050738977,0.00008383799,0.79227036,0.19451791,0.00007744026,0.000005674164,0.00038542922],"about_ca_topic_score_codex":0.00008129961,"about_ca_topic_score_gemma":0.000025873238,"teacher_disagreement_score":0.18898855,"about_ca_system_score_codex":0.00033254398,"about_ca_system_score_gemma":0.000078563506,"threshold_uncertainty_score":0.9996669},"labels":[],"label_agreement":null},{"id":"W4388556968","doi":"10.20944/preprints202309.1149.v2","title":"Critical Review of Neural Network Generations and Models Design","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Interpretability; Artificial intelligence; Artificial neural network; Overfitting; Machine learning; Deep learning; Recurrent neural network; Convolutional neural network; Nervous system network models; Scalability; Types of artificial neural networks","score_opus":0.35302822704953507,"score_gpt":0.37955362484045546,"score_spread":0.02652539779092039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388556968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35958052,0.0599615,0.5690592,0.0009282637,0.00371382,0.0022034652,0.000047843932,0.0022221967,0.0022831855],"genre_scores_gemma":[0.9729664,0.019441398,0.0067703356,0.00016338234,0.00036864058,0.000100084384,0.000019499692,0.0000747275,0.00009554753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984637,0.00015512876,0.0005026311,0.00044154123,0.00015065068,0.00028637904],"domain_scores_gemma":[0.99870145,0.00047705334,0.00006791894,0.0005614965,0.00009168158,0.000100420206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063865876,0.0002517084,0.0004556055,0.000046995145,0.000076726676,0.000009826933,0.00023640295,0.00014192,0.00004459209],"category_scores_gemma":[0.0004737858,0.0002749171,0.000106839754,0.00012729873,0.00006066804,0.00011126995,0.0007470873,0.00064637914,0.00003990876],"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":[0.0000045922707,0.00000650486,0.00034910964,0.0047638514,0.000029716319,0.000008760047,0.000056788536,0.99185616,0.0017769731,0.0005348767,0.00016372475,0.00044893043],"study_design_scores_gemma":[0.00011955903,0.000015128593,0.0026954955,0.008699223,0.00014299473,0.00002158912,0.000008419566,0.9429319,0.015989438,0.028605642,0.00018756148,0.0005830418],"about_ca_topic_score_codex":0.0000026751713,"about_ca_topic_score_gemma":7.447855e-7,"teacher_disagreement_score":0.61338586,"about_ca_system_score_codex":0.00002737055,"about_ca_system_score_gemma":0.000024406558,"threshold_uncertainty_score":0.9999703},"labels":[],"label_agreement":null},{"id":"W4388760782","doi":"10.17760/d20581919","title":"Towards efficient deep neural network inference and training for ubiquitous AI","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Crossbar switch; Resistive random-access memory; Artificial intelligence; Deep learning; Scalability; Computer architecture; Computer engineering; Distributed computing; Machine learning; Engineering","score_opus":0.03333344622705265,"score_gpt":0.30576343211850615,"score_spread":0.2724299858914535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388760782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91195434,0.0008350248,0.07721305,0.000023411283,0.0049499297,0.00065013004,0.0000080585305,0.0015861437,0.0027799308],"genre_scores_gemma":[0.9964379,0.00003793988,0.00183174,0.000069400136,0.0005664399,0.000057373592,0.00019048009,0.00009252673,0.000716171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990395,0.0000080761365,0.00023101259,0.00024789694,0.00009535782,0.00037821024],"domain_scores_gemma":[0.999547,0.00020517972,0.000038144088,0.00010130495,0.00003796715,0.00007042082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086046995,0.000245979,0.0002756256,0.00005986029,0.00012802302,0.000044384706,0.000097232325,0.00014549901,0.000007288409],"category_scores_gemma":[0.000061803905,0.0002418885,0.00006584466,0.00015096324,0.000008874096,0.000040854207,0.000017838001,0.0002876377,0.0000036568558],"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":[0.000019229448,0.0000016432563,0.0000021589601,0.00028259982,0.000016083242,0.0000048830757,0.0010316044,0.8379929,0.0005532712,0.00023562353,0.00008373709,0.15977626],"study_design_scores_gemma":[0.00021847145,0.00006242055,0.0005856121,0.00015141399,0.00003486063,0.0000042170964,0.0008073592,0.9941291,0.0024782934,0.00086834707,0.00024811906,0.0004117904],"about_ca_topic_score_codex":0.0000021325855,"about_ca_topic_score_gemma":0.000078070916,"teacher_disagreement_score":0.15936446,"about_ca_system_score_codex":0.000020316007,"about_ca_system_score_gemma":0.000014292399,"threshold_uncertainty_score":0.9863927},"labels":[],"label_agreement":null},{"id":"W4388798446","doi":"10.1142/s2010324723500327","title":"On the Design of Power Attack Immune Spintronic Associative Memory","year":2023,"lang":"en","type":"article","venue":"SPIN","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Computer science; Implementation; Vulnerability (computing); Artificial neural network; Embedded system; Resilience (materials science); Power analysis; Bidirectional associative memory; Content-addressable memory; Associative property; Energy consumption; Side channel attack; Power consumption; Power (physics); Computer engineering; Computer security; Computer network; Computer architecture; Cryptography; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.03530948948322764,"score_gpt":0.2733728642911354,"score_spread":0.23806337480790773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388798446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882495,0.00018784551,0.007711097,0.00018185854,0.0004027463,0.00020484324,0.0000029973758,0.00029303238,0.0027660825],"genre_scores_gemma":[0.9994424,0.00001790258,0.0001003074,0.0000435227,0.000032696258,0.0000059911654,0.0000011054124,0.00001752534,0.00033856786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994893,0.000038978334,0.00012438782,0.000078011326,0.00008955409,0.00017978002],"domain_scores_gemma":[0.9995181,0.00027742126,0.000033361674,0.00014304338,0.000013357336,0.000014730216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002275318,0.0000807934,0.00011257405,0.000032988944,0.00004986127,0.000004365209,0.000112762835,0.000029354873,0.000058539492],"category_scores_gemma":[0.00011500151,0.000059899303,0.000045819426,0.00021585023,0.000019282663,0.00003312749,0.00002759718,0.00016101357,0.00019196539],"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":[0.000015584896,0.000009121632,0.0000054034776,0.000020573589,0.000050772844,0.000008446109,0.00055244775,0.8873265,0.10465571,0.001840161,0.003316415,0.002198861],"study_design_scores_gemma":[0.0007327328,0.00044483348,0.007400033,0.0002393511,0.000021863316,0.0000025783647,0.00083591935,0.14942926,0.833794,0.005831418,0.00079276913,0.0004752155],"about_ca_topic_score_codex":5.079561e-7,"about_ca_topic_score_gemma":2.109277e-7,"teacher_disagreement_score":0.7378972,"about_ca_system_score_codex":0.000039447397,"about_ca_system_score_gemma":0.0000069499265,"threshold_uncertainty_score":0.24673907},"labels":[],"label_agreement":null},{"id":"W4388825887","doi":"10.1063/5.0163124","title":"Realization of dual-functional resistive switching characteristics in Ag−In−Zn−S/sericin peptide-based memristive device","year":2023,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Postdoctoral Science Foundation","keywords":"Neuromorphic engineering; Memristor; Materials science; Realization (probability); Voltage; Switching time; Sericin; Optoelectronics; Nanotechnology; Synaptic weight; Computer science; Electronic engineering; SILK; Artificial neural network; Electrical engineering; Artificial intelligence; Engineering","score_opus":0.01919938406415949,"score_gpt":0.22822388792579684,"score_spread":0.20902450386163735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388825887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9095323,0.0000034118611,0.089626186,0.00004723736,0.00012528826,0.00016965247,0.00001025945,0.00014429043,0.00034137227],"genre_scores_gemma":[0.99910676,0.000004773492,0.00025362932,0.0002934618,0.00013589907,0.000028149012,0.00013688965,0.000036824724,0.0000036062224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904317,0.000022948565,0.00031901733,0.00021455073,0.00015797635,0.00024232683],"domain_scores_gemma":[0.99946254,0.0002602386,0.00009546811,0.00013307767,0.000020781006,0.000027869859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013781835,0.00016514024,0.00024337559,0.00013008581,0.000044667224,0.000008809147,0.000066322,0.000040067014,0.0000023261314],"category_scores_gemma":[0.000019136929,0.00019571384,0.00003533576,0.0007764995,0.000022437735,0.00008532987,0.000026029122,0.00023523316,0.000013067304],"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":[0.000041213727,0.000013256612,0.0008810842,0.00009359068,0.000008653688,0.000014333299,0.00030644122,0.5206271,0.47534674,0.0012577621,0.00010872339,0.0013011188],"study_design_scores_gemma":[0.0027245434,0.00004724572,0.3600758,0.00066943484,0.00005393192,0.000002071072,0.0005537975,0.17734094,0.45426992,0.0026834165,0.000306703,0.0012722072],"about_ca_topic_score_codex":0.000012702128,"about_ca_topic_score_gemma":0.0000081989765,"teacher_disagreement_score":0.3591947,"about_ca_system_score_codex":0.00009835425,"about_ca_system_score_gemma":0.000013408625,"threshold_uncertainty_score":0.7980979},"labels":[],"label_agreement":null},{"id":"W4388837988","doi":"10.1109/tc.2023.3334140","title":"Fast Inner-Product Algorithms and Architectures for Deep Neural Network Accelerators","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Advanced Memory and Neural Computing","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":"McMaster University","funders":"","keywords":"Computer science; Pipeline (software); Convolutional neural network; Systolic array; Algorithm; Throughput; Matrix multiplication; Floating point; Parallel computing; Gate array; Field-programmable gate array; Computer hardware; Embedded system; Very-large-scale integration; Artificial intelligence; Wireless","score_opus":0.021547431843284955,"score_gpt":0.24506001079589054,"score_spread":0.2235125789526056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388837988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33165464,0.000048784204,0.66544235,0.00006542744,0.0019296529,0.00018260846,0.0000074689233,0.000661804,0.0000072871876],"genre_scores_gemma":[0.9902557,0.000015412275,0.009164069,0.000110012246,0.00035696846,0.0000307007,0.0000033375463,0.000043427237,0.000020357598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913883,0.000017990707,0.0001565811,0.00026133138,0.00007945829,0.00034583558],"domain_scores_gemma":[0.99952245,0.00021635188,0.000016943353,0.00014480315,0.000015685006,0.00008375144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006475194,0.00018458781,0.0001599473,0.00012252823,0.00025600754,0.000037817637,0.00010588361,0.000038052982,0.0000025112931],"category_scores_gemma":[0.0000014707687,0.00018615917,0.0000723102,0.00036393994,0.000033220935,0.000049382725,0.000002052122,0.00023353839,0.0000056443314],"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":[0.0000111582,0.000003915859,0.0000023103237,0.00002796861,0.000020850739,0.0000035876894,0.00012003087,0.71181566,0.00066809024,0.0000017630043,0.00011365865,0.28721103],"study_design_scores_gemma":[0.0003117526,0.000080591824,0.00014312576,0.000026438223,0.000013289234,0.000018617615,0.000021601982,0.9812061,0.017644638,0.00010785022,0.00020878421,0.00021720055],"about_ca_topic_score_codex":0.0000012348507,"about_ca_topic_score_gemma":0.000006036735,"teacher_disagreement_score":0.6586011,"about_ca_system_score_codex":0.000018761188,"about_ca_system_score_gemma":0.000003324396,"threshold_uncertainty_score":0.75913507},"labels":[],"label_agreement":null},{"id":"W4388844356","doi":"10.48550/arxiv.2311.10686","title":"Realistic Cost to Execute Practical Quantum Circuits using Direct Clifford+T Lattice Surgery Compilation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Oak Ridge National Laboratory; Advanced Research Projects Agency; U.S. Air Force; Office of Science; Defense Advanced Research Projects Agency; UT-Battelle; Battelle; QuantERA; U.S. Department of Energy; U.S. Department of Defense","keywords":"Computer science; MAGIC (telescope); Compiler; Electronic circuit; Quantum computer; Computer engineering; Computation; Quantum; Parallel computing; Theoretical computer science; Algorithm; Programming language; Electrical engineering; Physics; Quantum mechanics; Engineering","score_opus":0.3178719298117436,"score_gpt":0.27003252682386253,"score_spread":0.047839402987881086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388844356","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62604463,0.000017469732,0.36958787,0.000035069115,0.0018232192,0.00041737614,0.00008523425,0.0010302505,0.00095886225],"genre_scores_gemma":[0.9988211,0.00009572059,0.0003941712,0.00004839229,0.00022781463,0.0000012174949,0.00009853562,0.00008725658,0.00022578235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982858,0.00012316549,0.00031247528,0.0007013641,0.000101057834,0.00047618273],"domain_scores_gemma":[0.9978451,0.0011425259,0.00014589733,0.0005238042,0.000101655285,0.00024098606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032412462,0.00035337024,0.0004957804,0.00033315763,0.00020290603,0.000059846916,0.00023052935,0.00024273495,0.00001071649],"category_scores_gemma":[0.00030805558,0.00046925998,0.0001747099,0.00068421307,0.000045528293,0.00024258156,0.0003765269,0.0006992112,0.00008671826],"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":[0.000027356798,0.000020444648,0.0006692061,0.00021324524,0.000063272564,0.00062135846,0.000052498985,0.995325,0.00034667546,0.0019943502,0.00044311577,0.00022342904],"study_design_scores_gemma":[0.00015290877,0.000013154222,0.00129316,0.000411518,0.00016369062,0.000015735037,0.000055483055,0.99440193,0.0004085934,0.002074098,0.0004182449,0.00059147406],"about_ca_topic_score_codex":0.000044248485,"about_ca_topic_score_gemma":0.000029035948,"teacher_disagreement_score":0.37277645,"about_ca_system_score_codex":0.000369508,"about_ca_system_score_gemma":0.00007891092,"threshold_uncertainty_score":0.9997759},"labels":[],"label_agreement":null},{"id":"W4388928172","doi":"","title":"A tunable 28nm FD-SOI crossbar output circuit forlow power analog SNN inference with eNVM synapses","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Crossbar switch; Silicon on insulator; Power (physics); Computer science; Spice; Electronic engineering; Electrical engineering; Physics; Engineering; Optoelectronics; Telecommunications; Silicon","score_opus":0.026125691152780146,"score_gpt":0.23504124922346326,"score_spread":0.2089155580706831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388928172","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5569665,0.0011890407,0.40965456,0.0009104842,0.0005955959,0.00075076555,0.00013510828,0.002412389,0.027385596],"genre_scores_gemma":[0.9826869,0.0002894671,0.008221797,0.00004581502,0.00003261578,0.00008308357,0.0002930683,0.00012474884,0.008222536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967894,0.00082043355,0.00053151377,0.00082894875,0.00039793647,0.00063178554],"domain_scores_gemma":[0.99536973,0.0014218065,0.00026170202,0.0018527181,0.00087502797,0.00021899944],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016910934,0.00050951476,0.000525698,0.00024158672,0.00040141912,0.00040750767,0.0011680702,0.00028932266,0.000074670636],"category_scores_gemma":[0.00075776567,0.00051987957,0.00017799853,0.00052264956,0.00020895306,0.00023562291,0.0009976084,0.0010954492,0.00009979118],"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":[0.00016611861,0.001550559,0.018243732,0.0059426115,0.0019200492,0.0007269887,0.054848794,0.62007725,0.054565832,0.1010687,0.008536283,0.13235305],"study_design_scores_gemma":[0.0042300317,0.0000142954705,0.03264091,0.025346443,0.000493618,0.00023783205,0.0013349672,0.31867376,0.4914059,0.047244027,0.07021911,0.008159102],"about_ca_topic_score_codex":0.00020410599,"about_ca_topic_score_gemma":0.0005787112,"teacher_disagreement_score":0.43684006,"about_ca_system_score_codex":0.00015115607,"about_ca_system_score_gemma":0.00015072848,"threshold_uncertainty_score":0.9997253},"labels":[],"label_agreement":null},{"id":"W4388983752","doi":"10.1109/tcsii.2023.3336299","title":"A Weight Mapping Strategy for More Fully Exploiting Data in CIM-Based CNN Accelerator","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Dataflow; Speedup; Computer science; Inference; Spare part; Parallel computing; Von Neumann architecture; Efficient energy use; Energy (signal processing); Scale (ratio); Computer engineering; Computer architecture; Artificial intelligence; Programming language","score_opus":0.10459661464636516,"score_gpt":0.2934815948186596,"score_spread":0.18888498017229444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388983752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25419468,0.00018970076,0.7409315,0.000041044455,0.0017019939,0.0010064841,0.00040942663,0.0013595741,0.00016557079],"genre_scores_gemma":[0.99854887,0.000025135407,0.00016578283,0.000052084466,0.00032150885,0.00046943023,0.0000779954,0.00012837566,0.00021084525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974952,0.00007264491,0.0007034983,0.00069134316,0.00030960853,0.0007277476],"domain_scores_gemma":[0.9984125,0.00041681487,0.00009669486,0.00085943134,0.00006650693,0.00014806961],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038876312,0.00038334445,0.00047428318,0.0004358673,0.00045008055,0.00011603322,0.00059927115,0.00016966353,0.000013097923],"category_scores_gemma":[0.00001696611,0.00043632518,0.0001097888,0.00082798564,0.00003634529,0.00066358224,0.0000075339653,0.00046510814,0.000023205532],"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":[0.000014536189,0.000044377764,0.000007845149,0.00067907455,0.000042489337,0.000041920634,0.0006182607,0.8902933,0.09631774,0.00002175727,0.00031115126,0.011607565],"study_design_scores_gemma":[0.0024978823,0.00012936555,0.00010843318,0.0018037017,0.000045333967,0.00003410997,0.0020862673,0.86420995,0.11839023,0.000033318778,0.009558288,0.0011030933],"about_ca_topic_score_codex":0.000031348816,"about_ca_topic_score_gemma":0.000017945411,"teacher_disagreement_score":0.7443542,"about_ca_system_score_codex":0.0001286002,"about_ca_system_score_gemma":0.00007237984,"threshold_uncertainty_score":0.99980885},"labels":[],"label_agreement":null},{"id":"W4389209482","doi":"10.1016/j.mtnano.2023.100439","title":"Research progress of artificial neural systems based on memristors","year":2023,"lang":"en","type":"article","venue":"Materials Today Nano","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Xi’an Jiaotong University; National Natural Science Foundation of China","keywords":"Memristor; Artificial neural network; Computer science; Artificial intelligence; Process (computing); Neural system; Engineering; Control engineering; Electronic engineering; Neuroscience","score_opus":0.06079025923780432,"score_gpt":0.3215654114013894,"score_spread":0.26077515216358504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389209482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965635,0.00004466997,0.00005091208,0.00003106268,0.0023448619,0.00026208893,0.000024958446,0.0004450805,0.0002328714],"genre_scores_gemma":[0.9994822,0.0000025985476,0.000039044062,0.0000047990216,0.0002878494,0.000037396483,0.000016692495,0.000037366277,0.0000920128],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872357,0.00014406786,0.00031166387,0.00017118797,0.00029132102,0.0003581882],"domain_scores_gemma":[0.99948114,0.00014824071,0.00003829738,0.00023311908,0.000051197647,0.0000480178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007478528,0.000118694785,0.00022825817,0.00023418473,0.00009473305,0.00004162372,0.00015351163,0.00006618554,0.000039493923],"category_scores_gemma":[0.000045210312,0.00010769876,0.000030514337,0.00045720732,0.00005554069,0.000052484313,0.00003730601,0.00009849345,0.00010630367],"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":[0.000057254187,0.0000128118945,0.000016121143,0.00028843893,0.000004546879,0.000027359498,0.00005546721,0.16206224,0.8356671,0.00020843971,0.00062300416,0.0009772637],"study_design_scores_gemma":[0.000116844276,0.000102285914,0.00015558043,0.00011873896,0.0000028307404,0.0000014553062,0.000049385497,0.03334864,0.96516144,0.000052074487,0.0007725669,0.000118156415],"about_ca_topic_score_codex":0.0000061973224,"about_ca_topic_score_gemma":5.360057e-7,"teacher_disagreement_score":0.12949438,"about_ca_system_score_codex":0.00003789573,"about_ca_system_score_gemma":0.000011063978,"threshold_uncertainty_score":0.43918282},"labels":[],"label_agreement":null},{"id":"W4389303944","doi":"10.3389/fnins.2023.1294954","title":"Experimental demonstration of coupled differential oscillator networks for versatile applications","year":2023,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Synchronization (alternating current); Relaxation oscillator; Initialization; Synchronization networks; Basis (linear algebra); CMOS; Electronic engineering; Relaxation (psychology); Topology (electrical circuits); Memristor; Voltage; Electrical engineering; Engineering; Telecommunications","score_opus":0.0161153655996588,"score_gpt":0.2514896806057892,"score_spread":0.23537431500613037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389303944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38043064,0.000041803967,0.61820805,0.0000042323136,0.00095510285,0.00022840929,0.0000036631739,0.000098580254,0.000029562872],"genre_scores_gemma":[0.9982117,0.000016037844,0.001619587,0.000010603502,0.000043118827,0.00006863001,0.000004379298,0.0000092645605,0.000016697846],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945205,0.0000064612345,0.000139208,0.00015263807,0.00007762874,0.00017203341],"domain_scores_gemma":[0.999812,0.000032528253,0.000025636813,0.00009147675,0.000008347784,0.000029982426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047466885,0.00006566541,0.00009351583,0.000080330334,0.00006919527,0.000009460444,0.00012382251,0.000025014495,8.692089e-7],"category_scores_gemma":[0.000011724155,0.000072042436,0.00002627912,0.00041045898,0.00006115741,0.00009966496,0.000020903557,0.000055716595,3.9231773e-7],"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":[0.000010181119,0.000012343511,0.0009141896,0.000018413491,8.0137653e-7,6.527931e-7,0.00006289732,0.49691886,0.49989256,0.00016036566,0.00040498652,0.001603756],"study_design_scores_gemma":[0.00017186275,0.0000252846,0.0013874746,0.0000060527304,0.0000014602997,5.03385e-7,0.0000685643,0.92083013,0.07711877,0.00006217659,0.00026283052,0.00006490888],"about_ca_topic_score_codex":2.7588362e-7,"about_ca_topic_score_gemma":2.4992747e-7,"teacher_disagreement_score":0.61778104,"about_ca_system_score_codex":0.000021674294,"about_ca_system_score_gemma":0.000006765491,"threshold_uncertainty_score":0.29378054},"labels":[],"label_agreement":null},{"id":"W4389475089","doi":"10.1038/s41467-023-43887-8","title":"Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Neuromorphic engineering; Computer science; Network topology; Hebbian theory; Leverage (statistics); Artificial neural network; Structural plasticity; Software; Artificial intelligence; Synaptic plasticity; MNIST database; Neuroscience; Computer network; Biology","score_opus":0.02955815720924175,"score_gpt":0.27654917566776493,"score_spread":0.24699101845852317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389475089","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9276125,0.0028125972,0.06280665,0.0013653055,0.00086533435,0.00084884115,0.00006869361,0.003045312,0.0005747912],"genre_scores_gemma":[0.9968579,0.0001728768,0.0024945573,0.0000939719,0.00009697268,0.0000807093,0.00012439884,0.000039409308,0.000039235292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993494,0.000036111185,0.00013922354,0.00013227902,0.000066735716,0.00027621223],"domain_scores_gemma":[0.9984925,0.0008764292,0.000029351635,0.00048063358,0.00006387854,0.000057220124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000041012303,0.00013262541,0.00013774679,0.000081984814,0.00055600225,0.00002746064,0.0004000791,0.00010983858,0.000004915833],"category_scores_gemma":[0.000098185104,0.00012467637,0.000046649842,0.0005829556,0.00007540213,0.00009340743,0.00006801468,0.0008349569,0.00000454849],"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":[0.00008745032,0.000026730719,0.00044645445,0.00006857787,0.00014040554,0.0000061706255,0.00015093198,0.923872,0.045298137,0.02510411,0.0024986782,0.0023003626],"study_design_scores_gemma":[0.0006562197,0.00010021751,0.0034434327,0.0000334203,0.000060976014,0.00006658068,0.000064558415,0.9858933,0.005169292,0.00039859882,0.0038032993,0.00031007454],"about_ca_topic_score_codex":0.0000010937879,"about_ca_topic_score_gemma":0.00011127449,"teacher_disagreement_score":0.0692454,"about_ca_system_score_codex":0.000026586968,"about_ca_system_score_gemma":0.000013874412,"threshold_uncertainty_score":0.50841546},"labels":[],"label_agreement":null},{"id":"W4389667633","doi":"10.1109/nssmicrtsd49126.2023.10338742","title":"Towards Efficient Data Processing on the Edge With Neuromorphic Computing for Instrumentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Neuromorphic engineering; Computer science; Spiking neural network; Deep learning; Artificial neural network; Artificial intelligence; Convolutional neural network; Edge device; Machine learning; Asynchronous communication; Enhanced Data Rates for GSM Evolution; Edge computing; Data processing; Cloud computing; Database","score_opus":0.10752938590621564,"score_gpt":0.29420393718151017,"score_spread":0.18667455127529453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389667633","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8751766,0.000007875909,0.1228774,0.0002538775,0.00015760596,0.00027118754,0.000005886374,0.0005789314,0.0006706467],"genre_scores_gemma":[0.99828315,0.0000014016983,0.0013933756,0.0001442696,0.00008485292,0.0000060924117,0.00003776523,0.000022973707,0.000026130541],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943197,0.000010017756,0.00010368436,0.00017145,0.00010158526,0.0001812778],"domain_scores_gemma":[0.9995966,0.0001379371,0.000022341137,0.00020417894,0.000016898355,0.000022047809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001685027,0.00009117217,0.00006887644,0.000036141977,0.00020209275,0.000040934443,0.00019302324,0.000013644158,0.0000028243649],"category_scores_gemma":[0.000029113506,0.00005693034,0.000010735784,0.0002815377,0.000016041204,0.00006909501,0.00006782168,0.00009435784,0.000010255504],"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":[0.000015895153,0.0000073467363,0.000027484673,0.00009132204,0.000006949561,0.0000034649952,0.00019942722,0.901126,0.004092416,0.00049992336,0.00036109262,0.09356863],"study_design_scores_gemma":[0.00018613607,0.000045963272,0.00051907403,0.000057103178,0.000005409103,0.0000046371993,0.0001652341,0.98230034,0.016265506,0.00003916338,0.0003244239,0.000087021544],"about_ca_topic_score_codex":5.367089e-7,"about_ca_topic_score_gemma":0.0000010497929,"teacher_disagreement_score":0.12310655,"about_ca_system_score_codex":0.000013403069,"about_ca_system_score_gemma":0.000009986146,"threshold_uncertainty_score":0.23215519},"labels":[],"label_agreement":null},{"id":"W4389683695","doi":"10.1007/s11571-023-10038-0","title":"Coincidence detection and integration behavior in spiking neural networks","year":2023,"lang":"en","type":"article","venue":"Cognitive Neurodynamics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"H2020 European Research Council; Deutsche Forschungsgemeinschaft; Universitätsklinikum Erlangen; European Commission","keywords":"Coincidence; Computer science; Spiking neural network; Coincidence detection in neurobiology; Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.01713245213632568,"score_gpt":0.2538633157002973,"score_spread":0.2367308635639716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389683695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9474791,0.000039085422,0.051464584,0.0000045214183,0.00042003367,0.0001782634,0.0000021686699,0.00035702248,0.000055175653],"genre_scores_gemma":[0.9997372,0.00008015555,0.000020188012,0.00003933997,0.000054784516,0.000023658527,0.000011659739,0.00002526335,0.000007776624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939084,0.000028535187,0.00014127854,0.0001805734,0.000059899212,0.0001988751],"domain_scores_gemma":[0.99968624,0.00017949713,0.000022228864,0.000050357,0.000026864092,0.000034824687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000703853,0.000116473515,0.000095927586,0.00012923099,0.00006673583,0.00002535163,0.0000389398,0.000038263075,8.2108136e-7],"category_scores_gemma":[0.00010124877,0.00013161135,0.000017183105,0.0004185729,0.000029213232,0.00019759296,0.000037921698,0.00031298996,0.0000033637843],"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":[0.000052186224,0.00001434143,0.007276946,0.00003458246,0.0000033862232,0.0003900723,0.00015413376,0.42095163,0.09282136,0.000026884443,0.0000012912176,0.47827318],"study_design_scores_gemma":[0.00015137707,0.000060909417,0.15741856,0.000034203706,0.0000072709713,0.00003952612,0.000066882996,0.840274,0.0018022637,0.000038368435,9.980236e-7,0.00010563283],"about_ca_topic_score_codex":0.0000019561744,"about_ca_topic_score_gemma":0.00015017783,"teacher_disagreement_score":0.47816756,"about_ca_system_score_codex":0.000025773103,"about_ca_system_score_gemma":0.000001785998,"threshold_uncertainty_score":0.53669554},"labels":[],"label_agreement":null},{"id":"W4389921164","doi":"10.3390/biomimetics8080621","title":"Complex-Exponential-Based Bio-Inspired Neuron Model Implementation in FPGA Using Xilinx System Generator and Vivado Design Suite","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Field-programmable gate array; Artificial neural network; Lookup table; Embedded system; Algorithm; Artificial intelligence","score_opus":0.09162844271760033,"score_gpt":0.3029680438418114,"score_spread":0.21133960112421107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389921164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76417553,0.00004646182,0.23499024,0.000010054454,0.00017543786,0.00023417572,0.000016539267,0.00034750917,0.000004025323],"genre_scores_gemma":[0.9820513,0.000010307758,0.017769706,0.00002830698,0.000054078442,0.000007247489,0.0000320285,0.000044966448,0.000002026919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902207,0.000058747908,0.00030403832,0.00021186787,0.000113651615,0.00028963014],"domain_scores_gemma":[0.9996578,0.0000681339,0.00004635123,0.00014216683,0.000020525164,0.000065009604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019091895,0.00016515853,0.00018364174,0.00022669033,0.0000952257,0.00003765852,0.000075053744,0.000054133143,0.0000017640195],"category_scores_gemma":[0.000008528343,0.00018448172,0.000030120933,0.00041536026,0.000022127815,0.00007869273,0.0000400806,0.00007233613,0.000005369187],"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":[0.000008024149,0.0000034789841,0.00009746316,0.00007974037,0.000003970439,0.000013375682,0.000037463466,0.5042178,0.49465615,0.000030990803,0.000013513706,0.0008380361],"study_design_scores_gemma":[0.00047135435,0.000021952015,0.0003701658,0.00002569984,0.0000132861815,0.0000035409503,0.00005838297,0.76271886,0.23613958,0.00001765041,0.000018379345,0.00014114744],"about_ca_topic_score_codex":0.000009069346,"about_ca_topic_score_gemma":0.0000054043167,"teacher_disagreement_score":0.25851658,"about_ca_system_score_codex":0.000086767,"about_ca_system_score_gemma":0.000020059744,"threshold_uncertainty_score":0.7522946},"labels":[],"label_agreement":null},{"id":"W4390014080","doi":"10.1038/s41586-023-06791-1","title":"Moiré synaptic transistor with room-temperature neuromorphic functionality","year":2023,"lang":"en","type":"article","venue":"Nature","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Neuromorphic engineering; Transistor; Materials science; Heterojunction; Optoelectronics; Graphene; Nanotechnology; Computer science; Electrical engineering; Artificial intelligence; Voltage; Artificial neural network; Engineering","score_opus":0.013474826892318388,"score_gpt":0.21551529843578074,"score_spread":0.20204047154346236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390014080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960624,0.0006469013,0.000227768,0.00043405624,0.00077663,0.00010946495,0.000016312151,0.0012262614,0.0005002359],"genre_scores_gemma":[0.9988076,0.00001594133,0.000107586966,0.000375451,0.00020949848,0.000007152009,0.000040239767,0.000034794288,0.00040172142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993163,0.000017106042,0.00008937117,0.00019295582,0.00016945599,0.00021483279],"domain_scores_gemma":[0.99966675,0.000064638174,0.000012112696,0.00016309052,0.000033214423,0.00006020476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059977254,0.00014727887,0.0001286546,0.00005649614,0.00010348958,0.00001621191,0.000094413175,0.00041210544,0.00003376818],"category_scores_gemma":[0.000019843466,0.00012100587,0.000043674627,0.0005005039,0.000015619215,0.0001023414,0.000009672979,0.0019705817,0.000044535787],"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":[0.0001760682,0.00003660817,0.00046134065,0.0005278746,0.00020606216,0.000772499,0.00033333918,0.7004715,0.2670809,0.0010913446,0.026047438,0.0027950145],"study_design_scores_gemma":[0.008710875,0.0015247111,0.21411341,0.0012716203,0.0005853285,0.0020504778,0.0007738897,0.15845227,0.22323053,0.005609469,0.37707597,0.0066014268],"about_ca_topic_score_codex":2.6968337e-7,"about_ca_topic_score_gemma":0.000005653821,"teacher_disagreement_score":0.54201925,"about_ca_system_score_codex":0.000026822569,"about_ca_system_score_gemma":0.000010390778,"threshold_uncertainty_score":0.8561306},"labels":[],"label_agreement":null},{"id":"W4390322833","doi":"10.1016/j.cap.2023.12.015","title":"Evolution between RS and NRS behaviors in BiFeO3@egg albumen nanocomposite based memristor","year":2023,"lang":"en","type":"article","venue":"Current Applied Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Fujian Normal University; Xi’an Jiaotong University; National Natural Science Foundation of China","keywords":"Memristor; Materials science; Voltage; Optoelectronics; Nanotechnology; Commutation; Nanocomposite; Coupling (piping); Low voltage; Electronic engineering; Electrical engineering; Composite material","score_opus":0.024897174658149238,"score_gpt":0.2656698312564757,"score_spread":0.24077265659832647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390322833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914685,0.00013215025,0.0072551807,0.000005590973,0.0002930274,0.00024729237,0.0000135911805,0.00042620453,0.00015849281],"genre_scores_gemma":[0.9994413,0.00001530254,0.00017152258,0.000004197399,0.000211156,0.00003796784,0.00008008935,0.000033431435,0.000005000124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991877,0.000011294161,0.00018195919,0.00021913626,0.000121060315,0.00027886144],"domain_scores_gemma":[0.99968684,0.00006475655,0.000033180073,0.00013830687,0.000008702613,0.00006820235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007202501,0.0001734362,0.00018871752,0.00008607677,0.000075623575,0.000014518814,0.00009121772,0.000042758074,0.000001310497],"category_scores_gemma":[0.000001874943,0.00019568771,0.0000347637,0.00047265136,0.000029914436,0.000066274006,0.000046663743,0.00024230265,0.000030001473],"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":[0.00002644498,0.0000744263,0.021271523,0.00047691114,0.000020596555,0.0000062101785,0.00068776676,0.43492928,0.27514458,0.0014394908,0.0004027672,0.26552],"study_design_scores_gemma":[0.0048185214,0.00011393056,0.18903942,0.0006038858,0.00021632381,0.0000017021437,0.00024082369,0.3330767,0.45161048,0.008944977,0.008543732,0.0027895218],"about_ca_topic_score_codex":0.0000014157082,"about_ca_topic_score_gemma":0.0000011022371,"teacher_disagreement_score":0.26273048,"about_ca_system_score_codex":0.00009187453,"about_ca_system_score_gemma":0.000009143398,"threshold_uncertainty_score":0.7979913},"labels":[],"label_agreement":null},{"id":"W4390841993","doi":"10.1007/978-3-031-42478-6_4","title":"Is Neuromorphic Computing the Key to Power-Efficient Neural Networks: A Survey","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Neuromorphic engineering; Spiking neural network; Artificial neural network; Computer science; Artificial intelligence; Key (lock); Process (computing); Computer architecture; Power (physics); Computer security; Physics","score_opus":0.04400205733661624,"score_gpt":0.2439467185501311,"score_spread":0.19994466121351484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390841993","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038964305,0.0070053823,0.09168093,0.0011796665,0.017162682,0.002428012,0.00015739915,0.0052293437,0.8361923],"genre_scores_gemma":[0.8757116,0.000032506257,0.000099162695,0.0018381936,0.0006322428,0.000003469011,0.000023634662,0.00028550715,0.12137369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813145,0.000027122602,0.00047521564,0.0005595895,0.00027984596,0.00052674685],"domain_scores_gemma":[0.9987103,0.0004832414,0.000057336525,0.0005383728,0.00004581132,0.00016497614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003265482,0.0005813759,0.00043563984,0.00012665226,0.00018059906,0.00013942741,0.0004554295,0.00018231987,0.00019544645],"category_scores_gemma":[0.000021999318,0.00043548568,0.00021280815,0.00015549183,0.000046936097,0.000029031473,0.0003599954,0.0012290981,0.00037505888],"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":[0.0000077624145,0.0000026203263,0.0000023541386,0.000037744376,0.000050780127,0.000086234555,0.00018985789,0.98938495,0.000035906,0.0029906586,0.005004715,0.0022064259],"study_design_scores_gemma":[0.000072777264,0.00007837914,0.00007520361,0.0002159491,0.000035665966,0.000071880044,0.000008362556,0.95856607,0.000060752292,0.00027367912,0.039924484,0.0006167752],"about_ca_topic_score_codex":0.0000065639306,"about_ca_topic_score_gemma":0.000018109406,"teacher_disagreement_score":0.8367473,"about_ca_system_score_codex":0.000062903266,"about_ca_system_score_gemma":0.000009478619,"threshold_uncertainty_score":0.9998097},"labels":[],"label_agreement":null},{"id":"W4390876841","doi":"10.1117/12.3017278","title":"Advancing machine learning tasks with field-programmable gate arrays: advantages, applications, challenges, and future perspectives","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Field-programmable gate array; Computer science; Machine learning; Artificial intelligence; Field (mathematics); Computer architecture; Preprocessor; Deep learning; Artificial neural network; Inference; Embedded system","score_opus":0.006570787780938765,"score_gpt":0.227994882270759,"score_spread":0.22142409448982026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390876841","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03471901,0.65402716,0.28401607,0.001777832,0.00032169858,0.0008167588,0.0000048547868,0.0049224035,0.019394197],"genre_scores_gemma":[0.92174536,0.056285806,0.020375272,0.000043108346,0.000752628,0.00008531136,0.000012791539,0.00008609011,0.0006136111],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992089,0.000014597773,0.00011459766,0.00031135356,0.00008238536,0.0002681664],"domain_scores_gemma":[0.9996627,0.00009506496,0.000013813357,0.00013153022,0.000018917599,0.00007800605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008030892,0.00018541199,0.0001464114,0.000066988025,0.00012571517,0.000051165494,0.000064572174,0.000050137158,0.000030086414],"category_scores_gemma":[0.000005287649,0.00014862564,0.000026250747,0.00013822403,0.000017956481,0.0002644365,0.000029025865,0.00043792144,0.0000079455185],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020954392,0.000014460049,0.000052842537,0.00057426473,0.00006809723,0.000051098985,0.0020329657,0.020509586,0.002833437,0.007394371,0.000028698472,0.9664192],"study_design_scores_gemma":[0.0004762484,0.0003998702,0.0001352981,0.000289549,0.000058028985,0.0002996845,0.014486544,0.099286824,0.0058649736,0.0010829527,0.87678933,0.00083071634],"about_ca_topic_score_codex":0.0000022149625,"about_ca_topic_score_gemma":0.000021626049,"teacher_disagreement_score":0.9655885,"about_ca_system_score_codex":0.00002962428,"about_ca_system_score_gemma":0.0000064762366,"threshold_uncertainty_score":0.60607773},"labels":[],"label_agreement":null},{"id":"W4390959395","doi":"10.1126/sciadv.adj4411","title":"Wired together, change together: Spike timing modifies transmission in converging assemblies","year":2024,"lang":"en","type":"article","venue":"Science Advances","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"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":"Azrieli Foundation; United States-Israel Binational Science Foundation; Canadian Institutes of Health Research; Rosetrees Trust; International Development Research Centre","keywords":"Postsynaptic potential; Neuroscience; Optogenetics; Neurotransmission; Inhibitory postsynaptic potential; Spike-timing-dependent plasticity; Biology; Spike (software development); Excitatory postsynaptic potential; Stimulation; Computer science; Receptor","score_opus":0.04455654750130584,"score_gpt":0.3044716381794864,"score_spread":0.2599150906781806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390959395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9540761,0.025891311,0.015773723,0.00018219042,0.0013452994,0.00019442037,0.0000017800588,0.00069291354,0.0018422514],"genre_scores_gemma":[0.997069,0.0006294738,0.0020010576,0.00005267638,0.000117275,0.000019037672,6.8447684e-7,0.000020596683,0.00009021734],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998761,0.000014212625,0.00019002949,0.00034620016,0.0002558047,0.0004327606],"domain_scores_gemma":[0.99965054,0.0001091795,0.000016227397,0.00013177266,0.00001845015,0.00007385208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035020596,0.00016373409,0.00015554199,0.00026886968,0.00016983539,0.000105165476,0.00027222492,0.000034024062,0.000017594013],"category_scores_gemma":[0.000025709154,0.00014253354,0.00003846106,0.0009911216,0.00017667141,0.0021255754,0.00003591883,0.0001740513,0.000013527285],"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":[0.0000039119263,0.000005264038,0.0001337534,0.00017561157,0.000002110994,0.000035034416,0.002895864,0.055205114,0.3682265,0.00019558158,0.0000057828406,0.57311547],"study_design_scores_gemma":[0.00018847994,0.000052876603,0.00031638163,0.0010397802,0.000006733435,0.000021835614,0.0013762278,0.2534983,0.7181746,0.002520139,0.022318942,0.0004857183],"about_ca_topic_score_codex":0.0000044845387,"about_ca_topic_score_gemma":0.000010620552,"teacher_disagreement_score":0.57262975,"about_ca_system_score_codex":0.00007256959,"about_ca_system_score_gemma":0.00002533736,"threshold_uncertainty_score":0.58123493},"labels":[],"label_agreement":null},{"id":"W4390970688","doi":"10.1109/biocas58349.2023.10389106","title":"MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Successive approximation ADC; Electronic engineering; Delta-sigma modulation; Comparator; Computer science; Memristor; Effective number of bits; Integrator; Noise shaping; Artificial neural network; Control theory (sociology); CMOS; Engineering; Voltage; Artificial intelligence; Electrical engineering; Bandwidth (computing)","score_opus":0.045990866921732994,"score_gpt":0.2727894215698374,"score_spread":0.2267985546481044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390970688","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6761509,0.00012251468,0.3177342,0.000093924595,0.0007441687,0.0005150774,0.000017706649,0.0027618727,0.0018596045],"genre_scores_gemma":[0.99835294,0.0000075809944,0.0007769463,0.000104806415,0.0002746771,0.00007641697,0.000010630367,0.000057301484,0.00033868555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988663,0.000018515193,0.00025112115,0.00025419507,0.00011703545,0.000492832],"domain_scores_gemma":[0.9990465,0.00062161917,0.00003906622,0.00014473265,0.000051619198,0.000096471376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014356946,0.0001944795,0.00022099039,0.000111003545,0.00020209562,0.000030399728,0.0001452567,0.00006420918,0.00004475753],"category_scores_gemma":[0.00020326677,0.00018731458,0.000112198715,0.0003980791,0.000017747208,0.00016679759,0.000048963753,0.00018074592,0.000040436593],"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":[0.000026557533,0.000008566485,0.00007477162,0.00020083075,0.00008291563,0.00007204066,0.0005079477,0.57228476,0.25992918,0.00050985086,0.0019415172,0.16436107],"study_design_scores_gemma":[0.0005173395,0.000093297116,0.0003344903,0.00004431675,0.000026023741,0.000022350345,0.00036537103,0.6082567,0.38609582,0.0022460115,0.0015786801,0.00041958905],"about_ca_topic_score_codex":0.0000022516945,"about_ca_topic_score_gemma":0.000007947935,"teacher_disagreement_score":0.32220206,"about_ca_system_score_codex":0.000041441916,"about_ca_system_score_gemma":0.000007839734,"threshold_uncertainty_score":0.7638467},"labels":[],"label_agreement":null},{"id":"W4390993409","doi":"10.1109/biocas58349.2023.10388882","title":"STDG: Fast and Lightweight SNN Training Technique Using Spike Temporal Locality","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Spiking neural network; Spike (software development); MNIST database; Computer science; Asynchronous communication; Neuromorphic engineering; Locality; Efficient energy use; Artificial intelligence; Artificial neural network; Energy (signal processing); Face (sociological concept); Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.054493132378378224,"score_gpt":0.27533397038928764,"score_spread":0.22084083801090942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390993409","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7500329,0.00004699593,0.24571529,0.000027802167,0.00015798863,0.00015295752,0.0000024571318,0.0014952268,0.0023684043],"genre_scores_gemma":[0.9897654,0.000008131119,0.0099170115,0.000028169914,0.00010495718,0.000005468038,0.0000037691773,0.000028074932,0.00013902457],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928564,0.000014782026,0.00017252419,0.00017700173,0.0000784588,0.0002715765],"domain_scores_gemma":[0.99973154,0.000046402274,0.000017056193,0.0001180031,0.000011164194,0.000075842094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016993076,0.00013636,0.00015603051,0.00008172126,0.00010813143,0.00002298069,0.00006478133,0.00006195152,0.000018217304],"category_scores_gemma":[0.0000120065015,0.00012627529,0.000029523211,0.00030287812,0.000029725681,0.00015178713,0.000056146997,0.00017268647,0.000009809172],"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":[0.000016947675,0.000016513766,0.0022286267,0.00041211853,0.00004695329,0.0003133901,0.0015513384,0.13760506,0.77201444,0.0016605002,0.00042872597,0.08370538],"study_design_scores_gemma":[0.00034492242,0.00005391381,0.000956656,0.00016165947,0.000015229913,0.00014958932,0.00075841346,0.5456665,0.44525665,0.0022815762,0.0036983779,0.0006564759],"about_ca_topic_score_codex":0.000004645585,"about_ca_topic_score_gemma":0.000003772879,"teacher_disagreement_score":0.40806147,"about_ca_system_score_codex":0.000023455661,"about_ca_system_score_gemma":0.0000075080306,"threshold_uncertainty_score":0.5149357},"labels":[],"label_agreement":null},{"id":"W4390993482","doi":"10.1109/biocas58349.2023.10388869","title":"A 9.5ms-Latency 6.2µJ/Inference Spiking CNN for Patient-Specific Seizure Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","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":"York University","funders":"","keywords":"Computer science; Inference; Latency (audio); Artificial intelligence","score_opus":0.028695164363577566,"score_gpt":0.2510585138758583,"score_spread":0.22236334951228076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390993482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7045756,0.00008974754,0.29025012,0.00002196221,0.00095407106,0.00027230926,0.000003296206,0.0017921515,0.0020407543],"genre_scores_gemma":[0.9977319,0.00004166409,0.001800442,0.00002376714,0.00011216059,0.00002878151,0.000006492653,0.000029183688,0.00022559363],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992904,0.000006627472,0.00016730183,0.00017733,0.00007684337,0.00028151556],"domain_scores_gemma":[0.9996645,0.00011340146,0.000021104031,0.00012608459,0.00002953256,0.000045393084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055888264,0.00012221852,0.00010535329,0.00008432084,0.00013502903,0.000023642904,0.00007029463,0.000052477833,0.000023224324],"category_scores_gemma":[0.000027615957,0.00011763425,0.000053792177,0.00032243226,0.000007869347,0.00015213183,0.000028696719,0.000120936005,0.0000961963],"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":[0.000020111018,0.000007872591,0.000054051932,0.000114695686,0.000014487649,0.000014349788,0.00044415268,0.19574545,0.22509307,0.0005544428,0.0005710044,0.5773663],"study_design_scores_gemma":[0.00046497348,0.0001784181,0.00067424274,0.000083272076,0.000009540461,0.0000127194835,0.00038096512,0.46083823,0.50404435,0.0030687072,0.02972771,0.00051687955],"about_ca_topic_score_codex":8.9963106e-7,"about_ca_topic_score_gemma":0.000004460132,"teacher_disagreement_score":0.57684946,"about_ca_system_score_codex":0.000030229865,"about_ca_system_score_gemma":0.0000031088025,"threshold_uncertainty_score":0.47969854},"labels":[],"label_agreement":null},{"id":"W4391092839","doi":"10.1109/bigdata59044.2023.10386821","title":"Analogous Analogues: Digital Twins and Hardware Tracking in GLAM Collections","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Tracking (education); Computer graphics (images); Computer hardware; Computer vision; Psychology","score_opus":0.02289255560364838,"score_gpt":0.2509920707166515,"score_spread":0.22809951511300314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391092839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845062,0.000060654686,0.0024804769,0.000039181843,0.0001511976,0.00007762481,0.000009189992,0.0008482969,0.011827189],"genre_scores_gemma":[0.99898756,0.000033961587,0.00008083147,0.000016556633,0.00003132353,0.0000036445792,0.000010911495,0.000013214598,0.000822003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955535,0.0000043611394,0.00010969543,0.000112273076,0.00004243226,0.00017587312],"domain_scores_gemma":[0.9998177,0.00007198766,0.0000063599914,0.000059530696,0.000006415882,0.000037979702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032909244,0.00007557552,0.000091998736,0.00017776669,0.000082938044,0.00005586678,0.00003636782,0.000027991726,0.000008277698],"category_scores_gemma":[0.00002765787,0.00007618421,0.000019860607,0.000839801,0.000012673802,0.00023287379,0.000022566306,0.00010679395,0.000015455493],"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":[0.000017807157,0.000046375677,0.015951693,0.00020579065,0.00006944082,0.00055162853,0.002411777,0.8621108,0.015355618,0.001982161,0.00559976,0.09569714],"study_design_scores_gemma":[0.0025057958,0.00027767505,0.24935839,0.00035000997,0.00003248808,0.00031572304,0.0053155436,0.6707259,0.023920288,0.0077285157,0.037568904,0.0019008018],"about_ca_topic_score_codex":0.000003625466,"about_ca_topic_score_gemma":0.000104422405,"teacher_disagreement_score":0.2334067,"about_ca_system_score_codex":0.000022787086,"about_ca_system_score_gemma":0.0000036092013,"threshold_uncertainty_score":0.3106702},"labels":[],"label_agreement":null},{"id":"W4391097221","doi":"10.1109/lmag.2024.3356815","title":"Radiation-Immune Spintronic Binary Synapse and Neuron for Process-in-Memory Architecture","year":2024,"lang":"en","type":"article","venue":"IEEE Magnetics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"","keywords":"XNOR gate; Computer science; Transistor; Standby power; Logic gate; Electronic circuit; Power–delay product; Carbon nanotube field-effect transistor; Materials science; Electrical engineering; Electronic engineering; Field-effect transistor; Engineering; NAND gate; Voltage","score_opus":0.0055272886792400635,"score_gpt":0.22161644883853984,"score_spread":0.21608916015929977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391097221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98744637,0.004313132,0.0057127303,0.0012322264,0.0007553539,0.00027041868,0.000005260093,0.00023511765,0.0000293964],"genre_scores_gemma":[0.99870807,0.00009409004,0.0004990234,0.0003558097,0.00023928867,0.000027968452,0.000004890963,0.00004195544,0.00002888386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993111,0.0000111139025,0.00015787421,0.00020019089,0.00005980845,0.00025990643],"domain_scores_gemma":[0.9997183,0.00012442861,0.000011737227,0.00010482093,0.000005103615,0.00003560456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005950588,0.0001419084,0.0001195564,0.00011947815,0.000037220485,0.000035457968,0.00007684296,0.00003799275,0.000006021432],"category_scores_gemma":[0.000013436758,0.00014665183,0.000035259018,0.00013529311,0.000032282773,0.000069505615,0.000010444173,0.00024858594,0.0000038850467],"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":[0.000012680183,0.000004458172,0.000013769295,0.00067633146,0.000007578544,0.000055567947,0.00034772445,0.56953853,0.39242035,0.0000144559735,0.00027956325,0.036628984],"study_design_scores_gemma":[0.0018706339,0.0006880141,0.0054287063,0.0006089734,0.00009776644,0.0001916654,0.000114128394,0.7961067,0.18293662,0.0014162952,0.00924778,0.0012927363],"about_ca_topic_score_codex":6.1973674e-7,"about_ca_topic_score_gemma":0.0000010496832,"teacher_disagreement_score":0.22656813,"about_ca_system_score_codex":0.000027159585,"about_ca_system_score_gemma":0.000007609321,"threshold_uncertainty_score":0.5980288},"labels":[],"label_agreement":null},{"id":"W4391307866","doi":"10.1109/smc53992.2023.10394149","title":"Learning a Policy for Pursuit-Evasion Games Using Spiking Neural Networks and the STDP Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Pursuer; Pursuit-evasion; Spiking neural network; Computer science; Point (geometry); Artificial intelligence; Evasion (ethics); Spike (software development); Artificial neural network; Control (management); Game theory; Algorithm; Mathematical optimization; Mathematics; Mathematical economics","score_opus":0.023873934849921098,"score_gpt":0.2774436207510811,"score_spread":0.25356968590115997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307866","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51238924,0.00033834326,0.48541963,0.00020724184,0.00033387064,0.00030911274,7.5857184e-7,0.0008283372,0.00017343767],"genre_scores_gemma":[0.9955656,0.0000908584,0.0033150292,0.000109146225,0.00065453904,0.0000110486535,0.0000035990615,0.000039817885,0.00021038631],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926955,0.000029066387,0.00014114629,0.00014539703,0.000076582415,0.00033827315],"domain_scores_gemma":[0.999453,0.00037761743,0.00002641414,0.00008436985,0.00001559095,0.000042997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027088795,0.00012637304,0.00014869419,0.000078959325,0.00030642046,0.00006743203,0.00008598548,0.000041683306,0.0000020736452],"category_scores_gemma":[0.00008401853,0.00009084577,0.000055107383,0.00031743184,0.000039850984,0.00012432296,0.000088924986,0.00021139614,0.0000010199695],"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":[0.000008543201,5.846137e-7,0.000020818206,0.000015968779,0.0000070461037,0.0000022005088,0.00012397677,0.7953472,0.0012108178,0.0002181399,0.000020078633,0.2030246],"study_design_scores_gemma":[0.0005403979,0.00002301144,0.000091693284,0.000024724826,0.00001010628,0.000019261097,0.00022987487,0.9972617,0.0007980347,0.0003818615,0.0005023633,0.000116981464],"about_ca_topic_score_codex":0.000009004794,"about_ca_topic_score_gemma":0.0000014906428,"teacher_disagreement_score":0.48317632,"about_ca_system_score_codex":0.00002098669,"about_ca_system_score_gemma":0.000004151162,"threshold_uncertainty_score":0.3704583},"labels":[],"label_agreement":null},{"id":"W4391324355","doi":"10.1007/s11071-024-09286-4","title":"A novel circuit based on memristor-memcapacitor with extreme multistability","year":2024,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"","keywords":"Multistability; Memristor; Electronic engineering; Computer science; Electrical engineering; Control theory (sociology); Mathematics; Materials science; Topology (electrical circuits); Engineering; Physics; Artificial intelligence; Nonlinear system","score_opus":0.02650034662949476,"score_gpt":0.2267630434448104,"score_spread":0.20026269681531564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391324355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2689097,0.00009468647,0.72647256,0.00006213887,0.00088084943,0.00020665071,0.00014285526,0.0012370575,0.0019935078],"genre_scores_gemma":[0.97075844,0.0000035906278,0.028537484,0.000058114165,0.0003099717,0.000009502958,0.00005812185,0.00008146902,0.0001833009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990903,0.000010483279,0.00017205393,0.00029437774,0.0001809218,0.00025187898],"domain_scores_gemma":[0.9994131,0.00016912892,0.000015105486,0.00029087893,0.000030164862,0.000081677834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009895126,0.00020741616,0.0001601068,0.000076597666,0.000062270316,0.000042014828,0.00011604906,0.000074728836,0.000020918005],"category_scores_gemma":[0.000028680437,0.00018258647,0.00006304881,0.00023555923,0.00003706869,0.00009741843,0.000012428429,0.00039463156,0.000028114524],"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":[0.00006418035,0.00012575407,0.00020489759,0.0006007978,0.000042103646,0.00011749833,0.00018352078,0.9357107,0.02865122,0.00069294655,0.000056789082,0.03354956],"study_design_scores_gemma":[0.00024046439,0.00007138792,0.00008767656,0.00012780172,0.000012593464,0.000014766306,0.00001859606,0.9950799,0.0015751941,0.000034246383,0.00250565,0.00023176227],"about_ca_topic_score_codex":0.000004339574,"about_ca_topic_score_gemma":0.00007019065,"teacher_disagreement_score":0.70184875,"about_ca_system_score_codex":0.00037094543,"about_ca_system_score_gemma":0.00003210324,"threshold_uncertainty_score":0.7445661},"labels":[],"label_agreement":null},{"id":"W4391331234","doi":"10.1109/smc53992.2023.10393866","title":"Spiking Neural Networks for sEMG-Based Hand Gesture Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Gesture recognition; Gesture; Speech recognition; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Spiking neural network; Computer vision","score_opus":0.03503567723504062,"score_gpt":0.2501071812419587,"score_spread":0.21507150400691807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391331234","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55850357,0.00006487154,0.43864042,0.00009351905,0.00075596664,0.00021986986,0.000003916033,0.0013201123,0.0003977803],"genre_scores_gemma":[0.99794,0.0000045850024,0.0013336085,0.00018624055,0.00032816597,0.000019395959,0.00005681189,0.000028212826,0.00010300257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995165,0.0000061307433,0.00009785589,0.00011308342,0.000041136973,0.0002252829],"domain_scores_gemma":[0.999723,0.00014323478,0.000011989422,0.000068152425,0.000018119455,0.00003547091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006623555,0.00009109697,0.00008216725,0.00004463341,0.00011631823,0.000028064476,0.000044133736,0.00004948118,0.000012483162],"category_scores_gemma":[0.000019687079,0.00008679539,0.00004551675,0.00018559776,0.0000084860385,0.00007670642,0.000009783031,0.00010900609,0.000010972965],"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":[0.0000083884515,0.0000014573808,0.000026530026,0.000035795292,0.0000034923446,0.000004323057,0.000014407412,0.9430762,0.0055098296,0.0000062087565,0.00082363444,0.050489757],"study_design_scores_gemma":[0.000263197,0.000025265377,0.00018681661,0.00002426811,0.000005840083,0.0000020565185,0.000014776339,0.97138935,0.026776383,0.00021109643,0.0009771772,0.00012377699],"about_ca_topic_score_codex":3.7128044e-7,"about_ca_topic_score_gemma":0.0000045980833,"teacher_disagreement_score":0.43943644,"about_ca_system_score_codex":0.000011164494,"about_ca_system_score_gemma":0.0000018072985,"threshold_uncertainty_score":0.35394135},"labels":[],"label_agreement":null},{"id":"W4391363637","doi":"10.1007/978-3-031-38684-8","title":"Shared-Memory Synchronization","year":2024,"lang":"en","type":"book","venue":"Synthesis lectures on computer architecture","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Synchronization (alternating current); Computer network","score_opus":0.0070813198091430815,"score_gpt":0.20271209697582127,"score_spread":0.19563077716667818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391363637","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041589985,0.012139572,0.5341496,0.000543776,0.010104572,0.0017168744,0.00044473776,0.009371001,0.431114],"genre_scores_gemma":[0.27885354,0.0009785786,0.048571426,0.009428339,0.07536261,0.0006660902,0.002095044,0.006796172,0.5772482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99765325,0.0000686752,0.0004335415,0.0008679702,0.0004314056,0.0005451571],"domain_scores_gemma":[0.99826866,0.00074285205,0.000079651385,0.0007128036,0.00003765279,0.00015836884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009682915,0.0009669832,0.0007154929,0.00054028205,0.00016213092,0.00018680195,0.0005895556,0.0005792266,0.00020676604],"category_scores_gemma":[0.00003596894,0.000850173,0.00038699247,0.00019608822,0.00007079929,0.00005707036,0.00016679153,0.002013288,0.00052550185],"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":[0.000014913625,0.000008019369,6.916786e-8,0.0009219682,0.00018523553,0.00014936517,0.00018567982,0.58315986,0.00014714345,0.0001768007,0.08369017,0.33136076],"study_design_scores_gemma":[0.00041819765,0.00042706978,0.0000192025,0.010120264,0.0005824471,0.0005713621,0.000004221869,0.17117131,0.02234421,0.053381998,0.7363702,0.0045895316],"about_ca_topic_score_codex":3.5533006e-7,"about_ca_topic_score_gemma":0.0000053814647,"teacher_disagreement_score":0.65268004,"about_ca_system_score_codex":0.00035635478,"about_ca_system_score_gemma":0.000096549105,"threshold_uncertainty_score":0.9993949},"labels":[],"label_agreement":null},{"id":"W4391379387","doi":"10.1021/acs.nanolett.3c04671","title":"Light-Triggerable and Gate-Tunable Negative Differential Resistance in Small Molecules Heterojunction","year":2024,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Research Foundation of Korea","keywords":"Materials science; Optoelectronics; Heterojunction; Voltage; Reproducibility; Gate voltage; Nanotechnology; Chemistry; Transistor; Electrical engineering","score_opus":0.008304073993501039,"score_gpt":0.19712220439554892,"score_spread":0.1888181304020479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391379387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99047387,0.0009870614,0.006742872,0.00043510628,0.0005098036,0.000090298054,0.0000013725617,0.00024220959,0.0005174064],"genre_scores_gemma":[0.99882495,0.000052418534,0.0006774767,0.00014977566,0.00008533867,0.0000097609,0.0000015793554,0.00002256678,0.00017610514],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941385,0.000020211091,0.00012298507,0.00019201568,0.000050252656,0.00020068875],"domain_scores_gemma":[0.99982685,0.000053797918,0.000009415529,0.000074785974,0.0000049126816,0.000030238865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003763559,0.00011939209,0.00011113577,0.00009696827,0.00004468843,0.000057002762,0.00004464161,0.000037068112,0.000007906669],"category_scores_gemma":[0.000007842167,0.00011476557,0.000026404492,0.00016502051,0.000012375483,0.00013945781,0.000017339991,0.00014834096,0.000008503457],"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":[0.0000167709,0.0000032744674,0.000018369155,0.00015216127,0.000010605666,0.00008048554,0.00023930082,0.008652062,0.98942035,0.00012798519,0.00042986064,0.0008487912],"study_design_scores_gemma":[0.00075402594,0.000035486013,0.00055556913,0.00068279344,0.000022188344,0.000018723838,0.000052027775,0.032992974,0.9528463,0.0011354468,0.0103702415,0.00053426204],"about_ca_topic_score_codex":0.0000037309012,"about_ca_topic_score_gemma":0.000042695327,"teacher_disagreement_score":0.036574073,"about_ca_system_score_codex":0.00005380977,"about_ca_system_score_gemma":0.000003512557,"threshold_uncertainty_score":0.4680004},"labels":[],"label_agreement":null},{"id":"W4391481803","doi":"10.1016/j.orgel.2024.107002","title":"Resistive switching kinetics of electrolyte-gated polyaniline-based memristive devices","year":2024,"lang":"en","type":"article","venue":"Organic Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Foundation for Basic Research","keywords":"Memristor; Neuromorphic engineering; Polyaniline; Materials science; Switching time; Resistive random-access memory; Nanotechnology; Electrical conductor; Optoelectronics; Conductive polymer; Ranging; Electrode; Computer science; Polymer; Electronic engineering; Chemistry; Artificial neural network; Engineering; Telecommunications; Artificial intelligence; Polymerization","score_opus":0.005580860009568259,"score_gpt":0.22041039509317087,"score_spread":0.2148295350836026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391481803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8218065,0.024072215,0.1510626,0.00014697837,0.00036102164,0.0002575968,0.0000130823355,0.0013522386,0.0009277913],"genre_scores_gemma":[0.9987019,0.00019365108,0.0007576789,0.00005228427,0.000119521676,0.0000026345424,0.000019636842,0.00008829529,0.00006443329],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870574,0.00003204675,0.00032772246,0.00026991873,0.0001744092,0.00049016724],"domain_scores_gemma":[0.99937314,0.00025121745,0.000048353453,0.00019919148,0.00006471957,0.00006335282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117146206,0.0002429649,0.00026078598,0.00012525002,0.000054936467,0.000031364347,0.00019202416,0.00010014384,0.000052000498],"category_scores_gemma":[0.00005708989,0.00024209273,0.00007046039,0.00065706635,0.000022230008,0.000102613856,0.000025330244,0.0005516106,0.00002065213],"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":[0.000027447435,0.00001679218,0.000008485849,0.00018453783,0.00007231517,0.000016414428,0.00008043498,0.008319437,0.9857596,0.0008256274,0.00009584169,0.0045930543],"study_design_scores_gemma":[0.00017969545,0.00024239343,0.000034856654,0.00011733182,0.00006311789,0.00001225122,0.000019039255,0.07089253,0.9239864,0.0004123115,0.0038049277,0.00023516477],"about_ca_topic_score_codex":0.0000019709657,"about_ca_topic_score_gemma":0.00004046666,"teacher_disagreement_score":0.1768954,"about_ca_system_score_codex":0.00023903135,"about_ca_system_score_gemma":0.00010438701,"threshold_uncertainty_score":0.98722553},"labels":[],"label_agreement":null},{"id":"W4391522116","doi":"10.22541/au.170709088.85277279/v1","title":"BITLITE: Light Bit-wise Operative Vector Matrix Multiplication for Low-Resolution Platforms","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Autoencoder; Computer science; Multiplication (music); Computer engineering; Computation; Artificial neural network; Deep learning; Convolutional neural network; Encoding (memory); Transformation (genetics); Matrix multiplication; Artificial intelligence; Algorithm; Computer architecture; Mathematics; Telecommunications","score_opus":0.018654980911158886,"score_gpt":0.2976039936204196,"score_spread":0.27894901270926076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391522116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47227094,0.0040018293,0.509389,0.00034891116,0.004598498,0.0032586032,0.00022632988,0.003729106,0.0021767898],"genre_scores_gemma":[0.97695434,0.00009055298,0.019470545,0.00005240608,0.00093150744,0.00046703787,0.00022844455,0.00013260964,0.0016725773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987012,0.00000654046,0.00038218693,0.00048535323,0.000117957454,0.0003067326],"domain_scores_gemma":[0.9993506,0.00009798018,0.000052442854,0.00034510408,0.000078967976,0.000074897485],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011319268,0.00035663537,0.00030116225,0.00014543587,0.00009711486,0.000099080644,0.00019709735,0.0002871666,0.000025700421],"category_scores_gemma":[0.00004116426,0.00030247754,0.00017394742,0.00013561487,0.000015031187,0.00012057416,0.00031175636,0.0006187201,0.00015237555],"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":[0.000057934576,0.00003167271,0.000003628703,0.002763211,0.00013293735,0.000006906128,0.000820975,0.82373667,0.15512879,0.006571425,0.0026200009,0.008125872],"study_design_scores_gemma":[0.00025857307,0.000033865777,0.000018948005,0.00042805544,0.000041710817,0.0000046610776,0.000043544107,0.7970129,0.19009617,0.006779401,0.0047946144,0.0004875361],"about_ca_topic_score_codex":0.0000032917658,"about_ca_topic_score_gemma":0.0000053144922,"teacher_disagreement_score":0.5046834,"about_ca_system_score_codex":0.00020755129,"about_ca_system_score_gemma":0.00003108932,"threshold_uncertainty_score":0.9999427},"labels":[],"label_agreement":null},{"id":"W4391575969","doi":"10.21203/rs.3.rs-3931670/v1","title":"Perylene-Based Columnar Liquid Crystal: Reveling Unipolar Resistive Switching for Nonvolatile Memory Devices","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Laboratório Central de Microscopia Eletrônica, Universidade Federal de Santa Catarina; Horizon 2020 Framework Programme; Universidade Federal de Santa Catarina; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Instituto Nacional de Ciência e Tecnologia em Eletrônica Orgânica; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico; European Commission","keywords":"Materials science; Optoelectronics; Perylene; Non-volatile memory; Resistive touchscreen; Fast switching; Resistive random-access memory; Liquid crystal; Nanotechnology; Electrical engineering; Optics; Voltage; Engineering; Physics","score_opus":0.06513409881385651,"score_gpt":0.37211299409389337,"score_spread":0.30697889528003686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391575969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91837955,0.020413691,0.050304513,0.00045753605,0.0017085954,0.0041257236,0.0005330897,0.0021274427,0.0019498643],"genre_scores_gemma":[0.99267507,0.0001428536,0.004787012,0.000035923058,0.0011042989,0.00045144212,0.00015897951,0.00028023994,0.0003641642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961332,0.00029135536,0.0006120772,0.0009945586,0.0008427122,0.0011260642],"domain_scores_gemma":[0.9964494,0.0018216993,0.00008563551,0.00077602465,0.0005976401,0.00026961902],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00219009,0.0005255129,0.0006164959,0.0006759593,0.00062580383,0.00032624832,0.00067915116,0.0004531013,0.00003875666],"category_scores_gemma":[0.0006954844,0.00056367053,0.00034772957,0.0005653968,0.00007706238,0.00013467962,0.0010621741,0.0035419334,0.00004163421],"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":[0.00068754074,0.00007134361,0.00004052993,0.0447718,0.00030381075,0.00025631965,0.0020208254,0.82957953,0.112406224,0.00030522555,0.0020301484,0.007526674],"study_design_scores_gemma":[0.00086552347,0.0007385715,0.00010196362,0.019444356,0.00013527895,0.000010911861,0.002218188,0.8148346,0.13781454,0.007334805,0.014742092,0.0017591937],"about_ca_topic_score_codex":0.000049362614,"about_ca_topic_score_gemma":0.00010892134,"teacher_disagreement_score":0.07429554,"about_ca_system_score_codex":0.00062514073,"about_ca_system_score_gemma":0.00043748177,"threshold_uncertainty_score":0.9996815},"labels":[],"label_agreement":null},{"id":"W4392189166","doi":"10.36227/techrxiv.170905886.62702188/v1","title":"Accelerating Spiking Neural Networks with Parallelizable Leaky Integrate-and-Fire Neurons","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"Alliance de recherche numérique du Canada; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Parallelizable manifold; Spiking neural network; Computer science; Artificial neural network; Artificial intelligence; Algorithm","score_opus":0.0266031885729469,"score_gpt":0.24190873926125153,"score_spread":0.21530555068830462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392189166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91977745,0.0045943055,0.061359424,0.00018743056,0.0021170366,0.00051812886,0.000004819991,0.0029273885,0.008514019],"genre_scores_gemma":[0.9940063,0.00021318732,0.0044309283,0.00014985049,0.0005259523,0.00003515197,0.000019988434,0.00014870898,0.00046996181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831295,0.000029697007,0.00036328327,0.00062512164,0.00013881229,0.0005301648],"domain_scores_gemma":[0.9993408,0.000102639846,0.000056109617,0.00034094552,0.000030782758,0.0001287787],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00009785633,0.0005595696,0.00045580257,0.00008024615,0.00016298387,0.00035913568,0.0002440709,0.0002194874,0.000029741532],"category_scores_gemma":[0.000012796175,0.00044480528,0.00009292491,0.00018089247,0.000045438046,0.00012228877,0.0008116962,0.002499061,0.000007146632],"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":[0.000008998725,0.000003501225,0.00006793138,0.0003474641,0.00004373202,0.0001120305,0.000095194315,0.97540915,0.00019807117,0.00029620357,0.00020733941,0.023210406],"study_design_scores_gemma":[0.00011107691,0.00004386662,0.000040048075,0.0005015726,0.00005112688,0.00008205323,0.00008040128,0.99719584,0.0005662793,0.00053848006,0.0002661402,0.00052313285],"about_ca_topic_score_codex":0.000023090943,"about_ca_topic_score_gemma":0.000037748257,"teacher_disagreement_score":0.07422882,"about_ca_system_score_codex":0.000049931423,"about_ca_system_score_gemma":0.00001914403,"threshold_uncertainty_score":0.99980223},"labels":[],"label_agreement":null},{"id":"W4392236109","doi":"10.3389/fnins.2023.1333238","title":"The silence of the neurons: an application to enhance performance and energy efficiency","year":2024,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Carleton University; University of Windsor","funders":"","keywords":"Computer science; Field-programmable gate array; Artificial neural network; Spike (software development); Energy consumption; Task (project management); Energy (signal processing); Power consumption; Field (mathematics); Efficient energy use; Silence; Power (physics); Computer engineering; Embedded system; Artificial intelligence","score_opus":0.005081636826208407,"score_gpt":0.22690044195921702,"score_spread":0.2218188051330086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392236109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83210826,0.00074259785,0.16508137,0.000081054466,0.0016722991,0.00008496992,9.539465e-7,0.00006708156,0.00016141903],"genre_scores_gemma":[0.9991453,0.0003327248,0.0002907929,0.00010714319,0.00001995851,0.000015010222,4.6696034e-8,0.0000076019714,0.00008138361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932337,0.0000224397,0.0001257299,0.00023067665,0.00012164828,0.00017610795],"domain_scores_gemma":[0.99966586,0.00004717274,0.00001496904,0.00022995238,0.000007717798,0.000034325785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013116705,0.00007348575,0.00005836745,0.00004251806,0.00015223437,0.00003513281,0.00038987605,0.0000138178,8.477726e-8],"category_scores_gemma":[0.000044591507,0.000048247588,0.000011985736,0.0006605195,0.00014620365,0.00019630879,0.000068169626,0.00011698834,2.6774003e-7],"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":[0.000006161844,0.000008803066,0.0011967862,0.00005922359,3.9434784e-7,0.0000013734226,0.0003981964,0.2713341,0.34819025,0.00065874844,0.0001950526,0.3779509],"study_design_scores_gemma":[0.000014174703,0.000046959773,0.006061054,0.000046815654,0.0000011893442,0.0000058628934,0.000036798163,0.8877002,0.098846786,0.00016374678,0.006997639,0.00007875737],"about_ca_topic_score_codex":0.0000013052578,"about_ca_topic_score_gemma":0.0000017634059,"teacher_disagreement_score":0.6163661,"about_ca_system_score_codex":0.000014480659,"about_ca_system_score_gemma":0.000010866344,"threshold_uncertainty_score":0.19674796},"labels":[],"label_agreement":null},{"id":"W4392310839","doi":"10.1039/d4lf00003j","title":"Exploring response time and synaptic plasticity in P3HT ion-gated transistors for neuromorphic computing: impact of P3HT molecular weight and film thickness","year":2024,"lang":"en","type":"article","venue":"RSC Applied Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Neuromorphic engineering; Materials science; Transistor; Thin-film transistor; Ion; Optoelectronics; Synaptic plasticity; Computer science; Nanotechnology; Artificial neural network; Artificial intelligence; Physics; Electrical engineering; Engineering; Voltage; Medicine; Internal medicine","score_opus":0.034355552098785984,"score_gpt":0.24726240804585517,"score_spread":0.21290685594706918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392310839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9842322,0.00062283146,0.014454415,0.000019218907,0.00012230182,0.00028636452,0.000018431867,0.00020867912,0.000035579582],"genre_scores_gemma":[0.9995531,0.000038396956,0.00031734048,0.0000048924767,0.000021384372,0.0000123294685,0.0000032702058,0.00004447958,0.0000048238885],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905133,0.000037471946,0.00029271262,0.00029250217,0.000078604346,0.00024737112],"domain_scores_gemma":[0.99907404,0.000728621,0.000029942306,0.00008758998,0.000015020376,0.00006475799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001813007,0.00022633506,0.00031708527,0.00018112308,0.000045970843,0.000033320393,0.00008786455,0.0000632537,0.0000074496747],"category_scores_gemma":[0.00003870359,0.00021031412,0.000047446316,0.00021097262,0.000070764676,0.00012714845,0.00004175418,0.00025057865,0.0000024533842],"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":[0.0004696748,0.0000090427075,0.0000175962,0.00036726877,0.00006717439,0.000022053931,0.00086576445,0.18922411,0.8073193,0.00006988077,0.000008517208,0.0015596118],"study_design_scores_gemma":[0.00040118856,0.00018210355,0.0007912341,0.00045429065,0.000034220222,0.00003225656,0.00006906213,0.5247982,0.47284475,0.00010653402,0.000036024638,0.00025014946],"about_ca_topic_score_codex":0.000003460555,"about_ca_topic_score_gemma":0.0000011349854,"teacher_disagreement_score":0.3355741,"about_ca_system_score_codex":0.000047501297,"about_ca_system_score_gemma":0.000012798368,"threshold_uncertainty_score":0.8576361},"labels":[],"label_agreement":null},{"id":"W4392505127","doi":"10.1021/jacs.3c12820","title":"Spatially and Temporally Resolved Dynamic Response of Co-Based Composite Interface during the Oxygen Evolution Reaction","year":2024,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced Memory and Neural Computing","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 Waterloo","funders":"Jilin Scientific and Technological Development Program; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Chemistry; Composite number; Interface (matter); Oxygen; Oxygen evolution; Chemical engineering; Chemical physics; Physical chemistry; Composite material; Organic chemistry; Molecule","score_opus":0.006347334579487242,"score_gpt":0.2509063959366274,"score_spread":0.24455906135714015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392505127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907639,0.00042337793,0.008045826,0.00055144506,0.00011533892,0.0000534942,0.0000034110387,0.00003696863,0.0000062472996],"genre_scores_gemma":[0.99871266,0.000035915422,0.0011213532,0.00004107577,0.0000612911,5.4086246e-7,2.827334e-7,0.000015156906,0.00001175347],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993159,0.000067206136,0.00027005293,0.000077309865,0.00015987904,0.00010962453],"domain_scores_gemma":[0.9993605,0.00027320714,0.00018380694,0.00011136049,0.00003668875,0.000034440876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003171175,0.0000924276,0.00017017449,0.00001751393,0.00006511664,0.000017558283,0.00015998683,0.000026825863,9.063649e-7],"category_scores_gemma":[0.0000466281,0.000056762678,0.00018773503,0.00019747265,0.00017911712,0.00007924404,0.00003834079,0.00042194527,3.1619146e-7],"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":[0.00030525375,0.000006554401,0.00013407819,0.00005901311,0.00004869227,0.0000017446935,0.00020698518,0.013141266,0.98543906,5.9890147e-7,0.000077175784,0.0005795483],"study_design_scores_gemma":[0.0002304849,0.000067796864,0.015151219,0.00025536088,0.000039803796,0.000078806785,0.00018803941,0.06734863,0.9162999,0.00004871315,0.00019882292,0.00009246506],"about_ca_topic_score_codex":0.0000043396294,"about_ca_topic_score_gemma":3.7266165e-7,"teacher_disagreement_score":0.069139235,"about_ca_system_score_codex":0.00022433218,"about_ca_system_score_gemma":0.000029513327,"threshold_uncertainty_score":0.23147148},"labels":[],"label_agreement":null},{"id":"W4392615529","doi":"10.1038/s41598-024-55784-1","title":"Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Calgary","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Division of Emerging Frontiers in Research and Innovation; Consejo Nacional de Ciencia y Tecnología; National Institutes of Health; National Science Foundation","keywords":"Neuromorphic engineering; Computer science; Spiking neural network; Realization (probability); Memristor; Electrical element; Electronic circuit; Electric fish; Artificial neural network; Topology (electrical circuits); Neuroscience; Electronic engineering; Physics; Artificial intelligence; Mathematics; Fish <Actinopterygii>","score_opus":0.01986855042110296,"score_gpt":0.23174750585360193,"score_spread":0.21187895543249896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392615529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9695197,0.000455644,0.007525869,0.000064010026,0.020500308,0.0001649574,0.000003798322,0.0005745618,0.0011911786],"genre_scores_gemma":[0.9982679,0.000008077561,0.00019575853,0.000024645044,0.00026104375,0.000009788378,0.000030112033,0.000021861146,0.0011808446],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985955,0.000018186065,0.00027488705,0.0006352345,0.00028066215,0.00019556233],"domain_scores_gemma":[0.9995324,0.000043676813,0.000037605223,0.00025607913,0.000060521634,0.00006971837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045209465,0.000119135315,0.00008613751,0.000113363654,0.00021291134,0.00019337099,0.000033080734,0.000030012432,0.000101021855],"category_scores_gemma":[0.00009958947,0.000114621806,0.000030419718,0.0003931556,0.00008300549,0.0003855937,0.00003513107,0.00022438155,0.000018928602],"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":[0.00003684242,0.00005742522,0.002391175,0.00043814516,0.00013899524,0.0022788034,0.00046627148,0.06930273,0.8358983,0.0005543411,0.037203003,0.051233962],"study_design_scores_gemma":[0.0003429227,0.000234873,0.01804141,0.0003775259,0.00015545246,0.005472231,0.00013350039,0.21351998,0.1744852,0.033576317,0.5525942,0.0010663937],"about_ca_topic_score_codex":5.605323e-7,"about_ca_topic_score_gemma":7.584521e-7,"teacher_disagreement_score":0.6614131,"about_ca_system_score_codex":0.000023609324,"about_ca_system_score_gemma":0.00002785167,"threshold_uncertainty_score":0.46741414},"labels":[],"label_agreement":null},{"id":"W4393007705","doi":"10.1101/2024.03.19.584837","title":"Synapse specific and plasticity-regulated AMPAR mobility tunes synaptic integration","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"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 Institutes of Health Research; Centre National de la Recherche Scientifique; LabEx BRAIN; Université de Bordeaux; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche","keywords":"Synaptic plasticity; Synapse; Neuroscience; Metaplasticity; Plasticity; AMPA receptor; Synaptic scaling; Chemistry; Biology; Physics; Glutamate receptor","score_opus":0.013031735914332924,"score_gpt":0.2077917943115805,"score_spread":0.19476005839724758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393007705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98168904,0.00385456,0.010208655,0.000032204418,0.0016663921,0.000554664,0.00008756889,0.0018994901,0.000007450497],"genre_scores_gemma":[0.9972891,0.000316155,0.0017430113,0.00001839197,0.00040194887,0.00007318056,4.5867864e-7,0.00015543979,0.0000023127932],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99773103,0.00007464231,0.0005586167,0.00092935306,0.00022090258,0.00048544604],"domain_scores_gemma":[0.9987283,0.00016505174,0.00011116829,0.0006180265,0.00015642302,0.00022107897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002842709,0.0006530163,0.00058617635,0.00025510514,0.00012887025,0.0002406065,0.00026685224,0.00045138953,0.000021064156],"category_scores_gemma":[0.00012689193,0.00067703676,0.00011244075,0.0004151425,0.00013515024,0.00014332787,0.000446737,0.0013848761,0.00006289831],"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":[0.000021040152,0.000034332432,0.00009336452,0.0013520286,0.00015536071,0.00008754632,0.000020844127,0.014203961,0.98329073,0.0006344332,0.00008731355,0.00001903217],"study_design_scores_gemma":[0.00026563447,0.00004794425,0.011181865,0.0017069841,0.00016413195,1.6569936e-7,0.0000066415637,0.060288213,0.9246319,0.00007209876,0.0004658614,0.0011685498],"about_ca_topic_score_codex":0.0000041862263,"about_ca_topic_score_gemma":0.000001606807,"teacher_disagreement_score":0.058658835,"about_ca_system_score_codex":0.0002466576,"about_ca_system_score_gemma":0.000063644045,"threshold_uncertainty_score":0.9995681},"labels":[],"label_agreement":null},{"id":"W4393252848","doi":"10.1109/lnet.2024.3382955","title":"Necessary and Sufficient Condition for Triggering ECN Before PFC in Shared Memory Switches","year":2024,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Huawei Technologies","keywords":"Computer science; Computer network","score_opus":0.01446804937160483,"score_gpt":0.24086520517354745,"score_spread":0.2263971558019426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393252848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975649,0.0013940868,0.019672979,0.00025128672,0.0023314343,0.00022907952,0.0000049080704,0.0004105107,0.000056687997],"genre_scores_gemma":[0.9982616,0.000029023775,0.00028353345,0.00029718442,0.0010278944,0.000031891617,0.000012894138,0.00004396839,0.00001201933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991511,0.0000106728385,0.00020240786,0.00024214927,0.000073581155,0.00032006763],"domain_scores_gemma":[0.9996838,0.00016645715,0.0000185853,0.00008531437,0.00000501312,0.00004082993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001538494,0.00015594282,0.0001586022,0.00011792975,0.00007432458,0.000075896205,0.00006959238,0.000051028986,0.000002301683],"category_scores_gemma":[0.0000048780375,0.00016164935,0.000048483937,0.00019114786,0.000022957456,0.00018039226,0.000015820167,0.0002193699,0.0000019947051],"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":[0.00001772712,0.0000048381303,0.00018796259,0.00048744076,0.000026753629,0.00007740264,0.0011221672,0.8707462,0.06783266,0.000018315202,0.002111089,0.05736745],"study_design_scores_gemma":[0.00067193346,0.000060352668,0.00090923853,0.0014718381,0.00003387614,0.00004693774,0.00011729978,0.95970494,0.027147101,0.00027436105,0.009037502,0.0005246482],"about_ca_topic_score_codex":0.0000017141863,"about_ca_topic_score_gemma":0.000009062876,"teacher_disagreement_score":0.088958725,"about_ca_system_score_codex":0.00007321422,"about_ca_system_score_gemma":0.0000047828257,"threshold_uncertainty_score":0.6591869},"labels":[],"label_agreement":null},{"id":"W4394584683","doi":"10.1039/d3tc03609j","title":"Investigating versatile capabilities of organic field-effect transistors incorporated with vacuum-deposited metal nanoparticles","year":2024,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Materials science; Nanoparticle; Field-effect transistor; Transistor; Optoelectronics; Trapping; Metal; Nanotechnology; Charge (physics); Field (mathematics); Electrical engineering; Metallurgy; Voltage","score_opus":0.007150231747207311,"score_gpt":0.2020049778038755,"score_spread":0.1948547460566682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394584683","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9990001,0.0003490027,0.00021054187,0.000023231449,0.0002512939,0.00004414844,0.000005087969,0.000075547876,0.00004107925],"genre_scores_gemma":[0.9992879,0.000007785409,0.00053567416,0.000005539881,0.00012370595,0.0000011980416,0.0000019681447,0.00002395107,0.000012247488],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992112,0.000032433603,0.00042149183,0.00007993504,0.00013335813,0.00012161211],"domain_scores_gemma":[0.9995248,0.00017312079,0.000116290874,0.00007770018,0.000050615326,0.000057529927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002112777,0.0001345393,0.000303481,0.000029701341,0.000026646338,0.00003254088,0.00009093699,0.00005466459,0.0000970309],"category_scores_gemma":[0.0000605723,0.00010182035,0.00004960868,0.00014615849,0.00003907116,0.00016700596,0.000010730514,0.00014066158,9.707624e-7],"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":[0.000041274958,0.000005887754,0.000046787107,0.0012748827,0.000087315566,0.00004444704,0.00031460758,0.0022868733,0.99579304,0.0000013243601,0.000024135921,0.00007939989],"study_design_scores_gemma":[0.00022776579,0.00015199417,0.000021919492,0.00048465858,0.00008215341,0.00019983173,0.00008989449,0.0002593652,0.99831825,0.000044859447,0.00001615731,0.000103163395],"about_ca_topic_score_codex":0.0000014234356,"about_ca_topic_score_gemma":4.2786462e-7,"teacher_disagreement_score":0.0025251731,"about_ca_system_score_codex":0.000041025974,"about_ca_system_score_gemma":0.00003546077,"threshold_uncertainty_score":0.41521135},"labels":[],"label_agreement":null},{"id":"W4394683235","doi":"10.2351/7.0001345","title":"Laser writing of memristive logic gates and crossbar arrays","year":2024,"lang":"en","type":"article","venue":"Journal of Laser Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Crossbar switch; Memristor; Laser; Logic gate; Optoelectronics; Electronic engineering; Optics; Physics; Engineering","score_opus":0.013617514530229117,"score_gpt":0.27153242332858774,"score_spread":0.2579149087983586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394683235","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79053736,0.0030291965,0.20448238,0.00020212845,0.000119864,0.00013430747,0.000018615292,0.00009188982,0.0013842746],"genre_scores_gemma":[0.9951562,0.00012968728,0.0044700536,0.00001689472,0.00017163914,0.0000054754537,0.0000010392616,0.000012025797,0.00003697151],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950904,0.0000074787904,0.00026605694,0.000064620595,0.00006843114,0.00008434953],"domain_scores_gemma":[0.99960077,0.00017530436,0.000055876844,0.000060776958,0.00006014598,0.000047112204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011649687,0.00006734019,0.00012246564,0.00006363351,0.00004937092,0.000024714042,0.00006447476,0.000026070911,0.000011884313],"category_scores_gemma":[0.000011487948,0.000057849327,0.000045747827,0.00015059362,0.000035454315,0.0001292348,0.000014516771,0.00018271257,0.0000045920287],"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":[0.00003464295,0.00012645622,0.0009629298,0.001484877,0.00037581005,0.000100459176,0.0018385508,0.43986708,0.45150524,0.011306306,0.00250714,0.0898905],"study_design_scores_gemma":[0.00059911876,0.00016776523,0.0018793932,0.0006937404,0.0001853777,0.00043369303,0.0011079815,0.05742807,0.88623387,0.023179136,0.027655773,0.00043610425],"about_ca_topic_score_codex":2.3087243e-7,"about_ca_topic_score_gemma":3.0536708e-7,"teacher_disagreement_score":0.4347286,"about_ca_system_score_codex":0.000015925743,"about_ca_system_score_gemma":0.000010161722,"threshold_uncertainty_score":0.23590271},"labels":[],"label_agreement":null},{"id":"W4394693818","doi":"10.1109/mwc.001.2300358","title":"Toward Zero-Trust 6GC: A Software Defined Perimeter Approach with Dynamic Moving Target Defense Mechanism","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Advanced Memory and Neural Computing","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":"Western University; University of Guelph","funders":"","keywords":"Computer science; Perimeter; Mechanism (biology); Software; Zero (linguistics); Distributed computing; Computer security; Operating system; Mathematics","score_opus":0.028667077530897964,"score_gpt":0.24909409503436283,"score_spread":0.22042701750346488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394693818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15604074,0.001953134,0.8384009,0.00009965294,0.0002342025,0.00025371913,0.00002955209,0.0017088017,0.0012792856],"genre_scores_gemma":[0.869825,0.00015829921,0.12957078,0.000044276712,0.00002628318,0.00010049685,0.00008214629,0.000097885604,0.000094801115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889636,0.00006608046,0.00026373065,0.00028505575,0.0001464297,0.00034233314],"domain_scores_gemma":[0.9984119,0.0002408166,0.0000335177,0.0011803549,0.000039204344,0.000094198265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011585099,0.00027057165,0.00024492742,0.00013700366,0.00031242237,0.00011492257,0.0007011993,0.000094388604,0.000007872992],"category_scores_gemma":[0.000013914456,0.00025297786,0.000092330294,0.00041151955,0.00009070497,0.00027397918,0.00016216202,0.0006022046,0.000039600494],"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":[0.000084973355,0.00040583086,0.00021782612,0.0025237259,0.00091125094,0.00015959141,0.0148653425,0.6451784,0.20272264,0.07498359,0.0002807613,0.057666104],"study_design_scores_gemma":[0.00025820636,0.000047878253,0.00006619413,0.0003003731,0.00006792683,0.00013511597,0.00026533764,0.9836225,0.0087320125,0.004681287,0.001232869,0.000590306],"about_ca_topic_score_codex":0.0000063369807,"about_ca_topic_score_gemma":0.000008392217,"teacher_disagreement_score":0.7137843,"about_ca_system_score_codex":0.00011515998,"about_ca_system_score_gemma":0.000037885275,"threshold_uncertainty_score":0.99999225},"labels":[],"label_agreement":null},{"id":"W4394857932","doi":"10.1016/j.mne.2024.100251","title":"Damascene versus subtractive line CMP process for resistive memory crossbars BEOL integration","year":2024,"lang":"en","type":"article","venue":"Micro and Nano Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Resistive random-access memory; Back end of line; Computer science; Scalability; Resistive touchscreen; Neuromorphic engineering; Memristor; Non-volatile memory; Crossbar switch; Computer architecture; Transistor; Materials science; Electronic engineering; Electrical engineering; Voltage; Engineering; Computer hardware; Interconnection; Telecommunications","score_opus":0.018738389329155614,"score_gpt":0.273782405841805,"score_spread":0.2550440165126494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394857932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8702703,0.004032424,0.12325607,0.000027734619,0.0012596182,0.00028260294,0.000032731998,0.0006757224,0.00016279958],"genre_scores_gemma":[0.99748695,0.00009402495,0.0018937819,0.0000070149185,0.0002809158,0.000039359067,0.00002280991,0.00005401335,0.000121138815],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992926,0.000003815893,0.00016957849,0.00023270326,0.0000575752,0.00024370472],"domain_scores_gemma":[0.9996127,0.00020189115,0.000012729594,0.00007939016,0.000036320103,0.00005696285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087913344,0.0001942778,0.00015924471,0.000104180785,0.00007846935,0.000061163155,0.00006155609,0.0000747287,0.0000035844946],"category_scores_gemma":[0.000052855972,0.00019336099,0.00005148125,0.0001758994,0.000016813778,0.00025218725,0.000015451753,0.0001981495,0.0000031285037],"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":[0.00009071936,0.000005365044,0.0000013480717,0.0004998472,0.000047518155,0.000010038745,0.00053781207,0.11906214,0.8666806,0.00011841356,0.00004718839,0.012898967],"study_design_scores_gemma":[0.000406487,0.000068467685,0.00003721092,0.00024201746,0.000028293793,0.000011449058,0.00010559715,0.15565978,0.8415875,0.00002994646,0.0015912291,0.00023201054],"about_ca_topic_score_codex":0.0000010895913,"about_ca_topic_score_gemma":0.0000021140268,"teacher_disagreement_score":0.12721665,"about_ca_system_score_codex":0.000047692243,"about_ca_system_score_gemma":0.0000120663235,"threshold_uncertainty_score":0.7885032},"labels":[],"label_agreement":null},{"id":"W4394906544","doi":"10.48550/arxiv.2404.10694","title":"Towards scalable cryogenic quantum dot biasing using memristor-based DC sources","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"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":"Fonds de recherche du Québec – Nature et technologies; Canada First Research Excellence Fund; École Centrale de Lyon; Institut National des Sciences Appliquées de Lyon; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems; Université de Sherbrooke; Centre National de la Recherche Scientifique; Indian National Science Academy","keywords":"Memristor; Biasing; Quantum dot; Scalability; Optoelectronics; Nanotechnology; Physics; Materials science; Computer science; Voltage; Quantum mechanics","score_opus":0.09645538858202893,"score_gpt":0.206467753728169,"score_spread":0.11001236514614007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394906544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92130893,0.0010373516,0.07337593,0.000013295743,0.001711969,0.0002024084,0.000023838511,0.001151122,0.001175138],"genre_scores_gemma":[0.9983651,0.00007086652,0.0008629138,0.000030497742,0.0002665081,5.046853e-7,0.000014843317,0.00011215392,0.00027658913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815917,0.000070289345,0.00027573548,0.00085043994,0.00010404682,0.0005403013],"domain_scores_gemma":[0.9989639,0.00009413282,0.00010703881,0.0005877356,0.00006617295,0.00018105186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001772567,0.0005059853,0.00046633463,0.000390101,0.0002217668,0.000105426414,0.0005116869,0.00033220902,0.000053484975],"category_scores_gemma":[0.000026267133,0.00061339466,0.00034689804,0.00060200406,0.00009938163,0.00013888728,0.0006729203,0.0011815743,0.00007096922],"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":[0.00003468199,0.000015367881,0.000082901686,0.00061202364,0.00009686569,0.00038794582,0.000081161204,0.9882315,0.008979319,0.0011578442,0.000045172605,0.0002752461],"study_design_scores_gemma":[0.00027959296,0.00002728388,0.000024473658,0.0006016519,0.0002466831,0.000009994999,0.000103096296,0.9681213,0.020072721,0.009277754,0.00052685465,0.00070861675],"about_ca_topic_score_codex":0.000072463474,"about_ca_topic_score_gemma":0.000013372969,"teacher_disagreement_score":0.07705618,"about_ca_system_score_codex":0.000574351,"about_ca_system_score_gemma":0.00015569845,"threshold_uncertainty_score":0.99963176},"labels":[],"label_agreement":null},{"id":"W4394929630","doi":"10.1016/j.molliq.2024.124757","title":"Perylene-Based columnar liquid Crystal: Revealing resistive switching for nonvolatile memory devices","year":2024,"lang":"en","type":"article","venue":"Journal of Molecular Liquids","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Horizon 2020 Framework Programme; Universidade Federal de Santa Catarina; Laboratório Central de Microscopia Eletrônica, Universidade Federal de Santa Catarina; Ministerstwo Edukacji i Nauki; Horizon 2020; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Instituto Nacional de Ciência e Tecnologia em Eletrônica Orgânica; Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção; European Commission","keywords":"Perylene; Materials science; Non-volatile memory; Optoelectronics; Hysteresis; Thermal conduction; Reset (finance); Electrode; Chemical physics; Molecule; Condensed matter physics; Chemistry; Physics; Composite material","score_opus":0.010875246909996403,"score_gpt":0.2551200413410654,"score_spread":0.244244794431069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394929630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63559437,0.005282749,0.35778737,0.00012862349,0.000659114,0.00014759059,0.00000529927,0.00013105443,0.00026381417],"genre_scores_gemma":[0.9867467,0.00002849202,0.012405883,0.00020200542,0.0004849669,0.0000071238946,0.0000026800367,0.000084602194,0.000037517035],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986268,0.000056193527,0.000573822,0.00018263361,0.00026142527,0.0002990775],"domain_scores_gemma":[0.99912965,0.00031142498,0.00011465285,0.00014880384,0.00016402714,0.00013142188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006048877,0.00021415386,0.0003290632,0.00022368466,0.00011070829,0.000090169524,0.0002107451,0.00009664269,0.000011353606],"category_scores_gemma":[0.00014094281,0.00020693634,0.00030565605,0.00022481532,0.000018577455,0.00027687883,0.000027400116,0.00044389738,0.0000022549646],"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":[0.00022908405,0.000012199134,0.0000020741506,0.0005675183,0.00010387529,0.00033111792,0.00019610063,0.20322253,0.79371065,0.00009387695,0.0002578703,0.0012731355],"study_design_scores_gemma":[0.000592732,0.0009509625,0.000010340735,0.0018050452,0.00016109078,0.000150644,0.00018780123,0.11370697,0.87435335,0.0003220818,0.0073740673,0.00038494103],"about_ca_topic_score_codex":8.362992e-7,"about_ca_topic_score_gemma":0.0000015658424,"teacher_disagreement_score":0.35115236,"about_ca_system_score_codex":0.00012066381,"about_ca_system_score_gemma":0.00009643515,"threshold_uncertainty_score":0.8438619},"labels":[],"label_agreement":null},{"id":"W4394930718","doi":"10.36227/techrxiv.171340305.59520871/v1","title":"A small tamper-resistant anti-recycling IC sensor with a reused I/O interface and DC signalling","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Interface (matter); Signalling; Tamper resistance; Computer science; Embedded system; Cell biology; Computer security; Operating system; Biology","score_opus":0.030208795806914927,"score_gpt":0.24305729574411042,"score_spread":0.21284849993719548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394930718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90144575,0.0025738222,0.09263759,0.000085745574,0.00048794123,0.00034415233,0.000010599086,0.0012863463,0.0011280541],"genre_scores_gemma":[0.9799114,0.0001703306,0.019004285,0.000034421995,0.00019577229,0.000015067103,0.0000059898994,0.00013603835,0.000526702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983608,0.000030727224,0.00039576963,0.0006695373,0.00013956559,0.00040356032],"domain_scores_gemma":[0.9992511,0.00017588605,0.000062704254,0.00034430213,0.000041620046,0.00012436426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016643862,0.00050328986,0.0005187439,0.00015591571,0.000081151295,0.00018264448,0.0002046906,0.00020545979,0.000017140439],"category_scores_gemma":[0.000027306189,0.00041628184,0.00008676958,0.00014447597,0.000042525706,0.000044533732,0.00061101327,0.0013964459,0.000016807136],"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":[0.00004568849,0.000008080774,0.000016138449,0.0018607597,0.00014239282,0.00018322455,0.0005218989,0.5804889,0.4150546,0.000048526035,0.000020279916,0.0016095024],"study_design_scores_gemma":[0.0006822875,0.00017238565,0.00007796013,0.007708165,0.00029635153,0.00014478937,0.00068067317,0.42226598,0.5629642,0.0021682866,0.00087673333,0.0019622257],"about_ca_topic_score_codex":0.000012896332,"about_ca_topic_score_gemma":0.00004180032,"teacher_disagreement_score":0.15822296,"about_ca_system_score_codex":0.000071780494,"about_ca_system_score_gemma":0.000029097382,"threshold_uncertainty_score":0.9998289},"labels":[],"label_agreement":null},{"id":"W4394933962","doi":"10.1101/2024.04.16.589741","title":"Connectome-driven neural inventory of a complete visual system","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Wellcome Trust","keywords":"Connectome; Computer science; Neural system; Neuroscience; Artificial intelligence; Psychology; Functional connectivity","score_opus":0.01829709678992888,"score_gpt":0.22692880355747166,"score_spread":0.20863170676754278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394933962","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890796,0.0027196575,0.0012931221,0.000017605049,0.0038288801,0.0005568425,0.00020330992,0.0022723028,0.000028696033],"genre_scores_gemma":[0.99817497,0.00004347544,0.00081015076,0.000027881653,0.0006306123,0.00007122808,4.282448e-7,0.00023936806,0.0000018676058],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974487,0.0001012434,0.00077846687,0.0007412455,0.0003493436,0.00058102334],"domain_scores_gemma":[0.9984582,0.0000947773,0.00023493347,0.0007714542,0.00019756489,0.00024306438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025496577,0.00065824325,0.0008775844,0.00037231308,0.00008693201,0.00009429419,0.0005196667,0.00038223565,0.000011833628],"category_scores_gemma":[0.000049573482,0.00073283503,0.0002702241,0.0004865327,0.00010721606,0.00009757397,0.0007416162,0.0012858355,0.00007888949],"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":[0.000018666045,0.000029909452,0.00035041998,0.007736547,0.00025705365,0.00020807271,0.000020759335,0.045905396,0.9442712,0.0010594862,0.00013917661,0.000003290453],"study_design_scores_gemma":[0.0005860088,0.00010586318,0.0024138677,0.0037283704,0.0002800315,2.0885066e-7,0.000015800482,0.50084114,0.4894626,0.00000512044,0.0010325547,0.0015284175],"about_ca_topic_score_codex":0.000007641739,"about_ca_topic_score_gemma":8.02099e-7,"teacher_disagreement_score":0.45493576,"about_ca_system_score_codex":0.00034452864,"about_ca_system_score_gemma":0.00012393077,"threshold_uncertainty_score":0.99951226},"labels":[],"label_agreement":null},{"id":"W4394998395","doi":"10.1145/3620665.3640357","title":"Marple: Scalable Spike Sorting for Untethered Brain-Machine Interfacing","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Vector Institute; McMaster University; University of Toronto","funders":"","keywords":"Spike sorting; Computer science; Spike (software development); Interfacing; Scalability; Pipeline (software); Process (computing); Sorting; Software; Computer hardware; Embedded system; Algorithm","score_opus":0.01577930869280372,"score_gpt":0.26669829056497,"score_spread":0.2509189818721663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394998395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33920524,0.0021070864,0.63573164,0.0004875755,0.0015106249,0.00033845878,0.000009654459,0.002781253,0.017828463],"genre_scores_gemma":[0.9908936,0.0000053494773,0.0056008613,0.00013749536,0.0001833718,0.000010981728,0.00000453069,0.000053820215,0.0031099815],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992735,0.0000065922777,0.00020667716,0.00018418134,0.000054293163,0.00027476836],"domain_scores_gemma":[0.9995264,0.00029768547,0.000010697441,0.000105600375,0.000013085824,0.000046566605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015618312,0.00013429095,0.00012942494,0.00005857346,0.000072894814,0.00006036634,0.000082518665,0.000036918722,0.00011836364],"category_scores_gemma":[0.00006993342,0.00011901717,0.00006761198,0.00014857712,0.0000096130925,0.000116199284,0.00005144488,0.00018623762,0.00003932276],"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":[0.0000195109,0.000008790882,0.00004086345,0.001163285,0.00007380778,0.000021007045,0.0004212564,0.171278,0.5272915,0.0043519256,0.0047114734,0.29061863],"study_design_scores_gemma":[0.00015563144,0.00003280198,0.0000054939073,0.00019093888,0.000008523678,0.000017144212,0.00006766664,0.7599159,0.22215004,0.00283248,0.014425577,0.0001977909],"about_ca_topic_score_codex":0.0000031255215,"about_ca_topic_score_gemma":0.000009247561,"teacher_disagreement_score":0.6516884,"about_ca_system_score_codex":0.000037717244,"about_ca_system_score_gemma":0.000006175151,"threshold_uncertainty_score":0.48533794},"labels":[],"label_agreement":null},{"id":"W4394998989","doi":"10.1145/3620665.3640419","title":"Design of Novel Analog Compute Paradigms with Ark","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Analogue electronics; Compiler; Computation; Context (archaeology); Field-programmable analog array; Electronic circuit; Computer engineering; Analog computer; Computer architecture; Theoretical computer science; Programming language; Computer hardware; Analog multiplier; Analog signal; Electrical engineering; Engineering; Digital signal processing","score_opus":0.03287696895849183,"score_gpt":0.2350242551183852,"score_spread":0.20214728615989339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394998989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034475513,0.00025279733,0.9622478,0.000010566634,0.000120166296,0.00005847775,7.9564603e-7,0.0003645341,0.0024693746],"genre_scores_gemma":[0.9497908,0.00000844965,0.050028726,0.000016813117,0.000036956862,0.0000012075452,8.565664e-7,0.000014346635,0.00010184036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967337,0.0000035472322,0.00008614133,0.00008572794,0.00004977529,0.00010146288],"domain_scores_gemma":[0.99980235,0.00008772954,0.0000055895453,0.00007273691,0.0000068158356,0.000024789275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039205832,0.00007113164,0.00008787885,0.000036638747,0.000013369559,0.000009903884,0.00004988036,0.0000160763,0.000010512238],"category_scores_gemma":[0.0000012877156,0.00005258442,0.00001553393,0.00015755025,0.000013833835,0.00006718459,0.000008236481,0.00007584072,0.0000058706537],"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":[0.000004448849,0.0000049106043,0.000005639304,0.000063673986,0.0000223069,0.00001399595,0.000047318077,0.96092176,0.03279331,0.0023247954,0.00014075317,0.0036570674],"study_design_scores_gemma":[0.00011110032,0.000055118333,0.00004285792,0.00008012664,0.000007558258,0.000030011804,0.000007038087,0.93591315,0.062898055,0.0001303208,0.0006301629,0.0000944815],"about_ca_topic_score_codex":6.0086194e-7,"about_ca_topic_score_gemma":3.853321e-7,"teacher_disagreement_score":0.9153153,"about_ca_system_score_codex":0.000008052876,"about_ca_system_score_gemma":0.0000056419112,"threshold_uncertainty_score":0.21443303},"labels":[],"label_agreement":null},{"id":"W4395003179","doi":"10.1007/978-3-031-38684-8_10","title":"Correction to: Shared-Memory Synchronization","year":2024,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on computer architecture","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Synchronization (alternating current); Computer network","score_opus":0.007694660549121674,"score_gpt":0.20612400637414202,"score_spread":0.19842934582502034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395003179","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024585758,0.0023307197,0.5222976,0.00047616832,0.021950742,0.0013031468,0.00015903596,0.0049789706,0.44625774],"genre_scores_gemma":[0.40413618,0.00039040396,0.0199441,0.0059569734,0.03138711,0.0002924462,0.0004824169,0.0033294645,0.5340809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791497,0.000025034931,0.00039764142,0.00083427277,0.00037562323,0.00045247463],"domain_scores_gemma":[0.99845004,0.0006632877,0.00006780317,0.00057980977,0.000047172874,0.0001918723],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007909608,0.00085846445,0.00058930466,0.0006244119,0.00016716159,0.00015575202,0.0004005287,0.0003738588,0.00030770552],"category_scores_gemma":[0.000045449076,0.000790216,0.00031684418,0.000136705,0.000038828006,0.000048996688,0.00014610066,0.0015950943,0.00071993476],"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":[0.000021827847,0.000004196586,6.734339e-8,0.00022396783,0.00010854888,0.00006121833,0.0001810104,0.6143169,0.00026867617,0.00037173968,0.02772716,0.35671473],"study_design_scores_gemma":[0.00028355332,0.0006841107,0.000029882201,0.011575021,0.00043239756,0.00057120173,0.0000052632845,0.21828847,0.034529265,0.02807008,0.7013925,0.004138262],"about_ca_topic_score_codex":0.0000011138754,"about_ca_topic_score_gemma":0.000019943396,"teacher_disagreement_score":0.67366534,"about_ca_system_score_codex":0.00027039443,"about_ca_system_score_gemma":0.000029665305,"threshold_uncertainty_score":0.99945486},"labels":[],"label_agreement":null},{"id":"W4395087787","doi":"10.21203/rs.3.rs-4019510/v1","title":"Molybdenum-based Metallic Cluster-type Memristor exhibiting Stochastic Switching and Analog-state Programmable Characteristics and its Utilization for Homomorphic Encryption Hardware","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National NanoFab Center; Korea Advanced Institute of Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Encryption; Memristor; Homomorphic encryption; Computer science; Key (lock); Hardware security module; Computer hardware; Embedded system; Cryptography; Computer network; Electronic engineering; Algorithm; Operating system; Engineering","score_opus":0.10298483592916413,"score_gpt":0.3674681756178777,"score_spread":0.26448333968871357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395087787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9164581,0.0063884365,0.073586695,0.0000802972,0.0004147215,0.002275258,0.0001368469,0.00064766634,0.0000119959395],"genre_scores_gemma":[0.99791694,0.0003289685,0.00073134486,0.000009882936,0.000268124,0.00025686936,0.0002566696,0.00016251807,0.0000686564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976242,0.00015370607,0.00043539156,0.0006891186,0.0004294695,0.00066810485],"domain_scores_gemma":[0.998478,0.00051853206,0.0000965637,0.00026127952,0.00043959258,0.00020602692],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010869468,0.0003791577,0.000452038,0.0004693569,0.00037911336,0.00038401267,0.00016234815,0.00022544988,0.0000055362225],"category_scores_gemma":[0.00061995815,0.00039820324,0.00008409473,0.00037482433,0.000050942446,0.00013696887,0.0004883268,0.0014547748,0.000008492455],"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":[0.0007485098,0.00014821261,0.00018328484,0.10213673,0.00049816555,0.00028694235,0.0032144,0.5440583,0.19951195,0.0011613052,0.00015399249,0.1478982],"study_design_scores_gemma":[0.00043772048,0.00022681874,0.00018569114,0.0032292688,0.00008124837,0.000019213296,0.000118434145,0.9821282,0.009348496,0.0035049082,0.00022827674,0.0004917333],"about_ca_topic_score_codex":0.000006484668,"about_ca_topic_score_gemma":0.000013405472,"teacher_disagreement_score":0.43806988,"about_ca_system_score_codex":0.000207959,"about_ca_system_score_gemma":0.00011374438,"threshold_uncertainty_score":0.999847},"labels":[],"label_agreement":null},{"id":"W4395456619","doi":"10.1002/eng2.12902","title":"A reliable non‐volatile in‐memory computing associative memory based on spintronic neurons and synapses","year":2024,"lang":"en","type":"article","venue":"Engineering Reports","topic":"Advanced Memory and Neural Computing","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":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Content-addressable memory; Computer science; Associative property; Non-volatile memory; Spintronics; Neuroscience; Computer architecture; Psychology; Computer hardware; Artificial neural network; Artificial intelligence; Mathematics; Physics","score_opus":0.004973967040086656,"score_gpt":0.20989758682654658,"score_spread":0.20492361978645993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395456619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98424345,0.001081278,0.010832024,0.000053677773,0.0015522358,0.00023503425,0.0000020824602,0.0011014332,0.0008988117],"genre_scores_gemma":[0.99863625,0.000014620192,0.00095608167,0.000047574704,0.00017870059,0.000011863601,0.000005155565,0.00007811985,0.000071638264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865216,0.0000086855935,0.0003731672,0.00038810313,0.00016065051,0.00041721217],"domain_scores_gemma":[0.9993176,0.00031688288,0.000040367875,0.0002249642,0.000013785434,0.000086388136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028432376,0.00026485458,0.00028340888,0.00024098638,0.000059196784,0.00006059028,0.000057992096,0.00008473781,0.000009156207],"category_scores_gemma":[0.00014733302,0.0002855973,0.00006322302,0.0003381095,0.000015010054,0.00015911322,0.000042456642,0.00058216014,0.000004704918],"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":[0.0000024432386,0.000008721694,0.000263782,0.00025193693,0.000015677733,0.0012340986,0.00017205034,0.9880376,0.0074240766,0.000010250955,0.00018865564,0.0023907023],"study_design_scores_gemma":[0.00012159376,0.000052108673,0.003989131,0.0006596598,0.00001242507,0.00011252094,0.00002126382,0.9839814,0.010238997,0.000033112377,0.00048628965,0.00029152256],"about_ca_topic_score_codex":0.000005895275,"about_ca_topic_score_gemma":0.0000017580111,"teacher_disagreement_score":0.014392822,"about_ca_system_score_codex":0.00018155434,"about_ca_system_score_gemma":0.000027075232,"threshold_uncertainty_score":0.99995965},"labels":[],"label_agreement":null},{"id":"W4395675340","doi":"10.21203/rs.3.rs-4306732/v1","title":"28 nm FD-SOI embedded phase change memory exhibiting near-zero drift at 12 K for cryogenic spiking neural networks (SNNs)","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Silicon on insulator; Phase-change memory; Zero (linguistics); Spiking neural network; Materials science; Artificial neural network; Phase (matter); Computer science; Physics; Optoelectronics; Phase change; Artificial intelligence; Quantum mechanics; Thermodynamics","score_opus":0.11857147478985589,"score_gpt":0.40002601480125766,"score_spread":0.2814545400114018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395675340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9523192,0.018360829,0.015801594,0.00025343063,0.003759357,0.005269191,0.0002532725,0.0022900775,0.0016930334],"genre_scores_gemma":[0.9918835,0.00032738512,0.0013828017,0.000076035954,0.0036834995,0.0011232515,0.00037271157,0.00043739853,0.00071340404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994171,0.00030547028,0.0008247526,0.0014059619,0.0009782475,0.0023145361],"domain_scores_gemma":[0.9968581,0.0011715083,0.00013429785,0.0010520455,0.00028267736,0.000501341],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017961909,0.0008321402,0.00086744124,0.00050501083,0.00095511315,0.00053360674,0.00083423936,0.00067726336,0.00011715567],"category_scores_gemma":[0.00030043093,0.0008812582,0.0006556915,0.000576134,0.00017824958,0.0002408457,0.0032063026,0.0047324896,0.000068879664],"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":[0.00033064498,0.00008819682,0.000042092837,0.0100733545,0.00022907935,0.00069322676,0.0021816934,0.8377909,0.010377343,0.00013930073,0.0034416383,0.13461253],"study_design_scores_gemma":[0.0011450215,0.00024831638,0.00003107879,0.0025079597,0.000064659835,0.000046832352,0.00024317358,0.9819281,0.008177001,0.0012855626,0.0034025572,0.00091975264],"about_ca_topic_score_codex":0.000024388084,"about_ca_topic_score_gemma":0.00006752739,"teacher_disagreement_score":0.14413719,"about_ca_system_score_codex":0.00070508337,"about_ca_system_score_gemma":0.00009978644,"threshold_uncertainty_score":0.99936384},"labels":[],"label_agreement":null},{"id":"W4396508111","doi":"10.22215/etd/2024-15966","title":"Memristor Based Gain-Varying PI Control for Erbium-Doped Fiber Amplifiers (EDFAs)","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Automatic gain control; Robustness (evolution); Memristor; Optical amplifier; Electronic engineering; Computer science; Root locus; Optical communication; Engineering; Control system; Amplifier; Electrical engineering; CMOS; Physics","score_opus":0.015040387247982064,"score_gpt":0.2621888200649677,"score_spread":0.24714843281698562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396508111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085098855,0.0070734965,0.66662157,0.00019435944,0.018932274,0.006241569,0.00036714712,0.008524511,0.20694622],"genre_scores_gemma":[0.9346359,0.000019540636,0.0044648936,0.00037588686,0.0008737791,0.0003326918,0.001172708,0.00037709906,0.057747543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856627,0.000016398337,0.00039817468,0.0004280217,0.00016380173,0.0004273507],"domain_scores_gemma":[0.9991464,0.0003654121,0.000064340005,0.00024101367,0.0000721799,0.00011064289],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012496849,0.00043593955,0.00045420325,0.0001706336,0.00013041546,0.00006872438,0.00017840257,0.00025442927,0.0002921298],"category_scores_gemma":[0.00006396668,0.00042803405,0.00028194586,0.00015069354,0.00000981268,0.000097543896,0.0000072385556,0.00046277477,0.00014395094],"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":[0.00063287973,0.000039338527,0.0000030358017,0.008834276,0.0005706347,0.00008305323,0.0008051798,0.8049858,0.1158477,0.0011232548,0.03266553,0.03440929],"study_design_scores_gemma":[0.0031092174,0.000167517,0.000015550222,0.0013535236,0.0007023313,0.000007969282,0.00050836726,0.6333838,0.2346819,0.0013951717,0.122319795,0.002354822],"about_ca_topic_score_codex":0.0000037401653,"about_ca_topic_score_gemma":0.000018385343,"teacher_disagreement_score":0.849537,"about_ca_system_score_codex":0.00013373209,"about_ca_system_score_gemma":0.000050207793,"threshold_uncertainty_score":0.99981713},"labels":[],"label_agreement":null},{"id":"W4396572048","doi":"10.1016/j.apmt.2024.102214","title":"An implantable memristor towards biomedical applications","year":2024,"lang":"en","type":"article","venue":"Applied Materials Today","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Fujian Normal University; Xi’an Jiaotong University; National Natural Science Foundation of China","keywords":"Memristor; Neuromorphic engineering; Quantum tunnelling; Computer science; Field (mathematics); Electronic engineering; Nanotechnology; Memistor; Electrical engineering; Resistive random-access memory; Materials science; Engineering; Voltage; Artificial intelligence; Artificial neural network; Optoelectronics","score_opus":0.008880037434914707,"score_gpt":0.2515732349587514,"score_spread":0.2426931975238367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396572048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7598283,0.0009419465,0.18983719,0.00009845632,0.0041812025,0.0011393907,0.0003391972,0.0070576896,0.036576584],"genre_scores_gemma":[0.9967187,0.000039455048,0.0020641456,0.000050157967,0.0007204321,0.00017001007,0.00012297434,0.000048016012,0.000066104934],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991959,0.0000086033515,0.00021421336,0.00023015258,0.000100508354,0.0002506343],"domain_scores_gemma":[0.9996404,0.000028367063,0.000012009561,0.00021347108,0.0000062210515,0.00009954132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014222173,0.00014549731,0.00017381884,0.00006212136,0.00007327933,0.000089080124,0.00015078057,0.00007774749,0.00022000946],"category_scores_gemma":[0.0000014756758,0.00013272752,0.00001838135,0.00014357483,0.000028959746,0.000106218096,0.000027653618,0.00009026864,0.0003134017],"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":[0.0000050544645,0.00000978231,7.170267e-8,0.00015156921,0.000011699129,0.0000046816376,0.00006721032,0.00034289106,0.9729574,0.0057748165,0.0026016454,0.01807323],"study_design_scores_gemma":[0.000070619055,0.00001455707,0.0000064673104,0.000016825545,0.0000131018005,0.00002560382,0.00001972358,0.00029168362,0.8138661,0.0020117087,0.18348613,0.00017749997],"about_ca_topic_score_codex":0.000002970856,"about_ca_topic_score_gemma":4.6905134e-7,"teacher_disagreement_score":0.23689036,"about_ca_system_score_codex":0.000040408497,"about_ca_system_score_gemma":0.000015874337,"threshold_uncertainty_score":0.5412471},"labels":[],"label_agreement":null},{"id":"W4396596440","doi":"10.2139/ssrn.4814534","title":"Towards Scalable Cryogenic Quantum Dot Biasing Using Memristor-Based Dc Sources","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Biasing; Memristor; Quantum dot; Optoelectronics; Scalability; Materials science; Quantum; Physics; Nanotechnology; Voltage; Computer science; Quantum mechanics","score_opus":0.02417571656556987,"score_gpt":0.2649670667842605,"score_spread":0.24079135021869064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396596440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8731309,0.046064373,0.0762188,0.0001242201,0.003219562,0.0002042642,0.000009164524,0.0006682985,0.00036040234],"genre_scores_gemma":[0.9949302,0.0016668468,0.0013003948,0.000041778938,0.0016210148,0.0000058590517,0.000009340861,0.0002257727,0.0001987877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995071,0.00011302555,0.0007020865,0.0005420301,0.00045477742,0.0031170591],"domain_scores_gemma":[0.9990428,0.00007748656,0.00021985732,0.0003889037,0.00008850085,0.00018241793],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013603121,0.0006459952,0.0006278157,0.00044660547,0.00038330417,0.0002989919,0.00059157045,0.00036537027,0.000028513252],"category_scores_gemma":[0.00005857387,0.00064173015,0.0005135554,0.00034176753,0.00006137594,0.00013295062,0.0003500335,0.009814937,0.00003365727],"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":[0.000036739293,0.00002104981,0.000022868411,0.00044760777,0.00038708578,0.000063582025,0.00014362688,0.9512715,0.033769194,0.0022584533,0.00004356533,0.0115347365],"study_design_scores_gemma":[0.00071340945,0.00019314422,0.000012792981,0.0018761834,0.0005614504,0.0014945179,0.0006206324,0.58170354,0.04457139,0.36490124,0.0016606671,0.0016910086],"about_ca_topic_score_codex":0.000041897518,"about_ca_topic_score_gemma":0.00006551513,"teacher_disagreement_score":0.36956793,"about_ca_system_score_codex":0.0034551113,"about_ca_system_score_gemma":0.0027648993,"threshold_uncertainty_score":0.9996034},"labels":[],"label_agreement":null},{"id":"W4396918003","doi":"10.1109/cicc60959.2024.10529012","title":"Modular Flexible 80-dB-DR Artifact-Resilient EEG Headset with Distributed Pulse-Based Feature Extraction and Multiplier-Less Neuromorphic Boosted Seizure Classifier","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"York University","funders":"","keywords":"Computer science; Neuromorphic engineering; Feature extraction; Wearable computer; Modular design; Artificial intelligence; Support vector machine; Computer hardware; Speech recognition; Pattern recognition (psychology); Embedded system; Artificial neural network","score_opus":0.026484646075138205,"score_gpt":0.2484440715938985,"score_spread":0.2219594255187603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396918003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8193643,0.00066438684,0.17626955,0.0007901462,0.00052355893,0.00040683584,0.00009694382,0.0016402788,0.00024399685],"genre_scores_gemma":[0.9975792,0.000018595227,0.0010022882,0.00010529856,0.00009812072,0.00002136351,0.00020788428,0.00007118236,0.00089602824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986453,0.00003808236,0.00021416093,0.0004771105,0.00021740317,0.00040791044],"domain_scores_gemma":[0.9993412,0.00013297552,0.00002966195,0.00027761277,0.000043746353,0.00017481885],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008132014,0.00034055274,0.00025738345,0.00012173648,0.00015990346,0.00014683793,0.000082781866,0.00015717736,0.000045566605],"category_scores_gemma":[0.0000162263,0.00027585175,0.000060103714,0.00042195382,0.000050354643,0.00033406905,0.000028106704,0.00065587746,0.000019249108],"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":[0.00014630878,0.00004422523,0.00020717703,0.00040685132,0.000058394187,0.00038290492,0.00004998733,0.64558977,0.34143656,0.00011200522,0.0024784333,0.009087404],"study_design_scores_gemma":[0.0006762168,0.00013463756,0.006211699,0.000244342,0.000054192205,0.00016735564,0.00007969809,0.79844034,0.17822598,0.00005135278,0.0152395,0.0004746775],"about_ca_topic_score_codex":0.0000041713884,"about_ca_topic_score_gemma":0.000021583719,"teacher_disagreement_score":0.17821494,"about_ca_system_score_codex":0.00007785215,"about_ca_system_score_gemma":0.000025525835,"threshold_uncertainty_score":0.99996936},"labels":[],"label_agreement":null},{"id":"W4399089855","doi":"10.1007/978-3-031-62076-8_2","title":"Compositional Reversible Computation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Computation; Theoretical computer science; Algorithm","score_opus":0.015140598516267534,"score_gpt":0.24318721270008817,"score_spread":0.22804661418382063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399089855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055460434,0.0007739733,0.9852891,0.000092987335,0.002258149,0.00014703997,0.0000081250455,0.00036773816,0.010508271],"genre_scores_gemma":[0.841393,0.00007277091,0.15509392,0.0006115046,0.0015791488,0.0000044359977,0.000052697287,0.00012345292,0.0010690636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863064,0.000004245763,0.00024769924,0.000494277,0.00033641828,0.00028670763],"domain_scores_gemma":[0.99946034,0.00018852175,0.000037431422,0.0001898374,0.00005375664,0.000070095404],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001471536,0.00028421424,0.00024063789,0.00038240218,0.00011116259,0.00010962046,0.0003314196,0.00013730883,0.000022646263],"category_scores_gemma":[0.000005985673,0.00028991062,0.00007084286,0.0002437329,0.00019405216,0.00017727308,0.0001720482,0.0006698692,0.000105843974],"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":[0.0000019796548,0.0000023511243,0.0000012514156,0.000095422765,0.000006759238,0.00006801506,0.000079488666,0.90724325,0.0016321056,0.0024246743,0.000038874656,0.0884058],"study_design_scores_gemma":[0.00009903006,0.00004478725,0.000010419587,0.000674864,0.000010037647,0.00008910291,7.7679104e-8,0.79051363,0.007114893,0.19965008,0.0013711195,0.00042193997],"about_ca_topic_score_codex":6.3809716e-7,"about_ca_topic_score_gemma":0.000003810396,"teacher_disagreement_score":0.8408384,"about_ca_system_score_codex":0.00022828268,"about_ca_system_score_gemma":0.000049077917,"threshold_uncertainty_score":0.9999553},"labels":[],"label_agreement":null},{"id":"W4399436263","doi":"10.1016/b978-0-323-99738-6.00004-6","title":"Conveying bioenergy","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"","keywords":"Bioenergy; Environmental science; Renewable energy; Biology; Ecology","score_opus":0.015902554182114104,"score_gpt":0.2226842715984001,"score_spread":0.20678171741628598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399436263","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008337964,0.0053681904,0.00003160879,0.00000863966,0.0016493025,0.00012720126,0.000012136223,0.0008860049,0.9918335],"genre_scores_gemma":[0.0048958953,0.0001510441,0.00014811219,0.000092425296,0.0007293162,0.000007870132,0.000011325548,0.00020158711,0.99376243],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990155,0.0000032773548,0.00028611626,0.00030031998,0.00013339483,0.00026137795],"domain_scores_gemma":[0.99955195,0.000043942186,0.00003642299,0.00025993472,0.000018814982,0.000088942004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005624433,0.00038532424,0.00034312575,0.0001223649,0.000055403536,0.00003970448,0.00016428607,0.00021234107,0.000102468555],"category_scores_gemma":[0.0000027500967,0.00038263042,0.0001732451,0.000010631731,0.000046383564,0.000029972056,0.00008787818,0.0006246963,0.0005748195],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015185351,3.4930306e-7,3.249385e-8,0.00021399428,0.0000708074,0.00013711773,0.000046859666,0.000557743,0.0010550746,0.015180635,0.00008717987,0.9826487],"study_design_scores_gemma":[0.000054085933,0.000013003094,7.859748e-8,0.00065068784,0.000049918362,0.000032503704,0.000002066967,0.00046242747,0.003989489,0.019031525,0.9752957,0.00041849646],"about_ca_topic_score_codex":1.7828556e-8,"about_ca_topic_score_gemma":0.0000013541523,"teacher_disagreement_score":0.9822302,"about_ca_system_score_codex":0.00006750701,"about_ca_system_score_gemma":0.000012870859,"threshold_uncertainty_score":0.99986255},"labels":[],"label_agreement":null},{"id":"W4399489854","doi":"10.1016/j.biosystems.2024.105247","title":"Thermodynamic model for memory","year":2024,"lang":"en","type":"article","venue":"Biosystems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"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":"Department of Biology, University of Dayton; Memorial University of Newfoundland; McGill University","keywords":"Computer science","score_opus":0.021681781368673474,"score_gpt":0.2454014862057184,"score_spread":0.22371970483704492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399489854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.482217,0.004456466,0.505538,0.000025594709,0.0022771857,0.00036201856,0.000029112744,0.0017309359,0.0033636875],"genre_scores_gemma":[0.99845576,0.0000102706235,0.000414756,0.000010420162,0.00024083341,0.000023506063,0.0000035040139,0.000033788445,0.00080717244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960685,0.0000035279945,0.000102206206,0.00011041624,0.000040810562,0.00013618119],"domain_scores_gemma":[0.9998306,0.00003749225,0.0000050975036,0.00009365388,0.000007007307,0.00002613496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000636885,0.00008012657,0.00008138096,0.000033442127,0.000030663763,0.000024202844,0.00006136454,0.000035235367,0.0000020197683],"category_scores_gemma":[0.0000033377223,0.00006977999,0.000051337494,0.0000574097,0.0000044268904,0.00006592928,0.000007722567,0.000054016426,0.0000252144],"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":[0.0000052866126,0.0000036486958,0.0000011683741,0.0014610831,0.000039674418,0.000009608351,0.00040420267,0.49371323,0.4787629,0.002546601,0.0017267563,0.02132586],"study_design_scores_gemma":[0.000047586662,0.0000065796585,0.0000011004043,0.00008841835,0.000004966256,0.000007880768,0.00001652604,0.9862708,0.011042653,0.00036481352,0.0020566087,0.00009206487],"about_ca_topic_score_codex":3.667174e-7,"about_ca_topic_score_gemma":0.0000012315455,"teacher_disagreement_score":0.51623875,"about_ca_system_score_codex":0.000031519143,"about_ca_system_score_gemma":0.0000060740877,"threshold_uncertainty_score":0.28455457},"labels":[],"label_agreement":null},{"id":"W4399510304","doi":"10.3389/fnsyn.2024.1432259","title":"Editorial: Insights in synaptic neuroscience 2022","year":2024,"lang":"en","type":"editorial","venue":"Frontiers in Synaptic Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Montreal General Hospital","funders":"National Institute on Aging","keywords":"Neuroscience; Psychology; Cognitive science","score_opus":0.008317833749115447,"score_gpt":0.2386284309656512,"score_spread":0.23031059721653574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399510304","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038161266,0.002405405,0.0034356199,0.000027267486,0.9889803,0.00047818437,0.00003378405,0.00056388543,0.0002594373],"genre_scores_gemma":[0.034734827,0.0018176858,0.00046396622,0.00011543465,0.9620752,0.00010733418,0.000013695084,0.00021910461,0.00045277263],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942234,0.00016702287,0.00094015035,0.0018075345,0.0015629047,0.0012989843],"domain_scores_gemma":[0.9982545,0.0004878578,0.00014466746,0.00079766463,0.00006498106,0.0002503781],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006572673,0.00079422974,0.0009210773,0.0012071113,0.00017297138,0.0002785437,0.0019168865,0.0007257714,0.0000028889249],"category_scores_gemma":[0.0022912188,0.0008254636,0.00014771239,0.0029306994,0.00052945456,0.00074305746,0.0004747671,0.0038970711,0.00002754226],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018555158,0.00003317197,0.000021926851,0.00028485162,0.000003865449,0.000613059,0.00016841719,0.04365855,0.008974397,0.0000190534,0.94562155,0.00058260607],"study_design_scores_gemma":[0.0003542623,0.00016893666,0.000026972502,0.0005417259,0.000024460443,0.000014190436,0.000047070123,0.11466499,0.0004001467,0.0011838201,0.8816127,0.0009607221],"about_ca_topic_score_codex":0.000010030452,"about_ca_topic_score_gemma":0.00001934585,"teacher_disagreement_score":0.07100643,"about_ca_system_score_codex":0.0005831255,"about_ca_system_score_gemma":0.0003067197,"threshold_uncertainty_score":0.9994196},"labels":[],"label_agreement":null},{"id":"W4399573545","doi":"10.5772/intechopen.1003837","title":"Non-Idealities in Memristor Devices and Methods of Mitigating Them","year":2024,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Memristor; Context (archaeology); Computer science; Software; Quality (philosophy); Resistive random-access memory; Electronic engineering; Engineering; Electrical engineering","score_opus":0.03192432067023161,"score_gpt":0.2989100065396777,"score_spread":0.2669856858694461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399573545","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004238269,0.0016950488,0.0014244635,0.0000075062158,0.00034546555,0.0002600728,0.000013454652,0.00016912268,0.9918466],"genre_scores_gemma":[0.5928177,0.00014644186,0.031818353,0.0001002178,0.00031001598,0.000041540097,0.000007916093,0.00031904964,0.37443876],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905056,0.000009955585,0.00044024384,0.0002422032,0.0000901274,0.00016690948],"domain_scores_gemma":[0.9994246,0.00024309148,0.00008336948,0.00018240933,0.000025237687,0.000041286246],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002606054,0.000282421,0.00046457865,0.00014377745,0.000025359232,0.00002767072,0.00017401752,0.00018030549,0.000031651365],"category_scores_gemma":[0.000015032957,0.00027297708,0.00007942094,0.0000148712425,0.000090130656,0.000047725578,0.00016027647,0.00061733485,0.000011231868],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057834757,0.000006352201,0.000027794369,0.009186399,0.0005444869,0.00033702806,0.008525341,0.0013774008,0.07832276,0.2200758,0.00035625685,0.68118256],"study_design_scores_gemma":[0.00050287653,0.00012291169,0.000011227606,0.015835946,0.00019334242,0.00015177431,0.0010944391,0.006324574,0.43561128,0.34190732,0.19646478,0.0017795155],"about_ca_topic_score_codex":0.000012596075,"about_ca_topic_score_gemma":0.00003776574,"teacher_disagreement_score":0.679403,"about_ca_system_score_codex":0.00005544919,"about_ca_system_score_gemma":0.000014098629,"threshold_uncertainty_score":0.9999722},"labels":[],"label_agreement":null},{"id":"W4399729040","doi":"10.1109/syscon61195.2024.10553446","title":"3D Convolutional Spiking Neural Network for Human Action Recognition Using Modulating STDP With Global Error Feedback","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Convolutional neural network; Action recognition; Artificial intelligence; Spiking neural network; Pattern recognition (psychology); Action (physics); Speech recognition; Artificial neural network","score_opus":0.09625214450620186,"score_gpt":0.32371354995828805,"score_spread":0.2274614054520862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399729040","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6122014,0.000091906644,0.38595212,0.000008556484,0.00066875544,0.00016074457,0.000008688774,0.0005475254,0.00036033694],"genre_scores_gemma":[0.9338789,8.415863e-7,0.06480984,0.00003079093,0.0011655584,0.000008879747,0.000046428366,0.00003263278,0.000026154623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915695,0.000012849553,0.00019122368,0.0002252847,0.00010168521,0.00031197857],"domain_scores_gemma":[0.9997525,0.000060521066,0.00002732139,0.00006573545,0.00004801494,0.000045858014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000088836925,0.0001604291,0.00012337428,0.000029953077,0.00026949178,0.00007194865,0.000042554162,0.00005405854,0.000028874432],"category_scores_gemma":[0.000008585668,0.00015076934,0.000052801814,0.00022445622,0.000018841058,0.00038188865,0.000016907412,0.0001496543,0.000004380012],"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":[0.000020309426,0.0000032514808,0.00015585405,0.00014091466,0.000027333439,0.0000046832183,0.000016970222,0.94802946,0.008457471,0.00027907346,0.000054906697,0.042809803],"study_design_scores_gemma":[0.00021798092,0.00005481192,0.0004790334,0.00020875887,0.0000322991,0.000055175125,0.00004222449,0.99468637,0.0014700212,0.0024156722,0.0001217479,0.0002159259],"about_ca_topic_score_codex":0.000007520518,"about_ca_topic_score_gemma":0.000031965654,"teacher_disagreement_score":0.3216775,"about_ca_system_score_codex":0.00016928389,"about_ca_system_score_gemma":0.000009660759,"threshold_uncertainty_score":0.61481947},"labels":[],"label_agreement":null},{"id":"W4399771712","doi":"10.1016/j.cjph.2024.06.014","title":"Dynamics modeling of a memristor-based Rucklidge chaotic system: Multistability, offset boosting control and FPGA implementation","year":2024,"lang":"en","type":"article","venue":"Chinese Journal of Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Nankai University","keywords":"Chaotic; Attractor; Computer science; Multistability; Boosting (machine learning); Offset (computer science); Field-programmable gate array; Memristor; Complex dynamics; Bifurcation diagram; Control theory (sociology); Statistical physics; Bifurcation; Physics; Nonlinear system; Mathematics; Artificial intelligence; Computer hardware; Control (management)","score_opus":0.010775233137914867,"score_gpt":0.26895147064729763,"score_spread":0.2581762375093828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399771712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62111366,0.000537022,0.37793872,0.000015187635,0.00025641342,0.00006674069,0.00001970068,0.000038858485,0.000013714237],"genre_scores_gemma":[0.9986712,0.0000089652,0.0010337039,0.000005822213,0.00024797468,0.0000010464997,0.000005624366,0.000025051795,6.2309107e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913543,0.000029941724,0.00047211364,0.00008825767,0.00015211644,0.0001221232],"domain_scores_gemma":[0.9994606,0.00018469682,0.0001236697,0.00007863676,0.0000999377,0.00005248717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026053537,0.00014059777,0.00029546465,0.00006599969,0.00004797207,0.000025608508,0.00006969403,0.000026920148,8.573504e-7],"category_scores_gemma":[0.00002966282,0.00011321252,0.000097910255,0.00015001108,0.000017558017,0.00023461852,0.000014202335,0.00021918846,2.9354553e-7],"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":[0.00002116622,0.000011609064,0.0005295297,0.0015903702,0.000051827876,0.000017420076,0.00034950717,0.9653032,0.019753525,0.00025410316,0.0000031285765,0.012114605],"study_design_scores_gemma":[0.00056586793,0.00005106758,0.0001329295,0.0003594627,0.000060271497,0.000034762605,0.0002254756,0.99530107,0.002449842,0.0007237354,0.0000012590415,0.00009427534],"about_ca_topic_score_codex":0.0000040454643,"about_ca_topic_score_gemma":0.000004956407,"teacher_disagreement_score":0.37755755,"about_ca_system_score_codex":0.00014325824,"about_ca_system_score_gemma":0.000028663795,"threshold_uncertainty_score":0.46166724},"labels":[],"label_agreement":null},{"id":"W4399893426","doi":"10.20944/preprints202406.1486.v1","title":"A Comprehensive Review on Processing-in-Memory Architectures for Deep Neural Networks","year":2024,"lang":"en","type":"review","venue":"Preprints.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial neural network; Computer architecture; Artificial intelligence; Cognitive science; Psychology","score_opus":0.15043386007398904,"score_gpt":0.39195540207624996,"score_spread":0.24152154200226092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399893426","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033915567,0.99551934,0.00043995786,0.000017069657,0.0006885212,0.0020739366,0.000007010585,0.00052589976,0.00038912147],"genre_scores_gemma":[0.0028307342,0.9955877,0.00007787788,0.00022646617,0.0004490122,0.00054897246,0.000031143503,0.00017025716,0.000077827084],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977351,0.00009723745,0.0007539691,0.0007587156,0.00014491835,0.00051005324],"domain_scores_gemma":[0.99892336,0.00025831224,0.00014534972,0.0005369731,0.00003697665,0.00009905638],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020036729,0.0006640905,0.0016009717,0.00018590674,0.000068198875,0.000016345259,0.00043886868,0.00020789096,0.000024175688],"category_scores_gemma":[0.00009213543,0.0005458195,0.00054511783,0.00040883303,0.000037821934,0.000029836423,0.00020119293,0.0014228701,0.00014959504],"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":[0.0000052728874,0.000010030338,0.0000026859777,0.17788796,0.000035018955,0.000037206508,0.000034347962,0.07846819,0.0000021821488,0.0000031968534,0.000024988762,0.7434889],"study_design_scores_gemma":[0.00018952311,0.00004001689,0.000024595674,0.15618785,0.0006943464,0.00014006626,0.0000060969182,0.046408523,0.00003188863,0.00013538304,0.7951328,0.0010088857],"about_ca_topic_score_codex":5.098202e-7,"about_ca_topic_score_gemma":0.0000013762085,"teacher_disagreement_score":0.79510784,"about_ca_system_score_codex":0.00012038215,"about_ca_system_score_gemma":0.000026521186,"threshold_uncertainty_score":0.99969935},"labels":[],"label_agreement":null},{"id":"W4400010536","doi":"10.21203/rs.3.rs-4575664/v1","title":"Flexible Self-rectifying Synapse Array for Energy-efficient Edge Multiplication in Electrocardiogram Diagnosis","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Multiplication (music); Enhanced Data Rates for GSM Evolution; Computer science; Energy (signal processing); Synapse; Telecommunications; Physics; Neuroscience; Mathematics; Biology; Combinatorics","score_opus":0.05400928199033496,"score_gpt":0.37086242351679743,"score_spread":0.3168531415264625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400010536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70302874,0.034572914,0.24514641,0.00044481675,0.0022349302,0.0060516833,0.00016056767,0.0050931843,0.0032667418],"genre_scores_gemma":[0.99132574,0.0015162464,0.0022915811,0.0000075558387,0.0005287633,0.0040366068,0.00009188045,0.00012993568,0.00007167933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972837,0.00016442462,0.00038049044,0.0007750413,0.00045323052,0.0009430978],"domain_scores_gemma":[0.9981113,0.0009610122,0.0000350005,0.0005497945,0.00020102038,0.00014184084],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010639409,0.00032794106,0.00038882752,0.0008597547,0.00018513402,0.0001570631,0.0003757627,0.0003263166,0.0000053566214],"category_scores_gemma":[0.00024227312,0.00034716725,0.00025917107,0.0009914075,0.00003150975,0.000043231266,0.000446026,0.0019206642,0.0000248793],"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":[0.000040335737,0.00012482054,0.00018070129,0.0051258625,0.00012745355,0.000023140587,0.00064360356,0.92738336,0.021274144,0.00056496146,0.000792404,0.0437192],"study_design_scores_gemma":[0.00045690825,0.00018109808,0.00025791195,0.0026834174,0.000040100032,0.000008143671,0.00022214951,0.3823664,0.5920167,0.0066332733,0.014342681,0.000791228],"about_ca_topic_score_codex":0.000034049084,"about_ca_topic_score_gemma":0.00002812973,"teacher_disagreement_score":0.57074255,"about_ca_system_score_codex":0.00083995494,"about_ca_system_score_gemma":0.00010640723,"threshold_uncertainty_score":0.999898},"labels":[],"label_agreement":null},{"id":"W4400066938","doi":"10.1088/2634-4386/ad5c97","title":"Efficient sparse spiking auto-encoder for reconstruction, denoising and classification","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","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 Toronto; York University","funders":"","keywords":"MNIST database; Spiking neural network; Neuromorphic engineering; Computer science; Artificial intelligence; Encoder; Pattern recognition (psychology); Inference; Noise reduction; Encoding (memory); Spike (software development); Noise (video); Machine learning; Deep learning; Artificial neural network","score_opus":0.029966217576607623,"score_gpt":0.23263226529352554,"score_spread":0.20266604771691793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400066938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6928352,0.0011578262,0.30398634,0.00003956839,0.0010022564,0.00011327912,0.0000018043082,0.0008261216,0.00003759587],"genre_scores_gemma":[0.9939183,0.00004271584,0.0056740935,0.000013729697,0.00028435676,0.0000046226864,0.0000021693204,0.0000515608,0.000008430173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910283,0.000008982026,0.0002322478,0.0003126738,0.0000719264,0.00027132817],"domain_scores_gemma":[0.99950093,0.00028169152,0.000019050007,0.00009719189,0.000020813168,0.00008032078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017992954,0.00018808439,0.00016424866,0.00014173394,0.00015918605,0.00013559904,0.00004684155,0.00005670702,0.0000011386604],"category_scores_gemma":[0.00004970917,0.00020525289,0.00003525078,0.00017980675,0.000025353054,0.00006779383,0.000034878463,0.0002516588,0.0000015171693],"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":[0.0000025548181,0.0000026123262,0.00005328341,0.0005233124,0.00001574648,0.000012255044,0.00023533333,0.79134494,0.11862081,0.0007045017,0.000018892648,0.088465735],"study_design_scores_gemma":[0.00012732326,0.00002137554,0.0011102417,0.000394425,0.000019990794,0.0003999886,0.000026363308,0.9950894,0.0016884047,0.00006294087,0.00085140014,0.00020810947],"about_ca_topic_score_codex":5.977394e-7,"about_ca_topic_score_gemma":1.9671658e-7,"teacher_disagreement_score":0.30108312,"about_ca_system_score_codex":0.00003073958,"about_ca_system_score_gemma":0.000007011178,"threshold_uncertainty_score":0.836997},"labels":[],"label_agreement":null},{"id":"W4400222598","doi":"10.48550/arxiv.2406.19667","title":"Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"CMC Microsystems","keywords":"Neuromorphic engineering; Memristor; CMOS; Analogue electronics; Electronic circuit; Computer science; Electronic engineering; Computer architecture; Memistor; Electrical engineering; Resistive random-access memory; Artificial intelligence; Voltage; Engineering; Artificial neural network","score_opus":0.09446519308435226,"score_gpt":0.18621953286796242,"score_spread":0.09175433978361015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400222598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91234696,0.00038163728,0.07659635,0.000033701555,0.004497598,0.0007863737,0.0002241289,0.0018532217,0.0032800455],"genre_scores_gemma":[0.998056,0.00012829833,0.000068206595,0.00005378206,0.0002250163,0.0000029719238,0.00009329118,0.00010917394,0.0012632852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983366,0.000053557098,0.00023732735,0.00086825166,0.000063238665,0.00044101465],"domain_scores_gemma":[0.99894977,0.00016101921,0.00008581444,0.00054975133,0.000087669854,0.00016598521],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009853997,0.00046140142,0.00041981658,0.0002642259,0.00012626089,0.000056198525,0.000505299,0.00028110432,0.000038114624],"category_scores_gemma":[0.000049891703,0.000560639,0.0002973298,0.00046196024,0.00006128863,0.000106215615,0.0005450171,0.0011824935,0.000086953405],"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":[0.000030221405,0.000019873482,0.000031293624,0.00068514,0.00011650416,0.0005767198,0.00009242968,0.989471,0.003171788,0.0022856004,0.0017728584,0.0017465345],"study_design_scores_gemma":[0.0005193163,0.00011700352,0.0001000158,0.00030475616,0.0003181752,0.000015518655,0.00006718672,0.9708614,0.003554545,0.01649122,0.0068092826,0.00084158557],"about_ca_topic_score_codex":0.00001457609,"about_ca_topic_score_gemma":0.000018456216,"teacher_disagreement_score":0.085709035,"about_ca_system_score_codex":0.00025239267,"about_ca_system_score_gemma":0.0000747446,"threshold_uncertainty_score":0.9996845},"labels":[],"label_agreement":null},{"id":"W4400229854","doi":"10.1109/iscas58744.2024.10558666","title":"Configurable and Intelligent Switched CMOS Current Driver Powering Arrays of Electrothermal Actuators for MEMS Switches","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"","keywords":"Actuator; Microelectromechanical systems; CMOS; Electrical engineering; Current (fluid); Computer science; Electronic engineering; Materials science; Engineering; Optoelectronics","score_opus":0.01758088937829489,"score_gpt":0.2622069539633517,"score_spread":0.2446260645850568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400229854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53269136,0.001874341,0.46411857,0.000014051324,0.00036129518,0.00021528579,0.0000023202515,0.0002490172,0.0004737777],"genre_scores_gemma":[0.99878323,0.00017906785,0.00083827274,0.00000875249,0.00007385079,0.000015464473,0.0000020157383,0.000031374475,0.000067990295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993537,0.0000025879935,0.00018949895,0.00016610813,0.00006379487,0.00022427809],"domain_scores_gemma":[0.9997149,0.000115817435,0.000015217567,0.00008259607,0.00001792845,0.000053512806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007899446,0.00014134684,0.00016599886,0.000058997713,0.000033124503,0.000023952529,0.00006203248,0.00003338354,0.00004151609],"category_scores_gemma":[0.000012401268,0.000121619734,0.00005831656,0.00009021597,0.000017814891,0.00011789713,0.00001555514,0.00013214674,0.0000036918111],"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":[0.000017150407,0.000010414578,0.000024668981,0.0006369285,0.000059418984,0.000001602922,0.0007451519,0.008654468,0.8363879,0.0015356481,0.0000911727,0.1518355],"study_design_scores_gemma":[0.00010853261,0.000044944252,0.000014267467,0.00013285113,0.000015421638,0.000005204481,0.000113975206,0.05961926,0.9349061,0.0006341828,0.004253563,0.00015166237],"about_ca_topic_score_codex":0.0000013581179,"about_ca_topic_score_gemma":0.0000014800731,"teacher_disagreement_score":0.46609187,"about_ca_system_score_codex":0.000026894866,"about_ca_system_score_gemma":0.0000104216315,"threshold_uncertainty_score":0.49595088},"labels":[],"label_agreement":null},{"id":"W4400230130","doi":"10.1109/iscas58744.2024.10558103","title":"Spiking Auto-Encoder Using Error Modulated Spike Timing Dependant Plasticity","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Spike (software development); Computer science; Encoder; Spike-timing-dependent plasticity; Speech recognition; Algorithm; Synaptic plasticity","score_opus":0.05343842019079829,"score_gpt":0.289597649187131,"score_spread":0.2361592289963327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400230130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57296085,0.0001633871,0.42385656,0.000008035862,0.00063407305,0.00006132068,0.0000015025923,0.0012315289,0.0010827146],"genre_scores_gemma":[0.98638755,0.0000041943686,0.01318996,0.00003216034,0.00018408797,0.0000014203196,0.000001809168,0.00005452565,0.00014427325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990131,0.000011242021,0.00023192546,0.0002563089,0.00012887147,0.0003585123],"domain_scores_gemma":[0.99969435,0.00009611025,0.000013134036,0.00010298312,0.000015880214,0.00007753671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007732026,0.00019557761,0.00016502169,0.000108602944,0.0001212395,0.00007915595,0.00009449815,0.00006728642,0.00012134648],"category_scores_gemma":[0.000023538896,0.0001794989,0.000064787964,0.0002572108,0.000015856347,0.00029991742,0.000058018904,0.00030137567,0.000049943897],"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":[0.0000020560358,0.000002767231,0.000013716871,0.000096100666,0.000017092347,0.00010039208,0.00008921586,0.70541614,0.28942636,0.00013627224,0.000021246398,0.0046786563],"study_design_scores_gemma":[0.00006701502,0.0000090453605,0.00007070419,0.00019732188,0.000019572011,0.00007338463,0.000028593357,0.8994707,0.099204645,0.00017093655,0.00046856512,0.00021953387],"about_ca_topic_score_codex":0.000008141433,"about_ca_topic_score_gemma":0.000005447731,"teacher_disagreement_score":0.4134267,"about_ca_system_score_codex":0.000092127186,"about_ca_system_score_gemma":0.000014879205,"threshold_uncertainty_score":0.7319752},"labels":[],"label_agreement":null},{"id":"W4400230160","doi":"10.1109/iscas58744.2024.10558182","title":"In-Memory Transformer Self-Attention Mechanism Using Passive Memristor Crossbar","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Memristor; Transformer; Computer science; Mechanism (biology); Electrical engineering; Physics; Engineering; Voltage; Telecommunications","score_opus":0.01102041135886275,"score_gpt":0.24700266320018355,"score_spread":0.2359822518413208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400230160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83202684,0.00033335318,0.16173965,0.000044086413,0.0014914771,0.00015350744,0.0000021111757,0.0008933083,0.0033156537],"genre_scores_gemma":[0.9962756,0.00002962227,0.0030973852,0.000055744527,0.00015520204,0.0000056137947,0.0000014023452,0.000035011155,0.00034440364],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993235,0.000009738933,0.00017957645,0.00017374339,0.00008522284,0.00022821798],"domain_scores_gemma":[0.99983364,0.000030360507,0.0000068423615,0.00007746572,0.00000983874,0.00004183925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000644334,0.00013093304,0.00011458416,0.0001090198,0.000053065203,0.000036359957,0.000055381795,0.00006051668,0.000101710764],"category_scores_gemma":[0.000002431695,0.00012478692,0.00006382073,0.00019331352,0.000008072326,0.00040480087,0.00000701578,0.00019784062,0.000053507378],"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":[0.000005469287,0.000016532154,0.0000032879527,0.000399753,0.000038422942,0.00021457131,0.0007198475,0.04269756,0.94202757,0.0067384867,0.00014131065,0.0069971904],"study_design_scores_gemma":[0.00029478554,0.000027373197,0.000039974802,0.00015209305,0.000028849443,0.000077541794,0.00021596673,0.42679608,0.5650764,0.004996345,0.0019227569,0.00037181884],"about_ca_topic_score_codex":0.0000035058836,"about_ca_topic_score_gemma":0.0000041084068,"teacher_disagreement_score":0.38409853,"about_ca_system_score_codex":0.00013382774,"about_ca_system_score_gemma":0.000011714873,"threshold_uncertainty_score":0.5088663},"labels":[],"label_agreement":null},{"id":"W4400232793","doi":"10.1109/iscas58744.2024.10558615","title":"BITLITE: Light Bit-wise Operative Vector Matrix Multiplication for Low-Resolution Platforms","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Crossbar switch; Autoencoder; Computer science; Multiplication (music); Computer engineering; Matrix multiplication; Computation; Artificial neural network; Deep learning; Convolutional neural network; Encoding (memory); Matrix (chemical analysis); Transformation (genetics); Memristor; Artificial intelligence; Algorithm; Computer hardware; Parallel computing; Electronic engineering; Engineering; Telecommunications; Mathematics","score_opus":0.013933239593184632,"score_gpt":0.28710659554788254,"score_spread":0.2731733559546979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400232793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34978986,0.0014136524,0.6444922,0.00016782952,0.0008722563,0.0006494906,0.000020447218,0.0016786876,0.0009155552],"genre_scores_gemma":[0.99097186,0.000023631248,0.0075992825,0.00003548388,0.0002936846,0.00007387372,0.000025221409,0.000035883397,0.0009410669],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940693,0.000002779645,0.00016221144,0.0001837446,0.0000589476,0.00018539968],"domain_scores_gemma":[0.9997186,0.0000922481,0.000009302225,0.00011037915,0.000027523034,0.000041942418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006248733,0.00012241071,0.00009614321,0.00006704664,0.00008022376,0.00005207898,0.00006397992,0.00005596567,0.000027156604],"category_scores_gemma":[0.000020914353,0.00009725196,0.000056429602,0.0001631556,0.000008034823,0.00032313686,0.000017000424,0.00010477957,0.00011018154],"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":[0.00003538668,0.000019152008,0.000007456051,0.0005090702,0.00004441493,0.000005818376,0.0007268109,0.1518467,0.8071836,0.017158588,0.0024979464,0.019965004],"study_design_scores_gemma":[0.00015727224,0.000031524873,0.000028526769,0.00007093066,0.00000735444,0.000004513107,0.000034306693,0.7059032,0.27844378,0.0005036189,0.014665673,0.00014930095],"about_ca_topic_score_codex":8.76958e-7,"about_ca_topic_score_gemma":0.0000020271816,"teacher_disagreement_score":0.641182,"about_ca_system_score_codex":0.0000705124,"about_ca_system_score_gemma":0.000008201015,"threshold_uncertainty_score":0.396582},"labels":[],"label_agreement":null},{"id":"W4400234622","doi":"10.1109/iscas58744.2024.10557966","title":"SAR-MemPipe: A Hybrid Pipeline-SAR Memristive ADC for Analog Resistive Arrays","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Successive approximation ADC; Pipeline (software); Resistive touchscreen; Computer science; Synthetic aperture radar; Electronic engineering; Artificial intelligence; Electrical engineering; Capacitor; Engineering; Computer vision; Voltage","score_opus":0.01927429833523163,"score_gpt":0.2666665630822616,"score_spread":0.24739226474702997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400234622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030268548,0.0015417057,0.95061255,0.00017148728,0.0011043593,0.0003955538,0.00009988808,0.0016095806,0.01419634],"genre_scores_gemma":[0.9845678,0.00005280747,0.011803864,0.00015047255,0.0006107206,0.000014377539,0.000036690344,0.00006520256,0.002698016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896556,0.000013080098,0.00024501106,0.00033137453,0.00010262846,0.0003423485],"domain_scores_gemma":[0.9992984,0.00037780573,0.000016740765,0.00016680556,0.00005193495,0.00008831066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012928419,0.00021929434,0.00022083889,0.00010042769,0.00010241626,0.00004472935,0.00012269251,0.00004504603,0.000065199856],"category_scores_gemma":[0.000093391536,0.00019699054,0.0001298316,0.00021155672,0.000029976201,0.00016551808,0.000032479842,0.0002263209,0.00007963179],"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":[0.00035426908,0.000092601935,0.00005048007,0.002547274,0.0006047142,0.0008638357,0.0017925738,0.33716986,0.13706502,0.020595385,0.2570228,0.24184117],"study_design_scores_gemma":[0.00038490212,0.00010519875,0.000027219783,0.00021270973,0.0000686179,0.000038961345,0.00026556387,0.52345103,0.34512874,0.005417819,0.124336354,0.0005628545],"about_ca_topic_score_codex":0.0000030847734,"about_ca_topic_score_gemma":0.0000054912725,"teacher_disagreement_score":0.95429933,"about_ca_system_score_codex":0.00007222474,"about_ca_system_score_gemma":0.00001938977,"threshold_uncertainty_score":0.80330414},"labels":[],"label_agreement":null},{"id":"W4400234677","doi":"10.1109/iscas58744.2024.10557875","title":"Advancing Image Classification with Phase-coded Ultra-Efficient Spiking Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Spiking neural network; Pattern recognition (psychology); Image (mathematics); Contextual image classification; Computer vision","score_opus":0.012886546208594045,"score_gpt":0.26076948402612293,"score_spread":0.24788293781752888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400234677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26593596,0.0003863843,0.72909606,0.000045846118,0.00042422663,0.00012717953,9.0593437e-7,0.0012854898,0.002697929],"genre_scores_gemma":[0.99501103,0.000015342495,0.0045663924,0.00004604617,0.00022509319,0.0000093062445,0.000008975736,0.000048466045,0.000069324844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991095,0.000012355703,0.00018673019,0.00024750942,0.00010961361,0.0003343118],"domain_scores_gemma":[0.9996382,0.00010215243,0.000016315942,0.00015164519,0.000020457543,0.000071233335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009622555,0.00016996323,0.00012325315,0.00006895051,0.000092561146,0.00009107204,0.00008243507,0.000037246762,0.000030192778],"category_scores_gemma":[0.000008965514,0.00013633327,0.000041316434,0.00031875478,0.000023464445,0.00022439145,0.000009791791,0.00028802053,0.000012417686],"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":[0.000009445913,0.000009179059,0.0000045182,0.000055708184,0.000008497859,0.000033223438,0.00007225655,0.8022542,0.16950084,0.0003340716,0.000074895695,0.027643152],"study_design_scores_gemma":[0.0002127676,0.000043721586,0.000024428477,0.00009720242,0.0000130991375,0.000041830095,0.00009605028,0.9490332,0.04979588,0.000018162338,0.00043727667,0.0001863781],"about_ca_topic_score_codex":7.030505e-7,"about_ca_topic_score_gemma":0.0000021590192,"teacher_disagreement_score":0.7290751,"about_ca_system_score_codex":0.000062171435,"about_ca_system_score_gemma":0.0000058356304,"threshold_uncertainty_score":0.55595094},"labels":[],"label_agreement":null},{"id":"W4400254354","doi":"10.20944/preprints202407.0130.v1","title":"A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Neuromorphic engineering; Von Neumann architecture; Computer science; Spiking neural network; Computer architecture; Artificial neural network; Spike (software development); Memristor; Artificial intelligence; Reservoir computing; Electronic engineering; Recurrent neural network; Engineering","score_opus":0.2736677353099857,"score_gpt":0.3702834801673866,"score_spread":0.09661574485740088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400254354","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9749984,0.00028634662,0.018425792,0.00007907062,0.0034956,0.0008705995,0.00013528067,0.0011867085,0.0005221919],"genre_scores_gemma":[0.9980587,0.00002462983,0.0006560182,0.00006908041,0.0006984326,0.00015414729,0.000067456494,0.00015331325,0.00011822735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974759,0.00008656718,0.0006028831,0.0010572512,0.00024740337,0.00052995427],"domain_scores_gemma":[0.99834037,0.00054788496,0.000100300575,0.0008045659,0.00007068395,0.00013616688],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007256321,0.0005434463,0.000498876,0.00024132639,0.00012535333,0.000052847103,0.0006015079,0.00026923997,0.000056156132],"category_scores_gemma":[0.00041072778,0.000571198,0.0002611812,0.0002066574,0.000064090505,0.000015392096,0.0011558774,0.0021454894,0.00044441424],"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":[0.00007856504,0.000032698186,0.0013526683,0.0006574317,0.000099663244,0.00004603268,0.00045171106,0.9638188,0.008309321,0.00022021204,0.000021777563,0.024911117],"study_design_scores_gemma":[0.000092493734,0.00009024189,0.028788142,0.001067355,0.00008330139,0.00001978167,0.00002985814,0.38716447,0.5027065,0.07826665,0.00038706826,0.0013041206],"about_ca_topic_score_codex":0.000025907586,"about_ca_topic_score_gemma":0.000023329985,"teacher_disagreement_score":0.5766543,"about_ca_system_score_codex":0.00009375637,"about_ca_system_score_gemma":0.00004780468,"threshold_uncertainty_score":0.99967396},"labels":[],"label_agreement":null},{"id":"W4400405332","doi":"10.1101/2024.07.04.602047","title":"Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Toronto; Canadian Institute for Theoretical Astrophysics; Université Laval","funders":"","keywords":"Calcium; Neuroscience; Artificial intelligence; Psychology; Chemistry; Computer science","score_opus":0.019100220981192486,"score_gpt":0.2549864273020907,"score_spread":0.2358862063208982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400405332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9665589,0.004717322,0.026472252,0.000014318298,0.0010269166,0.00037266337,0.00019890694,0.00063152006,0.000007211984],"genre_scores_gemma":[0.9919103,0.00011137737,0.0076442063,0.00002038459,0.000113645925,0.000020270598,0.0000013973576,0.00017689941,0.0000015546694],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973638,0.00015784419,0.0007081318,0.0008437072,0.0003513344,0.00057516916],"domain_scores_gemma":[0.9984296,0.00021810176,0.00027386937,0.00054544705,0.00034656108,0.00018643051],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031748728,0.000667445,0.0011274726,0.0009528825,0.00013260114,0.00009023187,0.00035464295,0.0006781418,0.00001572858],"category_scores_gemma":[0.00013001334,0.0007441085,0.00044530528,0.0022626054,0.00009966484,0.00012066721,0.00027289987,0.0022832537,0.000011694816],"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":[0.000028662207,0.000025015755,0.00020152442,0.0006778816,0.0030074464,0.000083918254,0.00009905116,0.39196417,0.6037291,0.0001792832,0.0000016973921,0.0000022079475],"study_design_scores_gemma":[0.00029823696,0.00007620532,0.0058209966,0.0010502003,0.004151134,3.0843392e-8,0.000025700901,0.6509862,0.33664992,0.0000032135729,0.000029124574,0.0009090376],"about_ca_topic_score_codex":0.000013004558,"about_ca_topic_score_gemma":0.0000025987085,"teacher_disagreement_score":0.2670792,"about_ca_system_score_codex":0.0003075144,"about_ca_system_score_gemma":0.00010346271,"threshold_uncertainty_score":0.999501},"labels":[],"label_agreement":null},{"id":"W4400466529","doi":"10.1021/acsaenm.4c00293","title":"Asymmetric-Resistive-Switching Device with Reconfigurable Synaptic Functions for Logic-In-Memory","year":2024,"lang":"en","type":"article","venue":"ACS Applied Engineering Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Computer science; Materials science","score_opus":0.013776117160764559,"score_gpt":0.21828338771767145,"score_spread":0.20450727055690687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400466529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82461613,0.0008258159,0.16923031,0.00003099904,0.0013486367,0.00066908135,0.000030358413,0.0017034779,0.001545218],"genre_scores_gemma":[0.99592096,0.000035181416,0.0032964514,0.000024224728,0.0002634324,0.00025892883,0.000022252165,0.00011219355,0.00006639179],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987855,0.0000066161806,0.00032829758,0.00033526175,0.00009404413,0.00045029214],"domain_scores_gemma":[0.99935156,0.00035102255,0.000024108864,0.00019238434,0.00001855498,0.00006236503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026509096,0.000289758,0.00033423599,0.00028176195,0.00006958795,0.00013465517,0.0001333875,0.000096818665,0.00002124494],"category_scores_gemma":[0.000039039245,0.00026948255,0.000030565116,0.00045776326,0.00000834227,0.00016510837,0.000018095894,0.00019124086,0.00004593713],"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":[0.000021266482,0.0000046073606,5.486853e-7,0.00062564237,0.0000506549,0.000015140752,0.000036437865,0.34616938,0.6492089,0.0014598832,0.00006247421,0.0023450847],"study_design_scores_gemma":[0.00047781935,0.00006570785,0.00015772086,0.0004672683,0.00006508049,0.000040572533,0.000057914927,0.012039142,0.98190266,0.00035062016,0.0037105372,0.0006649744],"about_ca_topic_score_codex":0.0000035053108,"about_ca_topic_score_gemma":0.0000019623542,"teacher_disagreement_score":0.33413023,"about_ca_system_score_codex":0.00011032105,"about_ca_system_score_gemma":0.000014142778,"threshold_uncertainty_score":0.99997574},"labels":[],"label_agreement":null},{"id":"W4400479983","doi":"10.48550/arxiv.2407.04964","title":"ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zero (linguistics); Overhead (engineering); Artificial neural network; Binary number; Computer science; Mathematics; Algorithm; Arithmetic; Artificial intelligence; Operating system","score_opus":0.0815489315809812,"score_gpt":0.18738573537003203,"score_spread":0.10583680378905083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400479983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6191481,0.00043460948,0.3790639,0.0000037809925,0.0005004044,0.00027668875,0.000008981187,0.00040545128,0.00015809198],"genre_scores_gemma":[0.99622285,0.00024360031,0.003150274,0.000018311437,0.0000434552,0.0000013242338,0.000017765537,0.00009114353,0.00021128275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839145,0.0001031707,0.00027856624,0.00068729185,0.000079985904,0.00045954366],"domain_scores_gemma":[0.9989136,0.00023416882,0.00012704139,0.00051915366,0.00006824448,0.0001377768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014190923,0.00046978187,0.0005482904,0.00021385953,0.00009145218,0.000043886386,0.00043191086,0.000267813,0.000018159202],"category_scores_gemma":[0.000009610795,0.0004823398,0.0001763256,0.0005381807,0.00009798109,0.0001605525,0.0004678426,0.0011268316,0.000013631813],"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":[0.00032815262,0.000019466603,0.00008266845,0.00031930752,0.00018853418,0.0010292485,0.000047337497,0.9957748,0.0014604154,0.00036576367,0.000100988414,0.00028333376],"study_design_scores_gemma":[0.00048677737,0.00014590676,0.00003109328,0.00036020405,0.00020775426,0.000022956456,0.00003607593,0.9914942,0.0043363436,0.0023554075,0.000008896057,0.00051441026],"about_ca_topic_score_codex":0.000026058291,"about_ca_topic_score_gemma":0.000004695387,"teacher_disagreement_score":0.37707475,"about_ca_system_score_codex":0.00011456332,"about_ca_system_score_gemma":0.000049213646,"threshold_uncertainty_score":0.99976283},"labels":[],"label_agreement":null},{"id":"W4400612293","doi":"","title":"Fully analog 28nm FD-SOI hardware solution for drift and variability mitigation of embedded PCM memories in spiking neural networks","year":2024,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Silicon on insulator; Computer science; Artificial neural network; Spiking neural network; Computer hardware; Electronic engineering; Materials science; Engineering; Optoelectronics; Artificial intelligence; Silicon","score_opus":0.00910375493985592,"score_gpt":0.22070161022937168,"score_spread":0.21159785528951577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400612293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40392065,0.00080765603,0.59339607,0.0005100613,0.00018632512,0.00023313197,0.000011027071,0.00019691091,0.0007381861],"genre_scores_gemma":[0.985902,0.00006695473,0.013772676,0.00001196818,0.000026844511,0.000020658614,0.000073749725,0.000020897836,0.000104229635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853086,0.00054223737,0.00033062897,0.0002804239,0.00010203251,0.00021383923],"domain_scores_gemma":[0.99818015,0.0011114214,0.00006509118,0.0003274669,0.00026232592,0.0000535289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001899034,0.00013682638,0.00018338306,0.00009916602,0.00012677864,0.00008549479,0.00015995037,0.00008236659,0.0000067793844],"category_scores_gemma":[0.00045904252,0.00014957,0.00006320692,0.00035162675,0.00010336727,0.00026435728,0.00008433235,0.0002102953,4.3338403e-7],"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":[0.00007604357,0.00021100542,0.0061350735,0.0024241728,0.00011422153,0.000012683327,0.017714549,0.22347133,0.1500614,0.08168113,0.00034935735,0.517749],"study_design_scores_gemma":[0.00019861317,6.960148e-7,0.002392229,0.0004902569,0.000014165925,0.000004762139,0.00005729715,0.94346315,0.05037496,0.0024694027,0.0003965081,0.00013797279],"about_ca_topic_score_codex":0.000019966914,"about_ca_topic_score_gemma":0.00023078143,"teacher_disagreement_score":0.7199918,"about_ca_system_score_codex":0.000051278566,"about_ca_system_score_gemma":0.000020643136,"threshold_uncertainty_score":0.6099288},"labels":[],"label_agreement":null},{"id":"W4400677064","doi":"10.3390/computers13070174","title":"A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks","year":2024,"lang":"en","type":"review","venue":"Computers","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":21,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Computer architecture; Artificial intelligence; Neuroscience; Cognitive science; Psychology","score_opus":0.03881419212555036,"score_gpt":0.32122931801905547,"score_spread":0.2824151258935051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400677064","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007777691,0.9621657,0.035177417,0.0000059854024,0.0011906526,0.0011703104,0.0000087032895,0.00024491298,0.000028526565],"genre_scores_gemma":[0.00007850518,0.9969291,0.002258779,0.000119787794,0.0003659535,0.00009190644,0.000041460975,0.00010924145,0.0000052368446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833685,0.000056945548,0.00076156424,0.00038027824,0.00010299643,0.0003613452],"domain_scores_gemma":[0.99918926,0.00028971003,0.00015581447,0.00025888506,0.000040769715,0.00006554989],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009226603,0.00049748045,0.0017756706,0.0002038132,0.000031219275,0.000019885147,0.0003671749,0.00013492626,0.0000015941332],"category_scores_gemma":[0.000015535923,0.0004115205,0.00054950407,0.00045857517,0.000046909765,0.000022930531,0.00011282437,0.000626136,0.0000022946701],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010044678,0.0000027803726,1.6907926e-8,0.32963952,0.00002112787,0.000014423424,0.000016746239,0.09208834,1.3291165e-7,0.0000014467313,0.00013499492,0.57807946],"study_design_scores_gemma":[0.00011149695,0.00004229723,3.3398234e-7,0.26089546,0.00038799955,0.000098566336,0.000002594866,0.45565033,0.0000011427643,0.000035501926,0.28232685,0.0004474201],"about_ca_topic_score_codex":2.3055851e-7,"about_ca_topic_score_gemma":4.1366752e-7,"teacher_disagreement_score":0.57763207,"about_ca_system_score_codex":0.000061489205,"about_ca_system_score_gemma":0.000025122603,"threshold_uncertainty_score":0.99983364},"labels":[],"label_agreement":null},{"id":"W4400907098","doi":"10.1101/2024.07.19.604308","title":"A Burst-Dependent Algorithm for Neuromorphic On-Chip Learning of Spiking Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Ottawa","funders":"","keywords":"Neuromorphic engineering; Spiking neural network; Computer science; Chip; Artificial neural network; Artificial intelligence; Algorithm; Computer architecture; Telecommunications","score_opus":0.019275103799690694,"score_gpt":0.22188748317466558,"score_spread":0.2026123793749749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400907098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82020783,0.003095821,0.1670524,0.000047004392,0.0062084803,0.0011927523,0.00015621641,0.0020269016,0.000012599401],"genre_scores_gemma":[0.99351203,0.00013498937,0.00445678,0.00006527839,0.0013104776,0.00015194231,7.3562643e-7,0.00036220267,0.0000055315063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970979,0.00008783708,0.00073316164,0.00095311063,0.00034169605,0.00078631233],"domain_scores_gemma":[0.9983,0.00028303958,0.0002749075,0.0007341648,0.00018861535,0.00021926672],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00046024058,0.00076412014,0.0007985206,0.0003365569,0.00016983291,0.00015358235,0.00051949185,0.0004595008,0.000007891391],"category_scores_gemma":[0.00012752078,0.00085445953,0.00032710927,0.0004140025,0.0000676621,0.00008968603,0.00052276853,0.0024942958,0.000011154741],"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":[0.000025785763,0.000037633406,0.000080848935,0.0012280821,0.00017089874,0.00014543657,0.000010491353,0.84802765,0.14969467,0.00024371818,0.000043668366,0.0002911259],"study_design_scores_gemma":[0.000368962,0.00015488596,0.00045579157,0.0009133076,0.00014319293,1.2186169e-7,0.0000024602393,0.8685971,0.12817144,0.000011274249,0.00038710222,0.0007943702],"about_ca_topic_score_codex":0.0000037051877,"about_ca_topic_score_gemma":2.8851704e-7,"teacher_disagreement_score":0.17330423,"about_ca_system_score_codex":0.00017315286,"about_ca_system_score_gemma":0.00007395885,"threshold_uncertainty_score":0.999807},"labels":[],"label_agreement":null},{"id":"W4400947877","doi":"10.1039/d4lp00121d","title":"N and P-type zwitterion gated organic field effect transistors","year":2024,"lang":"en","type":"article","venue":"RSC Applied Polymers","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Zwitterion; Transistor; Materials science; Capacitance; Field-effect transistor; Vinyl alcohol; Type (biology); Voltage; Optoelectronics; Chemistry; Electrical engineering; Composite material; Electrode; Organic chemistry; Physical chemistry; Engineering; Polymer; Molecule","score_opus":0.004545057226530054,"score_gpt":0.20507846734499804,"score_spread":0.20053341011846798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400947877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98966086,0.0024329347,0.0030356206,0.00005554854,0.0006592062,0.00011803541,8.5743494e-7,0.0007357779,0.0033011725],"genre_scores_gemma":[0.9995739,0.0000660394,0.000048814785,0.00008898171,0.0000745176,0.0000026566781,0.0000037833245,0.000034832312,0.0001064729],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952143,0.0000069999865,0.00009313482,0.00015615429,0.00005210777,0.00017018612],"domain_scores_gemma":[0.9997569,0.00010207602,0.000005596573,0.000079822385,0.0000019886265,0.00005367008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000039611215,0.00013102248,0.00011892166,0.00005097551,0.000046585235,0.000022303457,0.000042724918,0.000060165537,0.0001095015],"category_scores_gemma":[0.0000028400607,0.00012245629,0.000023816214,0.00019675835,0.000015094309,0.00004240901,0.000010138307,0.00017344454,0.000036443],"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":[0.000029192684,0.0000015538678,0.000010708056,0.00021197747,0.000030547166,0.000017390099,0.0003113663,0.00036620384,0.89395255,0.0001337512,0.00025140322,0.10468335],"study_design_scores_gemma":[0.00019033367,0.00008413539,0.000046167945,0.000057221947,0.00003525743,0.000019086623,0.000030005813,0.003534065,0.9879449,0.000047364832,0.0077780876,0.00023337232],"about_ca_topic_score_codex":0.0000015512078,"about_ca_topic_score_gemma":9.469754e-7,"teacher_disagreement_score":0.10444997,"about_ca_system_score_codex":0.000019647545,"about_ca_system_score_gemma":0.0000046738614,"threshold_uncertainty_score":0.49936226},"labels":[],"label_agreement":null},{"id":"W4401014422","doi":"10.3390/electronics13152963","title":"A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Neuromorphic engineering; Computer science; Von Neumann architecture; Spiking neural network; Computer architecture; Artificial neural network; Spike (software development); Memristor; Artificial intelligence; Reservoir computing; Electronic engineering; Engineering; Recurrent neural network","score_opus":0.05584676384919752,"score_gpt":0.2911568294105837,"score_spread":0.23531006556138617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401014422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61811787,0.009445522,0.36971658,0.000086239015,0.0011573483,0.00029829054,0.00002480051,0.0010284077,0.00012492288],"genre_scores_gemma":[0.99887186,0.00006689337,0.0006115921,0.000054564334,0.0002790804,0.000013293238,0.000015194952,0.000048903563,0.00003863108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916697,0.000017848128,0.00015101276,0.00021016794,0.000082896804,0.00037113013],"domain_scores_gemma":[0.99944013,0.00038668432,0.000009612836,0.0001110656,0.000013600853,0.000038919196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001945994,0.00013970528,0.000111931186,0.00007406022,0.00008017006,0.000049185786,0.00010971612,0.00004055835,0.0000057369684],"category_scores_gemma":[0.000057902143,0.00013555113,0.000055442797,0.00021637075,0.000014317163,0.00001898908,0.0000129493665,0.00039836392,0.000018134584],"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":[0.000028625074,0.00000824642,0.000003488169,0.00006162859,0.00002230473,0.000015671945,0.00011093624,0.55615944,0.009561222,0.00242097,0.00014271724,0.43146476],"study_design_scores_gemma":[0.000025736157,0.0002903562,0.00008820605,0.000054916018,0.000008423557,0.000013708375,0.000004265147,0.80998814,0.16984603,0.013926873,0.0055240304,0.00022930022],"about_ca_topic_score_codex":6.74139e-7,"about_ca_topic_score_gemma":0.00002202065,"teacher_disagreement_score":0.43123546,"about_ca_system_score_codex":0.00004933321,"about_ca_system_score_gemma":0.000024502318,"threshold_uncertainty_score":0.5527615},"labels":[],"label_agreement":null},{"id":"W4401023952","doi":"10.24963/ijcai.2024/347","title":"Heterogeneous Temporal Hypergraph Neural Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute of Automation, Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"Spiking neural network; Computer science; Neuromorphic engineering; Boosting (machine learning); Artificial intelligence; Machine learning; Artificial neural network; Computer architecture","score_opus":0.012359676305770133,"score_gpt":0.22388765267327757,"score_spread":0.21152797636750742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401023952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96030265,0.0051756795,0.02309327,0.000052317217,0.002361291,0.00008401806,0.0000011883936,0.0031085717,0.0058210157],"genre_scores_gemma":[0.9978632,0.000021381411,0.0012096401,0.00009063827,0.0004504449,0.0000027134745,0.000002435053,0.000027549271,0.00033203565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995146,0.000006517228,0.00010059193,0.00012024938,0.00004081309,0.0002172334],"domain_scores_gemma":[0.9998194,0.00003672322,0.0000028924399,0.00008934005,0.0000038722173,0.00004777627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000030553812,0.00009973106,0.00007717695,0.000027253436,0.000041407042,0.00003668862,0.00006174484,0.000027751532,0.00004944375],"category_scores_gemma":[0.0000015506058,0.00008554133,0.00005904549,0.00016989643,0.000009445112,0.000063785315,0.000019222178,0.00012856351,0.000054464774],"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":[0.0000016996042,0.0000013503711,0.000086416185,0.0000402303,0.000015271262,0.00013289503,0.000016513077,0.96429604,0.0025973863,0.0004152904,0.0013285638,0.03106837],"study_design_scores_gemma":[0.00006463416,0.000035856,0.000053038773,0.000042816428,0.000009666606,0.0001962014,0.000006467639,0.9460046,0.014332605,0.0015700698,0.037409842,0.0002741676],"about_ca_topic_score_codex":9.2825366e-7,"about_ca_topic_score_gemma":0.0000046932823,"teacher_disagreement_score":0.037560515,"about_ca_system_score_codex":0.000009186261,"about_ca_system_score_gemma":0.0000021193146,"threshold_uncertainty_score":0.34882742},"labels":[],"label_agreement":null},{"id":"W4401052711","doi":"10.1016/j.neucom.2024.128275","title":"SITU: Stochastic input encoding and weight update thresholding for efficient memristive neural network in-situ training","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"In situ; Computer science; Encoding (memory); Artificial neural network; Thresholding; Artificial intelligence; Pattern recognition (psychology); Training (meteorology); Machine learning; Image (mathematics); Chemistry","score_opus":0.02303172755659986,"score_gpt":0.25518566122472697,"score_spread":0.23215393366812712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401052711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79288536,0.001474957,0.2023833,0.00006653759,0.0016158506,0.0004047937,0.0000021351527,0.0007092761,0.0004577888],"genre_scores_gemma":[0.99585783,0.000013032012,0.0028627142,0.00013449416,0.0010166023,0.000017800943,0.00000470665,0.00008743434,0.000005351045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980443,0.00003767894,0.00043635254,0.00056225,0.00014367132,0.0007757066],"domain_scores_gemma":[0.99878675,0.0009030952,0.000047803456,0.0001285606,0.000020719344,0.0001130659],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036094408,0.00033204886,0.00034993942,0.00018987032,0.0002813907,0.00015261174,0.00015430301,0.00007475874,0.0000015556329],"category_scores_gemma":[0.000059841677,0.0003478138,0.00008697291,0.00046367815,0.000038058526,0.00016272013,0.00014249096,0.0005892289,0.0000032546727],"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":[0.000012922157,0.000004221855,0.000023761191,0.00018077179,0.000013419643,0.00007989003,0.0011267674,0.9333857,0.020059144,0.00061625603,0.00003382005,0.04446331],"study_design_scores_gemma":[0.00033647844,0.00004161752,0.00023008359,0.0005435761,0.00002202645,0.000089666726,0.0001031081,0.9944961,0.0029423593,0.0004641379,0.00038913218,0.00034168363],"about_ca_topic_score_codex":5.078753e-7,"about_ca_topic_score_gemma":0.000003025694,"teacher_disagreement_score":0.2029725,"about_ca_system_score_codex":0.00006820289,"about_ca_system_score_gemma":0.000015404365,"threshold_uncertainty_score":0.99989736},"labels":[],"label_agreement":null},{"id":"W4401211711","doi":"10.1109/isca59077.2024.00068","title":"Realizing the AMD Exascale Heterogeneous Processor Vision : Industry Product","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Product (mathematics); Computer architecture; Computer graphics (images)","score_opus":0.01456483201779985,"score_gpt":0.2664916633379022,"score_spread":0.25192683132010235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211711","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97800416,0.0029623306,0.00543525,0.000524098,0.0006683548,0.00023748878,0.0000015361501,0.0018495189,0.010317262],"genre_scores_gemma":[0.9982332,0.00002734586,0.00020701176,0.00006890807,0.00032879767,0.000008809015,0.0000012648336,0.000028535633,0.0010961558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941385,0.00001122662,0.00011904343,0.00018115883,0.0000890205,0.00018569433],"domain_scores_gemma":[0.9997337,0.000050173043,0.000006668598,0.00016358373,0.0000109875555,0.000034906036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000094940944,0.000105875675,0.00007112003,0.000028090073,0.00008767854,0.000066611254,0.000111321664,0.000053649197,0.00003730432],"category_scores_gemma":[0.000014629176,0.00006669636,0.00003206779,0.00020395196,0.000016849692,0.00014112733,0.000039496066,0.0003964033,0.000038156057],"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":[0.000011714872,0.00002093665,0.00006356552,0.0008621636,0.000053659904,0.0001779495,0.0008114729,0.46857318,0.18067704,0.00070056156,0.0071034385,0.34094432],"study_design_scores_gemma":[0.00008323941,0.000049404607,0.00011089522,0.00028559857,0.000018831906,0.00027336823,0.00012785335,0.16122004,0.77972454,0.0007093984,0.05705882,0.00033803083],"about_ca_topic_score_codex":0.0000012767263,"about_ca_topic_score_gemma":0.0000026618213,"teacher_disagreement_score":0.5990475,"about_ca_system_score_codex":0.000020830543,"about_ca_system_score_gemma":0.0000069303555,"threshold_uncertainty_score":0.27197987},"labels":[],"label_agreement":null},{"id":"W4401329579","doi":"10.1109/iolts60994.2024.10616090","title":"ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Overhead (engineering); Zero (linguistics); Artificial neural network; Binary number; Arithmetic; Artificial intelligence; Mathematics; Operating system","score_opus":0.029546408433241612,"score_gpt":0.2362666546888419,"score_spread":0.2067202462556003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401329579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40647477,0.0010175434,0.59128904,0.000011153793,0.00028941108,0.00013723089,9.5360764e-7,0.00053422485,0.00024567184],"genre_scores_gemma":[0.9714073,0.00005644708,0.028265255,0.00004016613,0.000030469813,0.000006725634,0.0000034581135,0.0000500232,0.00014018077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911326,0.000033831075,0.0002274094,0.00021015324,0.00010831026,0.00030705793],"domain_scores_gemma":[0.9994598,0.00027985743,0.000018736577,0.00015563097,0.000019942318,0.000066047876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010722055,0.00019095276,0.00022465929,0.00007057827,0.000050072016,0.000041744832,0.00010543456,0.00005592133,0.000039641665],"category_scores_gemma":[0.0000062673244,0.00014650942,0.00005176961,0.0003231518,0.000030220535,0.0002592568,0.000024847606,0.00023787242,0.000008416179],"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":[0.00008923682,0.000006146888,0.000016346772,0.00007648511,0.000041081545,0.000119592514,0.000033899676,0.9681917,0.026690362,0.000091021575,0.00032583356,0.00431832],"study_design_scores_gemma":[0.00022890184,0.00017459522,0.000020399784,0.000096228956,0.000022228318,0.00006130198,0.000017025493,0.94187516,0.05718613,0.000082867766,0.00004408104,0.00019106481],"about_ca_topic_score_codex":0.0000063760413,"about_ca_topic_score_gemma":0.0000012497798,"teacher_disagreement_score":0.5649325,"about_ca_system_score_codex":0.000020094985,"about_ca_system_score_gemma":0.000011097737,"threshold_uncertainty_score":0.5974481},"labels":[],"label_agreement":null},{"id":"W4401362110","doi":"10.1109/iccsc62074.2024.10616720","title":"Memristor-based efficient Combinational circuit designs using Material Implication","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Combinational logic; Computer science; Memristor; Parallel computing; Logic gate; Electronic engineering; Algorithm; Engineering","score_opus":0.05022712557431907,"score_gpt":0.27297724756094066,"score_spread":0.22275012198662159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401362110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5175724,0.000050286744,0.48019814,0.000015744861,0.0006542305,0.00007437231,0.0000042577503,0.00055127963,0.00087929616],"genre_scores_gemma":[0.9984914,4.5395808e-7,0.001316602,0.000020137439,0.00010434458,0.000004738453,0.000013805266,0.000019237217,0.000029274172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955493,0.000008957947,0.00011430615,0.00011873933,0.000082978,0.00012006823],"domain_scores_gemma":[0.9998238,0.00004983724,0.000007251933,0.00007396144,0.000015516967,0.000029649966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065489156,0.00007717835,0.00005818997,0.000061034403,0.000058519036,0.00004221621,0.00004940356,0.000028148728,0.000117694384],"category_scores_gemma":[0.000004574502,0.00007634592,0.000028237213,0.000120645374,0.000010388671,0.000051142706,0.000007984472,0.00005964541,0.000026511923],"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":[9.931393e-7,0.000005089377,0.0000018291557,0.00004373852,0.0000036016038,0.0000028522675,0.000016131537,0.5209465,0.4735419,0.0038948837,0.000054390915,0.0014880816],"study_design_scores_gemma":[0.00006319449,0.000007722988,0.00006601026,0.000022603966,0.000005577089,0.0000072908315,0.0000036944166,0.7197042,0.27922747,0.00037164183,0.0004312198,0.00008936489],"about_ca_topic_score_codex":0.0000010680951,"about_ca_topic_score_gemma":1.313494e-7,"teacher_disagreement_score":0.480919,"about_ca_system_score_codex":0.0001143761,"about_ca_system_score_gemma":0.000016174386,"threshold_uncertainty_score":0.3113296},"labels":[],"label_agreement":null},{"id":"W4401383180","doi":"10.1038/s41467-024-51178-z","title":"Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Henan Normal University","keywords":"Chimera (genetics); Polarimetry; Computer science; Computer vision; Artificial intelligence; Machine vision; Physics; Optics; Biology","score_opus":0.014103672852790598,"score_gpt":0.29168285883777295,"score_spread":0.2775791859849824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401383180","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91677654,0.026159788,0.04895717,0.002246665,0.0005065789,0.00089375547,0.00037037293,0.0016828905,0.0024062302],"genre_scores_gemma":[0.98433024,0.00064581516,0.014184701,0.000049429887,0.00005824507,0.000005861913,0.0006634322,0.000033273507,0.000029018878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953854,0.000028799208,0.00012462428,0.00014340022,0.000060004568,0.00010466052],"domain_scores_gemma":[0.9992302,0.00027912075,0.000021037085,0.00038164144,0.000054610173,0.00003338354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009511586,0.00011225529,0.00010102566,0.00008472659,0.00017758689,0.00006570526,0.00017040898,0.000110185516,0.000006532119],"category_scores_gemma":[0.000018701108,0.000099253215,0.000030956155,0.00020167937,0.000036390687,0.00023998335,0.000069155554,0.00058825576,0.000008806772],"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":[0.000054098768,0.00009799381,0.00060612184,0.0007769455,0.00024799717,0.000011443724,0.0014527164,0.0013030457,0.24629277,0.0011383878,0.0036437295,0.74437475],"study_design_scores_gemma":[0.0012877508,0.00039506005,0.0066820593,0.003296015,0.00028124065,0.00025869408,0.00031786202,0.8737804,0.042634506,0.0018055971,0.06805271,0.0012080751],"about_ca_topic_score_codex":0.0000035655041,"about_ca_topic_score_gemma":0.00000976114,"teacher_disagreement_score":0.8724774,"about_ca_system_score_codex":0.00004016964,"about_ca_system_score_gemma":0.0000065351405,"threshold_uncertainty_score":0.40474287},"labels":[],"label_agreement":null},{"id":"W4401391022","doi":"10.1021/acsnano.4c05137","title":"Volatile and Nonvolatile Programmable Iontronic Memristor with Lithium Imbued TiO<sub><i>x</i></sub> for Neuromorphic Computing Applications","year":2024,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canada First Research Excellence Fund; Innovation, Science and Economic Development Canada","keywords":"Neuromorphic engineering; Memristor; Materials science; Lithium (medication); Nanotechnology; Non-volatile memory; Optoelectronics; Computer science; Electrical engineering; Artificial neural network; Artificial intelligence; Engineering; Psychology","score_opus":0.010089326880392869,"score_gpt":0.21018083714540342,"score_spread":0.20009151026501054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401391022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83251405,0.0027249947,0.16168061,0.000089078785,0.00029584754,0.0012092008,0.00002009121,0.0011872979,0.0002788337],"genre_scores_gemma":[0.9960799,0.000031473184,0.0032322842,0.000036035613,0.0002525851,0.00016834107,0.000028106348,0.00007184514,0.00009943724],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990126,0.000012757033,0.00020728575,0.0003214392,0.00009610169,0.0003497976],"domain_scores_gemma":[0.99943155,0.00021455999,0.000033449156,0.00019446731,0.00004413446,0.00008185793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110315705,0.00019420887,0.00018684116,0.000072120405,0.00022112827,0.00009702488,0.000099752164,0.0000590951,0.0000017077716],"category_scores_gemma":[0.000010832748,0.0001804092,0.000042106145,0.000296202,0.000041649535,0.00018949724,0.00003833155,0.00020826666,0.000011795258],"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":[0.00004475582,0.00003634972,0.00008825295,0.0010290801,0.00009232915,0.000016805696,0.0003244415,0.030127915,0.87329006,0.0011548708,0.0009938474,0.09280129],"study_design_scores_gemma":[0.0010015211,0.00036937383,0.00016858874,0.0003354941,0.0001429191,0.00013669743,0.000051191826,0.48471656,0.4305697,0.0011510127,0.08063248,0.0007244464],"about_ca_topic_score_codex":0.0000011293906,"about_ca_topic_score_gemma":0.0000058158435,"teacher_disagreement_score":0.45458865,"about_ca_system_score_codex":0.000048048918,"about_ca_system_score_gemma":0.00003171185,"threshold_uncertainty_score":0.7356874},"labels":[],"label_agreement":null},{"id":"W4401413224","doi":"10.1162/neco_a_01693","title":"Efficient Hyperdimensional Computing With Spiking Phasors","year":2024,"lang":"en","type":"article","venue":"Neural Computation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Phasor; Computer science; Representation (politics); Theoretical computer science; Encoding (memory); Spiking neural network; Vector space; Bit array; Function (biology); Algorithm; Artificial neural network; Artificial intelligence; Mathematics; Power (physics); Electric power system","score_opus":0.012245341300940892,"score_gpt":0.24727495012122316,"score_spread":0.23502960882028226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401413224","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83860517,0.0003880052,0.15854476,0.000051968826,0.00068046944,0.00009859436,0.0000010332006,0.0010708676,0.0005591298],"genre_scores_gemma":[0.9958811,9.836292e-7,0.003810975,0.000048106904,0.0002004626,0.0000015512502,0.0000087910375,0.00003704038,0.0000110013225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991792,0.0000165252,0.00016743813,0.0002243709,0.00018844355,0.00022401096],"domain_scores_gemma":[0.99970853,0.0001286925,0.000016810542,0.000059696744,0.000033181277,0.00005310567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066909764,0.0001578093,0.000118803815,0.00014288152,0.00013070893,0.00007383445,0.000054617678,0.000029328352,0.0000056256804],"category_scores_gemma":[0.000005162939,0.00013471434,0.000037023965,0.00046490086,0.000022766155,0.000081947735,0.000024330093,0.00022550946,0.000028552635],"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":[0.000006021263,0.000004567106,0.000026531889,0.000060096558,0.000010031309,0.00006170647,0.00012268843,0.9390837,0.010558705,0.0001690915,0.00003376502,0.049863115],"study_design_scores_gemma":[0.00015474859,0.000050031555,0.00026767654,0.00016334889,0.000010988971,0.00013726947,0.000024571207,0.9941228,0.0046122656,0.00014155275,0.00014363725,0.00017109081],"about_ca_topic_score_codex":4.1200045e-7,"about_ca_topic_score_gemma":2.6653626e-7,"teacher_disagreement_score":0.15727592,"about_ca_system_score_codex":0.00004929654,"about_ca_system_score_gemma":0.000009484518,"threshold_uncertainty_score":0.5493491},"labels":[],"label_agreement":null},{"id":"W4401632231","doi":"10.22215/etd/2024-16126","title":"Optimization of Low-Temperature Solution-Processed Antimony Tin Oxide Thin-Film Field-Effect Transistor Fabrication Procedure for Neuromorphic Applications","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Materials science; Optoelectronics; Fabrication; Annealing (glass); Passivation; Wafer; Tin oxide; Gate oxide; Transistor; Nanotechnology; Voltage; Layer (electronics); Doping; Electrical engineering; Composite material","score_opus":0.00804428378177186,"score_gpt":0.23806950186555434,"score_spread":0.23002521808378248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401632231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4256432,0.0030557327,0.5596216,0.00017396077,0.0016900828,0.0057669817,0.00011241176,0.0018925107,0.0020435723],"genre_scores_gemma":[0.98816687,0.00015951965,0.0047256127,0.00006209219,0.0002838262,0.0009393162,0.0028628085,0.00015631734,0.002643667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986529,0.00001972278,0.00048460896,0.00044211012,0.00017226713,0.00022842987],"domain_scores_gemma":[0.9990985,0.00021102247,0.00013997786,0.0002429381,0.0002508867,0.000056686848],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010010194,0.00036928285,0.00039557737,0.00019228671,0.00014011383,0.000051522107,0.00020450403,0.00039712325,0.000020211211],"category_scores_gemma":[0.00009316366,0.00036114056,0.00018160741,0.0004933898,0.000011541948,0.00020427899,0.000006950528,0.00043758433,0.0000068314157],"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":[0.00009010374,0.000029965126,0.0000019285874,0.012020308,0.000057406854,9.760216e-7,0.0002744693,0.7019643,0.2826079,0.00012757181,0.0019104533,0.00091461703],"study_design_scores_gemma":[0.00026944507,0.000104795305,0.000027164788,0.0005693118,0.00017669745,0.0000039541096,0.00009651191,0.35169888,0.64620626,0.000119072356,0.00033918815,0.00038873046],"about_ca_topic_score_codex":0.000002118387,"about_ca_topic_score_gemma":0.000013804834,"teacher_disagreement_score":0.56252366,"about_ca_system_score_codex":0.000046089626,"about_ca_system_score_gemma":0.00007925413,"threshold_uncertainty_score":0.99988407},"labels":[],"label_agreement":null},{"id":"W4401687973","doi":"10.1101/2024.08.16.608322","title":"FORCE trained spiking networks do not benefit from faster learning while parameter matched rate networks do","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.01449449166377182,"score_gpt":0.2070888896743565,"score_spread":0.1925943980105847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401687973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8846989,0.0062199784,0.10050035,0.000052254312,0.0048677856,0.00077037484,0.000081272534,0.00277434,0.000034734003],"genre_scores_gemma":[0.9919651,0.0003863147,0.004042412,0.00021790156,0.0026230616,0.0001607503,0.0000031928676,0.0005747236,0.000026530717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99545795,0.00015654604,0.0010405084,0.0016472487,0.00035725962,0.0013405185],"domain_scores_gemma":[0.9973282,0.000494571,0.00034442285,0.0012574069,0.00018217906,0.0003932159],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00068547606,0.0012758462,0.0011946333,0.0002828094,0.00028722626,0.00091945555,0.000764364,0.0010548987,0.00009508258],"category_scores_gemma":[0.00010981542,0.0014047431,0.00041373263,0.000563755,0.000081604354,0.0002714606,0.0010285298,0.0041840803,0.00008174364],"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":[0.000054910626,0.000017054628,0.00036727858,0.00030433078,0.00031480743,0.00016631444,0.00005845583,0.88958067,0.10881501,0.000120049124,0.00006712396,0.0001339892],"study_design_scores_gemma":[0.0007726971,0.00006133558,0.004896849,0.0030109964,0.00036756767,9.862133e-8,0.000014231513,0.9113024,0.07514315,0.00005958414,0.0018366943,0.0025344186],"about_ca_topic_score_codex":0.0000092939445,"about_ca_topic_score_gemma":0.000002474303,"teacher_disagreement_score":0.1072662,"about_ca_system_score_codex":0.00032128431,"about_ca_system_score_gemma":0.000065621454,"threshold_uncertainty_score":0.99999934},"labels":[],"label_agreement":null},{"id":"W4401696598","doi":"10.1149/ma2024-01311555mtgabs","title":"(Digital Presentation) Temporal Evolution of the Threshold Voltage in Organic Thin Film Memory Transistors","year":2024,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Thin-film transistor; Presentation (obstetrics); Threshold voltage; Transistor; Optoelectronics; Materials science; Computer science; Voltage; Electrical engineering; Nanotechnology; Engineering; Medicine","score_opus":0.010882134659395167,"score_gpt":0.22138646690923228,"score_spread":0.2105043322498371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401696598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96440774,0.0008475911,0.00017474638,0.000037829577,0.0008018771,0.00013671911,0.000006386705,0.00024568205,0.033341415],"genre_scores_gemma":[0.9993876,0.0000033862102,0.00006698761,0.0000059042095,0.0001066477,0.0000026653138,0.000004030035,0.000030212663,0.00039254333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991296,0.000012735139,0.00033587316,0.00016251637,0.00017119457,0.00018805984],"domain_scores_gemma":[0.9996241,0.00014106612,0.00004172763,0.00014380776,0.000016295977,0.00003301422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002026033,0.000125889,0.00012064269,0.00007929597,0.000052200543,0.00003944335,0.00014089786,0.000060033115,0.000016059485],"category_scores_gemma":[0.00010070737,0.00010659463,0.00006684421,0.00032945356,0.00003203294,0.00031974903,0.000023195427,0.00032992888,0.000013631592],"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":[0.0000042249117,0.000010834638,0.00093711435,0.0001835197,0.000009050637,0.0000179292,0.00061600894,0.9027708,0.094846964,0.00001351169,0.00013024054,0.00045979727],"study_design_scores_gemma":[0.0005225468,0.000044895,0.043987274,0.0016501447,0.000037921713,0.000045727043,0.0011923036,0.2970761,0.6515265,0.0017765783,0.0015733652,0.0005666439],"about_ca_topic_score_codex":0.000014230799,"about_ca_topic_score_gemma":0.000034523866,"teacher_disagreement_score":0.6056947,"about_ca_system_score_codex":0.00010529124,"about_ca_system_score_gemma":0.000034035507,"threshold_uncertainty_score":0.43468028},"labels":[],"label_agreement":null},{"id":"W4401806864","doi":"10.1109/mssc.2024.3419488","title":"A Brief History of SEDRA/SMITH: Microelectronic Circuits [Society News]","year":2024,"lang":"en","type":"article","venue":"IEEE Solid-State Circuits Magazine","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Microelectronics; Electronic circuit; Computer science; Engineering; Electrical engineering","score_opus":0.017809346649174,"score_gpt":0.2390244110181682,"score_spread":0.2212150643689942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401806864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79810417,0.06548429,0.089533806,0.00014388686,0.010485666,0.0009674977,0.0001518479,0.003936603,0.031192258],"genre_scores_gemma":[0.98739773,0.0010538761,0.00007937753,0.00020150561,0.00039737,0.000016569133,0.000016300772,0.00017174339,0.0106655555],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977133,0.00004022851,0.00060723315,0.0005417988,0.00027074546,0.0008266631],"domain_scores_gemma":[0.99906266,0.00015520852,0.00008117256,0.00043383107,0.00008807254,0.00017907222],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020224764,0.0004312978,0.0005081423,0.00016246631,0.000055526423,0.000039249968,0.00031198212,0.00012953812,0.000084261155],"category_scores_gemma":[0.000021528193,0.00047770643,0.00033357943,0.00050672435,0.00012853125,0.00036725175,0.000038178394,0.000679839,0.00019369498],"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":[0.0000039491833,0.00003171881,0.000013674208,0.0011482036,0.00016743717,0.000109906156,0.0016259026,0.032004744,0.8518082,0.0000714386,0.042926274,0.070088536],"study_design_scores_gemma":[0.0014058391,0.00031451523,0.00037135993,0.0009673966,0.00018682532,0.0003754151,0.00006344722,0.06648151,0.36216295,0.0012582013,0.5647798,0.0016327486],"about_ca_topic_score_codex":0.000005616636,"about_ca_topic_score_gemma":0.000010984799,"teacher_disagreement_score":0.5218535,"about_ca_system_score_codex":0.0008732596,"about_ca_system_score_gemma":0.00027685415,"threshold_uncertainty_score":0.9997675},"labels":[],"label_agreement":null},{"id":"W4401985244","doi":"10.1038/s41598-024-71038-6","title":"A high-density multi-electrode platform examining the effects of radiation on in vitro cortical networks","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ottawa Hospital Research Institute; University of Ottawa","keywords":"Neuroscience; Propidium iodide; Multielectrode array; Radiosurgery; Prefrontal cortex; In vitro; Radiation; Radiation therapy; Medicine; Biology; Computer science; Chemistry; Cognition; Apoptosis; Microelectrode; Programmed cell death; Internal medicine; Physics; Electrode","score_opus":0.013101291167462992,"score_gpt":0.23409345706500595,"score_spread":0.22099216589754295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401985244","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9785806,0.00041143375,0.013401327,0.000006258082,0.007238093,0.00018317136,1.6092248e-7,0.00014548675,0.000033480843],"genre_scores_gemma":[0.9996362,0.0000062168638,0.00017023251,0.000007905777,0.00007974861,0.0000067982787,0.0000074306204,0.000013046041,0.00007243902],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990431,0.000022143724,0.0002740596,0.00027578525,0.00016389352,0.00022101596],"domain_scores_gemma":[0.9993227,0.00033902394,0.00004009081,0.00025253836,0.0000121317225,0.000033516764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006012324,0.00009350259,0.00012789706,0.000092743794,0.000097837874,0.00006325004,0.000053349293,0.0000427935,0.0000016574498],"category_scores_gemma":[0.00017843251,0.00006991128,0.00003497365,0.00040855422,0.000042522784,0.00010694499,0.00002252116,0.0002954482,0.0000020820144],"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":[0.000013838557,0.00001832249,0.00010112479,0.00012852436,0.000012488415,0.0006110879,0.00041337812,0.19301319,0.7499176,0.00009274626,0.00022176871,0.055455867],"study_design_scores_gemma":[0.000065800945,0.00002298751,0.0026660122,0.00011279676,0.0000076976585,0.000046034293,0.000008448686,0.26455224,0.73197615,0.00037879476,0.000088632674,0.00007439803],"about_ca_topic_score_codex":0.000003316418,"about_ca_topic_score_gemma":0.000005141324,"teacher_disagreement_score":0.071539044,"about_ca_system_score_codex":0.000058460362,"about_ca_system_score_gemma":0.000014252796,"threshold_uncertainty_score":0.2850899},"labels":[],"label_agreement":null},{"id":"W4402035785","doi":"10.1002/cssc.202401157","title":"Computational Engineering of Non‐van der Waals 2D Magnetene for Enhanced Oxygen Evolution and Reduction Reactions","year":2024,"lang":"en","type":"article","venue":"ChemSusChem","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"van der Waals force; Oxygen reduction; Reduction (mathematics); Oxygen reduction reaction; Oxygen; Chemistry; Chemical reaction engineering; Materials science; Chemical physics; Computational chemistry; Catalysis; Physical chemistry; Molecule; Organic chemistry; Electrochemistry; Mathematics","score_opus":0.010355147195186694,"score_gpt":0.2388526699427497,"score_spread":0.22849752274756302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402035785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56096214,0.0009193085,0.43715307,0.00002328622,0.00033880226,0.00013657521,0.000005746169,0.00022230353,0.000238758],"genre_scores_gemma":[0.9837182,0.000023276412,0.015858846,0.0000020505884,0.00020380276,0.00002575687,0.000019212694,0.00002582693,0.00012298538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994703,0.0000023002563,0.00018307404,0.00015206094,0.00005943627,0.00013280904],"domain_scores_gemma":[0.99975777,0.00007690691,0.000019155674,0.000066379835,0.000041405,0.000038369504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065988155,0.00010353822,0.00011413166,0.00007261813,0.00004013846,0.000013452982,0.00003190726,0.00005177975,0.0000075665916],"category_scores_gemma":[0.000023887738,0.00011466334,0.000043242788,0.00014366477,0.000015420868,0.00016320775,0.000012048961,0.000099581026,0.000002911762],"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":[0.0000043322893,0.0000033333133,7.0761456e-7,0.000402852,0.00002039023,3.366177e-7,0.000093414965,0.24450018,0.7505854,0.00029695858,0.000059901256,0.0040322426],"study_design_scores_gemma":[0.0001260581,0.000019237768,0.00012354058,0.00010305271,0.000018384662,0.000016974343,0.00002468266,0.32782683,0.66999984,0.0006848184,0.000953532,0.00010306318],"about_ca_topic_score_codex":6.253372e-7,"about_ca_topic_score_gemma":1.1188706e-7,"teacher_disagreement_score":0.4227561,"about_ca_system_score_codex":0.00005604538,"about_ca_system_score_gemma":0.00001058226,"threshold_uncertainty_score":0.4675835},"labels":[],"label_agreement":null},{"id":"W4402127487","doi":"10.1038/s44335-024-00008-y","title":"28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs","year":2024,"lang":"en","type":"article","venue":"npj Unconventional Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"École Centrale de Lyon; Centre National de la Recherche Scientifique; Association Nationale de la Recherche et de la Technologie; Agence Nationale de la Recherche; Université de Sherbrooke; Indian National Science Academy","keywords":"Scalability; MNIST database; Spiking neural network; Computer science; Phase-change memory; Artificial neural network; Materials science; Computer architecture; Embedded system; Nanotechnology; Artificial intelligence; Operating system","score_opus":0.021523487975477117,"score_gpt":0.26400072585329704,"score_spread":0.2424772378778199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402127487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77839637,0.0013467687,0.21347933,0.00007520818,0.0027553583,0.00038388267,0.00002683892,0.0014891021,0.0020471332],"genre_scores_gemma":[0.985406,0.0000060209973,0.0124519495,0.0000965419,0.00087295787,0.000015717607,0.00009748392,0.000111540794,0.0009418211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978976,0.000041553543,0.00058127235,0.0005337085,0.00029215694,0.00065366365],"domain_scores_gemma":[0.99880445,0.00069538894,0.00007457921,0.00021132828,0.00007351781,0.00014073066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004448628,0.00034191035,0.000311689,0.00012169683,0.0006009524,0.0001810198,0.00021939806,0.00012306032,0.00012425914],"category_scores_gemma":[0.00008793124,0.0003774758,0.0003912531,0.00027164764,0.000056397035,0.0002452734,0.00018188759,0.0004079793,0.00013581355],"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":[0.000066587476,0.000054971297,0.00048516944,0.001904823,0.00037511246,0.00013423992,0.00093674305,0.8029101,0.121086046,0.013198568,0.0069923126,0.051855344],"study_design_scores_gemma":[0.0006909343,0.0000715667,0.00041937607,0.0006415258,0.000057055375,0.00012806634,0.000058980848,0.958584,0.0145089645,0.0054528643,0.01879798,0.0005886711],"about_ca_topic_score_codex":0.0000021714345,"about_ca_topic_score_gemma":0.000009238786,"teacher_disagreement_score":0.20700958,"about_ca_system_score_codex":0.00021646787,"about_ca_system_score_gemma":0.0000503183,"threshold_uncertainty_score":0.99986774},"labels":[],"label_agreement":null},{"id":"W4402195674","doi":"10.1109/fccm60383.2024.00044","title":"Stay Flexible: A High-Performance FPGA NPU Overlay for Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Overlay; Field-programmable gate array; Graph; Artificial neural network; Computer architecture; Computer network; Embedded system; Parallel computing; Artificial intelligence; Theoretical computer science; Operating system","score_opus":0.012792387590170215,"score_gpt":0.23415523761782528,"score_spread":0.22136285002765507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402195674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49505204,0.001460167,0.4965341,0.000056001878,0.0028456394,0.00025632605,0.0000066458965,0.0023294638,0.0014596195],"genre_scores_gemma":[0.9962712,0.00009215179,0.0019581818,0.00011845625,0.00052151084,0.000025911322,0.000009946854,0.000048673344,0.00095394946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919593,0.0000055368396,0.00017133518,0.00019931927,0.00007365817,0.00035424266],"domain_scores_gemma":[0.99964845,0.00012785092,0.000009106273,0.0001392433,0.0000150810365,0.00006025942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007410286,0.00016635835,0.0001370222,0.000069406015,0.00008522818,0.000056497385,0.000104547784,0.000055681812,0.0000518308],"category_scores_gemma":[0.0000044764897,0.0001430771,0.00006300688,0.00024629087,0.000015717053,0.00029094092,0.00002583531,0.00021174719,0.000013750793],"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":[0.000014514574,0.000003071648,0.0000137789075,0.0001663934,0.000021628108,0.000006749743,0.00002125331,0.9396558,0.0017268342,0.0011833765,0.0032950353,0.053891607],"study_design_scores_gemma":[0.00016412423,0.000065987835,0.00010862547,0.000046042845,0.000011285449,0.000011571292,0.0000071014347,0.96078044,0.029761983,0.00031289982,0.008521398,0.00020853884],"about_ca_topic_score_codex":9.9869e-7,"about_ca_topic_score_gemma":0.0000013061881,"teacher_disagreement_score":0.50121915,"about_ca_system_score_codex":0.000024648134,"about_ca_system_score_gemma":0.0000044207713,"threshold_uncertainty_score":0.58345145},"labels":[],"label_agreement":null},{"id":"W4402282296","doi":"10.1002/adfm.202401600","title":"Ultrafast Hybrid Computing Systems Enabled by Memristor‐Based Quadratic Programming Circuits","year":2024,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Intelligence Advanced Research Projects Activity; Office of the Director of National Intelligence","keywords":"Memristor; Materials science; Ultrashort pulse; Electronic circuit; Neuromorphic engineering; Quadratic equation; Quadratic programming; Computational science; Nanotechnology; Electronic engineering; Computer science; Electrical engineering; Artificial intelligence; Mathematical optimization; Mathematics; Artificial neural network; Engineering; Physics","score_opus":0.012205583602764894,"score_gpt":0.21858707855626966,"score_spread":0.20638149495350477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402282296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74662924,0.0024133413,0.23878235,0.000032855634,0.008912319,0.00052533136,0.00009201945,0.0023750567,0.00023746962],"genre_scores_gemma":[0.9983431,0.000014185922,0.00042432267,0.000058809608,0.00061282376,0.000069945316,0.0001749989,0.000096452975,0.00020532093],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982296,0.000061142615,0.0005340521,0.00042820728,0.00026447984,0.00048250606],"domain_scores_gemma":[0.99926805,0.00030811422,0.000066029475,0.00019235832,0.00005632771,0.000109115055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025591653,0.00033054815,0.00036336575,0.00011030492,0.00020715526,0.0002475807,0.00012140805,0.00005429345,0.000090330206],"category_scores_gemma":[0.00004964121,0.0003306278,0.000071240036,0.00023631436,0.00003585727,0.0003990069,0.00002178051,0.00019251325,0.00011382406],"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":[0.000013229407,0.000012421063,0.000002629521,0.0006499214,0.000035127403,0.00003829166,0.000021307591,0.2985816,0.69518316,0.00018309825,0.0004617074,0.004817515],"study_design_scores_gemma":[0.00040684213,0.000070271824,0.000016181288,0.0005737664,0.00003433087,0.00011808178,0.000054090393,0.035295386,0.94408643,0.00009300092,0.018747788,0.00050382526],"about_ca_topic_score_codex":0.0000032097741,"about_ca_topic_score_gemma":3.2251265e-7,"teacher_disagreement_score":0.26328623,"about_ca_system_score_codex":0.00020399682,"about_ca_system_score_gemma":0.000031791296,"threshold_uncertainty_score":0.9999146},"labels":[],"label_agreement":null},{"id":"W4402322132","doi":"10.3389/felec.2024.1366299","title":"LIF neuron —a memristive realization","year":2024,"lang":"en","type":"article","venue":"Frontiers in Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Realization (probability); Memristor; Computer science; Neuron; Neuroscience; Electrical engineering; Psychology; Engineering; Mathematics","score_opus":0.004675134335272384,"score_gpt":0.2115212008833958,"score_spread":0.2068460665481234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402322132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0819575,0.033794012,0.87547916,0.00006070468,0.0035491157,0.00019250737,0.000003646666,0.0010892976,0.0038740532],"genre_scores_gemma":[0.99589473,0.0016415135,0.0019326165,0.00004227409,0.00015731665,0.0000085526435,0.000016001917,0.000048787257,0.00025822574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936986,0.00001585005,0.00012301277,0.00015319075,0.0000656424,0.00027247134],"domain_scores_gemma":[0.999853,0.000020989868,0.000007375382,0.000086013075,0.0000068803497,0.000025716985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000663791,0.00009981597,0.00009960561,0.000108083375,0.000026749067,0.000022794076,0.00007395771,0.00004820394,0.0000033935623],"category_scores_gemma":[0.000014428881,0.00010845595,0.000026625723,0.00033735138,0.000010921724,0.00013360073,0.000010546324,0.00029872244,0.0000062324407],"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":[0.000028309438,0.000015750193,0.0003825232,0.00026565694,0.00005148431,0.0001090719,0.0006472313,0.69176334,0.0075045615,0.008137098,0.03305466,0.25804034],"study_design_scores_gemma":[0.00012293275,0.00007503747,0.000124395,0.00007284406,0.000011916382,0.000010873288,0.000039730865,0.8666336,0.012250422,0.010452203,0.10997872,0.00022731983],"about_ca_topic_score_codex":5.5164116e-7,"about_ca_topic_score_gemma":0.000003887522,"teacher_disagreement_score":0.9139372,"about_ca_system_score_codex":0.00019862033,"about_ca_system_score_gemma":0.000020270028,"threshold_uncertainty_score":0.44227052},"labels":[],"label_agreement":null},{"id":"W4402353736","doi":"10.1109/ijcnn60899.2024.10650635","title":"Design and Analysis of a Frequency-Driven LIF Model Neuron","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Frequency analysis; Biological neuron model; Artificial neural network; Artificial intelligence; Algorithm","score_opus":0.028438765340827135,"score_gpt":0.2485154172293148,"score_spread":0.22007665188848768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402353736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25822172,0.0004982974,0.740452,0.0000072445882,0.000028515213,0.00002927883,0.0000011239449,0.00017604318,0.0005858084],"genre_scores_gemma":[0.9721582,0.00007344296,0.027675444,0.000013497051,0.0000073255965,0.000001195783,9.386321e-7,0.000008641186,0.00006130663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997094,0.000006816837,0.00008614315,0.00009013967,0.000036371614,0.000071105314],"domain_scores_gemma":[0.999837,0.00006503546,0.000004405278,0.000064911335,0.000005696913,0.000022995342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000317672,0.000054574783,0.00010015916,0.0001117788,0.000011540272,0.000009260812,0.000031837622,0.000017005163,0.000010872462],"category_scores_gemma":[0.0000037169086,0.000047560156,0.000032416247,0.0002985521,0.000008405723,0.00007280986,0.00001131491,0.000056970857,0.0000013806746],"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":[7.058016e-7,9.766496e-7,0.000013887773,0.000027693466,0.000071231945,0.0000045622364,0.00008082618,0.91364825,0.08271553,0.00052698137,0.000015430165,0.0028939068],"study_design_scores_gemma":[0.000017547467,0.000010311673,0.000050197745,0.000009399587,0.00011194726,0.0000010904301,0.000003447367,0.9839453,0.015161936,0.0006317688,0.000007693208,0.000049345355],"about_ca_topic_score_codex":8.3973026e-7,"about_ca_topic_score_gemma":7.8713805e-7,"teacher_disagreement_score":0.7139365,"about_ca_system_score_codex":0.000005717681,"about_ca_system_score_gemma":0.0000033612628,"threshold_uncertainty_score":0.19394468},"labels":[],"label_agreement":null},{"id":"W4402437041","doi":"10.1109/ccta60707.2024.10666540","title":"Passivity-Based Gain-Scheduled Control with Scheduling Matrices","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Passivity; Scheduling (production processes); Gain scheduling; Automatic gain control; Control (management); Distributed computing; Control theory (sociology); Computer network; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.007750056518693475,"score_gpt":0.21900555599008242,"score_spread":0.21125549947138894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402437041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32848796,0.00091963005,0.6667058,0.00011976076,0.00021290076,0.00010338668,0.0000018332067,0.0015756717,0.0018730683],"genre_scores_gemma":[0.98416686,0.0000064707046,0.015413491,0.00013870547,0.00014293328,0.000010094893,0.0000018881311,0.000036050027,0.00008351403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993622,0.000012200036,0.000120130004,0.00017023933,0.000100884,0.0002343483],"domain_scores_gemma":[0.9995934,0.00021268273,0.000009372108,0.000103162514,0.00001589454,0.00006549386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084448635,0.00014357381,0.00013535781,0.000077628785,0.00005576157,0.00007256493,0.00007138665,0.000037014215,0.000052663672],"category_scores_gemma":[0.000009572988,0.00010638271,0.000042700263,0.00023467757,0.00001649444,0.00015542262,0.0000068365994,0.00020410404,0.000060431015],"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":[0.000018101731,0.0000055624805,0.00006554862,0.00021139091,0.00003526542,0.00008609516,0.000026506787,0.96277165,0.030039798,0.0009941688,0.000041632546,0.005704271],"study_design_scores_gemma":[0.00042348605,0.000039470553,0.00003844955,0.00014294531,0.000024232448,0.000011312163,0.000025337926,0.90678096,0.09121437,0.0001058546,0.0009935194,0.00020002977],"about_ca_topic_score_codex":0.0000010391533,"about_ca_topic_score_gemma":0.0000030614328,"teacher_disagreement_score":0.65567887,"about_ca_system_score_codex":0.000026980142,"about_ca_system_score_gemma":0.000017587661,"threshold_uncertainty_score":0.4338161},"labels":[],"label_agreement":null},{"id":"W4402464242","doi":"10.11159/cist24.002","title":"Reliable AI: From Legal Requirements to Neuromorphic Computing","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Neuromorphic engineering; Computer science; Computer architecture; Artificial intelligence; Artificial neural network","score_opus":0.012641099291014419,"score_gpt":0.22441190684429813,"score_spread":0.2117708075532837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402464242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9910681,0.0008074716,0.0040597273,0.00015037264,0.0032283738,0.00019772533,0.0000018868727,0.0003145298,0.00017180086],"genre_scores_gemma":[0.99905485,0.0000117333475,0.000464185,0.000073647374,0.0002451003,0.0000040525106,9.405192e-8,0.000017349375,0.00012898922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882513,0.0000024956987,0.00023200667,0.00035460666,0.00026716402,0.00031860836],"domain_scores_gemma":[0.99961334,0.000087843604,0.00002600275,0.00008328097,0.000057793764,0.00013175813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021628724,0.00016976862,0.00020952376,0.00022590329,0.00014753205,0.00036870586,0.00030143996,0.000024156898,2.8882653e-7],"category_scores_gemma":[0.000021777785,0.00012851325,0.000028680224,0.0010410561,0.00005381062,0.00025157892,0.00015793505,0.00028680966,9.806915e-7],"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":[0.000029214903,0.000031027044,0.0011055573,0.0009972899,0.00007640233,0.000022736394,0.0003125347,0.63127387,0.26256335,0.06980566,0.0029843738,0.03079803],"study_design_scores_gemma":[0.00007374209,0.00006815675,0.000702947,0.0008059681,0.0000073112437,0.000024396766,0.0000024369822,0.9810331,0.014267169,0.000031683405,0.0028302947,0.00015281061],"about_ca_topic_score_codex":0.0000084499425,"about_ca_topic_score_gemma":2.5313247e-7,"teacher_disagreement_score":0.34975925,"about_ca_system_score_codex":0.000038030674,"about_ca_system_score_gemma":0.000011163503,"threshold_uncertainty_score":0.5240618},"labels":[],"label_agreement":null},{"id":"W4402580846","doi":"10.1038/s41467-024-52259-9","title":"Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","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":"National Research Council Canada","funders":"Office of Science; SLAC National Accelerator Laboratory; Electronic Components and Systems for European Leadership; Technische Universität Dresden; Danmarks Grundforskningsfond; Office of Energy Efficiency; Kungliga Tekniska Högskolan; Staatssekretariat für Bildung, Forschung und Innovation; Deutsche Forschungsgemeinschaft; National Research Foundation; National Science Foundation; Statens Naturvidenskabelige Forskningsrad; Innosuisse - Schweizerische Agentur für Innovationsförderung; Division of Electrical, Communications and Cyber Systems; Office of Energy Efficiency and Renewable Energy; European Commission; U.S. Department of Energy","keywords":"Neuromorphic engineering; Computer science; Interoperability; Computer architecture; Software; Artificial neural network; Spiking neural network; Set (abstract data type); Computation; Scalability; Artificial intelligence; Theoretical computer science; Algorithm; Programming language","score_opus":0.05465863145305034,"score_gpt":0.32762999375610435,"score_spread":0.272971362303054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402580846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8941843,0.010868771,0.07196486,0.0073950654,0.0065460824,0.0012308332,0.00009659196,0.0041713165,0.0035421841],"genre_scores_gemma":[0.9941884,0.00015373716,0.004840668,0.00028619063,0.00018689931,0.000027474205,0.00017729127,0.00004062213,0.00009871566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924815,0.0000616752,0.00025261735,0.00019431346,0.000072149174,0.00017110612],"domain_scores_gemma":[0.9985311,0.00063593325,0.000029970728,0.00068673486,0.00007072443,0.000045555567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013892479,0.00013317165,0.00013412184,0.00012536222,0.0002549566,0.000098820645,0.000477553,0.00012353198,0.000005332292],"category_scores_gemma":[0.00015615909,0.00013759131,0.000075429714,0.00042158613,0.00005706909,0.00024976977,0.00015835927,0.00088688097,0.0000118048065],"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":[0.00013932594,0.00013194338,0.0006001964,0.0015717394,0.0006169434,0.000050571252,0.010284867,0.1670726,0.22403018,0.08866358,0.060651865,0.44618618],"study_design_scores_gemma":[0.00045894063,0.000053224107,0.000587073,0.00027677816,0.000034524663,0.000077114506,0.00025231307,0.82381696,0.010237813,0.0012174958,0.16269265,0.00029513316],"about_ca_topic_score_codex":0.0000015805839,"about_ca_topic_score_gemma":0.000028918392,"teacher_disagreement_score":0.65674436,"about_ca_system_score_codex":0.000058910446,"about_ca_system_score_gemma":0.00001617225,"threshold_uncertainty_score":0.5610811},"labels":[],"label_agreement":null},{"id":"W4402727498","doi":"10.1109/mwscas60917.2024.10658665","title":"A Neural Assembly-Based State Machine Design","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Carleton University","funders":"","keywords":"Computer science; State (computer science); Artificial neural network; Artificial intelligence; Programming language","score_opus":0.024056383537892516,"score_gpt":0.24865587346279838,"score_spread":0.22459948992490586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08473252,0.0006433152,0.91059965,0.000079868594,0.0004420142,0.00008370498,0.0000022201277,0.0018833342,0.0015333709],"genre_scores_gemma":[0.99268484,0.0000057957905,0.0067064506,0.000114824645,0.000046813053,0.0000039291363,0.00000214053,0.000028465809,0.00040673037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995497,0.000014230911,0.000094098774,0.000112728245,0.000058424805,0.00017080535],"domain_scores_gemma":[0.99974453,0.00012067931,0.0000033128952,0.00008163511,0.0000053511126,0.000044497883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000661589,0.00009860485,0.00007126742,0.00004892523,0.000028039913,0.000040654457,0.000056386452,0.00001592368,0.00005249664],"category_scores_gemma":[0.000005899187,0.00008182326,0.000034206932,0.0001395932,0.0000060696384,0.000102056845,0.000009711405,0.00013925595,0.00007064589],"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":[0.00000474886,0.0000023193936,0.0000039142524,0.000058754096,0.0000069568423,0.00008395186,0.000020003496,0.916344,0.051777557,0.000077600416,0.0005271965,0.03109302],"study_design_scores_gemma":[0.000059262777,0.00002101093,0.000015774689,0.000016748856,0.0000031360787,0.000007257084,0.000001411759,0.8513566,0.1467522,0.00019426849,0.00147805,0.00009424022],"about_ca_topic_score_codex":0.0000010376101,"about_ca_topic_score_gemma":0.0000011313243,"teacher_disagreement_score":0.9079523,"about_ca_system_score_codex":0.000017848852,"about_ca_system_score_gemma":0.0000065303398,"threshold_uncertainty_score":0.33366558},"labels":[],"label_agreement":null},{"id":"W4402753448","doi":"10.1109/mwscas60917.2024.10658688","title":"Manhattan Rule for Robust In-Situ Training of Memristive Deep Neural Network Accelerators","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; York University; University of Toronto","funders":"","keywords":"Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Physics; Meteorology","score_opus":0.04124591927515616,"score_gpt":0.26418662891298955,"score_spread":0.22294070963783338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402753448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7201938,0.0011582625,0.26998508,0.000029649207,0.0011001807,0.00026548497,0.000002956928,0.0004950209,0.006769556],"genre_scores_gemma":[0.98755753,0.0000070195874,0.012055775,0.000026664587,0.0002465873,0.000012466229,0.000004070339,0.000031285053,0.00005858694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999299,0.000008721832,0.00021437345,0.00015347455,0.000052700307,0.00027171258],"domain_scores_gemma":[0.99966735,0.00019715322,0.000013140079,0.00007169294,0.00001257322,0.00003809733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010000596,0.00012052898,0.00017536696,0.000059595863,0.000033801265,0.000020477413,0.00008049303,0.000042766114,0.00001915852],"category_scores_gemma":[0.000011951193,0.000113915514,0.00006083671,0.00024348535,0.000014429031,0.00015307555,0.000020097072,0.00015095767,0.0000023506802],"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":[0.00000878536,0.000002675738,0.000042825577,0.00012215605,0.000015044612,0.000013133004,0.00045270412,0.94453365,0.0057412526,0.0008450243,0.000093648356,0.048129108],"study_design_scores_gemma":[0.0002058742,0.00005122787,0.00045729178,0.000114845796,0.0000122510555,0.000008281532,0.00045041827,0.96976703,0.027326738,0.00096496055,0.00043566807,0.00020540462],"about_ca_topic_score_codex":7.551032e-7,"about_ca_topic_score_gemma":0.000026936865,"teacher_disagreement_score":0.26736373,"about_ca_system_score_codex":0.000029226268,"about_ca_system_score_gemma":0.000005807065,"threshold_uncertainty_score":0.46453398},"labels":[],"label_agreement":null},{"id":"W4402753594","doi":"10.1109/mwscas60917.2024.10658870","title":"Memristive-Based Full Adder","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Adder; Computer science; Computer architecture; Telecommunications","score_opus":0.01139724450202675,"score_gpt":0.23041226294306522,"score_spread":0.21901501844103846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402753594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2085102,0.0018073769,0.70149845,0.0001591999,0.0014611926,0.00010517639,0.0000039848437,0.004439136,0.08201525],"genre_scores_gemma":[0.99719805,0.000003466273,0.0018541256,0.00007074354,0.000108956854,0.0000023402706,0.0000017643011,0.000015623036,0.0007449091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997253,0.000003048576,0.000055357224,0.000078398836,0.000037326387,0.00010056804],"domain_scores_gemma":[0.9998563,0.000054622986,0.0000014926063,0.000056450674,0.00000460231,0.000026548405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000023595918,0.00005862004,0.000043727512,0.000028583632,0.000018759005,0.00001779382,0.00003324985,0.000017320632,0.00033495322],"category_scores_gemma":[0.000004109307,0.00004951708,0.000026034326,0.000090736015,0.0000062598606,0.000053209227,0.0000060192697,0.00008570718,0.00019988144],"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":[0.000010777367,0.000008827287,0.000012914746,0.00034258916,0.000036652676,0.00017255939,0.00011793968,0.68311244,0.18809158,0.0035302388,0.01818396,0.10637951],"study_design_scores_gemma":[0.00006551091,0.000019004212,0.000024427978,0.000044474597,0.000005333038,0.000006485587,0.000012883087,0.7660314,0.17443404,0.00034928665,0.058867637,0.00013954073],"about_ca_topic_score_codex":2.4811817e-7,"about_ca_topic_score_gemma":7.3246207e-7,"teacher_disagreement_score":0.7886879,"about_ca_system_score_codex":0.000012716709,"about_ca_system_score_gemma":0.000004127966,"threshold_uncertainty_score":0.3667503},"labels":[],"label_agreement":null},{"id":"W4402827756","doi":"10.1007/978-3-031-61531-3_9","title":"Condition Assessment of a Cantilevered I-Beam Using LSTM Deep Learning Algorithm","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cantilever; Artificial intelligence; Beam (structure); Algorithm; Computer science; Deep learning; Machine learning; Engineering; Structural engineering","score_opus":0.010679024914700934,"score_gpt":0.24588895621393134,"score_spread":0.2352099312992304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402827756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021254076,0.0043457523,0.98097086,0.0000041564986,0.0014383191,0.0002946653,0.00004311871,0.0006613419,0.01011635],"genre_scores_gemma":[0.98148894,0.00023862885,0.016775852,0.000011172848,0.00052094506,0.000008954637,0.0001036788,0.00033502447,0.00051680685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843377,0.0000066130547,0.0005220279,0.00038536484,0.00025744314,0.0003948083],"domain_scores_gemma":[0.99930257,0.0002716956,0.00009592185,0.00022159176,0.00004001912,0.00006819641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012477757,0.0005442894,0.0006457872,0.00048941723,0.00003984553,0.000030490115,0.00015397,0.0004019559,0.00009549636],"category_scores_gemma":[0.00004246768,0.00062167324,0.00017005982,0.00015420963,0.000024956107,0.00010158282,0.0000806673,0.0017344378,0.00000431064],"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":[0.0000013184247,0.0000028318295,0.0000047027133,0.00069955905,0.00008427429,0.00009968591,0.00009456517,0.9687739,0.017643118,0.000446634,0.000001186222,0.012148217],"study_design_scores_gemma":[0.00017595329,0.00003814788,0.000019081082,0.0020962912,0.00007613907,0.00005981951,0.0000020981274,0.98410237,0.009525113,0.0020684346,0.0012429992,0.00059353997],"about_ca_topic_score_codex":0.0000063704883,"about_ca_topic_score_gemma":0.000039010127,"teacher_disagreement_score":0.9793635,"about_ca_system_score_codex":0.00038210768,"about_ca_system_score_gemma":0.000029015644,"threshold_uncertainty_score":0.9996235},"labels":[],"label_agreement":null},{"id":"W4402834950","doi":"10.1109/vtc2024-spring62846.2024.10683049","title":"Efficient Hardware Acceleration of Spiking Neural Networks Using FPGA: Towards Real-Time Edge Neuromorphic Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Royal Military College of Canada","funders":"","keywords":"Neuromorphic engineering; Field-programmable gate array; Computer science; Spiking neural network; Hardware acceleration; Acceleration; Computer architecture; Enhanced Data Rates for GSM Evolution; Artificial neural network; Edge computing; Embedded system; Computational science; Parallel computing; Artificial intelligence","score_opus":0.04039281297494234,"score_gpt":0.26555638351202626,"score_spread":0.2251635705370839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402834950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7796383,0.0003094827,0.21708268,0.00001400443,0.0010291303,0.00014168704,0.0000020243351,0.0008169622,0.0009657611],"genre_scores_gemma":[0.9973751,0.000011787159,0.0020481283,0.000022555656,0.000441112,9.4233235e-7,0.000008564133,0.000058412832,0.00003345176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875313,0.00003767979,0.00040380537,0.00028595925,0.00016671586,0.00035269815],"domain_scores_gemma":[0.99955094,0.000117550146,0.000043670134,0.00017366196,0.0000458769,0.00006832244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017594316,0.00022469995,0.00026170688,0.00012300734,0.00012664951,0.00008676885,0.00013255927,0.00007187341,0.00004293277],"category_scores_gemma":[0.00001760948,0.00021791189,0.000103820115,0.00043423966,0.000028304768,0.00012169417,0.00009782147,0.0003038353,0.000006499282],"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":[0.0000036740144,0.0000054673806,0.000010265922,0.00012222667,0.000012007118,0.000035085715,0.00009707692,0.858192,0.124908514,0.00006343747,0.000029079656,0.016521156],"study_design_scores_gemma":[0.00009445411,0.00002793661,0.00021190221,0.00018728872,0.00002207982,0.00005303255,0.000018394978,0.9652735,0.03387456,0.0000097788625,0.000025902613,0.00020119666],"about_ca_topic_score_codex":0.000011897006,"about_ca_topic_score_gemma":8.4991524e-7,"teacher_disagreement_score":0.21773678,"about_ca_system_score_codex":0.00006610122,"about_ca_system_score_gemma":0.000015272512,"threshold_uncertainty_score":0.8886189},"labels":[],"label_agreement":null},{"id":"W4402943498","doi":"10.1016/j.brs.2024.09.012","title":"Tetra codes: A precise, concise notation system for scalp-based neuronavigation","year":2024,"lang":"en","type":"letter","venue":"Brain stimulation","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"National Institutes of Health; Chia Family Foundation; Canadian Institutes of Health Research; MagVenture; Fondation Brain Canada; Klarman Family Foundation; National Institute of Mental Health; Arrell Family Foundation; Ontario Brain Institute; Foundation for the National Institutes of Health","keywords":"Scalp; Notation; Tetra; Computer science; Programming language; Medicine; Mathematics; Biology; Arithmetic; Anatomy; Paleontology","score_opus":0.02490552501754463,"score_gpt":0.27104390948389057,"score_spread":0.24613838446634595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402943498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015598958,0.0003488136,0.9401905,0.03263225,0.0043367706,0.0030065533,0.00036712547,0.0031786347,0.00034038024],"genre_scores_gemma":[0.931444,0.0000022511738,0.005400333,0.04958729,0.005967581,0.00040965827,0.006141721,0.0003975167,0.0006496517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985139,0.00005820794,0.00045665586,0.00041604787,0.0002680515,0.0002871402],"domain_scores_gemma":[0.99830437,0.0012087635,0.00011237388,0.00023010834,0.00010579028,0.000038604358],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016738602,0.00033803436,0.00030726468,0.0001937938,0.00010495492,0.000099074176,0.000115470124,0.0004234849,0.0000052489986],"category_scores_gemma":[0.00011779461,0.00036842204,0.00014807333,0.00023040929,0.00002012191,0.00017019006,0.000012201851,0.0006328139,0.00004259471],"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":[0.000016807688,0.0000027527115,0.0000029236967,0.003001691,0.000018526958,0.000024310119,0.000056553483,0.83922476,0.0031348455,0.000024406128,0.14788762,0.006604795],"study_design_scores_gemma":[0.0004935681,0.00005735664,0.000050657556,0.00085423887,0.000079125566,0.000006482,0.000006313904,0.909995,0.0034515392,0.0002032794,0.08445108,0.00035135468],"about_ca_topic_score_codex":0.0000010588643,"about_ca_topic_score_gemma":6.065748e-7,"teacher_disagreement_score":0.9347902,"about_ca_system_score_codex":0.00026484317,"about_ca_system_score_gemma":0.000033063046,"threshold_uncertainty_score":0.9998768},"labels":[],"label_agreement":null},{"id":"W4403124238","doi":"10.1109/nocarc51382.2020.9234573","title":"High-Throughput Synthesizable Synchronization FIFOs for Mixed-Timing NoCs","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Throughput; Synchronization (alternating current); Computer science; Parallel computing; Computer network; Operating system; Wireless","score_opus":0.02812016090132021,"score_gpt":0.22990027996594836,"score_spread":0.20178011906462814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403124238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040575877,0.000098850825,0.95717263,0.0002726463,0.0002893391,0.0001922491,0.00000437632,0.0007100386,0.0006839962],"genre_scores_gemma":[0.95700336,0.000014281775,0.042211898,0.0003007256,0.00031720757,0.000012918655,0.0000110127485,0.00003717781,0.00009144318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994154,0.000006618567,0.00015254403,0.00016289369,0.000058630572,0.00020391392],"domain_scores_gemma":[0.99967545,0.00013248071,0.000018812247,0.0000869952,0.000022739974,0.00006350885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003540094,0.00011453841,0.00013694893,0.000016069622,0.00008977216,0.000020726646,0.00008425139,0.000042048803,0.00006349113],"category_scores_gemma":[0.00008215804,0.00011423163,0.000038429243,0.0001244409,0.00000785004,0.00017880382,0.00002399204,0.000069863665,0.00003656165],"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":[0.000008103712,0.000004440654,0.000008297765,0.00019839194,0.000015545713,0.0000027866529,0.000113791895,0.9514317,0.022537883,0.0010750944,0.0024018593,0.022202138],"study_design_scores_gemma":[0.00019129188,0.000037767106,0.000006658414,0.000021486094,0.000012158671,0.0000020099721,0.000047602483,0.6780835,0.3157842,0.00022962815,0.00541575,0.00016795579],"about_ca_topic_score_codex":0.0000011337645,"about_ca_topic_score_gemma":0.0000010585685,"teacher_disagreement_score":0.91642743,"about_ca_system_score_codex":0.000027928856,"about_ca_system_score_gemma":0.0000056633107,"threshold_uncertainty_score":0.46582308},"labels":[],"label_agreement":null},{"id":"W4403181367","doi":"10.36227/techrxiv.171340305.59520871/v2","title":"A small tamper-resistant anti-recycling IC sensor with a reused I/O interface and DC signalling","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Interface (matter); Tamper resistance; Signalling; Computer science; Embedded system; Chemistry; Cell biology; Biology; Operating system; Computer security","score_opus":0.030208795806914927,"score_gpt":0.24305729574411042,"score_spread":0.21284849993719548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403181367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90144575,0.0025738222,0.09263759,0.000085745574,0.00048794123,0.00034415233,0.000010599086,0.0012863463,0.0011280541],"genre_scores_gemma":[0.9799114,0.0001703306,0.019004285,0.000034421995,0.00019577229,0.000015067103,0.0000059898994,0.00013603835,0.000526702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983608,0.000030727224,0.00039576963,0.0006695373,0.00013956559,0.00040356032],"domain_scores_gemma":[0.9992511,0.00017588605,0.000062704254,0.00034430213,0.000041620046,0.00012436426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016643862,0.00050328986,0.0005187439,0.00015591571,0.000081151295,0.00018264448,0.0002046906,0.00020545979,0.000017140439],"category_scores_gemma":[0.000027306189,0.00041628184,0.00008676958,0.00014447597,0.000042525706,0.000044533732,0.00061101327,0.0013964459,0.000016807136],"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":[0.00004568849,0.000008080774,0.000016138449,0.0018607597,0.00014239282,0.00018322455,0.0005218989,0.5804889,0.4150546,0.000048526035,0.000020279916,0.0016095024],"study_design_scores_gemma":[0.0006822875,0.00017238565,0.00007796013,0.007708165,0.00029635153,0.00014478937,0.00068067317,0.42226598,0.5629642,0.0021682866,0.00087673333,0.0019622257],"about_ca_topic_score_codex":0.000012896332,"about_ca_topic_score_gemma":0.00004180032,"teacher_disagreement_score":0.15822296,"about_ca_system_score_codex":0.000071780494,"about_ca_system_score_gemma":0.000029097382,"threshold_uncertainty_score":0.9998289},"labels":[],"label_agreement":null},{"id":"W4403199863","doi":"10.21203/rs.3.rs-5029115/v1","title":"Chiroferromagnetic Quantum Dots for Chiroptical Synapse (ChiropS)","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Quantum dot; Synapse; Chemistry; Nanotechnology; Physics; Optoelectronics; Neuroscience; Materials science; Biology","score_opus":0.07782669973761897,"score_gpt":0.39562496090726385,"score_spread":0.3177982611696449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403199863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9737637,0.01025766,0.00481361,0.0005128912,0.0022612046,0.0025406631,0.00025718854,0.0016281168,0.003964974],"genre_scores_gemma":[0.9954133,0.00040138524,0.0013965408,0.00001321608,0.0012581983,0.000459863,0.00010990811,0.00021598762,0.00073157856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967947,0.00012712262,0.00043648854,0.0008110299,0.00066159456,0.0011690375],"domain_scores_gemma":[0.9981024,0.000636478,0.000025323965,0.0007567467,0.00017208159,0.0003069464],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007571868,0.00044646076,0.00049225905,0.00043512727,0.00023626845,0.00020249972,0.0005843311,0.00043588458,0.00007925622],"category_scores_gemma":[0.0004050445,0.00042081528,0.00030703613,0.0003613096,0.00014859317,0.00004864904,0.0014230498,0.0036528853,0.00030582986],"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":[0.00066969905,0.0005147121,0.00007679928,0.10471917,0.0009023252,0.0018525872,0.0028475246,0.5804219,0.105965205,0.09491831,0.028226634,0.07888511],"study_design_scores_gemma":[0.0007038639,0.00084602274,0.00058391853,0.0041430932,0.00008967164,0.00006364459,0.00027295796,0.791832,0.015228523,0.17553964,0.009276258,0.0014204497],"about_ca_topic_score_codex":0.0000047020603,"about_ca_topic_score_gemma":0.0000047260446,"teacher_disagreement_score":0.21141003,"about_ca_system_score_codex":0.00022590993,"about_ca_system_score_gemma":0.000111919035,"threshold_uncertainty_score":0.99982435},"labels":[],"label_agreement":null},{"id":"W4403428452","doi":"10.1038/s41598-024-76011-x","title":"Author Correction: Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function","year":2024,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; University of Calgary","funders":"Division of Emerging Frontiers in Research and Innovation; National Science Foundation","keywords":"Neuromorphic engineering; Order (exchange); Function (biology); Computer science; Neuroscience; Artificial intelligence; Biology; Artificial neural network; Economics; Evolutionary biology","score_opus":0.02327377903840074,"score_gpt":0.2455517991887639,"score_spread":0.22227802015036316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403428452","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014696689,0.0017254229,0.0027115662,0.00013926218,0.9620852,0.0005768653,0.000031051753,0.0012468126,0.016787125],"genre_scores_gemma":[0.09608629,0.00007796308,0.00021927943,0.00008804462,0.007730879,0.00009495126,0.0014484916,0.00023494578,0.8940191],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964399,0.000052679912,0.0007195927,0.0016300727,0.0007507691,0.0004069986],"domain_scores_gemma":[0.9986116,0.00005708164,0.00025724698,0.00068121875,0.00023549907,0.00015734456],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000792327,0.00041503512,0.00032774446,0.00033572715,0.0004996372,0.0003939225,0.00009399276,0.000250058,0.00019235768],"category_scores_gemma":[0.00034867285,0.0004230738,0.00010540797,0.00075959496,0.00018166503,0.00035803005,0.00012055551,0.001688725,0.000053772743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009885114,0.000014700371,0.000025293391,0.00020150369,0.000061172796,0.0003584435,0.00005885105,0.0041875443,0.0025893506,0.0000069402636,0.9894988,0.002987531],"study_design_scores_gemma":[0.000053863645,0.00006708019,0.0003588256,0.0002825375,0.00012324742,0.0016700282,0.000029209126,0.018811615,0.0013981801,0.001861542,0.97494465,0.00039921553],"about_ca_topic_score_codex":0.0000020855034,"about_ca_topic_score_gemma":0.0000058512956,"teacher_disagreement_score":0.95435435,"about_ca_system_score_codex":0.00010385694,"about_ca_system_score_gemma":0.00016471371,"threshold_uncertainty_score":0.9998221},"labels":[],"label_agreement":null},{"id":"W4403446571","doi":"10.1109/tbcas.2024.3481160","title":"BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"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":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Neuromorphic engineering; Computer science; Multiplier (economics); State (computer science); Computer hardware; Coprocessor; Bit (key); Parallel computing; Arithmetic; Electronic engineering; Artificial intelligence; Algorithm; Artificial neural network; Mathematics; Engineering","score_opus":0.03799952034486521,"score_gpt":0.2446836631765777,"score_spread":0.2066841428317125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403446571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2452172,0.0006266911,0.7444901,0.00038315635,0.0067661502,0.0007038784,0.000113634895,0.0012774229,0.0004218034],"genre_scores_gemma":[0.99841297,0.00014415703,0.000049642727,0.00008075443,0.00044164664,0.0001091247,0.000017935683,0.00007470436,0.0006690794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979819,0.00007673128,0.00059618603,0.00051034195,0.00039340268,0.00044140813],"domain_scores_gemma":[0.9988892,0.00044021767,0.000052845124,0.00024988016,0.000046795052,0.00032106086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030743706,0.0003406129,0.0003895695,0.0003268472,0.00025930433,0.00021591864,0.00017893508,0.00020803005,0.000022009946],"category_scores_gemma":[0.000016622027,0.0002920567,0.000113434515,0.0005650873,0.00016124082,0.00028187077,0.000002232189,0.0005673214,0.000053600037],"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":[0.00013645478,0.00020886747,0.000005740727,0.0034869185,0.00030320653,0.00029466898,0.0018383367,0.14298323,0.2949282,0.00026984318,0.0015090661,0.5540355],"study_design_scores_gemma":[0.0018860082,0.00024261785,0.00011473801,0.00070315413,0.00006819392,0.00028797652,0.00022805165,0.97382104,0.011796219,0.000075674914,0.010121694,0.00065460074],"about_ca_topic_score_codex":0.000009369171,"about_ca_topic_score_gemma":0.000008318589,"teacher_disagreement_score":0.83083785,"about_ca_system_score_codex":0.00007211242,"about_ca_system_score_gemma":0.000051925334,"threshold_uncertainty_score":0.99995315},"labels":[],"label_agreement":null},{"id":"W4403631537","doi":"10.1101/2024.10.19.619194","title":"Competitive interactions shape brain dynamics and computation across species","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Agencia Estatal de Investigación; Agència de Gestió d'Ajuts Universitaris i de Recerca; Canadian Institutes of Health Research; Wellcome Trust; HORIZON EUROPE Framework Programme; Canada Research Chairs; National Institutes of Health; Horizon 2020 Framework Programme; National Research Foundation; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; European Regional Development Fund; European Commission; Danmarks Grundforskningsfond; Simons Foundation Autism Research Initiative; National Alliance for Research on Schizophrenia and Depression","keywords":"Computer science; Modular design; Connectome; Computational neuroscience; Modularity (biology); Computational model; Computation; Distributed computing; Artificial intelligence; Neuroscience; Theoretical computer science; Psychology; Biology","score_opus":0.01563691296412302,"score_gpt":0.25428309651275105,"score_spread":0.23864618354862804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403631537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9780004,0.0009656,0.015400609,0.00026763167,0.0028829032,0.0003758622,0.00047698055,0.0015141028,0.00011593235],"genre_scores_gemma":[0.9963106,0.00011851845,0.0028382512,0.00008593142,0.0004570763,0.00003541442,0.0000014293882,0.0001353372,0.00001744258],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841,0.00004520745,0.00037792345,0.0006000154,0.00016228664,0.0004045883],"domain_scores_gemma":[0.99909467,0.00020233337,0.00010187496,0.0003132361,0.0001428847,0.0001450014],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018400772,0.0004581599,0.00039828353,0.00015302791,0.00018070202,0.00031627095,0.00018612041,0.00020724074,0.000015610818],"category_scores_gemma":[0.000068547655,0.00053486665,0.00009477421,0.00029662898,0.00011038255,0.00015311764,0.00059454126,0.001247248,0.000046279478],"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":[0.000039962964,0.00009222299,0.00067130144,0.0042403266,0.0006084293,0.00035265094,0.00031195473,0.107400306,0.8570313,0.02816325,0.00087635714,0.00021196187],"study_design_scores_gemma":[0.00042362607,0.000049459883,0.022992257,0.0019607162,0.000115543684,3.210431e-7,0.000094112635,0.86816627,0.0996858,0.00007387415,0.0048173824,0.0016206475],"about_ca_topic_score_codex":0.000003259893,"about_ca_topic_score_gemma":0.000012468603,"teacher_disagreement_score":0.76076597,"about_ca_system_score_codex":0.00034345462,"about_ca_system_score_gemma":0.000048006867,"threshold_uncertainty_score":0.99971026},"labels":[],"label_agreement":null},{"id":"W4403651570","doi":"10.3390/jlpea14040050","title":"Phase Change Memory Drift Compensation in Spiking Neural Networks Using a Non-Linear Current Scaling Strategy","year":2024,"lang":"en","type":"article","venue":"Journal of Low Power Electronics and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Association Nationale de la Recherche et de la Technologie; Agence Nationale de la Recherche","keywords":"Neuromorphic engineering; Phase-change memory; Spiking neural network; Compensation (psychology); MNIST database; Benchmark (surveying); Computer science; Chip; CMOS; Electronic engineering; Artificial neural network; Control theory (sociology); Computer hardware; Artificial intelligence; Engineering; Phase change; Control (management)","score_opus":0.029590021835309406,"score_gpt":0.3228131415276315,"score_spread":0.2932231196923221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403651570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6991649,0.015753238,0.2846671,0.000027655946,0.00016727929,0.00016970209,0.000001310829,0.000026018868,0.000022758915],"genre_scores_gemma":[0.9984832,0.00079162186,0.0002562466,0.000016733355,0.00041791765,0.000008476062,0.0000032676155,0.00002159796,9.5177273e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921346,0.000012589274,0.0003416761,0.000113907074,0.00008804215,0.00023031498],"domain_scores_gemma":[0.99971193,0.000047809368,0.00007297071,0.00006917262,0.000036306497,0.00006181167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018299831,0.00011659032,0.00015980954,0.00014149108,0.000074746094,0.000054879354,0.00007373994,0.00003972004,0.0000025084541],"category_scores_gemma":[0.0000023689377,0.00011287576,0.000052543917,0.00028976568,0.000014516562,0.0002453637,0.0000147291585,0.00056363,4.0850588e-7],"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":[0.000011961726,0.00007136782,0.000016889748,0.000100939484,0.00001875917,0.000013817128,0.0001965042,0.8262942,0.021862976,0.0008080029,0.000005436084,0.15059917],"study_design_scores_gemma":[0.00036232706,0.000072642186,0.00003326642,0.00018467533,0.000018662116,0.00008202048,0.00003901541,0.99621165,0.0015542412,0.00027803564,0.0010496702,0.00011381892],"about_ca_topic_score_codex":6.6661465e-7,"about_ca_topic_score_gemma":0.0000022938614,"teacher_disagreement_score":0.29931825,"about_ca_system_score_codex":0.000083362465,"about_ca_system_score_gemma":0.000027230684,"threshold_uncertainty_score":0.46029398},"labels":[],"label_agreement":null},{"id":"W4403908724","doi":"10.3389/fnins.2024.1511987","title":"Editorial: From theory to practice: the latest developments in neuromorphic computing applications","year":2024,"lang":"en","type":"editorial","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Carleton University","funders":"","keywords":"Neuromorphic engineering; Computer science; Cognitive science; Artificial intelligence; Psychology; Artificial neural network","score_opus":0.012946420590248411,"score_gpt":0.2654865729175506,"score_spread":0.2525401523273022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403908724","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012293084,0.0004977819,0.04993532,0.00010144294,0.9482168,0.0005880588,0.000052075782,0.00024348112,0.00024209503],"genre_scores_gemma":[0.0026929933,0.00027620312,0.0074393437,0.00029945103,0.9888745,0.0001200239,0.00002967483,0.00011421259,0.00015358347],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99728537,0.00017425143,0.0005010441,0.00082720193,0.0006764943,0.00053564354],"domain_scores_gemma":[0.99748194,0.0018176762,0.00009599218,0.00044323783,0.000059080467,0.00010204938],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000865856,0.0003581889,0.00034414534,0.0002992232,0.00017447029,0.00020663542,0.0011753811,0.00026693594,6.5993123e-7],"category_scores_gemma":[0.00213814,0.0003211381,0.00004476964,0.0014994142,0.000110372835,0.0002660867,0.00036430176,0.0022245694,0.000026338015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000142322,0.000015546731,0.000020960402,0.000039134968,0.0000029557768,0.000041955976,0.0003195204,0.057790447,0.00052974146,0.000016683109,0.9378922,0.003316647],"study_design_scores_gemma":[0.000119959754,0.000017585839,0.000047607384,0.0001893715,0.000011887269,0.0000017185396,0.00006640841,0.0053611323,0.00008233361,0.0009054164,0.9928841,0.00031249202],"about_ca_topic_score_codex":0.00001062773,"about_ca_topic_score_gemma":0.000008482324,"teacher_disagreement_score":0.054991912,"about_ca_system_score_codex":0.00023563358,"about_ca_system_score_gemma":0.00015523176,"threshold_uncertainty_score":0.99992406},"labels":[],"label_agreement":null},{"id":"W4404061367","doi":"10.1103/physreve.110.054303","title":"Fault-tolerant neural networks from biological error correction codes","year":2024,"lang":"en","type":"article","venue":"Physical review. E","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"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":"Natural Sciences and Engineering Research Council of Canada; National Defense Science and Engineering Graduate; Hertz Foundation; National Science Foundation","keywords":"Computer science; Fault tolerance; Artificial neural network; Error detection and correction; Algorithm; Artificial intelligence; Distributed computing","score_opus":0.03160467284377184,"score_gpt":0.3202653775074859,"score_spread":0.2886607046637141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404061367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87178844,0.07392863,0.048769426,0.00016844297,0.003204133,0.0002734707,0.00001012526,0.0013065558,0.00055075786],"genre_scores_gemma":[0.99502116,0.0035900453,0.000066549284,0.0002486746,0.0009906002,0.00001644296,0.000026520245,0.000021402342,0.000018589864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927413,0.00004288034,0.00017202098,0.00022738797,0.00007718825,0.00020638411],"domain_scores_gemma":[0.99950486,0.0002786284,0.000014764177,0.00012501725,0.0000109790435,0.00006576545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051666855,0.00016280999,0.00028994912,0.000012359124,0.000045889064,0.000025095416,0.00009422929,0.000031637777,0.000027216533],"category_scores_gemma":[0.000039272938,0.000119222634,0.00014443326,0.0001893156,0.000024710947,0.00010195852,0.00003184524,0.00037293037,0.00008632795],"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":[0.000015611635,0.00004905135,0.000062095576,0.00058741553,0.00006201433,0.0000757364,0.000092729875,0.43047455,0.026937371,0.00050034345,0.0068104067,0.53433263],"study_design_scores_gemma":[0.00003503012,0.000030671963,0.00017553409,0.00073259044,0.000026518517,0.0000063305524,0.0000031427332,0.98838735,0.0018298007,0.00065813557,0.00795642,0.00015850544],"about_ca_topic_score_codex":0.0000023120735,"about_ca_topic_score_gemma":8.4783045e-7,"teacher_disagreement_score":0.55791277,"about_ca_system_score_codex":0.000024083405,"about_ca_system_score_gemma":0.0000022835452,"threshold_uncertainty_score":0.48617578},"labels":[],"label_agreement":null},{"id":"W4404295989","doi":"10.1039/d4cp03242j","title":"Identifying winner-takes-all emergence in random nanowire networks: an inverse problem","year":2024,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Science Foundation Ireland; Compute Canada; Alliance de recherche numérique du Canada; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Nanowire; Inverse; Nanotechnology; Statistical physics; Computer science; Physics; Mathematics; Materials science","score_opus":0.02176294201113871,"score_gpt":0.265568653039465,"score_spread":0.24380571102832627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404295989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9835163,0.00032905702,0.012526084,0.000023970922,0.000198309,0.0001597863,0.000007810818,0.00093108445,0.0023076418],"genre_scores_gemma":[0.99775857,0.000023768876,0.0003902246,0.000033187403,0.0015673932,0.00003196103,0.00006983831,0.000078478566,0.000046583707],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983968,0.000016156986,0.00030984022,0.00052268174,0.00021630054,0.0005382259],"domain_scores_gemma":[0.9993341,0.0001240816,0.000029639868,0.00029437116,0.00002375726,0.00019406731],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007016456,0.00035704207,0.0003618067,0.000013644627,0.000041417803,0.000071215334,0.0003064145,0.00010731755,0.000018199185],"category_scores_gemma":[0.0000166535,0.00036795865,0.00017604115,0.00050097593,0.0000763782,0.000506415,0.000070135,0.00082062715,0.00002571974],"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":[0.00001800323,0.000076054814,0.00000407744,0.0005107935,0.000025346488,0.000034382287,0.00033096402,0.15414704,0.839889,0.00007323559,0.00024385322,0.004647255],"study_design_scores_gemma":[0.00025462604,0.0000049087894,9.951469e-7,0.00015759192,0.00001815012,0.0000037072207,0.00003547886,0.4368583,0.55837125,0.0038090772,0.00018886532,0.00029709568],"about_ca_topic_score_codex":0.0000021732317,"about_ca_topic_score_gemma":4.4752613e-7,"teacher_disagreement_score":0.28271124,"about_ca_system_score_codex":0.00009792711,"about_ca_system_score_gemma":0.000019429242,"threshold_uncertainty_score":0.9998772},"labels":[],"label_agreement":null},{"id":"W4404328185","doi":"10.3389/felec.2024.1377080","title":"Compact grounded memristor model with resistorless and tunability features","year":2024,"lang":"en","type":"article","venue":"Frontiers in Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Memristor; Memistor; Grounded theory; Computer science; Materials science; Electronic engineering; Electrical engineering; Resistive random-access memory; Engineering; Sociology; Qualitative research; Voltage","score_opus":0.005333570283542363,"score_gpt":0.20740328897064314,"score_spread":0.20206971868710077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404328185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7332836,0.05936199,0.20384333,0.00013054455,0.0006157169,0.0002496741,0.000005814268,0.00065237033,0.00185702],"genre_scores_gemma":[0.99398714,0.00029077136,0.0053545632,0.000019381188,0.000038508297,0.0000044454277,0.0000036171566,0.00003434351,0.0002672299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992222,0.000018163424,0.00011550775,0.00022790932,0.000091486385,0.0003247329],"domain_scores_gemma":[0.999761,0.000030518324,0.00000957593,0.00014043826,0.000008890974,0.000049577666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109489294,0.00015235152,0.00018481619,0.000087961,0.000051750296,0.000035653225,0.0000842896,0.000059109123,7.8669626e-7],"category_scores_gemma":[0.0000074067925,0.00013650741,0.000025155034,0.00018636529,0.000047045938,0.00015926787,0.000010449338,0.0004623813,3.6541738e-7],"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":[0.00019265068,0.00002487866,0.0006799851,0.00057207316,0.000093680675,0.000052317773,0.0009298879,0.9509227,0.0028135637,0.0033446592,0.013670381,0.026703171],"study_design_scores_gemma":[0.00030381966,0.000103067076,0.0006452122,0.0001009289,0.000025892045,0.000029100675,0.00008982497,0.97404426,0.0036329688,0.013466042,0.0072040567,0.00035484612],"about_ca_topic_score_codex":0.0000027742547,"about_ca_topic_score_gemma":0.00006187164,"teacher_disagreement_score":0.2607036,"about_ca_system_score_codex":0.0004434425,"about_ca_system_score_gemma":0.00004519828,"threshold_uncertainty_score":0.5566611},"labels":[],"label_agreement":null},{"id":"W4404363361","doi":"10.1049/ell2.70092","title":"Guest Editorial: Memristive electronic circuits, neural networks and neuromorphic computing","year":2024,"lang":"en","type":"editorial","venue":"Electronics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neuromorphic engineering; Memristor; Artificial neural network; Electronic circuit; Computer science; Computer architecture; Biological neural network; Electronic engineering; Artificial intelligence; Electrical engineering; Engineering; Machine learning","score_opus":0.005623823962764663,"score_gpt":0.2116595404693833,"score_spread":0.20603571650661862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404363361","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039953045,0.017603435,0.0032595666,0.00015770306,0.9731386,0.00036483354,0.000028093958,0.0014044613,0.000048053844],"genre_scores_gemma":[0.055173866,0.0017693928,0.000010024406,0.00017831603,0.94213897,0.000016750264,0.0002935139,0.00038082624,0.000038338585],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956127,0.0001227867,0.00062053197,0.0010340633,0.00068331393,0.0019266465],"domain_scores_gemma":[0.99805653,0.0010710779,0.00016120086,0.00043354477,0.000087372966,0.00019028994],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0004349393,0.0009615475,0.0008293046,0.00022886448,0.00026798347,0.00029900516,0.00051463506,0.00082307385,0.0000030716906],"category_scores_gemma":[0.00015804397,0.0010790038,0.00020565232,0.0004562093,0.00010800274,0.00020067522,0.00020233648,0.007529065,0.0000189391],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000131964525,0.0000061162914,7.512976e-7,0.0002356853,0.00015382351,0.000087729415,0.000049172988,0.08978079,0.0022849385,0.00003671351,0.90622735,0.0011237494],"study_design_scores_gemma":[0.0003788011,0.0001847999,9.1894674e-7,0.00017998462,0.0001969328,0.000039443858,0.000004610454,0.09162319,0.000325114,0.0001005835,0.90594935,0.0010162755],"about_ca_topic_score_codex":0.000005648952,"about_ca_topic_score_gemma":0.00001744914,"teacher_disagreement_score":0.051178563,"about_ca_system_score_codex":0.00069387065,"about_ca_system_score_gemma":0.00013466341,"threshold_uncertainty_score":0.999166},"labels":[],"label_agreement":null},{"id":"W4404462966","doi":"10.1002/aelm.202400500","title":"Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs","year":2024,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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 Ottawa","funders":"Office of Naval Research; University of Ottawa; Natural Sciences and Engineering Research Council of Canada; Faculty of Health Sciences, University of Ottawa; National Foundation for Science and Technology Development","keywords":"Materials science; Phthalocyanine; Silicon; Rapid prototyping; Nanotechnology; Optoelectronics; Composite material","score_opus":0.027071190789336667,"score_gpt":0.2697527238468835,"score_spread":0.2426815330575468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404462966","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923126,0.0039290697,0.001743942,0.000023354945,0.00024755998,0.0012193345,0.00001339197,0.00035428777,0.00015647207],"genre_scores_gemma":[0.9982008,0.0004825711,0.0006425437,0.000008890898,0.00006918243,0.0004331542,0.0000476857,0.000053870222,0.00006130497],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861324,0.00003277141,0.00050154614,0.00026624452,0.00008937127,0.0004968103],"domain_scores_gemma":[0.99961364,0.000106110245,0.00006516949,0.0001410845,0.000042951022,0.00003101612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003633655,0.00019961988,0.00029567163,0.00014122088,0.00007029353,0.000042342523,0.000121846235,0.00007397747,0.00004606446],"category_scores_gemma":[0.00005344766,0.00019836321,0.000030830208,0.000379531,0.000018623276,0.00058696204,0.000019259178,0.00021169413,0.0000036894817],"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":[0.00012592557,0.000015381816,0.000011441253,0.0017562379,0.000014257343,0.0000019550116,0.00015958908,0.04862669,0.89883494,0.00044963747,0.000010777515,0.049993187],"study_design_scores_gemma":[0.00049015915,0.00016767012,0.0001680763,0.0006118946,0.000009826815,0.000009678208,0.000021266373,0.022773638,0.97203904,0.0006987704,0.002798937,0.00021101712],"about_ca_topic_score_codex":9.224692e-7,"about_ca_topic_score_gemma":0.0000035621722,"teacher_disagreement_score":0.07320415,"about_ca_system_score_codex":0.00015896541,"about_ca_system_score_gemma":0.00006751902,"threshold_uncertainty_score":0.8089017},"labels":[],"label_agreement":null},{"id":"W4404534236","doi":"10.1039/d4dd00197d","title":"Quantitative analysis of miniature synaptic calcium transients using positive unlabeled deep learning","year":2024,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Canadian Institute for Advanced Research; University of Toronto; Canadian Institute for Theoretical Astrophysics; Université Laval","funders":"Next Generation Network for Neuroscience; Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Research Chairs; Canada Research Coordinating Committee; Canadian Institute for Advanced Research; Connaught Fund; National Science Foundation","keywords":"Calcium; Neuroscience; Artificial intelligence; Deep learning; Pattern recognition (psychology); Computer science; Psychology; Medicine; Internal medicine","score_opus":0.01856203608304035,"score_gpt":0.27499016164379475,"score_spread":0.2564281255607544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404534236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89257723,0.001298088,0.10502344,0.0000040342525,0.00017897734,0.00006114076,0.000095930394,0.00013128274,0.0006298654],"genre_scores_gemma":[0.9995904,0.000012423623,0.0001908159,0.000007425661,0.000020301179,0.0000011645469,0.0000843186,0.000028040426,0.00006513565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922854,0.000016196618,0.00021523479,0.00020505702,0.00013121491,0.00020374276],"domain_scores_gemma":[0.9996013,0.00022506028,0.000026805506,0.00008083482,0.000025603533,0.000040377276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003443348,0.00015885809,0.0002792152,0.00024672542,0.00004131857,0.00013863802,0.00006996056,0.00006906751,0.000004196409],"category_scores_gemma":[0.000038023365,0.00014962886,0.0002121885,0.0009832269,0.00004000498,0.0010544893,0.000021844433,0.00027286675,0.0000044210087],"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":[0.000038933806,0.00001990686,0.00025577162,0.00015046146,0.0017817948,0.00007625993,0.0012065511,0.9264205,0.066783234,0.0008033952,0.0000021330213,0.0024610357],"study_design_scores_gemma":[0.00014535242,0.00009745485,0.0016527561,0.00024875774,0.00066674396,0.000006663481,0.0006528066,0.9801024,0.016013794,0.00009954927,0.000031053514,0.0002826209],"about_ca_topic_score_codex":0.000001829394,"about_ca_topic_score_gemma":0.000001841247,"teacher_disagreement_score":0.107013136,"about_ca_system_score_codex":0.000050577535,"about_ca_system_score_gemma":0.000009892427,"threshold_uncertainty_score":0.6101688},"labels":[],"label_agreement":null},{"id":"W4404562648","doi":"10.1162/netn_a_00424","title":"Neural network embedding of functional microconnectome","year":2024,"lang":"en","type":"article","venue":"Network Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Genome British Columbia","funders":"Uehara Memorial Foundation","keywords":"Embedding; Computer science; Neuroscience; Artificial intelligence; Psychology","score_opus":0.02178316929737428,"score_gpt":0.25170532356678904,"score_spread":0.22992215426941476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404562648","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8814482,0.0036380424,0.09952572,0.000060926508,0.012500206,0.00017888754,0.000003758411,0.0011702054,0.0014740296],"genre_scores_gemma":[0.99795157,0.000046652713,0.00052214786,0.00019330232,0.0011355643,0.000004309431,0.0000011703621,0.000026128262,0.00011918464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988635,0.000021696613,0.00021987129,0.00028556402,0.00016547332,0.00044389625],"domain_scores_gemma":[0.99955195,0.0001953947,0.000022715938,0.00014445136,0.000015493784,0.000069983544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015746256,0.00014371473,0.00014361812,0.00004548566,0.00013847776,0.000051995787,0.00018051134,0.000035598587,0.000020798714],"category_scores_gemma":[0.000020761694,0.0001386662,0.00006999258,0.0008912518,0.00007979235,0.0002512222,0.00006712127,0.00026398577,0.000010588876],"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":[0.0000037073282,0.0000020184557,0.00010849609,0.00003928734,0.0000015979803,0.000021296286,0.000015363594,0.9228612,0.070327826,0.0005673381,0.0022008661,0.003850983],"study_design_scores_gemma":[0.000051910483,0.000036657726,0.0012325636,0.00009637998,0.000006150043,0.00007230955,0.0000029153655,0.9836324,0.0035964667,0.0006388051,0.010477152,0.00015630996],"about_ca_topic_score_codex":2.3626717e-7,"about_ca_topic_score_gemma":5.2247105e-7,"teacher_disagreement_score":0.11650331,"about_ca_system_score_codex":0.000017822433,"about_ca_system_score_gemma":0.000012979986,"threshold_uncertainty_score":0.5654644},"labels":[],"label_agreement":null},{"id":"W4404628195","doi":"10.1016/j.chaos.2024.115784","title":"Plasticity of parylene memristors: Compact phenomenological model and synaptic properties","year":2024,"lang":"en","type":"article","venue":"Chaos Solitons & Fractals","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Science Foundation; Basis Foundation","keywords":"Memristor; Materials science; Synaptic plasticity; Phenomenological model; Metaplasticity; Composite material; Condensed matter physics; Physics; Medicine; Quantum mechanics","score_opus":0.04149919583743753,"score_gpt":0.24688104570473823,"score_spread":0.2053818498673007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404628195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98753256,0.004471064,0.006285553,0.000048109436,0.00017500488,0.00012648673,0.000015297499,0.00040563478,0.00094029476],"genre_scores_gemma":[0.9994182,0.00012601724,0.000263015,0.000015540967,0.00009834157,0.0000061556307,0.0000020027046,0.000025478208,0.000045229946],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992084,0.00001624799,0.00023566495,0.00019576905,0.00009311215,0.0002508398],"domain_scores_gemma":[0.99962175,0.00014987704,0.00002229468,0.00010825983,0.000016769585,0.000081060396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006559049,0.00017101724,0.00027698124,0.000068837835,0.00005912225,0.000023812783,0.000088984874,0.00007193247,0.000015985326],"category_scores_gemma":[0.000046201447,0.00013595307,0.000048304675,0.00009302406,0.000114464565,0.00017618721,0.00004264203,0.00023518813,0.00001003721],"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":[0.000031098196,0.00004649775,0.0000449554,0.0008079793,0.00013644202,0.000035509052,0.0016964428,0.47240475,0.5204498,0.0011190308,0.00015733362,0.0030701149],"study_design_scores_gemma":[0.000118119984,0.00009096921,0.0001184464,0.00026886794,0.00003843324,0.000033198692,0.00017380249,0.90848774,0.08838877,0.0017539308,0.00028156213,0.00024614413],"about_ca_topic_score_codex":0.000002355205,"about_ca_topic_score_gemma":6.689754e-7,"teacher_disagreement_score":0.43608302,"about_ca_system_score_codex":0.000042341835,"about_ca_system_score_gemma":0.000014908568,"threshold_uncertainty_score":0.5544005},"labels":[],"label_agreement":null},{"id":"W4404645655","doi":"10.1038/s41598-024-80272-x","title":"Charge-trap synaptic device with polycrystalline silicon channel for low power in-memory computing","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation","keywords":"Trap (plumbing); Polycrystalline silicon; Channel (broadcasting); Charge (physics); Computer science; Optoelectronics; Power (physics); Silicon; Materials science; Electrical engineering; Physics; Nanotechnology; Computer network; Engineering","score_opus":0.012603742851958023,"score_gpt":0.24219734144537522,"score_spread":0.2295935985934172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404645655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9761824,0.0012598898,0.014199404,0.00004228256,0.006746261,0.00046943783,0.000002566146,0.000596523,0.00050122093],"genre_scores_gemma":[0.9987197,0.000002191518,0.00048012458,0.000025307372,0.0001319803,0.000014968342,0.000021377935,0.00005199281,0.0005523483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982108,0.000011672614,0.00043866603,0.0006425799,0.00021463868,0.0004816556],"domain_scores_gemma":[0.999328,0.00010888014,0.000059639275,0.00035323433,0.000054387427,0.00009585156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070544856,0.0002088645,0.00022680133,0.00023874301,0.00016217865,0.00019015947,0.00010044504,0.000058045396,0.00001876266],"category_scores_gemma":[0.00004313368,0.00018368005,0.000072471805,0.00062602275,0.00007396838,0.000254771,0.000034612756,0.00021270131,0.0000115496705],"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":[0.000036005567,0.000075602926,0.00014153392,0.0018650828,0.00010396446,0.0036501237,0.0022626033,0.4919661,0.48882365,0.00013340043,0.0015527281,0.009389196],"study_design_scores_gemma":[0.0004068347,0.0001338769,0.00019466458,0.0019358413,0.000039241768,0.0014045844,0.00034183383,0.7288286,0.25715417,0.0015501913,0.007093974,0.0009161917],"about_ca_topic_score_codex":0.0000015779881,"about_ca_topic_score_gemma":0.000008920709,"teacher_disagreement_score":0.23686248,"about_ca_system_score_codex":0.00006732272,"about_ca_system_score_gemma":0.00004661764,"threshold_uncertainty_score":0.7490255},"labels":[],"label_agreement":null},{"id":"W4404836419","doi":"10.1007/s40820-024-01579-y","title":"RGB Color-Discriminable Photonic Synapse for Neuromorphic Vision System","year":2024,"lang":"en","type":"article","venue":"Nano-Micro Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Neuromorphic engineering; Computer science; RGB color model; Artificial intelligence; Photonics; Computer vision; Artificial neural network; Optoelectronics; Materials science","score_opus":0.015554337777907804,"score_gpt":0.23209356814768842,"score_spread":0.21653923036978062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404836419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98398715,0.0008077318,0.010721291,0.00046783066,0.0020397878,0.00046849297,0.000023450344,0.0012619602,0.00022227956],"genre_scores_gemma":[0.9979827,0.000017540764,0.00091428787,0.0005373218,0.0002172323,0.00005173649,0.000015920099,0.0000841883,0.00017907411],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885553,0.000022618651,0.00024215675,0.00033973964,0.000102369726,0.0004375992],"domain_scores_gemma":[0.9994859,0.00018790466,0.000023194756,0.00021821569,0.000016747012,0.000068019384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013509147,0.0002248776,0.00021945995,0.0001130489,0.00014920929,0.000094297284,0.00017372874,0.000073138996,0.000009506622],"category_scores_gemma":[0.000013637794,0.00021783668,0.0001255036,0.00020007475,0.00003064213,0.00020497941,0.00004137546,0.00021522457,0.000083239815],"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":[0.000016978142,0.0000046673917,0.0000022332047,0.00115379,0.000022220527,0.00013288167,0.00010462219,0.016716093,0.97165066,0.00009112687,0.009358299,0.0007464531],"study_design_scores_gemma":[0.00037169957,0.00010306374,0.00001335737,0.0005619212,0.00006223926,0.00020318873,0.00008016703,0.09060286,0.87372136,0.000013087777,0.03387894,0.00038810546],"about_ca_topic_score_codex":0.000002672376,"about_ca_topic_score_gemma":0.000001071665,"teacher_disagreement_score":0.09792927,"about_ca_system_score_codex":0.00018727926,"about_ca_system_score_gemma":0.000013934846,"threshold_uncertainty_score":0.8883122},"labels":[],"label_agreement":null},{"id":"W4404865596","doi":"10.1088/2634-4386/ad962f","title":"Focus on benchmarks for neuromorphic computing","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Neuromorphic engineering; Focus (optics); Computer science; Artificial intelligence; Computer architecture; Artificial neural network; Physics; Optics","score_opus":0.023070976506613314,"score_gpt":0.22932413313624875,"score_spread":0.20625315662963545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404865596","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8674684,0.0014095553,0.124365166,0.00020232348,0.0031694695,0.0003607916,0.000014468457,0.0025281138,0.000481683],"genre_scores_gemma":[0.99709165,0.000050639974,0.0017789457,0.000086021595,0.0008105549,0.0000066667308,0.000015690495,0.00014200185,0.000017841672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981487,0.00002256758,0.00040856644,0.0005814142,0.00017844916,0.00066035124],"domain_scores_gemma":[0.99841887,0.0011006643,0.000029919136,0.00024086944,0.000027611342,0.00018209696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002621677,0.0004473139,0.00037406824,0.00025932046,0.0002246295,0.00019767172,0.0001851757,0.00010957741,0.0000075425305],"category_scores_gemma":[0.00009172476,0.0004769678,0.0001308181,0.00037043457,0.00003191746,0.00012412478,0.00009355745,0.0007178558,0.0000094078805],"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":[0.000012958979,0.0000141618975,0.000029483135,0.00086837355,0.00005173584,0.00019273322,0.00025385446,0.905229,0.029469345,0.0029446017,0.00060248707,0.060331296],"study_design_scores_gemma":[0.00030804882,0.00017975582,0.0003695217,0.00058288546,0.000028850174,0.00027160594,0.000011459604,0.9878722,0.0053572985,0.00023586737,0.004304504,0.00047799686],"about_ca_topic_score_codex":0.0000017553381,"about_ca_topic_score_gemma":3.987932e-7,"teacher_disagreement_score":0.1296232,"about_ca_system_score_codex":0.000043871405,"about_ca_system_score_gemma":0.000012361482,"threshold_uncertainty_score":0.9997682},"labels":[],"label_agreement":null},{"id":"W4404916119","doi":"10.1109/icons62911.2024.00034","title":"Versatile CMOS Analog LIF Neuron for Memristor-Integrated Neuromorphic Circuits","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Agence Nationale de la Recherche; CMC Microsystems","keywords":"Memristor; Neuromorphic engineering; CMOS; Electronic circuit; Computer science; Analogue electronics; Electronic engineering; Computer architecture; Materials science; Optoelectronics; Electrical engineering; Artificial neural network; Artificial intelligence; Engineering","score_opus":0.03284854512349168,"score_gpt":0.23751241733289782,"score_spread":0.20466387220940613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404916119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5548866,0.0019204452,0.403326,0.0003354699,0.008425818,0.0009953448,0.000080879625,0.0074705733,0.022558894],"genre_scores_gemma":[0.99824524,0.000021878986,0.00029204317,0.00013085909,0.00018324576,0.000015859594,0.000019751458,0.000053934393,0.0010371654],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992686,0.000012716856,0.00016317399,0.00023137213,0.00007448912,0.0002496294],"domain_scores_gemma":[0.99959797,0.00016188438,0.000009191195,0.00013933983,0.00002237046,0.00006926354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058758214,0.00015656454,0.00013748548,0.000079458165,0.00006199769,0.000046451845,0.000105231506,0.000046805442,0.00009333209],"category_scores_gemma":[0.000038200793,0.00014398596,0.0000781667,0.00024417895,0.000014488691,0.00014714802,0.000021408589,0.00020634456,0.00006358848],"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":[0.000027949258,0.000035040808,0.000033218246,0.0010736994,0.0001276029,0.0003545466,0.00047194495,0.15917964,0.55897504,0.004646791,0.056010995,0.21906354],"study_design_scores_gemma":[0.00029425352,0.00017550582,0.000106357475,0.00009197147,0.000040791325,0.00005134019,0.000043032298,0.7649957,0.0781614,0.0007965106,0.15483753,0.0004056317],"about_ca_topic_score_codex":0.0000024044377,"about_ca_topic_score_gemma":0.000003872111,"teacher_disagreement_score":0.605816,"about_ca_system_score_codex":0.000041190913,"about_ca_system_score_gemma":0.0000144551495,"threshold_uncertainty_score":0.5871577},"labels":[],"label_agreement":null},{"id":"W4405316970","doi":"10.1016/j.heliyon.2024.e41171","title":"Guanine-based spin valve with spin rectification effect for an artificial memory element","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Agence Universitaire de la Francophonie; European Commission; Ministerul Cercetării, Inovării şi Digitalizării","keywords":"Rectification; Spin valve; Spin (aerodynamics); Element (criminal law); Condensed matter physics; Materials science; Guanine; Physics; Nanotechnology; Engineering physics; Engineering; Mechanical engineering; Quantum mechanics; Biology; Political science; Genetics; Voltage; Gene","score_opus":0.024593596536302033,"score_gpt":0.29441765437777023,"score_spread":0.2698240578414682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405316970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8296929,0.0009728998,0.16693057,0.000048722937,0.0007064207,0.0005889956,0.0000074117734,0.0007751894,0.00027689332],"genre_scores_gemma":[0.9965429,0.000014048022,0.0027057403,0.000043821834,0.0004595747,0.000087091226,0.000043197444,0.000055263863,0.00004840826],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923193,0.000027845166,0.00017004914,0.00025222852,0.000102999635,0.00021492549],"domain_scores_gemma":[0.99962586,0.000100391124,0.00001861572,0.00017963693,0.000022703862,0.000052798576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021677758,0.00015249429,0.00012703748,0.000079804166,0.00007109181,0.00005113429,0.000065324064,0.000045556288,0.000014754596],"category_scores_gemma":[0.000019178606,0.00013112873,0.00006631218,0.00016578598,0.0000117941145,0.00012730119,0.000005806271,0.0001332714,0.00003277924],"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":[0.0001386625,0.000021356731,0.00001277929,0.0017525908,0.00003129258,0.000009523756,0.00014900812,0.1857514,0.704361,0.00025572456,0.00013044734,0.10738622],"study_design_scores_gemma":[0.00022318623,0.0007069694,0.00009091542,0.0004196306,0.00003793692,0.0000049102064,0.000027115302,0.09793765,0.8978223,0.000057608588,0.0024670379,0.00020469319],"about_ca_topic_score_codex":9.947885e-7,"about_ca_topic_score_gemma":0.000014650949,"teacher_disagreement_score":0.19346134,"about_ca_system_score_codex":0.00007177585,"about_ca_system_score_gemma":0.000016181164,"threshold_uncertainty_score":0.53472745},"labels":[],"label_agreement":null},{"id":"W4405333981","doi":"10.1021/acsami.4c17183","title":"Surmounting Erase-Operation Limit in Organic Charge-Trap Memories by Fine Tuning Electron Injection at Semiconductor/Heterobimetallic Electrode Contacts","year":2024,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Materials science; Electrode; Optoelectronics; Semiconductor; Voltage; Electron; Trap (plumbing); Threshold voltage; Organic semiconductor; Fermi level; Layer (electronics); Limit (mathematics); Nanotechnology; Transistor; Electrical engineering; Chemistry; Physics","score_opus":0.008844630812615533,"score_gpt":0.22010081304946905,"score_spread":0.21125618223685352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405333981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99605113,0.0016306527,0.00016967878,0.00003361086,0.00091656426,0.00031771138,0.000014857569,0.00071652204,0.00014927346],"genre_scores_gemma":[0.9989666,0.00018089404,0.00004608795,0.00004132766,0.00024020013,0.000049042992,0.00013044468,0.00009884791,0.00024655194],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983691,0.00004424013,0.0005028387,0.0004391158,0.00012924067,0.0005154441],"domain_scores_gemma":[0.99961644,0.00009501814,0.00006761019,0.0001614976,0.000020931599,0.000038487164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028199868,0.00035397336,0.00038017673,0.0001481417,0.000127948,0.00027705316,0.00015294492,0.00013419885,0.0003419176],"category_scores_gemma":[0.000020940077,0.00035731128,0.000017276227,0.00022735794,0.000023344866,0.0004605258,0.00007286836,0.0002963995,0.00011838076],"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":[0.00005564253,0.000009426298,0.000006688318,0.00016166728,0.000044596316,0.0000044346657,0.00042703314,0.0018930743,0.99660367,0.00011853772,0.00018989791,0.00048535632],"study_design_scores_gemma":[0.00021797701,0.00008308282,0.000007949966,0.000113296956,0.000022353317,0.000028542836,0.000046501435,0.00020072622,0.99856657,0.00009825919,0.00026747427,0.0003472895],"about_ca_topic_score_codex":0.000015939813,"about_ca_topic_score_gemma":0.00008445003,"teacher_disagreement_score":0.002915474,"about_ca_system_score_codex":0.0003345694,"about_ca_system_score_gemma":0.000017310003,"threshold_uncertainty_score":0.9998879},"labels":[],"label_agreement":null},{"id":"W4405362220","doi":"10.1109/aiotsys63104.2024.10780644","title":"Memristor Based Gain-Scheduling Controller for Erbium-Doped Fiber Amplifiers","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Erbium doped fiber amplifier; Erbium; Computer science; Gain scheduling; Fiber amplifier; Materials science; Memristor; Optical amplifier; Amplifier; Optoelectronics; Doping; Electronic engineering; CMOS; Engineering; Control (management); Physics; Optics; Artificial intelligence","score_opus":0.02023867395804399,"score_gpt":0.25774930738105706,"score_spread":0.23751063342301307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405362220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016128635,0.0007653029,0.97350115,0.00012952031,0.00067068415,0.0003524909,0.000008414739,0.0013373961,0.0071063917],"genre_scores_gemma":[0.9767864,0.0000038516937,0.019337779,0.00029038062,0.00030305923,0.000044618173,0.000009447931,0.00005760329,0.003166894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992736,0.0000084813655,0.0001777815,0.00020058571,0.000071347175,0.00026817963],"domain_scores_gemma":[0.9994389,0.0003546942,0.000009273459,0.000102311395,0.000022337801,0.000072478826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012318155,0.00014953969,0.0001640242,0.00006309911,0.00007190448,0.000048302172,0.00007608972,0.000051908675,0.0002593519],"category_scores_gemma":[0.000035272806,0.00013172206,0.00011860512,0.00011041572,0.000012443946,0.00009334264,0.0000099858835,0.00014276044,0.00014999734],"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":[0.00004086453,0.00000627747,0.000002155951,0.00032102154,0.000058476315,0.000012664965,0.000067768255,0.91480654,0.060405273,0.0017988995,0.0046127154,0.017867325],"study_design_scores_gemma":[0.00048614133,0.000024532002,0.0000015833836,0.000057107412,0.000021603775,0.0000024626902,0.000026554872,0.85776055,0.05260232,0.0003692007,0.08844024,0.00020770852],"about_ca_topic_score_codex":6.573733e-7,"about_ca_topic_score_gemma":9.44571e-7,"teacher_disagreement_score":0.9606577,"about_ca_system_score_codex":0.00005540553,"about_ca_system_score_gemma":0.000013814912,"threshold_uncertainty_score":0.537147},"labels":[],"label_agreement":null},{"id":"W4405710057","doi":"10.1109/biocas61083.2024.10798280","title":"Multiplierless Spiking Neural Network for Motor Signal Decoding in the Peripheral Nervous System","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoding methods; Computer science; Spiking neural network; Neural decoding; Artificial neural network; SIGNAL (programming language); Nervous system; Neuroscience; Peripheral; Speech recognition; Artificial intelligence; Algorithm; Biology","score_opus":0.020667363237661714,"score_gpt":0.24830207877720542,"score_spread":0.2276347155395437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405710057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7846874,0.0017791429,0.20948711,0.000045161458,0.001492901,0.0005139952,0.0000034465334,0.00090406596,0.0010867977],"genre_scores_gemma":[0.99522275,0.000003704363,0.0036243545,0.00005665438,0.0009394574,0.000054145097,0.0000021509238,0.000035962436,0.000060821953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913806,0.000025502086,0.00021117424,0.00017772133,0.00008146392,0.00036607866],"domain_scores_gemma":[0.9995025,0.0003450183,0.000010954203,0.00010230043,0.000008438368,0.000030783867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023175537,0.0001497102,0.00014585821,0.000038202834,0.00012425563,0.00011629949,0.00016221458,0.000043539814,0.00000648714],"category_scores_gemma":[0.000005986499,0.000107154825,0.00008379316,0.0001884317,0.000009641501,0.00014352088,0.000022107311,0.0002180305,0.0000031928485],"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":[0.000012062367,0.0000022316174,0.00007329172,0.00029144576,0.00000975994,0.00007405338,0.00038297925,0.937635,0.004721877,0.002280695,0.000117465635,0.054399133],"study_design_scores_gemma":[0.00012887825,0.00002373209,0.00014378082,0.0001669126,0.0000072521248,0.000058062156,0.0004532335,0.9966867,0.0006356593,0.0000729262,0.00147815,0.00014468846],"about_ca_topic_score_codex":0.0000030881738,"about_ca_topic_score_gemma":0.00001527782,"teacher_disagreement_score":0.21053538,"about_ca_system_score_codex":0.000087033804,"about_ca_system_score_gemma":0.0000054663915,"threshold_uncertainty_score":0.4369647},"labels":[],"label_agreement":null},{"id":"W4405811815","doi":"10.1109/lnet.2024.3522966","title":"Quantum-Safe Blockchain in Hyperledger Fabric","year":2024,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Algoma University","funders":"Algoma University","keywords":"Blockchain; Quantum; Computer science; Computer security; Physics","score_opus":0.015196688907093282,"score_gpt":0.22423976343598212,"score_spread":0.20904307452888884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405811815","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9724403,0.002138007,0.017343754,0.0003124964,0.0063743424,0.00009383828,5.8723487e-7,0.0007163115,0.00058036257],"genre_scores_gemma":[0.9970703,0.000037535487,0.00019024996,0.0007235707,0.0018745777,0.000008742076,0.0000010223096,0.00005325944,0.000040717605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990318,0.00002639203,0.00021070884,0.00022824171,0.00009786636,0.00040502584],"domain_scores_gemma":[0.9996289,0.00017671294,0.000010908072,0.00013586515,0.0000034571342,0.000044153916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014383678,0.00016961961,0.00015456362,0.00013807733,0.000045129385,0.00004645915,0.00011967103,0.000052070565,0.000008195125],"category_scores_gemma":[0.0000036676893,0.00017556513,0.00006145171,0.00044161006,0.000021471955,0.00005927872,0.00001541841,0.0004154105,0.00006739665],"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":[0.0000035505532,0.0000026767896,0.00015085434,0.00007121852,0.000014329245,0.00033869417,0.0002282895,0.8812532,0.07856747,0.0000466647,0.0046090554,0.034714032],"study_design_scores_gemma":[0.00019006585,0.000013421128,0.00017899396,0.0005776316,0.000011606844,0.00005722046,0.000014302006,0.9512292,0.008913054,0.00024890434,0.03812538,0.00044022658],"about_ca_topic_score_codex":0.000002392852,"about_ca_topic_score_gemma":0.000003726648,"teacher_disagreement_score":0.069976024,"about_ca_system_score_codex":0.00008237107,"about_ca_system_score_gemma":0.00000454598,"threshold_uncertainty_score":0.71593386},"labels":[],"label_agreement":null},{"id":"W4405867763","doi":"10.1145/3696409.3700171","title":"Accelerating Inference of Networks in the Frequency Domain","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Inference; Frequency domain; Domain (mathematical analysis); Artificial intelligence; Mathematics; Computer vision","score_opus":0.026191523857638146,"score_gpt":0.2683879847946556,"score_spread":0.24219646093701744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405867763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8176119,0.00085776317,0.15576206,0.0000365328,0.00019761521,0.00006732123,2.8065458e-7,0.00016111601,0.025305431],"genre_scores_gemma":[0.99691695,0.000019744459,0.002954317,0.00003300418,0.0000585315,0.0000029002904,4.986242e-7,0.0000055853884,0.000008490435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996751,0.000013522985,0.00011803977,0.000056787972,0.0000401937,0.00009633767],"domain_scores_gemma":[0.9997089,0.00020554083,0.00000472557,0.00006960225,0.0000036548292,0.0000075597627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000118629854,0.000048925005,0.00005328023,0.00002403543,0.000015474609,0.000018216988,0.000086723805,0.000019581988,0.000022213071],"category_scores_gemma":[0.000010657739,0.000032146592,0.000016036422,0.00020205285,0.000008349642,0.0000890753,0.00001124447,0.00016658989,0.0000023912307],"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":[0.0000012097228,0.0000067576143,0.0008092909,0.00015934887,0.000009249353,0.000054456894,0.0019078915,0.81159,0.036692772,0.108805925,0.000112756854,0.039850354],"study_design_scores_gemma":[0.000111855225,0.000035392743,0.0016860558,0.00030276267,0.0000040818886,0.00001646337,0.00062202296,0.9526621,0.009870839,0.034195583,0.00027836132,0.00021447534],"about_ca_topic_score_codex":0.0000033853244,"about_ca_topic_score_gemma":0.000013972676,"teacher_disagreement_score":0.17930505,"about_ca_system_score_codex":0.000007269525,"about_ca_system_score_gemma":0.0000032055682,"threshold_uncertainty_score":0.13109},"labels":[],"label_agreement":null},{"id":"W4406116755","doi":"10.1063/5.0242551","title":"Sliding ferroelectricity-induced triple barrier modulation in van der Waals boron arsenide tunnel junctions","year":2025,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"van der Waals force; Ferroelectricity; Materials science; Gallium arsenide; Condensed matter physics; Boron; Modulation (music); Quantum tunnelling; Optoelectronics; Chemistry; Physics; Molecule","score_opus":0.014984278078971961,"score_gpt":0.23172132038492813,"score_spread":0.21673704230595617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406116755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8519912,0.000014799351,0.1448679,0.000117561314,0.00036392215,0.00025307445,0.0000011250598,0.00022795361,0.0021624854],"genre_scores_gemma":[0.9984194,0.0000034392879,0.00044533808,0.0008542864,0.00015104469,0.000043440432,0.000013106137,0.000030172187,0.000039774153],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999087,0.000018495011,0.00023205864,0.0002435135,0.000099148514,0.00031977863],"domain_scores_gemma":[0.99964494,0.00008054718,0.00003403162,0.0001922615,0.000010828426,0.000037417518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007596574,0.00018670128,0.00020060397,0.000121002304,0.00013141125,0.000024692697,0.00009686229,0.000054040505,0.0000041703556],"category_scores_gemma":[0.000009791859,0.00021847404,0.00005595007,0.00061335776,0.000008585783,0.00015317378,0.000025849942,0.00032545382,0.0000131421275],"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":[0.0000066031207,0.0000076003817,0.00004045406,0.000016434657,0.000014186796,0.0000012740533,0.00009948297,0.3766299,0.61415404,0.001399892,0.00016764048,0.007462492],"study_design_scores_gemma":[0.000998039,0.000009499195,0.0025871429,0.00005878355,0.000039286777,0.000001104102,0.000059636972,0.1281468,0.8630189,0.003994467,0.00059474277,0.00049165986],"about_ca_topic_score_codex":0.000005526818,"about_ca_topic_score_gemma":0.000004870703,"teacher_disagreement_score":0.24886481,"about_ca_system_score_codex":0.0001487366,"about_ca_system_score_gemma":0.000010144721,"threshold_uncertainty_score":0.89091134},"labels":[],"label_agreement":null},{"id":"W4406137451","doi":"10.1109/tase.2024.3524358","title":"Passivity-Based Connectivity Maintenance of Teleoperated Multi-Robots Under DoS Attacks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Teleoperation; Passivity; Robot; Computer science; Telerobotics; Mobile robot; Engineering; Control theory (sociology); Artificial intelligence; Control (management); Electrical engineering","score_opus":0.016851056965736667,"score_gpt":0.26347094719919234,"score_spread":0.24661989023345568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406137451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36670595,0.000010583534,0.6325956,0.000036462945,0.00028314325,0.000080316204,0.000002514443,0.00025371625,0.000031708852],"genre_scores_gemma":[0.9959495,0.000008463021,0.0039393916,0.000054923934,0.0000046192204,0.000013547817,3.516997e-7,0.000008669305,0.000020513768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927795,0.000008843761,0.00018176739,0.00018306506,0.00015005481,0.00019833933],"domain_scores_gemma":[0.99958056,0.00011214111,0.000021638787,0.00013682418,0.0000979186,0.00005089572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022224257,0.000122428,0.00013964067,0.00031038644,0.00015983547,0.000032729997,0.00009421668,0.000042888027,0.000003403538],"category_scores_gemma":[0.000024948791,0.00012184332,0.000028061146,0.0009643681,0.00008391533,0.00033478922,0.0000012775074,0.00015837602,0.0000023147818],"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":[0.0000024423457,0.000013295133,0.000005146765,0.00005285402,0.0000046073224,4.1229933e-7,0.000029507839,0.7611707,0.23284356,0.000055989844,0.0000024931862,0.0058190017],"study_design_scores_gemma":[0.00021746615,0.000011245821,0.0016984004,0.00008882082,0.000004625542,0.0000013569476,0.000018486899,0.65049106,0.34737036,0.0000050998324,0.000014433831,0.00007862796],"about_ca_topic_score_codex":0.0000022449503,"about_ca_topic_score_gemma":0.0000039379443,"teacher_disagreement_score":0.62924355,"about_ca_system_score_codex":0.000091845555,"about_ca_system_score_gemma":0.00004809308,"threshold_uncertainty_score":0.49686262},"labels":[],"label_agreement":null},{"id":"W4406172019","doi":"10.1109/ted.2024.3521653","title":"State-Aware Multibit Write Algorithm for TiO<sub> <i>x</i> </sub>-Based Resistive Switching Memory Devices","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Reset (finance); Resistive random-access memory; State (computer science); Conductance; Algorithm; Voltage; Relaxation (psychology); Amplitude; Computer science; Electrical engineering; Mathematics; Physics; Engineering; Combinatorics; Quantum mechanics","score_opus":0.008248586864534469,"score_gpt":0.24465890705790616,"score_spread":0.2364103201933717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406172019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21771482,0.0004375565,0.7800194,0.000071673094,0.00048448,0.00048108757,0.000049980707,0.000649308,0.00009167455],"genre_scores_gemma":[0.99617004,0.000067445675,0.0028833828,0.00047985412,0.00006671341,0.00015227616,0.000012837944,0.00006695455,0.0001005082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829173,0.000055502827,0.00039774147,0.00046713414,0.00018082574,0.0006070555],"domain_scores_gemma":[0.9988991,0.00056677096,0.000077983066,0.00025851093,0.00010572369,0.00009188662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017535653,0.00038169613,0.0003458769,0.00031570726,0.0005141753,0.00006801806,0.00021659405,0.00011786751,0.000003942336],"category_scores_gemma":[0.000004560389,0.0004080272,0.00019186466,0.0004997364,0.00002820085,0.0003381713,0.0000014698122,0.00054215273,0.000013904677],"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":[0.00010381483,0.0000728467,0.000004435422,0.00032712542,0.00014408737,0.00000583266,0.000082431805,0.4961966,0.17792019,0.000005057408,0.000053446627,0.32508412],"study_design_scores_gemma":[0.0006675028,0.00011166174,0.00006077542,0.00020553095,0.0000877086,0.0000020359203,0.00006562817,0.22662939,0.7712883,0.00008646138,0.00047434444,0.0003206912],"about_ca_topic_score_codex":0.0000110823685,"about_ca_topic_score_gemma":0.0004973891,"teacher_disagreement_score":0.7784552,"about_ca_system_score_codex":0.00019984301,"about_ca_system_score_gemma":0.000074470336,"threshold_uncertainty_score":0.99983716},"labels":[],"label_agreement":null},{"id":"W4406265540","doi":"10.1109/sips62058.2024.00009","title":"MemNAS: Super-net Neural Architecture Search for Memristor-based DNN Accelerators","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Memristor; Computer science; Architecture; Artificial neural network; Computer architecture; Artificial intelligence; Engineering; Electronic engineering","score_opus":0.02480787702623328,"score_gpt":0.2677612228997359,"score_spread":0.24295334587350265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406265540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72464645,0.0014821502,0.26445502,0.0005593074,0.0024310157,0.00055749924,0.000026517777,0.0027958616,0.003046157],"genre_scores_gemma":[0.99576694,0.000004682763,0.0030705505,0.00018655512,0.0003661549,0.000025646754,0.000017824352,0.00006472062,0.0004969504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907583,0.000017065287,0.00016238532,0.00025291808,0.0001236933,0.00036812434],"domain_scores_gemma":[0.9994508,0.00026170147,0.000004337108,0.00016381386,0.000021395708,0.00009793345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010006521,0.00018810447,0.0001521284,0.00011474519,0.00008931561,0.000077699944,0.00015707049,0.00006399381,0.00008647747],"category_scores_gemma":[0.000013724098,0.00015849787,0.00011214785,0.0002171624,0.000019905163,0.000114814415,0.000024435232,0.00031362145,0.000020475401],"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":[0.000035803918,0.000012604107,0.000033850938,0.0007887686,0.000044473756,0.00004839543,0.00032273837,0.8386825,0.09789046,0.0010573472,0.008212525,0.052870538],"study_design_scores_gemma":[0.00028729427,0.0001036789,0.00002356772,0.00005810498,0.000016073835,0.000015091835,0.000058936348,0.60647064,0.34514302,0.00030630792,0.04716679,0.00035046207],"about_ca_topic_score_codex":0.0000020777932,"about_ca_topic_score_gemma":0.0000064211386,"teacher_disagreement_score":0.27112046,"about_ca_system_score_codex":0.000058509268,"about_ca_system_score_gemma":0.000022668639,"threshold_uncertainty_score":0.6463356},"labels":[],"label_agreement":null},{"id":"W4406276777","doi":"10.1109/qce60285.2024.00149","title":"Towards a Cryogenic CMOS-Memristor Neural Decoder for Quantum Error Correction","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"Fonds de recherche du Québec – Nature et technologies; Government of Canada; Ministry of Colleges and Universities; Institut National des Sciences Appliquées de Lyon; Université de Sherbrooke; CMC Microsystems; Indian National Science Academy; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Centre National de la Recherche Scientifique; Innovation, Science and Economic Development Canada; École Centrale de Lyon; National Science Foundation","keywords":"CMOS; Memristor; Computer science; Electronic engineering; Error detection and correction; Quantum; Decoding methods; Optoelectronics; Electrical engineering; Physics; Telecommunications; Engineering; Algorithm; Quantum mechanics","score_opus":0.027423870962455438,"score_gpt":0.2848080242883656,"score_spread":0.25738415332591014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406276777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41363296,0.0014770888,0.56876755,0.00014179571,0.011747465,0.00029664533,0.000006756426,0.002333286,0.0015964388],"genre_scores_gemma":[0.9956594,0.000012192611,0.0015802258,0.00008083144,0.00038544656,0.00003294445,0.0000065816544,0.000046918038,0.0021954419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935615,0.0000071948903,0.00015518464,0.00018666172,0.000064112566,0.0002306706],"domain_scores_gemma":[0.9997293,0.000090460875,0.000008148346,0.00009865543,0.000019041474,0.000054422773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063322426,0.00013254432,0.00011406539,0.000060365866,0.00006909967,0.000038930386,0.000067330635,0.000049727074,0.00007126892],"category_scores_gemma":[0.000027010785,0.000119333155,0.00010716986,0.00013902136,0.00001030523,0.0001703043,0.000015686906,0.00014490221,0.000039226703],"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":[0.00009500569,0.000029667548,0.000029705436,0.0008742811,0.00012918125,0.000051023144,0.0005769624,0.40148103,0.13044555,0.0025061322,0.07879932,0.38498214],"study_design_scores_gemma":[0.00010551491,0.000053385775,0.000043941654,0.000024329363,0.000016343096,0.000039531584,0.000047216625,0.9417696,0.030056441,0.00042401897,0.027253138,0.00016651425],"about_ca_topic_score_codex":0.0000036819295,"about_ca_topic_score_gemma":0.000020201624,"teacher_disagreement_score":0.5820264,"about_ca_system_score_codex":0.00006745626,"about_ca_system_score_gemma":0.000012503809,"threshold_uncertainty_score":0.4866265},"labels":[],"label_agreement":null},{"id":"W4406360241","doi":"10.3390/jsan14010007","title":"Event-Based Visual Simultaneous Localization and Mapping (EVSLAM) Techniques: State of the Art and Future Directions","year":2025,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Event (particle physics); Artificial intelligence; Computer vision; Simultaneous localization and mapping; Preprocessor; Robot; Mobile robot","score_opus":0.003990369855592725,"score_gpt":0.22498718274572277,"score_spread":0.22099681289013004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406360241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64973706,0.0019981216,0.34760064,0.00014207157,0.0003384175,0.00010403342,0.0000015389568,0.00003368257,0.000044443794],"genre_scores_gemma":[0.9976185,0.0013422328,0.0007360464,0.00010024697,0.00016438603,3.827445e-7,3.4350114e-7,0.000007841183,0.00003005081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955696,0.000023148594,0.00022057012,0.00005703519,0.00005696824,0.000085292224],"domain_scores_gemma":[0.9996315,0.00015131544,0.00009304708,0.000041016167,0.000049199967,0.0000338972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008624845,0.00008328526,0.00014339494,0.00006079981,0.00009726456,0.000018557475,0.000028117758,0.000043360764,9.832361e-7],"category_scores_gemma":[0.000021486061,0.000059876602,0.000029522998,0.00014559473,0.000035101857,0.000063213665,0.000014715244,0.0002056959,1.9819023e-8],"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":[0.000053532116,0.000016250122,0.0005232152,0.00013659097,0.000053037005,0.000013039787,0.0001902841,0.64880013,0.010390082,0.000010140663,0.00016762564,0.33964604],"study_design_scores_gemma":[0.00041907525,0.00008649043,0.00063869794,0.0005484328,0.00005262309,0.00008861055,0.00021985243,0.9504088,0.019592268,0.00010959877,0.027696012,0.00013949999],"about_ca_topic_score_codex":2.2146418e-7,"about_ca_topic_score_gemma":0.0000011355895,"teacher_disagreement_score":0.3478814,"about_ca_system_score_codex":0.000012773533,"about_ca_system_score_gemma":0.000007894801,"threshold_uncertainty_score":0.2441697},"labels":[],"label_agreement":null},{"id":"W4406657231","doi":"10.1371/journal.pcbi.1012101","title":"Balancing central control and sensory feedback produces adaptable and robust locomotor patterns in a spiking, neuromechanical model of the salamander spinal cord","year":2025,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"HORIZON EUROPE European Research Council; Max-Planck-Institut für Kognitions- und Neurowissenschaften; Max-Planck-Gesellschaft; European Commission","keywords":"Central pattern generator; Biological neural network; Sensory system; Computer science; Negative feedback; Neuroscience; Controllability; Feedback loop; Control theory (sociology); Biological system; Artificial intelligence; Biology; Physics; Control (management); Mathematics","score_opus":0.025194132342683986,"score_gpt":0.24023252248851584,"score_spread":0.21503839014583187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406657231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92740446,0.00016044131,0.07191999,0.0001530348,0.000103863116,0.00019431044,0.0000129551845,0.00003077312,0.000020175365],"genre_scores_gemma":[0.9984533,0.000011680793,0.0013348445,0.0001494116,0.00002796598,0.0000056351764,0.0000029401222,0.0000070828232,0.000007105274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993357,0.000047631926,0.00020196804,0.0001882034,0.000047356505,0.000179116],"domain_scores_gemma":[0.99970776,0.00014200082,0.000037838337,0.000061390034,0.00002603271,0.000024968247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005673664,0.00010365649,0.00018558769,0.000052138006,0.0000474155,0.000007445609,0.00006684109,0.000047293433,0.0000012535787],"category_scores_gemma":[0.000035481004,0.00008465449,0.000020116213,0.000074053794,0.00004997217,0.00004072058,0.00005880282,0.00016534606,1.4022713e-7],"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":[0.00009031366,0.00002146867,0.022628441,0.000120493394,0.000020584363,0.0000014255885,0.000030929335,0.9265705,0.047022596,0.0015732606,0.0000030972435,0.0019168971],"study_design_scores_gemma":[0.00056099327,0.00006501362,0.061791226,0.000101426354,0.000010281729,0.000007394477,0.000013141177,0.9320937,0.0023987629,0.0028793053,0.000002693438,0.00007607123],"about_ca_topic_score_codex":0.0000028193708,"about_ca_topic_score_gemma":0.000007335981,"teacher_disagreement_score":0.07104888,"about_ca_system_score_codex":0.000017960981,"about_ca_system_score_gemma":0.00001748375,"threshold_uncertainty_score":0.34521103},"labels":[],"label_agreement":null},{"id":"W4407140944","doi":"10.3390/electronics14030606","title":"Realization of Modified Electrical Equivalent of Memristor-Based Pavlov’s Associative Learning to Avoid Training Fallacies","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Memristor; Realization (probability); Associative property; Computer science; Artificial intelligence; Electronic engineering; Mathematics; Engineering; Pure mathematics; Statistics","score_opus":0.019245214037310696,"score_gpt":0.26720744803096713,"score_spread":0.24796223399365644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407140944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59113836,0.0017629362,0.4043723,0.000042554333,0.00009909778,0.00018673045,0.0000025012068,0.00018361045,0.0022119014],"genre_scores_gemma":[0.9990736,0.00009821864,0.00057892187,0.00003354535,0.000019417348,0.0000065931026,0.000008431723,0.000017281873,0.00016396724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903476,0.00006439843,0.00029561587,0.00014050843,0.0001513832,0.00031330317],"domain_scores_gemma":[0.9994681,0.00023423952,0.000086381624,0.000098042976,0.00008086677,0.000032383192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022253451,0.000121557794,0.00026423504,0.00014698967,0.00006312998,0.000005242306,0.00010901583,0.00006759631,0.0000029592197],"category_scores_gemma":[0.00029158144,0.00013623292,0.00006816678,0.0005468581,0.000014102616,0.000042705447,0.000018753344,0.00028316732,5.8100494e-7],"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":[0.000040013405,0.000017009883,0.000108033404,0.00009470251,0.000045147954,5.574415e-7,0.0010285609,0.81295174,0.16803205,0.0067621046,0.000065507375,0.010854565],"study_design_scores_gemma":[0.0006049772,0.0004143972,0.00033364978,0.0001737387,0.000047527956,5.502577e-7,0.00024246775,0.25861606,0.7348364,0.0018065016,0.0026688217,0.00025492272],"about_ca_topic_score_codex":0.0000013233571,"about_ca_topic_score_gemma":0.000008066807,"teacher_disagreement_score":0.56680435,"about_ca_system_score_codex":0.00029107556,"about_ca_system_score_gemma":0.00010065764,"threshold_uncertainty_score":0.55554175},"labels":[],"label_agreement":null},{"id":"W4407230387","doi":"10.1016/j.cogsys.2025.101334","title":"Combination of reward-modulated spike-timing dependent plasticity and temporal difference long-term potentiation in actor–critic spiking neural network","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Russian Science Foundation","keywords":"Long-term potentiation; Spike-timing-dependent plasticity; Spike (software development); Neuroscience; Spiking neural network; Term (time); Plasticity; Computer science; Neuroplasticity; Synaptic plasticity; Duration (music); Artificial neural network; Psychology; Artificial intelligence; Cognitive psychology; Biology; Physics","score_opus":0.06493077052038645,"score_gpt":0.35284284871533894,"score_spread":0.28791207819495246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407230387","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9714912,0.0007544448,0.026593281,0.00000818135,0.00033398188,0.00058004766,0.0000051185793,0.00006474283,0.00016900136],"genre_scores_gemma":[0.9997853,0.000031821877,0.000014027927,0.0000030998235,0.00005799025,0.000026630847,0.000017439643,0.000016087715,0.000047577265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983054,0.00030752618,0.0003876424,0.00026991568,0.0003190625,0.00041048642],"domain_scores_gemma":[0.9987346,0.0007664052,0.000051952098,0.00008484047,0.00030600355,0.000056226105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060058886,0.00014202435,0.00027173132,0.00033732786,0.00015493327,0.000063771186,0.00010625529,0.00009334303,0.0000020671384],"category_scores_gemma":[0.00041595096,0.00015041576,0.00002529026,0.0005817323,0.000079436475,0.00017079267,0.000119681434,0.00048130605,0.0000011413565],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023432952,0.00012312063,0.7674283,0.0038615623,0.000093116236,0.0001576053,0.00061002164,0.09013899,0.10494182,0.0004156773,0.0000074863465,0.031987995],"study_design_scores_gemma":[0.00085452886,0.00008164239,0.73345274,0.002818458,0.000013107667,0.0000086454675,0.00022121721,0.2501653,0.012032803,0.00019942285,3.6994408e-7,0.00015176278],"about_ca_topic_score_codex":0.00004718477,"about_ca_topic_score_gemma":0.000046952755,"teacher_disagreement_score":0.16002633,"about_ca_system_score_codex":0.00012098318,"about_ca_system_score_gemma":0.000024090923,"threshold_uncertainty_score":0.6133777},"labels":[],"label_agreement":null},{"id":"W4407309047","doi":"10.48550/arxiv.2502.04632","title":"Tight Bounds for Noisy Computation of High-Influence Functions, Connectivity, and Threshold","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"York University; Johns Hopkins University; University of California, San Diego; National Science Foundation","keywords":"Computation; Computer science; Statistical physics; Algorithm; Physics","score_opus":0.029590459350180388,"score_gpt":0.27377675047417405,"score_spread":0.24418629112399365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407309047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91798186,0.00058192015,0.0796959,0.000058378286,0.00090474274,0.00034040312,0.00004789643,0.00021048532,0.00017842335],"genre_scores_gemma":[0.99866414,0.00005784119,0.0008804321,0.00004395616,0.00011467532,0.00003848254,0.000040141418,0.000020013775,0.00014032287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99914956,0.000014547722,0.00027363194,0.0003137059,0.00007907023,0.00016949348],"domain_scores_gemma":[0.99933577,0.00023089278,0.0000923382,0.00020969873,0.00009216996,0.000039130548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001114746,0.00020765515,0.00032631675,0.00009683696,0.000104561586,0.000020141284,0.000110641886,0.00015176661,0.0000021154713],"category_scores_gemma":[0.000056023637,0.00022662601,0.00006824603,0.00010142722,0.00004805894,0.00010743961,0.00019906461,0.00032847474,0.0000020960629],"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":[0.000036063004,0.000024801751,0.029818153,0.0017947691,0.00010239996,0.000002366218,0.00012387345,0.9504149,0.013369861,0.000658578,0.00020429412,0.0034499518],"study_design_scores_gemma":[0.0024709671,0.0003065096,0.54607064,0.0026390054,0.00041851422,0.000018136143,0.00010949745,0.24665211,0.1733932,0.024087138,0.002098869,0.0017353835],"about_ca_topic_score_codex":0.000013003654,"about_ca_topic_score_gemma":0.000010501985,"teacher_disagreement_score":0.70376277,"about_ca_system_score_codex":0.000044784818,"about_ca_system_score_gemma":0.000027837788,"threshold_uncertainty_score":0.92415404},"labels":[],"label_agreement":null},{"id":"W4407386505","doi":"10.1038/s41467-025-56739-4","title":"The neurobench framework for benchmarking neuromorphic computing algorithms and systems","year":2025,"lang":"en","type":"review","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","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":"National Research Council Canada; University of Waterloo","funders":"Engineering and Physical Sciences Research Council","keywords":"Neuromorphic engineering; Benchmarking; Benchmark (surveying); Computer science; Set (abstract data type); Computer architecture; Field (mathematics); Data science; Artificial intelligence; Machine learning; Artificial neural network","score_opus":0.0647970251285108,"score_gpt":0.3577684499497226,"score_spread":0.29297142482121186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407386505","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.0292085e-7,0.9753103,0.02088806,0.00008359709,0.0020325216,0.0011091101,0.000044567274,0.00027841306,0.00025258702],"genre_scores_gemma":[0.00033475694,0.9880687,0.010914985,0.000043093914,0.00032249058,0.0001304117,0.00009600455,0.000059472837,0.000030087314],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985555,0.00022297422,0.0005265651,0.0002786107,0.000102887316,0.0003135004],"domain_scores_gemma":[0.9867642,0.0113500375,0.00018614382,0.0015633812,0.00008012209,0.000056110803],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00034990493,0.00035465878,0.0007062937,0.00010811416,0.0012722737,0.00019422208,0.0014059317,0.00053265295,2.2298303e-7],"category_scores_gemma":[0.00047881826,0.00027651325,0.00019689172,0.00049367617,0.000092374656,0.000054363558,0.0005498622,0.0030793275,9.991406e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.4241278e-7,0.000005515537,3.8631188e-7,0.0061056134,0.000064234075,9.185416e-7,0.000021121283,0.0010274398,3.400379e-7,0.024825761,0.0004007032,0.96754754],"study_design_scores_gemma":[0.000038224756,0.000010622684,0.0000013331039,0.009034457,0.00019455547,0.000033409346,0.00001055195,0.050466392,2.832347e-7,0.00037294728,0.93960255,0.00023465818],"about_ca_topic_score_codex":5.5511117e-7,"about_ca_topic_score_gemma":0.0000036258775,"teacher_disagreement_score":0.9673129,"about_ca_system_score_codex":0.000051642393,"about_ca_system_score_gemma":0.000050166695,"threshold_uncertainty_score":0.9999687},"labels":[],"label_agreement":null},{"id":"W4407399862","doi":"10.1088/2634-4386/adb511","title":"A burst-dependent algorithm for neuromorphic on-chip learning of spiking neural networks","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Ottawa","funders":"","keywords":"Neuromorphic engineering; Spiking neural network; Computer science; Artificial neural network; Chip; Artificial intelligence; Algorithm; Computer architecture; Telecommunications","score_opus":0.017269634536803424,"score_gpt":0.22462909813870446,"score_spread":0.20735946360190105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407399862","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53224623,0.0004025973,0.46545577,0.000028393035,0.0010703069,0.00020924387,0.0000032308596,0.00052165793,0.00006254831],"genre_scores_gemma":[0.99712235,0.000044610093,0.0023979314,0.00007913134,0.00024296531,0.000009148887,0.0000069708763,0.00007245082,0.000024471543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840766,0.000034963025,0.00046387105,0.0004084875,0.00014581204,0.00053918903],"domain_scores_gemma":[0.9987686,0.00078761653,0.000079155536,0.0002139042,0.000048467842,0.00010225425],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000246826,0.00036376854,0.00045406076,0.0002556968,0.00020895049,0.000052959145,0.00019467885,0.00010378622,0.0000014684222],"category_scores_gemma":[0.00012333687,0.00040993962,0.00011299425,0.00035792656,0.000035114947,0.00007435207,0.00011512042,0.0007566169,4.4356185e-7],"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":[0.000011123502,0.000012107868,0.00014827099,0.00023723232,0.00002936981,0.000021509404,0.000038286602,0.91626006,0.0064439196,0.00024039803,0.000013108144,0.076544605],"study_design_scores_gemma":[0.00063321006,0.00017600427,0.00092404603,0.00030635667,0.000040169107,0.000041206727,0.000018810035,0.99401593,0.0032776175,0.000030727297,0.00024245844,0.00029347054],"about_ca_topic_score_codex":0.0000024560718,"about_ca_topic_score_gemma":3.0274626e-7,"teacher_disagreement_score":0.46487606,"about_ca_system_score_codex":0.000029221865,"about_ca_system_score_gemma":0.000008350133,"threshold_uncertainty_score":0.99983525},"labels":[],"label_agreement":null},{"id":"W4407433764","doi":"10.1021/acsaelm.4c02275","title":"Optically Controlled P–Cu<sub><i>x</i></sub>O-Based Artificial Synaptic Device for Neuromorphic Applications","year":2025,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Defence Metallurgical Research Laboratory","keywords":"Neuromorphic engineering; Materials science; Computer science; Artificial neural network; Neuroscience; Optoelectronics; Artificial intelligence; Biology","score_opus":0.011973253114905132,"score_gpt":0.23326836220450062,"score_spread":0.2212951090895955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407433764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87511104,0.00016588421,0.12118253,0.00015908468,0.00020218267,0.0023486838,0.000022053186,0.0005775281,0.0002310079],"genre_scores_gemma":[0.9965125,0.000030439518,0.0002059251,0.00046606243,0.00020145344,0.002449704,0.00006281769,0.000061373525,0.000009765967],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99811095,0.000034551627,0.0005917908,0.00039605933,0.00011470155,0.00075195497],"domain_scores_gemma":[0.9989529,0.00047527195,0.000092918,0.00035588606,0.00005807586,0.000064943815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034832465,0.0003099431,0.0005769987,0.00011390102,0.00022185425,0.00008869416,0.00027255388,0.0001380522,0.000015741376],"category_scores_gemma":[0.000048497277,0.00032264655,0.00007528491,0.00027436262,0.00004569359,0.00004873432,0.00003574747,0.00019253309,0.000041492272],"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":[0.00038092837,0.00003348382,2.5023175e-7,0.0001948544,0.00008592193,8.663955e-7,0.0000043717887,0.0125098005,0.9476262,0.037147433,0.000059170656,0.0019567395],"study_design_scores_gemma":[0.0017451223,0.000058274694,0.0000063114467,0.000021729607,0.0001258223,0.0000021741707,0.0000050903905,0.0008739149,0.987869,0.007940907,0.0010758828,0.00027573277],"about_ca_topic_score_codex":4.365494e-7,"about_ca_topic_score_gemma":0.0000045341208,"teacher_disagreement_score":0.121401414,"about_ca_system_score_codex":0.000115449046,"about_ca_system_score_gemma":0.000119179575,"threshold_uncertainty_score":0.9999226},"labels":[],"label_agreement":null},{"id":"W4407637065","doi":"10.1038/s41699-024-00506-4","title":"Mobility and threshold voltage extraction in transistors with gate-voltage-dependent contact resistance","year":2025,"lang":"en","type":"article","venue":"npj 2D Materials and Applications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contact resistance; Overdrive voltage; Materials science; Threshold voltage; Optoelectronics; Gate voltage; Transistor; Voltage; Extraction (chemistry); Electrical engineering; Nanotechnology; Chemistry; Engineering; Chromatography","score_opus":0.00888585951329848,"score_gpt":0.2423975468260866,"score_spread":0.23351168731278812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407637065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977649,0.00023341735,0.020192668,0.000038739978,0.000051297804,0.00040111772,0.000022804476,0.00009925686,0.0013117245],"genre_scores_gemma":[0.9993668,0.00012478488,0.000112918,0.000035317844,0.000024400722,0.00016661573,0.000005248293,0.000009389683,0.00015450743],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949753,0.000007228141,0.00015621731,0.00019203972,0.000041325147,0.000105659274],"domain_scores_gemma":[0.99976176,0.000046650537,0.000021683318,0.00013221912,0.000010234425,0.000027424907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008939615,0.000099321514,0.00013426701,0.000039463528,0.00008974251,0.000031536194,0.000037059108,0.000039650164,0.000008547391],"category_scores_gemma":[0.0000026790085,0.00008940646,0.000007469301,0.00008229258,0.000024597632,0.00007493908,0.000007930109,0.00007609922,0.0000010975428],"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":[0.00008960455,0.000038092294,0.00021189085,0.0002542815,0.000008596228,0.000002709283,0.000062832056,0.0022894186,0.98259753,0.012302616,0.000022840532,0.0021196082],"study_design_scores_gemma":[0.0012310733,0.00006191963,0.03860888,0.00029050803,0.000039527647,0.0000066690714,0.00021601772,0.00040716678,0.93994063,0.00741569,0.011359317,0.00042257627],"about_ca_topic_score_codex":0.000006763902,"about_ca_topic_score_gemma":0.000077276774,"teacher_disagreement_score":0.042656854,"about_ca_system_score_codex":0.000027880978,"about_ca_system_score_gemma":0.0000051202987,"threshold_uncertainty_score":0.36458898},"labels":[],"label_agreement":null},{"id":"W4407667602","doi":"10.11591/ijai.v14.i2.pp1000-1021","title":"Adaptive silicon synapse and CMOS neuron for neuromorphic VLSI computing","year":2025,"lang":"en","type":"article","venue":"IAES International Journal of Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Neuromorphic engineering; Very-large-scale integration; Computer science; Synapse; CMOS; Computer architecture; Neuron; Silicon; Artificial neural network; Neuroscience; Artificial intelligence; Materials science; Embedded system; Optoelectronics","score_opus":0.04827601371223869,"score_gpt":0.31045451492861853,"score_spread":0.26217850121637987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407667602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5269786,0.00024278661,0.47029877,0.0002612262,0.0019595576,0.00008832765,0.000004613065,0.000033747787,0.00013237658],"genre_scores_gemma":[0.9973981,0.000063927415,0.0019867874,0.00018621252,0.0003356148,0.0000011594118,9.165276e-7,0.000011793385,0.000015488034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990486,0.000024842917,0.00050087675,0.00012747978,0.00015123235,0.00014697689],"domain_scores_gemma":[0.9990092,0.00045983697,0.00013354271,0.000062839346,0.00028680972,0.00004779903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001912656,0.00012229821,0.00017190097,0.00018063623,0.00007138933,0.00006219294,0.0002826851,0.000039458024,0.0000062509976],"category_scores_gemma":[0.00024457063,0.00012136108,0.00008020012,0.00010685453,0.00006466418,0.00019433492,0.000047981892,0.00024115208,0.0000020131897],"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":[0.00034978686,0.00005872144,0.00012020087,0.00004631421,0.00015658769,0.0000724755,0.00052841916,0.3946176,0.09483598,0.0179254,0.00013236846,0.49115616],"study_design_scores_gemma":[0.00013738398,0.00030785665,0.0003564936,0.00032266421,0.00003748251,0.00014652502,0.00046873913,0.57216364,0.40341064,0.021426478,0.0010015109,0.00022056668],"about_ca_topic_score_codex":0.0000014628143,"about_ca_topic_score_gemma":0.0000031456245,"teacher_disagreement_score":0.4909356,"about_ca_system_score_codex":0.000042863026,"about_ca_system_score_gemma":0.000019319039,"threshold_uncertainty_score":0.4948961},"labels":[],"label_agreement":null},{"id":"W4407732231","doi":"10.1088/2634-4386/adb7fe","title":"Accelerating spiking neural networks with parallelizable leaky integrate-and-fire neurons<sup>*</sup>","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Parallelizable manifold; Spiking neural network; Computer science; Artificial neural network; Artificial intelligence; Neuroscience; Environmental science; Biology; Algorithm","score_opus":0.01566510693462688,"score_gpt":0.2078539110774719,"score_spread":0.192188804142845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407732231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92868614,0.0014325123,0.068005174,0.00007351806,0.00032414004,0.00018278652,9.0962925e-7,0.0010646308,0.0002302147],"genre_scores_gemma":[0.99709535,0.000111958754,0.0023253798,0.00015670873,0.0001959678,0.0000063811844,0.0000046148343,0.00007649476,0.000027152193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983202,0.000033784745,0.00037744583,0.0004978341,0.00012517409,0.000645547],"domain_scores_gemma":[0.9992,0.0003369482,0.00004653683,0.00023694776,0.000034403252,0.00014519012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015302649,0.00045649812,0.00042353646,0.0001492313,0.0003602918,0.00020691249,0.00017786043,0.000101536105,0.0000025663742],"category_scores_gemma":[0.000055592696,0.00044400422,0.000048633607,0.00046917325,0.000051533953,0.00020587233,0.00018203523,0.00092330464,6.839086e-7],"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":[0.0000118330245,0.0000060233024,0.0010162306,0.00016214656,0.000026373835,0.000059242466,0.00013614068,0.9649381,0.0015065443,0.00026068508,0.000024939303,0.031851716],"study_design_scores_gemma":[0.00043060328,0.00007242984,0.0014592006,0.00037788154,0.00002998191,0.00021188646,0.00007819024,0.99611413,0.0003780967,0.000016268214,0.00044242182,0.00038889333],"about_ca_topic_score_codex":0.00000906835,"about_ca_topic_score_gemma":0.0000012210319,"teacher_disagreement_score":0.06840923,"about_ca_system_score_codex":0.000028910154,"about_ca_system_score_gemma":0.000010121222,"threshold_uncertainty_score":0.99980116},"labels":[],"label_agreement":null},{"id":"W4407930031","doi":"10.5220/0013261400003911","title":"First Results on Graph Similarity Search in Resting-State Functional Connectivity Networks Using Spectral and Graph Edit Distances","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"","keywords":"Computer science; Graph; Graph theory; Theoretical computer science; Similarity (geometry); Pattern recognition (psychology); Artificial intelligence; Mathematics; Combinatorics","score_opus":0.02941820725235409,"score_gpt":0.2610935779733812,"score_spread":0.2316753707210271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407930031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9097042,0.000156777,0.08715176,0.000106805775,0.00053970265,0.00012758904,0.0000072259068,0.00017565787,0.0020302974],"genre_scores_gemma":[0.99909973,0.00006402445,0.0005879371,0.000049138733,0.00011860892,0.0000022726058,0.000004056533,0.000009872414,0.000064330154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990322,0.00004007203,0.00021552622,0.00029085044,0.00010763397,0.00031371138],"domain_scores_gemma":[0.99919814,0.0005868397,0.000019607809,0.00012276342,0.000021654923,0.00005098074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029733186,0.00015136471,0.00016478087,0.00014954442,0.00020953473,0.00003585687,0.000059016034,0.0000539118,0.0000036794231],"category_scores_gemma":[0.00007802635,0.00014793305,0.000034662524,0.0005161701,0.000064285974,0.0001429242,0.00004142817,0.0004452507,3.3573204e-7],"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":[0.00019109549,0.000016214302,0.005491353,0.00004586195,0.000010705848,0.000017072327,0.0000382888,0.9922567,0.00030925754,0.00068286795,0.00012419421,0.00081640703],"study_design_scores_gemma":[0.0009920005,0.00006023396,0.119864345,0.00027840663,0.00000728719,0.0000059205718,0.000060153,0.8679303,0.0036951278,0.006681943,0.00014446881,0.00027982792],"about_ca_topic_score_codex":0.0000456707,"about_ca_topic_score_gemma":0.00089518086,"teacher_disagreement_score":0.12432639,"about_ca_system_score_codex":0.000060746817,"about_ca_system_score_gemma":0.000010722151,"threshold_uncertainty_score":0.6032535},"labels":[],"label_agreement":null},{"id":"W4407938603","doi":"10.1109/nmdc58214.2024.10894469","title":"BEOL Integration of Reconfigurable Li-Based Memristors on Foundry CMOS Chips","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"CMOS; Foundry; Memristor; Computer science; Computer architecture; Embedded system; Electronic engineering; Materials science; Engineering; Metallurgy","score_opus":0.02499631470430632,"score_gpt":0.2483287902021955,"score_spread":0.22333247549788918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407938603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7275702,0.00054756727,0.1614955,0.00019071424,0.0022516204,0.00026544894,0.00000556482,0.001530174,0.10614321],"genre_scores_gemma":[0.99814063,0.000008460729,0.0008243919,0.00005146999,0.00007403175,0.000004642833,0.0000057640536,0.000019526196,0.0008711038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955255,0.000010197628,0.00015152918,0.00011342183,0.00006802031,0.000104308485],"domain_scores_gemma":[0.99972326,0.000119128286,0.0000110945675,0.00010652952,0.000012564023,0.000027406108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007115179,0.0000921338,0.00009802633,0.00007732361,0.000027358325,0.0000143460575,0.00005170934,0.000037314752,0.00014072322],"category_scores_gemma":[0.000020013125,0.00007829096,0.000049787734,0.00014056724,0.000010985371,0.00009267339,0.0000026513492,0.00015663453,0.00004995011],"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":[0.000030260706,0.000025083189,0.000011500939,0.000427016,0.000033889908,0.00001683249,0.00027870576,0.42455444,0.42420623,0.006717556,0.0031767127,0.1405218],"study_design_scores_gemma":[0.00007280425,0.000067307265,0.00002088785,0.00018266623,0.000006014444,0.0000017255124,0.000034229535,0.23399046,0.76081187,0.00049400027,0.0042159534,0.00010206489],"about_ca_topic_score_codex":0.0000039538463,"about_ca_topic_score_gemma":0.0000074685454,"teacher_disagreement_score":0.33660567,"about_ca_system_score_codex":0.000045452212,"about_ca_system_score_gemma":0.000010182359,"threshold_uncertainty_score":0.31926128},"labels":[],"label_agreement":null},{"id":"W4408080864","doi":"10.1016/j.xpro.2025.103652","title":"Computational protocol for modeling and analyzing synaptic dynamics using SRPlasticity","year":2025,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Government of Ontario","keywords":"Computer science; Dynamics (music); Protocol (science); Neuroscience; Biology; Physics; Medicine","score_opus":0.04522368089347535,"score_gpt":0.35959887537571283,"score_spread":0.3143751944822375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408080864","genre_codex":"methods","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011573131,0.00000112917,0.6234088,0.000005516295,0.0000146994935,0.3648174,0.0000063243397,0.000118562246,0.000054422704],"genre_scores_gemma":[0.09324865,3.3254764e-8,0.06651469,0.000019664214,0.000036905356,0.84014606,0.000004005403,0.000021514268,0.00000848901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999252,0.000016441727,0.00025346508,0.00019001502,0.0000683629,0.000219717],"domain_scores_gemma":[0.99965066,0.0001266206,0.000032084467,0.000086727676,0.00006443883,0.00003945075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012820697,0.0001431372,0.00017263897,0.000088713976,0.00018812464,0.00004759846,0.000074309915,0.000049427275,0.0000024319104],"category_scores_gemma":[0.000047926766,0.00014799839,0.00003625664,0.00014730945,0.000022301765,0.00013328451,0.00004359535,0.00013278164,3.664239e-7],"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":[0.00005327303,0.0000094226325,0.00009706954,0.0008154514,0.000025246669,0.0000012657315,0.00001952897,0.9944206,0.0008229096,0.00166739,0.0000048346205,0.0020630113],"study_design_scores_gemma":[0.0008102346,0.00003759451,0.000013953195,0.00041251755,0.000009119229,0.0000035864016,0.000016450282,0.98827153,0.00073707575,0.009440159,0.0001067167,0.0001410474],"about_ca_topic_score_codex":0.0000012813732,"about_ca_topic_score_gemma":0.000002892643,"teacher_disagreement_score":0.5568941,"about_ca_system_score_codex":0.00010460245,"about_ca_system_score_gemma":0.00003465303,"threshold_uncertainty_score":0.6035199},"labels":[],"label_agreement":null},{"id":"W4408181536","doi":"10.1109/isscc49661.2025.10904733","title":"15.5 Event-Based Spatially Zooming Neural Interface IC with 10nW/Input Reconfigurable-Inverter Fabric and Input-Adaptive Quantization","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Krembil Foundation; University of Toronto","funders":"","keywords":"Zoom; Quantization (signal processing); Computer science; Artificial neural network; Interface (matter); Computer vision; Artificial intelligence; Engineering; Parallel computing","score_opus":0.011279503562359443,"score_gpt":0.23469434599333078,"score_spread":0.22341484243097134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408181536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38410366,0.00013198893,0.61339825,0.00009356574,0.00019877774,0.00017254968,0.0000011239235,0.00025071422,0.001649378],"genre_scores_gemma":[0.9967654,0.00000832088,0.0023725,0.0003019359,0.00003117493,0.000008125065,0.000005250811,0.000026315525,0.00048102473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991122,0.000038978396,0.00023504699,0.00027412875,0.000089055924,0.00025062633],"domain_scores_gemma":[0.99957955,0.00011569777,0.000043898686,0.0001537926,0.000049457984,0.000057584955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075108466,0.00021918085,0.00021097175,0.00012957474,0.000107933825,0.000052116957,0.00009449873,0.000059204893,0.000032819422],"category_scores_gemma":[0.000021038757,0.00018967842,0.000030815543,0.00028735134,0.00003187276,0.00025305685,0.000022543994,0.0002065129,0.0000071918535],"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":[0.00020620067,0.00002588761,0.0013247819,0.00022752631,0.00007063717,0.000020027293,0.00022173378,0.85328335,0.091301665,0.0004633709,0.00016156578,0.052693274],"study_design_scores_gemma":[0.0005980496,0.00011176464,0.0006505555,0.00021049954,0.00002207185,0.0000068034333,0.00008108737,0.88027996,0.11761293,0.000089918445,0.00011177132,0.00022461389],"about_ca_topic_score_codex":0.000016871807,"about_ca_topic_score_gemma":0.00009667427,"teacher_disagreement_score":0.61266166,"about_ca_system_score_codex":0.00005198696,"about_ca_system_score_gemma":0.000025191564,"threshold_uncertainty_score":0.77348614},"labels":[],"label_agreement":null},{"id":"W4408324761","doi":"10.1109/globecom52923.2024.10901461","title":"Energy Efficient Orchestration for O-RAN","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Orchestration; Ran; Computer science; C-RAN; Energy (signal processing); Telecommunications; Computer network; Radio access network; Physics; Art","score_opus":0.0172328434800466,"score_gpt":0.24536833823388754,"score_spread":0.22813549475384093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408324761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08476313,0.00024142792,0.9091076,0.000028401546,0.00039054287,0.00003527595,7.458748e-7,0.00057476555,0.0048580957],"genre_scores_gemma":[0.9976954,0.0000037939888,0.0016777031,0.00001978051,0.00011813234,0.0000067140186,0.0000027839942,0.00000934295,0.0004663822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979866,0.0000014033428,0.000048805337,0.00005848529,0.000021805012,0.00007082007],"domain_scores_gemma":[0.9998919,0.000055866047,0.000001393849,0.000032060325,0.0000040505906,0.000014724063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000022557328,0.00003709679,0.00002798312,0.000018462873,0.00001870066,0.00001521609,0.000018400184,0.000013546562,0.000009875731],"category_scores_gemma":[0.0000029140028,0.000031881267,0.000020414152,0.000049334758,0.0000025857628,0.00002610965,0.0000027771835,0.000023387565,0.000005608673],"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":[0.0000018909647,0.00000220444,2.2994217e-7,0.000071840484,0.000005338266,0.0000023167916,0.000052359363,0.8001794,0.09142152,0.035419606,0.0005417767,0.07230151],"study_design_scores_gemma":[0.00002699246,0.000009510777,0.0000017986407,0.000009799707,0.0000017968507,0.0000019925806,0.0000078594485,0.80161417,0.18307583,0.0006035131,0.014605655,0.00004107895],"about_ca_topic_score_codex":3.0501243e-7,"about_ca_topic_score_gemma":0.00000100139,"teacher_disagreement_score":0.9129322,"about_ca_system_score_codex":0.000010722783,"about_ca_system_score_gemma":0.000002428708,"threshold_uncertainty_score":0.13000803},"labels":[],"label_agreement":null},{"id":"W4408385078","doi":"10.1002/ail2.114","title":"On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method","year":2025,"lang":"en","type":"article","venue":"Applied AI Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Training (meteorology); Computer science; Artificial neural network; Artificial intelligence; Spiking neural network; Quantum; Machine learning; Physics","score_opus":0.015708621735505593,"score_gpt":0.2523177143841734,"score_spread":0.2366090926486678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408385078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23160847,0.000078522084,0.76642275,0.00033925628,0.0002496796,0.00013748702,0.0000026556756,0.0003069345,0.00085421966],"genre_scores_gemma":[0.9917277,0.000003469451,0.0042192396,0.003903205,0.000063034284,0.000012609012,0.000017458076,0.000040965395,0.000012299254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989356,0.00002671107,0.00030363514,0.00025850092,0.00011726172,0.0003583298],"domain_scores_gemma":[0.99938744,0.00033889175,0.000067065346,0.0001553764,0.000008167623,0.000043041044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019282817,0.00022782406,0.00031070109,0.00011304192,0.00010724149,0.000018787043,0.00016702787,0.00006654877,0.000004011353],"category_scores_gemma":[0.000024145693,0.00023831426,0.00007389523,0.00031847163,0.000030170333,0.00005990088,0.000042994394,0.0006751869,9.559168e-7],"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":[0.0000218334,0.000008003226,0.000011723191,0.000029917384,0.000027519121,0.0000022849217,0.0001587921,0.6612409,0.3220221,0.0017948203,0.00021246698,0.014469692],"study_design_scores_gemma":[0.00069653295,0.000022360588,0.000030332918,0.000072605464,0.000021136839,0.0000024275573,0.000051286766,0.9729865,0.024981024,0.00008822698,0.00083447056,0.00021305936],"about_ca_topic_score_codex":0.0000049987743,"about_ca_topic_score_gemma":0.0000013866771,"teacher_disagreement_score":0.7622035,"about_ca_system_score_codex":0.000037947393,"about_ca_system_score_gemma":0.0000034723746,"threshold_uncertainty_score":0.9718174},"labels":[],"label_agreement":null},{"id":"W4408555961","doi":"10.51846/jcsa.v1i2.3932","title":"Hybrid Neuromorphic-Deep Learning Systems for AI Acceleration in Edge Computing","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Science and Applications (JCSA) ISSN 3079-0867 (Onilne)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Neuromorphic engineering; Acceleration; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Edge computing; Deep learning; Computer architecture; Artificial neural network; Physics","score_opus":0.023935295233434035,"score_gpt":0.290722473664146,"score_spread":0.266787178430712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408555961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29654422,0.0017070528,0.7002916,0.0003750971,0.00042979373,0.00038033645,0.000005035181,0.00010102118,0.00016588427],"genre_scores_gemma":[0.9961835,0.00009916259,0.0030171708,0.00009060191,0.0005345584,0.000021947613,0.0000096349,0.000022112266,0.000021279362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834627,0.000033799486,0.00063249504,0.00027951022,0.0004059555,0.0003019994],"domain_scores_gemma":[0.9987003,0.0005118593,0.00015348119,0.00008542942,0.00041339,0.0001355377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010071539,0.00016807514,0.00024248482,0.00044525444,0.00048109915,0.00043455683,0.00027101545,0.000038634993,0.000003148498],"category_scores_gemma":[0.000082406135,0.00016269977,0.00006300868,0.00085644523,0.00012925411,0.0008613855,0.000049040722,0.0004888714,0.0000063108887],"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":[0.0000061028736,0.000018119454,0.000059882837,0.000118223994,0.000008592844,0.000007364314,0.00017285725,0.92547166,0.006190342,0.005825434,0.00011769087,0.062003747],"study_design_scores_gemma":[0.00029251626,0.000079655496,0.0013343917,0.00016334919,0.00001504692,0.00031137976,0.0001207588,0.97998387,0.0012184925,0.0045576133,0.011737208,0.00018573648],"about_ca_topic_score_codex":0.0000018988759,"about_ca_topic_score_gemma":7.401329e-7,"teacher_disagreement_score":0.6996393,"about_ca_system_score_codex":0.00015176849,"about_ca_system_score_gemma":0.00016994643,"threshold_uncertainty_score":0.6634704},"labels":[],"label_agreement":null},{"id":"W4408660629","doi":"10.1021/acsaelm.4c02350","title":"Termination-Dependent Resistive Switching in SrTiO<sub>3</sub> Valence Change Memory Cells","year":2025,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"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":"Staatssekretariat für Bildung, Forschung und Innovation; Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Valence (chemistry); Materials science; Condensed matter physics; Resistive random-access memory; Optoelectronics; Physics; Voltage; Quantum mechanics","score_opus":0.007013861392381054,"score_gpt":0.21599882378962262,"score_spread":0.20898496239724157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408660629","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966552,0.00027490998,0.0012237546,0.000033271477,0.00037718302,0.00056698883,0.000007768864,0.00025383424,0.000607087],"genre_scores_gemma":[0.99907285,0.0002984889,0.000023573404,0.0001454381,0.00014568231,0.00022488547,0.000012230995,0.00003375191,0.000043085725],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984772,0.000040902123,0.00039313364,0.0003428553,0.0001344377,0.0006114468],"domain_scores_gemma":[0.9995328,0.000101265505,0.00007320967,0.00024230222,0.000016400694,0.000033986904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038134304,0.00024350724,0.00030806937,0.00017634715,0.00010174765,0.000048176844,0.0002250457,0.00010834395,0.000016366534],"category_scores_gemma":[0.000015532662,0.00026859588,0.00002300377,0.00025149967,0.000016224134,0.00014769824,0.00008532788,0.00025533867,0.00003339794],"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":[0.00003523325,0.000013779103,0.0000022824456,0.00016582104,0.00001414369,0.000006728935,0.00020364118,0.0018355415,0.98550016,0.0018225785,0.00002997396,0.010370147],"study_design_scores_gemma":[0.0003540439,0.000015489959,0.0003299054,0.00009635993,0.000014362049,0.0000022718189,0.000051333947,0.000049344624,0.9954226,0.0033448427,0.00008069791,0.00023876685],"about_ca_topic_score_codex":0.000006291304,"about_ca_topic_score_gemma":0.000024909046,"teacher_disagreement_score":0.0101313805,"about_ca_system_score_codex":0.00030341576,"about_ca_system_score_gemma":0.00003293143,"threshold_uncertainty_score":0.99997663},"labels":[],"label_agreement":null},{"id":"W4408701618","doi":"10.1088/2058-9565/adc3ba","title":"A memristive neural decoder for cryogenic fault-tolerant quantum error correction","year":2025,"lang":"en","type":"article","venue":"Quantum Science and Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Institut Périmètre de physique théorique; Centre National de la Recherche Scientifique","keywords":"Fault tolerance; Quantum; Computer science; Error detection and correction; Artificial neural network; Electronic engineering; Algorithm; Physics; Engineering; Artificial intelligence; Quantum mechanics; Distributed computing","score_opus":0.013431308340949145,"score_gpt":0.28122570578388595,"score_spread":0.2677943974429368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408701618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9466762,0.0005571824,0.049494006,0.00053704914,0.0017467827,0.00025206988,0.0000025851084,0.00059270154,0.000141454],"genre_scores_gemma":[0.9993409,0.0000324335,0.0003892223,0.00008868901,0.000022505621,0.00004262174,8.1353517e-7,0.000009201059,0.0000735999],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900055,0.000006096252,0.00017973504,0.0003319657,0.000094829404,0.0003868425],"domain_scores_gemma":[0.9995291,0.00009277521,0.000031780197,0.00017358322,0.00013248478,0.000040264564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019902398,0.00013540204,0.00017618264,0.0004432511,0.00040409106,0.000029342853,0.00023442479,0.000099729295,0.000001920745],"category_scores_gemma":[0.00024571468,0.00012524473,0.000027231235,0.0012593953,0.00043513646,0.00019441829,0.00007469862,0.00020567572,0.0000029071953],"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":[0.000096822994,0.00006046389,0.0008731284,0.00018879224,0.00004114418,0.000021396665,0.00029226614,0.022974305,0.62441653,0.07672315,0.0019363688,0.27237567],"study_design_scores_gemma":[0.00024982585,0.0001096598,0.00023374558,0.00003061733,0.000011582242,0.000033832428,0.00040247297,0.9023197,0.08459292,0.010085677,0.0017851493,0.00014479675],"about_ca_topic_score_codex":0.0000028092998,"about_ca_topic_score_gemma":0.0000120767745,"teacher_disagreement_score":0.8793454,"about_ca_system_score_codex":0.000058425256,"about_ca_system_score_gemma":0.000050659448,"threshold_uncertainty_score":0.5107332},"labels":[],"label_agreement":null},{"id":"W4408765236","doi":"10.1002/aelm.202400807","title":"Emulation of Synaptic Plasticity in WO<sub>3</sub>‐Based Ion‐Gated Transistors","year":2025,"lang":"en","type":"article","venue":"Advanced Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Oak Ridge National Laboratory; Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Office of Science; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; U.S. Department of Energy","keywords":"Emulation; Materials science; Transistor; Ion; Nanotechnology; Optoelectronics; Thin-film transistor; Engineering physics; Electrical engineering; Voltage; Engineering; Psychology; Physics","score_opus":0.004756571884427007,"score_gpt":0.21721893255069652,"score_spread":0.2124623606662695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408765236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96446085,0.00018636706,0.034521524,0.000014472657,0.00029352168,0.00025544837,0.0000071756517,0.00019935287,0.00006128074],"genre_scores_gemma":[0.99970216,0.000051968236,0.0001387681,0.00002607184,0.000018116349,0.00001782336,0.000017298345,0.000023085662,0.000004687779],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988043,0.000051729385,0.00044636207,0.00020605099,0.0000838331,0.00040776748],"domain_scores_gemma":[0.99959815,0.0001464186,0.000066669,0.00013444347,0.000028280374,0.000026045735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010406831,0.00018219328,0.0003435567,0.00017425655,0.00003899,0.000007591804,0.00010082208,0.00008125415,0.000016613254],"category_scores_gemma":[0.000058847283,0.00020068114,0.0000389745,0.00036616228,0.000026159549,0.00012951554,0.000010239602,0.00013962455,0.0000033515848],"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":[0.000085974425,0.000012205461,0.000008511963,0.00014774424,0.000013165211,0.0000013659641,0.000014530858,0.34039748,0.65721613,0.00045361152,0.0000015741997,0.0016476811],"study_design_scores_gemma":[0.00073328026,0.000057932393,0.00045979038,0.00020215691,0.000016229502,9.0768106e-7,0.0000057674733,0.0080617145,0.98901427,0.001218473,0.00006990995,0.00015958866],"about_ca_topic_score_codex":0.000002391837,"about_ca_topic_score_gemma":0.000027178896,"teacher_disagreement_score":0.33233577,"about_ca_system_score_codex":0.00022047183,"about_ca_system_score_gemma":0.000050815295,"threshold_uncertainty_score":0.81835395},"labels":[],"label_agreement":null},{"id":"W4408774371","doi":"10.7554/elife.105043.1.sa2","title":"eLife Assessment: Fast and slow synaptic plasticity enables concurrent control and learning","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Plasticity; Synaptic plasticity; Control (management); Computer science; Neuroscience; Psychology; Artificial intelligence; Biology; Physics","score_opus":0.013622472621451286,"score_gpt":0.27835756653401783,"score_spread":0.26473509391256655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408774371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004817216,0.3403431,0.60726994,0.0031250857,0.006360775,0.0024426975,0.00034070775,0.0021254201,0.033175092],"genre_scores_gemma":[0.74517894,0.13969605,0.0027665254,0.0032158052,0.0012131765,0.00017401169,0.00042849,0.00022192963,0.107105054],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99867195,0.000086136264,0.0003636227,0.00037488007,0.00017555752,0.00032786073],"domain_scores_gemma":[0.9987641,0.00086767727,0.00008210658,0.00010262381,0.000065286076,0.00011818576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020194304,0.00039596306,0.0007777102,0.00008457425,0.00017155809,0.00005964554,0.00010188469,0.00014660753,0.000047444515],"category_scores_gemma":[0.00024334196,0.0003572137,0.00006168475,0.00009140048,0.000050727434,0.00008333052,0.00010973897,0.001135985,0.0000032688176],"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":[0.000019515599,0.000052471194,0.00019209323,0.0729559,0.0012883755,0.00011895064,0.000093212686,0.10431576,0.001591819,0.0015471022,0.22129321,0.59653157],"study_design_scores_gemma":[0.0010866908,0.000113433016,0.00013632842,0.0105361305,0.00053559546,0.000042316726,0.00004114224,0.3153626,0.00011715739,0.000029564339,0.67115426,0.00084474013],"about_ca_topic_score_codex":0.000004827953,"about_ca_topic_score_gemma":0.000007094212,"teacher_disagreement_score":0.74036175,"about_ca_system_score_codex":0.00005083285,"about_ca_system_score_gemma":0.000039213028,"threshold_uncertainty_score":0.999888},"labels":[],"label_agreement":null},{"id":"W4408891683","doi":"10.1063/5.0249837","title":"Biomimetic spider web sensor designed with memristive oscillators for location-resolved disturbance detection","year":2025,"lang":"en","type":"article","venue":"Applied Physics Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Spider; Disturbance (geology); Neuromorphic engineering; Nanotechnology; Memristor; Computer science; Materials science; Engineering; Electronic engineering; Artificial intelligence; Ecology; Biology","score_opus":0.008748753630696285,"score_gpt":0.21358190650296122,"score_spread":0.20483315287226495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408891683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37871036,0.000019890826,0.62038463,0.000054435994,0.0001293043,0.00034306096,0.0000026064454,0.00018058161,0.00017515066],"genre_scores_gemma":[0.9951387,0.0000013957798,0.004080011,0.00047552804,0.0001304073,0.000114884155,0.000007762051,0.00003244555,0.000018863118],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993172,0.00000784266,0.00013763354,0.00023910202,0.000074883814,0.00022330081],"domain_scores_gemma":[0.9995524,0.00017239411,0.000040742292,0.00017192104,0.00003200287,0.000030527142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004080082,0.0001821639,0.0001672915,0.000041706062,0.00014409759,0.000021729125,0.00007799382,0.000032778913,5.8842994e-7],"category_scores_gemma":[0.0000065321665,0.00017429153,0.000037522947,0.00039643518,0.000049683134,0.00006184844,0.0000117775435,0.00010648245,0.00000533577],"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":[0.000059416998,0.000008644817,0.000017419134,0.00008530165,0.000049560902,5.133134e-7,0.000050794493,0.19719864,0.79590124,0.0006502304,0.00014882226,0.005829404],"study_design_scores_gemma":[0.0008019152,0.000027214106,0.00023724981,0.000048836573,0.000060898503,5.7685617e-7,0.0000487707,0.01831876,0.97886,0.0003166579,0.00096746447,0.00031165304],"about_ca_topic_score_codex":0.0000014358762,"about_ca_topic_score_gemma":0.0000016483058,"teacher_disagreement_score":0.6164284,"about_ca_system_score_codex":0.00008109271,"about_ca_system_score_gemma":0.000011885874,"threshold_uncertainty_score":0.71074027},"labels":[],"label_agreement":null},{"id":"W4409019733","doi":"10.1109/tetci.2025.3551934","title":"Reconfigurable Digital FPGA Implementations for Neuromorphic Computing: A Survey on Recent Advances and Future Directions","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Neuromorphic engineering; Implementation; Field-programmable gate array; Computer architecture; Computer science; Reconfigurable computing; Embedded system; Artificial intelligence; Software engineering; Artificial neural network","score_opus":0.050108907699487545,"score_gpt":0.32871339850187,"score_spread":0.2786044908023824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409019733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017590057,0.00044744884,0.9781974,0.00071334577,0.0019980192,0.00033287474,0.000108899,0.00016641665,0.00044555034],"genre_scores_gemma":[0.9952404,0.0017711183,0.0024665988,0.00020553506,0.00009688769,0.000038558446,0.000039472925,0.00001999089,0.00012146467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989893,0.000036492744,0.0003625535,0.0002906564,0.0001072814,0.00021373869],"domain_scores_gemma":[0.9987606,0.00094925525,0.00003906106,0.0001081597,0.00009973347,0.00004321704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013029622,0.00017344429,0.0001622687,0.00026048307,0.000308424,0.00006109274,0.00010573655,0.00004711047,0.000017601717],"category_scores_gemma":[0.000022769209,0.000197814,0.000047429454,0.0005341456,0.00004031022,0.00017923182,0.0000016451569,0.00028465435,0.000002663113],"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":[0.000019781712,0.000037847785,0.000024914187,0.00003114722,0.000013627964,7.1589125e-7,0.00008971062,0.662553,0.000011723218,0.0006878957,0.000037704867,0.3364919],"study_design_scores_gemma":[0.00067861954,0.00030585928,0.0037983102,0.0002628668,0.000030042303,0.000012571642,0.0004229012,0.8509723,0.014834079,0.030947344,0.09702778,0.0007073335],"about_ca_topic_score_codex":0.000005222134,"about_ca_topic_score_gemma":0.00008320652,"teacher_disagreement_score":0.97765034,"about_ca_system_score_codex":0.000083586536,"about_ca_system_score_gemma":0.000027143573,"threshold_uncertainty_score":0.8066621},"labels":[],"label_agreement":null},{"id":"W4409149101","doi":"10.1038/s44335-025-00020-w","title":"Solving Boolean satisfiability problems with resistive content addressable memories","year":2025,"lang":"en","type":"article","venue":"npj Unconventional Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Research Laboratory; Defense Sciences Office, DARPA; Advanced Research Projects Agency; Defense Advanced Research Projects Agency","keywords":"Boolean satisfiability problem; Resistive touchscreen; Satisfiability; Computer science; Content (measure theory); Arithmetic; Theoretical computer science; Mathematics; Operating system","score_opus":0.024690578229014847,"score_gpt":0.23433229802560382,"score_spread":0.20964171979658897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409149101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78047806,0.00072763494,0.20074339,0.0001307965,0.00085984654,0.00048979104,0.0000094053285,0.0008191287,0.015741946],"genre_scores_gemma":[0.9946313,0.000003779584,0.004487886,0.00007370399,0.00012684388,0.00001077724,0.000018862362,0.000024622275,0.00062226056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854195,0.000058774003,0.00041454978,0.00036352326,0.00023337628,0.00038781916],"domain_scores_gemma":[0.9991169,0.00034523066,0.00008402502,0.00021349482,0.00017367161,0.00006668361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035516566,0.00024250208,0.00028936827,0.00009934687,0.00039887795,0.00006800751,0.00017433216,0.000060824423,0.000038507183],"category_scores_gemma":[0.00009301803,0.00022778368,0.000090475965,0.00033962177,0.000105216735,0.00019903653,0.00010865716,0.00035303054,0.000009136362],"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":[0.00014470718,0.00010854854,0.029631918,0.001493163,0.00039510604,0.000035155237,0.00047912908,0.89676374,0.020401292,0.030891228,0.0010711612,0.018584875],"study_design_scores_gemma":[0.0069884476,0.0005556396,0.22590487,0.007219082,0.00030819626,0.000109095265,0.0025389127,0.53686357,0.15132502,0.05556636,0.009651694,0.0029691276],"about_ca_topic_score_codex":0.000013464051,"about_ca_topic_score_gemma":0.000059968053,"teacher_disagreement_score":0.35990015,"about_ca_system_score_codex":0.00015785804,"about_ca_system_score_gemma":0.00005415586,"threshold_uncertainty_score":0.92887485},"labels":[],"label_agreement":null},{"id":"W4409156951","doi":"10.1109/ieeeconf60004.2024.10943038","title":"Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Normalization (sociology); Computer science; Outlier; Minification; Parallel computing; Computer engineering; Artificial intelligence; Algorithm; Programming language","score_opus":0.020324500675859582,"score_gpt":0.25314236791784894,"score_spread":0.23281786724198936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409156951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25394964,0.0001412205,0.7426094,0.000056824723,0.00082238216,0.00037025937,0.00001394697,0.00082880136,0.001207519],"genre_scores_gemma":[0.99755037,0.000011550163,0.0015799763,0.00012672105,0.00018247329,0.000014353285,0.000111988054,0.000057968893,0.000364586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999119,0.000013913103,0.0002697634,0.00025284436,0.00009403846,0.00025046093],"domain_scores_gemma":[0.9996512,0.00016182203,0.000018955952,0.00009335281,0.000028841292,0.000045857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009206212,0.00017074852,0.00014870567,0.00013775825,0.000084235355,0.00007866413,0.00008653071,0.000079758545,0.00004103292],"category_scores_gemma":[0.000023279672,0.0001681087,0.000045531142,0.00034099622,0.000009454841,0.00027314608,0.000021167112,0.00014290123,0.000015128628],"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":[0.000009114368,0.000012646815,0.00016020109,0.00021322611,0.0000070438527,0.0000039336333,0.00020558084,0.9770092,0.0017590924,0.00024231215,0.0020720572,0.01830562],"study_design_scores_gemma":[0.00027564386,0.000027878254,0.00010274671,0.00016019041,0.000006369426,0.0000027888461,0.00009829909,0.9721302,0.025927564,0.000031756314,0.0010276905,0.00020891677],"about_ca_topic_score_codex":0.0000011329297,"about_ca_topic_score_gemma":0.000005338201,"teacher_disagreement_score":0.7436007,"about_ca_system_score_codex":0.000080384096,"about_ca_system_score_gemma":0.000008794401,"threshold_uncertainty_score":0.6855274},"labels":[],"label_agreement":null},{"id":"W4409229075","doi":"10.1007/s11227-025-07176-z","title":"Synergizing spintronics and quaternary logic: a hardware accelerator for neural networks with optimized quantization algorithm","year":2025,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Computer science; Quantization (signal processing); Spintronics; Artificial neural network; Computer architecture; Algorithm; Artificial intelligence; Physics","score_opus":0.013886908048315258,"score_gpt":0.24550396923516007,"score_spread":0.23161706118684483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409229075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38974482,0.0013977219,0.60829854,0.000094780466,0.00030538088,0.00010672574,4.839065e-7,0.000042490694,0.000009025096],"genre_scores_gemma":[0.94846326,0.00014640618,0.05088114,0.00013472368,0.00034237813,0.000001065795,0.0000012840399,0.000023253404,0.0000065103386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999146,0.000055018845,0.00036681164,0.00009213292,0.00009216808,0.0002478855],"domain_scores_gemma":[0.99923503,0.0004178713,0.00009822622,0.00009102949,0.00011439474,0.000043432337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043551248,0.00015924452,0.00027021772,0.000076848155,0.0002482665,0.00004770353,0.00017816456,0.000046385914,0.0000010008243],"category_scores_gemma":[0.00003632647,0.00010305828,0.00005528984,0.0001685348,0.000033377117,0.00023936314,0.00005425465,0.00033710268,7.699654e-8],"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":[0.00008296523,0.0000053298827,0.00011829266,0.000047436926,0.00005459083,0.000007938829,0.00017307069,0.96751535,0.0012429424,0.000053459826,0.000027092112,0.030671526],"study_design_scores_gemma":[0.0008861755,0.00011875189,0.000119988064,0.00025515555,0.00006767272,0.00017106657,0.00037523793,0.99618125,0.0015370332,0.00012877984,0.000042248645,0.000116624375],"about_ca_topic_score_codex":0.0000015590011,"about_ca_topic_score_gemma":7.020384e-7,"teacher_disagreement_score":0.55871844,"about_ca_system_score_codex":0.000038868773,"about_ca_system_score_gemma":0.000015528543,"threshold_uncertainty_score":0.42025948},"labels":[],"label_agreement":null},{"id":"W4409271510","doi":"10.31399/asm.amp.2025-02.p036","title":"Do Shape Memory Alloys Have Standards?","year":2025,"lang":"en","type":"article","venue":"AM&P Technical Articles","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ATS Automation Tooling Systems (Canada)","funders":"","keywords":"Shape-memory alloy; Psychology; Materials science; Metallurgy","score_opus":0.01388807131630622,"score_gpt":0.27452942439974715,"score_spread":0.26064135308344094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409271510","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96888524,0.00069715804,0.013766868,0.0003782628,0.00018192756,0.00017235776,0.0000069265698,0.0015070792,0.014404168],"genre_scores_gemma":[0.9975755,0.0000352384,0.001913436,0.000258061,0.000050326435,0.00001317454,8.368054e-7,0.000018007642,0.00013540499],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908024,0.00001816062,0.00023754757,0.00020157691,0.00016917921,0.0002933083],"domain_scores_gemma":[0.9994678,0.00014484776,0.000015821373,0.00026187295,0.000039578328,0.000070108224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001923558,0.00013623225,0.00016760893,0.000059631246,0.00009567302,0.000033568907,0.00018521465,0.00006979639,0.00009178353],"category_scores_gemma":[0.00013958744,0.00012629924,0.00006782476,0.00021451146,0.00007168158,0.00012142252,0.0001051826,0.00023889408,0.000028480845],"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":[0.00004853454,0.00006635189,0.0006826953,0.00010350859,0.000041752985,0.000056541667,0.00008912311,0.019926058,0.54092705,0.0060379035,0.0065136324,0.42550686],"study_design_scores_gemma":[0.00082172547,0.00009802845,0.0042855367,0.00024512465,0.00004489909,0.00002365191,0.00020121135,0.038261965,0.9078332,0.020820048,0.026820071,0.00054453016],"about_ca_topic_score_codex":0.0000011098998,"about_ca_topic_score_gemma":0.00000738737,"teacher_disagreement_score":0.42496234,"about_ca_system_score_codex":0.000071085684,"about_ca_system_score_gemma":0.000013564304,"threshold_uncertainty_score":0.51503336},"labels":[],"label_agreement":null},{"id":"W4409323412","doi":"10.36227/techrxiv.174431756.65547803/v1","title":"Long-Context Efficient Transformers: A Comprehensive Survey of Techniques, Applications, and Future Directions","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformer; Computer science; Context (archaeology); Systems engineering; Engineering; Electrical engineering; Geography; Voltage","score_opus":0.01946895872485314,"score_gpt":0.2715784337193104,"score_spread":0.25210947499445724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409323412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11085014,0.023999978,0.851337,0.000113512935,0.0008373595,0.0027887963,0.0004836695,0.0016351161,0.007954387],"genre_scores_gemma":[0.98933154,0.005571381,0.004293149,0.000056716697,0.00015033917,0.0002426687,0.00014281776,0.000031172232,0.00018022988],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921507,0.000034704375,0.00028825193,0.00025630987,0.00007227474,0.00013341097],"domain_scores_gemma":[0.9994251,0.00014457802,0.000045708588,0.00021780058,0.00012399092,0.00004279894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082709914,0.00020913026,0.00032336102,0.00012910416,0.000062268635,0.000011925145,0.00011048398,0.00017243388,0.000008522136],"category_scores_gemma":[0.0000040327454,0.00020628856,0.00006303056,0.00021287623,0.00004113526,0.000019546336,0.00008877657,0.00042238046,6.8356627e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027159937,0.0000626317,0.00028782958,0.002355259,0.00015165457,0.0000014601122,0.00031201978,0.029189425,0.0015985601,0.00040589963,0.00018605337,0.96542203],"study_design_scores_gemma":[0.0017257156,0.00020719787,0.08010825,0.002851007,0.0005084927,0.00007544864,0.0016254702,0.10318035,0.4558415,0.0010574555,0.34879988,0.004019192],"about_ca_topic_score_codex":0.00008347371,"about_ca_topic_score_gemma":0.00014308385,"teacher_disagreement_score":0.96140283,"about_ca_system_score_codex":0.000037677764,"about_ca_system_score_gemma":0.000025395317,"threshold_uncertainty_score":0.8412204},"labels":[],"label_agreement":null},{"id":"W4409723737","doi":"10.1109/tetc.2025.3562136","title":"HyperXArray: Low-Power and Compact Memristive Architecture for In-Memory Encryption on Edge","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encryption; Architecture; Enhanced Data Rates for GSM Evolution; Non-volatile memory; Memristor; Computer architecture; Embedded system; Computer network; Computer hardware; Electronic engineering; Telecommunications; Engineering","score_opus":0.013067604114430512,"score_gpt":0.27202836096676614,"score_spread":0.2589607568523356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409723737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50316274,0.00005598074,0.49461824,0.00015069447,0.0009664734,0.00021081886,0.0000029152147,0.00012080898,0.0007113567],"genre_scores_gemma":[0.997698,0.000017990993,0.0019603039,0.00014951597,0.0000790881,0.00000763366,0.0000011936226,0.000021025142,0.0000652581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990141,0.00003679528,0.00029224393,0.00028475554,0.00008014829,0.0002919658],"domain_scores_gemma":[0.99932534,0.00044092303,0.000029356674,0.00014928801,0.000019031573,0.00003603943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016267864,0.00019958687,0.00023458486,0.00037553604,0.00017345628,0.00002056399,0.000095949414,0.00008595481,0.000003504371],"category_scores_gemma":[0.000016606213,0.00022042649,0.000063272004,0.00031853252,0.000031150146,0.000058648802,0.0000022204724,0.0005136488,9.0132994e-7],"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":[0.000038625163,0.00003847961,0.00004319562,0.000115898016,0.0000134121265,0.000004572441,0.00073543703,0.9129157,0.003293928,0.00013589098,0.000017356726,0.08264748],"study_design_scores_gemma":[0.002902942,0.00024115716,0.0037857024,0.002320659,0.000031323616,0.000013007272,0.00069081096,0.84156704,0.14286214,0.0033865026,0.0013622602,0.00083647814],"about_ca_topic_score_codex":0.0000065713684,"about_ca_topic_score_gemma":0.00004187264,"teacher_disagreement_score":0.49453527,"about_ca_system_score_codex":0.00013145624,"about_ca_system_score_gemma":0.000012839166,"threshold_uncertainty_score":0.89887315},"labels":[],"label_agreement":null},{"id":"W4409725008","doi":"10.1109/icipnp62754.2023.00043","title":"Design and Implementation of Series-Parallel Connectable Memristor Simulators Based on Combination of Virtuality and Reality","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Virtuality (gaming); Computer science; Series (stratigraphy); Memristor; Computer graphics (images); Operating system; Engineering; Electrical engineering","score_opus":0.031293125178527004,"score_gpt":0.2952116736416192,"score_spread":0.2639185484630922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409725008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91725534,0.000008446788,0.08228328,0.000033942313,0.000034191482,0.00017264394,0.0000046603272,0.000095631214,0.00011185558],"genre_scores_gemma":[0.99881166,0.000011117453,0.0011310028,0.000010911692,0.0000033065896,0.0000029513817,0.000008820836,0.000006554637,0.000013643601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995672,0.000040530947,0.00016556929,0.00008467063,0.000068567206,0.000073445],"domain_scores_gemma":[0.9996157,0.00023177128,0.000038769027,0.00006825901,0.000022910213,0.000022537219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026002977,0.00006027672,0.00011529858,0.00004788379,0.000030909392,0.000003110651,0.000019491714,0.000021075057,0.000010863267],"category_scores_gemma":[0.000020419948,0.000059619364,0.000010095437,0.00011933204,0.000019818499,0.000095427786,0.000010703227,0.000031382304,2.6558686e-7],"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":[0.000079607235,0.0000127647845,0.0012377924,0.00025417202,0.000011755424,7.3537495e-7,0.00026831302,0.94112754,0.051216703,0.0026329071,0.000088351735,0.0030693698],"study_design_scores_gemma":[0.00090389,0.00024200676,0.013607635,0.000018783745,0.000008850166,4.0791133e-7,0.00028000155,0.60984606,0.37376893,0.0011957557,0.00002886576,0.00009880114],"about_ca_topic_score_codex":0.000015744281,"about_ca_topic_score_gemma":0.000004924091,"teacher_disagreement_score":0.33128145,"about_ca_system_score_codex":0.000013414041,"about_ca_system_score_gemma":0.0000052074747,"threshold_uncertainty_score":0.24312072},"labels":[],"label_agreement":null},{"id":"W4409775770","doi":"10.1002/adma.202419245","title":"Toward Switching and Fusing Neuromorphic Computing: Vertical Bulk Heterojunction Transistors with Multi‐Neuromorphic Functions for Efficient Deep Learning","year":2025,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of China; National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Spiking neural network; Artificial neural network; Computer science; Transistor; Coding (social sciences); Spike (software development); Materials science; Efficient energy use; Heterojunction; Voltage; Electronic circuit; Synaptic weight; Artificial intelligence; Computer architecture; Electronic engineering; Optoelectronics; Electrical engineering; Engineering","score_opus":0.026966137680382616,"score_gpt":0.23849534357330127,"score_spread":0.21152920589291865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409775770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57541937,0.000103202205,0.42293414,0.000033938435,0.0008568591,0.00028578375,0.0000026899886,0.0003472308,0.000016798038],"genre_scores_gemma":[0.99531525,0.000021018492,0.0043843025,0.000075417636,0.00008044515,0.000028708382,0.000013119688,0.00005752182,0.000024229385],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864775,0.000058109446,0.00036585552,0.0004065303,0.00012358799,0.00039819058],"domain_scores_gemma":[0.99942994,0.00022595494,0.000049441416,0.00014521502,0.00007170379,0.000077719465],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018201416,0.00028458604,0.00036177234,0.00012994876,0.00041286863,0.00007548448,0.00007735458,0.00007049435,0.000005942301],"category_scores_gemma":[0.00011358444,0.00027463125,0.000047353486,0.00019559417,0.00004785493,0.00014185789,0.00003742705,0.00022142242,0.000002441939],"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":[0.00012968069,0.000015034225,0.000018256154,0.00020493042,0.000017432085,0.000006700809,0.00011648604,0.44495252,0.55100065,0.000045703873,0.0000011652538,0.0034914382],"study_design_scores_gemma":[0.0032910034,0.00040123338,0.0012723244,0.00048148003,0.00017078711,0.00011213167,0.00023962413,0.25957158,0.7325378,0.000084794854,0.0011721272,0.00066508685],"about_ca_topic_score_codex":0.0000018740869,"about_ca_topic_score_gemma":0.0000032695355,"teacher_disagreement_score":0.4198959,"about_ca_system_score_codex":0.00007152549,"about_ca_system_score_gemma":0.000012334575,"threshold_uncertainty_score":0.9999706},"labels":[],"label_agreement":null},{"id":"W4409883144","doi":"10.1109/ted.2025.3561703","title":"Stack Optimization of TiO<i> <sub>x</sub> </i>-Based Resistive Switching Devices Through Interface Engineering","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Stack (abstract data type); Resistive touchscreen; Materials science; Interface (matter); Optoelectronics; Electrical engineering; Electronic engineering; Computer science; Nanotechnology; Engineering; Operating system; Composite material","score_opus":0.007778485957451694,"score_gpt":0.23960176408273545,"score_spread":0.23182327812528375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409883144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3162461,0.0003724186,0.6824804,0.0000328388,0.0002465602,0.00014139783,0.00000448586,0.00030662757,0.00016916754],"genre_scores_gemma":[0.9961795,0.000089091875,0.003554095,0.00007681588,0.000022984023,0.000022333048,0.0000028355546,0.00003758276,0.000014764113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989096,0.000032659787,0.00034478618,0.0002592692,0.00013913987,0.0003145814],"domain_scores_gemma":[0.99939126,0.00024258895,0.00007182092,0.00019435973,0.00006480419,0.000035140183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008922296,0.00024894252,0.0002562587,0.00020988403,0.00013886543,0.000028983988,0.0001598348,0.00008964801,0.0000053829963],"category_scores_gemma":[0.0000067762094,0.00027249538,0.0000928885,0.00058588386,0.000016819175,0.00035440054,0.0000013907174,0.00038679581,0.0000037627028],"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":[0.000042298117,0.000026085127,0.000003308335,0.00021446728,0.00005665677,7.618312e-7,0.0000721695,0.7673265,0.22996455,0.000026384429,0.0000052169385,0.0022616305],"study_design_scores_gemma":[0.00021022111,0.000061181556,0.000024897237,0.0002697446,0.000044480246,8.527577e-7,0.000030880474,0.3099719,0.6891372,0.000018112789,0.00007865359,0.00015185856],"about_ca_topic_score_codex":0.0000050379385,"about_ca_topic_score_gemma":0.000052644828,"teacher_disagreement_score":0.6799334,"about_ca_system_score_codex":0.00012839468,"about_ca_system_score_gemma":0.000036980862,"threshold_uncertainty_score":0.9999727},"labels":[],"label_agreement":null},{"id":"W4409956774","doi":"10.1039/d5tc00997a","title":"Electrically erasable multi-level charge trapping memory with metal nanoparticle engineering for organic synaptic transistors","year":2025,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Research Foundation of Korea; Hanbat National University; National Research Foundation","keywords":"Materials science; Trapping; Nanoparticle; Charge (physics); Transistor; Optoelectronics; Metal; Nanotechnology; Non-volatile memory; Electrical engineering; Voltage; Metallurgy","score_opus":0.013438158357702207,"score_gpt":0.21285739344258997,"score_spread":0.19941923508488776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409956774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95255446,0.0003556283,0.04653886,0.00004011168,0.00029792526,0.00010475557,0.000006524417,0.00007598971,0.000025724346],"genre_scores_gemma":[0.9955582,0.000020429825,0.0040682233,0.000019452,0.0001413592,0.000006940785,0.0000016307038,0.000033104847,0.00015065516],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990342,0.000009280507,0.00045647292,0.00011576376,0.00009982544,0.00028444163],"domain_scores_gemma":[0.9995609,0.00008236628,0.000105843836,0.00009812638,0.00008069515,0.00007205365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023097254,0.00017841182,0.00037182684,0.000047954123,0.000059173362,0.00003315725,0.00015389502,0.00006734073,0.000054486318],"category_scores_gemma":[0.00008392064,0.00015672944,0.00007521564,0.00016114044,0.00001103241,0.00014703524,0.000009089359,0.00014981326,0.000001100811],"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":[0.000091420996,0.000026972146,0.0000015050455,0.00054482586,0.0001640447,0.000016163343,0.000042754105,0.014981264,0.9839073,0.0000034159782,0.000035205983,0.00018516004],"study_design_scores_gemma":[0.0010505801,0.000044868677,0.000018549692,0.00019119939,0.000093007475,0.00010209172,0.000019688421,0.0028222406,0.99534214,0.0000074447316,0.0001452698,0.00016289615],"about_ca_topic_score_codex":1.4633906e-7,"about_ca_topic_score_gemma":1.2905559e-7,"teacher_disagreement_score":0.043003723,"about_ca_system_score_codex":0.000094738105,"about_ca_system_score_gemma":0.00004632776,"threshold_uncertainty_score":0.6391241},"labels":[],"label_agreement":null},{"id":"W4409992894","doi":"10.1021/acsami.5c00535","title":"Real-Time, Dual-Physical-Layer Encryption Directly within an Optical Sensor on a Silicon Platform","year":2025,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Encryption; Materials science; Robustness (evolution); Silicon; Image sensor; Computer science; Heterojunction; Electronics; Optoelectronics; Nanotechnology; Embedded system; Electrical engineering; Artificial intelligence; Engineering; Computer network","score_opus":0.01720915004943565,"score_gpt":0.26623303616288035,"score_spread":0.2490238861134447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409992894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98849505,0.000006890386,0.000070352595,0.00002329002,0.00052771514,0.00030467243,0.000013162904,0.0007480688,0.009810766],"genre_scores_gemma":[0.99875695,0.000016196147,0.000591128,0.000058402544,0.00021397942,0.00004676197,0.000019311992,0.00004419433,0.00025305754],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988006,0.000022681652,0.00035758287,0.0003651059,0.00013248049,0.00032160257],"domain_scores_gemma":[0.99941355,0.00014599465,0.0000636661,0.00029581698,0.000022285418,0.000058682494],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016149489,0.00030283132,0.00039160717,0.00008789035,0.000102433616,0.00011276993,0.00015328742,0.00011395314,0.00006508101],"category_scores_gemma":[0.000019759447,0.00027029373,0.000017068149,0.00011797735,0.000049042927,0.0001872246,0.000077981975,0.00016627953,0.00022759128],"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":[0.00021544928,0.00004615853,3.7274995e-7,0.000077173616,0.000032900447,0.0000050843396,0.00043482066,0.013756371,0.9788045,0.0048953393,0.00006576359,0.0016660449],"study_design_scores_gemma":[0.00028243888,0.00011224305,0.00004839624,0.000095663214,0.000024895542,0.000002900312,0.000117466014,0.0004458086,0.9969736,0.001599994,0.000029853501,0.00026673972],"about_ca_topic_score_codex":0.0000054529455,"about_ca_topic_score_gemma":0.0000014547011,"teacher_disagreement_score":0.018169079,"about_ca_system_score_codex":0.00006285121,"about_ca_system_score_gemma":0.000009084247,"threshold_uncertainty_score":0.9999749},"labels":[],"label_agreement":null},{"id":"W4410009330","doi":"10.1088/2634-4386/add293","title":"Enhancing temporal learning in recurrent spiking networks for neuromorphic applications","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada","keywords":"Neuromorphic engineering; Spiking neural network; Computer science; Artificial intelligence; Neuroscience; Computer architecture; Artificial neural network; Psychology","score_opus":0.016164953710232383,"score_gpt":0.23407184734959233,"score_spread":0.21790689363935994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410009330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4269181,0.0009904832,0.5703444,0.00004516241,0.0005952275,0.00034614914,9.145151e-7,0.00069645623,0.000063123385],"genre_scores_gemma":[0.99707454,0.00008999207,0.002443121,0.00005059626,0.00021999286,0.000037032678,0.000010513465,0.00005201967,0.000022199609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998571,0.0000262716,0.00043920975,0.0003859376,0.00007806352,0.00049953436],"domain_scores_gemma":[0.99915755,0.0005273869,0.00004835811,0.00015951387,0.00002849617,0.00007867737],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028791963,0.00027797784,0.00032183446,0.00026872495,0.00020657414,0.00006550732,0.00014403083,0.00008697598,9.460865e-7],"category_scores_gemma":[0.00008675069,0.00033967465,0.00006192174,0.00054725766,0.000020026735,0.000087985034,0.00009575437,0.0007124882,6.9313285e-7],"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":[0.0000060031625,0.0000095084215,0.0009076134,0.00034424727,0.00000967164,0.0000095347,0.000055742217,0.96456456,0.010720824,0.00035515317,0.0000102543545,0.023006875],"study_design_scores_gemma":[0.00042406554,0.0000445287,0.0023774263,0.00044298437,0.000013917034,0.000024200635,0.000020746902,0.99099594,0.0016474099,0.00005878413,0.0036656428,0.0002843771],"about_ca_topic_score_codex":0.0000027613216,"about_ca_topic_score_gemma":0.0000061283868,"teacher_disagreement_score":0.57015646,"about_ca_system_score_codex":0.000048744198,"about_ca_system_score_gemma":0.000013027764,"threshold_uncertainty_score":0.9999055},"labels":[],"label_agreement":null},{"id":"W4410023076","doi":"10.1088/2634-4386/add36c","title":"NeuroMorse: a temporally structured dataset for neuromorphic computing","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Neuromorphic engineering; Computer science; Artificial intelligence; Artificial neural network","score_opus":0.02195744381126712,"score_gpt":0.24195547067536402,"score_spread":0.2199980268640969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410023076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8501341,0.00055700593,0.1447141,0.00017385227,0.0022935541,0.0005510891,0.00016914686,0.0013254293,0.00008173743],"genre_scores_gemma":[0.994283,0.000030170322,0.004696028,0.00035006716,0.00030406888,0.000007784196,0.00020072938,0.000103623475,0.000024508945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774164,0.00004050552,0.00061209337,0.0006710206,0.00017394379,0.00076077104],"domain_scores_gemma":[0.9985659,0.0006623231,0.00008371032,0.00044771365,0.000056675883,0.00018364096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027177986,0.00054138666,0.0005561916,0.00031049008,0.0003655666,0.00015629957,0.0003676805,0.00013187127,0.000003763463],"category_scores_gemma":[0.00020011074,0.000612988,0.000104579754,0.0005466724,0.00005647502,0.00016397801,0.00024808515,0.0006772204,0.00000262661],"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":[0.00003574997,0.000025897396,0.00037396493,0.0011780079,0.000084924446,0.000120635195,0.00013565083,0.88571423,0.09122789,0.0008141096,0.0029422243,0.017346745],"study_design_scores_gemma":[0.0010520712,0.00009754242,0.0034104085,0.000293297,0.000058659814,0.00021068742,0.000017514649,0.9763931,0.004608786,0.00015354673,0.013101697,0.00060266064],"about_ca_topic_score_codex":0.000004183212,"about_ca_topic_score_gemma":0.0000017591875,"teacher_disagreement_score":0.14414893,"about_ca_system_score_codex":0.000036936242,"about_ca_system_score_gemma":0.000026088748,"threshold_uncertainty_score":0.9996321},"labels":[],"label_agreement":null},{"id":"W4410226406","doi":"10.1109/mmm.2025.3548346","title":"Focus Issue on High-Performance Multifunctional and Reconfigurable Integrated Passive Circuits and Components [From the Guest Editors’ Desk]","year":2025,"lang":"en","type":"article","venue":"IEEE Microwave Magazine","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Desk; Focus (optics); Electronic circuit; Computer science; Engineering; Systems engineering; Computer architecture; Electrical engineering; Telecommunications; Mechanical engineering; Physics","score_opus":0.011959451596618926,"score_gpt":0.20944780047130934,"score_spread":0.1974883488746904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410226406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98930496,0.00039210665,0.0013628529,0.00030022816,0.0066247694,0.00019454914,0.000042534244,0.00013806009,0.0016399453],"genre_scores_gemma":[0.99690074,0.00012411275,0.00013370243,0.00021278804,0.002013353,0.000010432121,0.000035077126,0.000020655736,0.0005491644],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992194,0.000029584771,0.00020005657,0.00025660187,0.000078013,0.00021632435],"domain_scores_gemma":[0.9994008,0.00030061422,0.000037441445,0.0001613884,0.000051023846,0.000048762893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000064314314,0.0002102649,0.0001903247,0.00005505069,0.00018228464,0.000046293433,0.00010000647,0.00007420601,0.000017262197],"category_scores_gemma":[0.000028410022,0.0001634442,0.000019227204,0.00015151,0.00007408397,0.000108900684,0.00001821071,0.00033145133,0.00005927934],"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":[0.00003842703,0.000017001885,0.00029195176,0.00003828041,0.00005374956,0.000008391723,0.00007295574,0.0025516052,0.9087251,0.00001695195,0.020678448,0.06750716],"study_design_scores_gemma":[0.0009210707,0.000070499176,0.017080026,0.00033095662,0.000029794779,0.000013905884,0.00002515815,0.0069809225,0.9193166,0.00018529728,0.05479018,0.00025559554],"about_ca_topic_score_codex":0.000021504276,"about_ca_topic_score_gemma":0.000019427449,"teacher_disagreement_score":0.06725156,"about_ca_system_score_codex":0.000049836937,"about_ca_system_score_gemma":0.000009269518,"threshold_uncertainty_score":0.6665061},"labels":[],"label_agreement":null},{"id":"W4410267946","doi":"10.1002/advs.202416305","title":"Residual Stresses and Micro‐voids Propel Metal Diffusion for Filament‐Based Memristors","year":2025,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Materials science; Protein filament; Residual stress; Composite material; Diffusion; Metallurgy; Metal; Thermodynamics","score_opus":0.009247012625250333,"score_gpt":0.2640247387349556,"score_spread":0.2547777261097052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410267946","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95183676,0.00071095047,0.04592857,0.00007935305,0.00042185825,0.00030707207,0.000008667943,0.00018315007,0.0005236098],"genre_scores_gemma":[0.97703654,0.000029368524,0.02251759,0.00009046961,0.000022732378,0.000023143166,0.0000022600166,0.000009827085,0.0002680627],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991253,0.000007674315,0.00014573823,0.00030421384,0.00012596087,0.00029110277],"domain_scores_gemma":[0.99956405,0.0001360413,0.000026637903,0.00016442558,0.00005053152,0.0000583077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014293645,0.00012693657,0.00012967896,0.000119688004,0.00034344656,0.000035627123,0.00019771045,0.000023134273,0.000002170932],"category_scores_gemma":[0.0001510251,0.000113239854,0.000023238734,0.00040772484,0.00020906822,0.00034067582,0.000068708934,0.00009109517,7.923989e-7],"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":[0.00001990847,0.000009985356,0.00007623897,0.00008125508,0.0000024115639,0.0000013455283,0.00004272241,0.03301429,0.94590276,0.00025907758,0.000040421874,0.020549554],"study_design_scores_gemma":[0.0004839996,0.000059667724,0.00063900504,0.00009631397,0.000009678322,0.0000012738816,0.00008275156,0.019572575,0.97523403,0.00063555274,0.00302088,0.00016425025],"about_ca_topic_score_codex":7.791051e-7,"about_ca_topic_score_gemma":0.0000033000163,"teacher_disagreement_score":0.029331263,"about_ca_system_score_codex":0.000054796907,"about_ca_system_score_gemma":0.00004328968,"threshold_uncertainty_score":0.4617787},"labels":[],"label_agreement":null},{"id":"W4410342948","doi":"10.1109/tcasai.2025.3569509","title":"Design of Highly-Accurate and Hardware-Efficient Spiking Neural Networks","year":2025,"lang":"en","type":"article","venue":"IEEE transactions on circuits and systems for artificial intelligence.","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Computer science; Computer hardware; Spiking neural network; Artificial neural network; Computer architecture; Artificial intelligence; Embedded system","score_opus":0.06252776760931862,"score_gpt":0.28224782255418807,"score_spread":0.21972005494486946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410342948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048536763,0.000798328,0.948199,0.000012709127,0.0017647422,0.00053451903,0.000012004191,0.00010377729,0.00003816713],"genre_scores_gemma":[0.99963474,0.00012051508,0.0000784265,0.000012490326,0.000053086387,0.000047554684,6.858403e-7,0.000017039005,0.000035462406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989967,0.000035822977,0.00044066677,0.00022253262,0.00007470508,0.00022959376],"domain_scores_gemma":[0.99939275,0.00032363864,0.000050210052,0.00012015835,0.00005838353,0.000054878008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002174282,0.00016569118,0.00026356525,0.00013562899,0.00023305157,0.000059350794,0.00007202251,0.00008101322,0.0000024335802],"category_scores_gemma":[0.0000060426078,0.00015985804,0.000053065614,0.00019648008,0.00005864295,0.00006944699,0.000001201059,0.00015289354,6.2512225e-7],"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":[0.000021034804,0.0000174651,8.2759703e-7,0.00014021678,0.00002583361,0.0000011046573,0.00014253613,0.8920252,0.0075184237,0.0009593822,0.0000039409033,0.09914409],"study_design_scores_gemma":[0.000044039854,0.00008504762,0.00000284415,0.00014434238,0.000029697747,0.0000059737563,0.00019873027,0.91953635,0.07956812,0.0002176686,0.000036068388,0.00013113927],"about_ca_topic_score_codex":0.000008936652,"about_ca_topic_score_gemma":0.000002808613,"teacher_disagreement_score":0.95109797,"about_ca_system_score_codex":0.000023447845,"about_ca_system_score_gemma":0.000008567001,"threshold_uncertainty_score":0.6518822},"labels":[],"label_agreement":null},{"id":"W4410614342","doi":"10.1109/tvlsi.2025.3566949","title":"<i>S</i> <sup>3</sup>A-NPU: A High-Performance Hardware Accelerator for Spiking Self-Supervised Learning With Dynamic Adaptive Memory Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; National Research Foundation of Korea","keywords":"Computer science; Computer hardware; Computer architecture; Artificial intelligence","score_opus":0.007721111867427429,"score_gpt":0.20980369269899632,"score_spread":0.20208258083156888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410614342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22538972,0.00009334109,0.7713212,0.000019127889,0.00093522784,0.00095608603,0.000059879363,0.00096689333,0.00025855654],"genre_scores_gemma":[0.990109,0.000068776644,0.008292097,0.000062201485,0.000102593,0.00046141775,0.00005859384,0.00009107549,0.00075420673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980853,0.000109181245,0.00056207617,0.0005001069,0.00027028003,0.0004730288],"domain_scores_gemma":[0.99903595,0.00018715377,0.000108177795,0.0003035413,0.0002728153,0.000092362934],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024623697,0.00043955594,0.0004521494,0.00034818132,0.0007389524,0.00012401765,0.00019913506,0.00021006083,0.00001790429],"category_scores_gemma":[0.000007734502,0.00041164417,0.00013039986,0.00065963785,0.00002933578,0.00087800913,0.00000266355,0.0006422891,0.000012391267],"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":[0.0002042637,0.0000730401,0.0000112311145,0.00032889645,0.00011635923,0.0000024339065,0.0007561288,0.9904626,0.0035140028,0.000024696754,0.00003067879,0.004475685],"study_design_scores_gemma":[0.0013262545,0.000253158,0.000009287135,0.0008709603,0.00009834337,0.000012456219,0.0020427732,0.93742085,0.05732484,0.0000026861467,0.000250055,0.0003883408],"about_ca_topic_score_codex":0.000009922557,"about_ca_topic_score_gemma":0.000034896926,"teacher_disagreement_score":0.7647193,"about_ca_system_score_codex":0.00041184813,"about_ca_system_score_gemma":0.000069295114,"threshold_uncertainty_score":0.9998335},"labels":[],"label_agreement":null},{"id":"W4410617336","doi":"10.3389/fncom.2025.1569374","title":"Reinforced liquid state machines—new training strategies for spiking neural networks based on reinforcements","year":2025,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Bundesministerium für Bildung und Forschung","keywords":"Reinforcement learning; Computer science; Artificial neural network; Adaptability; Artificial intelligence; Adaptation (eye); Reinforcement; State (computer science); Spiking neural network; Reservoir computing; Machine learning; Recurrent neural network; Engineering; Neuroscience","score_opus":0.021097433617318744,"score_gpt":0.2702712606263056,"score_spread":0.24917382700898683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410617336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042925823,0.00003234145,0.95334715,0.00009376382,0.0026633535,0.00031295794,0.0000049137398,0.00018355488,0.00043617404],"genre_scores_gemma":[0.9785669,0.000002797948,0.019697003,0.0014730019,0.000057049372,0.000017229302,0.0000150899405,0.000018335513,0.00015259192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986908,0.000022674298,0.00034748454,0.0003256841,0.00020917221,0.00040419085],"domain_scores_gemma":[0.99950224,0.00021268235,0.00006047379,0.00012291293,0.00003034573,0.00007136404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016324392,0.0002028924,0.00019883459,0.0002907181,0.00019585858,0.00010322572,0.00028317003,0.000037396625,0.0000011896741],"category_scores_gemma":[0.000082941275,0.00021968654,0.000058910187,0.00060526485,0.000061730105,0.00037034837,0.000032631753,0.00024300252,2.1904566e-7],"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":[0.00012058822,0.0000032939158,0.00005586532,0.000029270175,0.0000018351811,0.000005626846,0.00006843918,0.9898275,0.00044240418,0.0008788293,0.00036143122,0.008204904],"study_design_scores_gemma":[0.0006514352,0.00013619951,0.00037023146,0.000079285404,0.0000028493594,0.0000012817586,0.000036757345,0.99560297,0.00023947055,0.0024509646,0.00024628243,0.00018229854],"about_ca_topic_score_codex":0.0000015001365,"about_ca_topic_score_gemma":7.535426e-7,"teacher_disagreement_score":0.93564105,"about_ca_system_score_codex":0.00007376482,"about_ca_system_score_gemma":0.000106862775,"threshold_uncertainty_score":0.8958557},"labels":[],"label_agreement":null},{"id":"W4410632639","doi":"10.22215/etd/2025-16465","title":"Hardware Oriented Evolutionary Spiking Neural Network","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Spiking neural network; Computer science; Artificial neural network; Artificial intelligence; Neuroscience; Cognitive science; Computer architecture; Biology; Psychology","score_opus":0.010390525237862866,"score_gpt":0.24071259360294808,"score_spread":0.23032206836508523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410632639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5476565,0.014007912,0.029979954,0.000043652093,0.03535344,0.0011828047,0.000035461075,0.0067332825,0.365007],"genre_scores_gemma":[0.8165637,0.00036164687,0.012428314,0.00034235272,0.003929417,0.00009886271,0.0054359017,0.00027460768,0.16056517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906105,0.000014274094,0.00025672463,0.00024345165,0.000103559316,0.00032094776],"domain_scores_gemma":[0.999612,0.00006705499,0.000040708157,0.0001785756,0.00005402361,0.00004769123],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000032417407,0.00026868808,0.00024349564,0.00008295286,0.00015892544,0.000016736392,0.00013699071,0.00017589392,0.00008065078],"category_scores_gemma":[0.000022849916,0.0002828967,0.00010527036,0.00030536344,0.0000044477965,0.000077427,0.000022832752,0.00046760426,0.000015298601],"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":[0.000036582274,0.0000068458726,0.000068526155,0.0005724899,0.000060159386,0.00003305416,0.00008003442,0.95285046,0.0006984561,0.0012507983,0.010657148,0.033685416],"study_design_scores_gemma":[0.0010743891,0.00010943491,0.007420736,0.0038385643,0.00031348606,0.000036723275,0.001050259,0.7941801,0.017811352,0.0033129053,0.16771692,0.0031351356],"about_ca_topic_score_codex":0.0000024981368,"about_ca_topic_score_gemma":0.00004438261,"teacher_disagreement_score":0.26890725,"about_ca_system_score_codex":0.00006552262,"about_ca_system_score_gemma":0.000023047496,"threshold_uncertainty_score":0.99996233},"labels":[],"label_agreement":null},{"id":"W4410698181","doi":"10.1002/adfm.202504688","title":"All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfO<sub>x</sub> ReRAM Devices","year":2025,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"H2020 Leadership in Enabling and Industrial Technologies; H2020 Excellent Science; Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; CHIST-ERA; European Commission","keywords":"Resistive random-access memory; Materials science; Inference; Electrical conductor; Chip; Optoelectronics; Training (meteorology); Oxide; Nanotechnology; Electrical engineering; Computer science; Artificial intelligence; Voltage; Metallurgy; Composite material; Engineering","score_opus":0.04357057439404371,"score_gpt":0.26755674636243476,"score_spread":0.22398617196839105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410698181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99009675,0.00019121991,0.008061204,0.00013664344,0.0006285247,0.00024287503,0.000026077463,0.00023652577,0.0003801563],"genre_scores_gemma":[0.99754393,0.00007345956,0.0013712922,0.00076200825,0.000080914986,0.00006486587,0.000041004078,0.000030572086,0.000031944546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986806,0.00005158944,0.00035335348,0.00041242968,0.00015717502,0.00034486994],"domain_scores_gemma":[0.9993136,0.00031051313,0.000071619805,0.00017398974,0.00006340905,0.00006689373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016602963,0.00028519912,0.00041584708,0.0001810969,0.000117014395,0.000057006346,0.000089040936,0.000080167294,0.000026852616],"category_scores_gemma":[0.00010856199,0.00026853822,0.00002723782,0.0002767936,0.00007344299,0.0005175384,0.000041772648,0.00022891715,0.00001063887],"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":[0.00020803894,0.000022451602,0.00017330424,0.000115930015,0.00006930478,0.000013804374,0.00006572033,0.10548132,0.8858845,0.0031571693,0.000011755816,0.00479667],"study_design_scores_gemma":[0.0010635249,0.00013705924,0.024232948,0.00046180977,0.000034590128,0.000016909658,0.00012816898,0.00024506467,0.9677466,0.0049535045,0.0005962755,0.00038354515],"about_ca_topic_score_codex":0.000002515274,"about_ca_topic_score_gemma":0.000017933427,"teacher_disagreement_score":0.105236255,"about_ca_system_score_codex":0.000064777865,"about_ca_system_score_gemma":0.000033612298,"threshold_uncertainty_score":0.9999767},"labels":[],"label_agreement":null},{"id":"W4410810840","doi":"10.1021/acsnano.5c03052","title":"Voltage-Driven All-Solid-State Ionic Control on Co/CoO Antiferromagnet/Ferromagnet Exchange Bias","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Canada First Research Excellence Fund; Innovation, Science and Economic Development Canada","keywords":"Spintronics; Exchange bias; Materials science; Antiferromagnetism; Ferromagnetism; Magnetism; Magnetization; Condensed matter physics; Context (archaeology); Magnetic anisotropy; Nanotechnology; Optoelectronics; Magnetic field; Physics","score_opus":0.023261794920698096,"score_gpt":0.2778366779743868,"score_spread":0.25457488305368875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410810840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9870445,0.0008760642,0.0032096019,0.00017784805,0.0009903214,0.0004040187,0.000039968767,0.000760878,0.0064968076],"genre_scores_gemma":[0.9947585,0.00019076692,0.00009589212,0.0010269386,0.000120318204,0.000025578098,0.00002377871,0.000046670037,0.003711542],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859697,0.000052178006,0.00032291294,0.00033765903,0.00016478356,0.0005254729],"domain_scores_gemma":[0.9991613,0.0002510628,0.000051305182,0.00040164846,0.000038477472,0.00009618767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013466335,0.0003011203,0.00034731292,0.00018512913,0.00013759942,0.000046164783,0.00028635986,0.00010123338,0.000076528784],"category_scores_gemma":[0.00006616104,0.0002979218,0.00009088617,0.00030618114,0.00004365032,0.00015634864,0.00005372315,0.0003015695,0.00022159463],"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":[0.00017717858,0.00011473865,0.0007208215,0.00037128016,0.00022192308,0.0002840024,0.00044195834,0.22061236,0.72589207,0.0006100188,0.024489278,0.026064372],"study_design_scores_gemma":[0.008063083,0.0013844747,0.008952444,0.00077681744,0.00022181623,0.00005914754,0.00009879471,0.11798398,0.6577179,0.0017427389,0.20096524,0.0020335536],"about_ca_topic_score_codex":0.00000523057,"about_ca_topic_score_gemma":0.000013923009,"teacher_disagreement_score":0.17647596,"about_ca_system_score_codex":0.000074837415,"about_ca_system_score_gemma":0.000021077787,"threshold_uncertainty_score":0.9999473},"labels":[],"label_agreement":null},{"id":"W4410872413","doi":"10.1039/d5qm00274e","title":"Improving synergism in Ni-prussian blue analog/CNT composite <i>via</i> coordination engineering for highly stable K<sup>+</sup>-ion capacitor","year":2025,"lang":"en","type":"article","venue":"Materials Chemistry Frontiers","topic":"Advanced Memory and Neural Computing","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":"University of Alberta","funders":"Science and Engineering Research Board; Indian Institute of Technology Indore","keywords":"Prussian blue; Materials science; Composite number; Capacitor; Ion; Chemical engineering; Nanotechnology; Composite material; Chemistry; Electrode; Electrical engineering; Electrochemistry; Voltage; Organic chemistry; Engineering","score_opus":0.0036467860699125427,"score_gpt":0.18386328751250086,"score_spread":0.18021650144258833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410872413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8072498,0.00022213376,0.1903509,0.0000543122,0.0013393221,0.0002798634,0.00005236163,0.00032079543,0.00013051378],"genre_scores_gemma":[0.9924968,0.000012242069,0.006416614,0.000027867978,0.0003014834,0.00009174088,0.0001246517,0.000054135715,0.00047446328],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859804,0.000016662543,0.00046005956,0.00034314283,0.00009870328,0.00048338732],"domain_scores_gemma":[0.999543,0.000043472934,0.000075309974,0.00023064064,0.00003432908,0.00007322213],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019776082,0.00030448686,0.0004146259,0.00009325174,0.00016541369,0.00014935451,0.0001999004,0.00017766956,0.0000074937484],"category_scores_gemma":[0.0000480363,0.00035321753,0.000060904247,0.00021604222,0.000024331743,0.00023914165,0.000050740266,0.00016413543,0.0000012537156],"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":[0.000038531616,0.000010640553,0.000029061464,0.0008985538,0.000026885169,0.0000053240024,0.000059780756,0.20231292,0.79487574,0.000010476953,0.0008259198,0.00090615975],"study_design_scores_gemma":[0.00056877226,0.000008376825,0.000021385393,0.00016147853,0.000022398783,0.0000030726503,0.000088581386,0.17362635,0.8236882,0.000050689756,0.0014805051,0.00028019765],"about_ca_topic_score_codex":0.000031311632,"about_ca_topic_score_gemma":9.871849e-7,"teacher_disagreement_score":0.18524702,"about_ca_system_score_codex":0.00031648198,"about_ca_system_score_gemma":0.000020156553,"threshold_uncertainty_score":0.999892},"labels":[],"label_agreement":null},{"id":"W4410947088","doi":"10.1088/1402-4896/addfb8","title":"Design and analysis of a 2D discrete memristive map","year":2025,"lang":"en","type":"article","venue":"Physica Scripta","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Materials science","score_opus":0.01528233578550082,"score_gpt":0.2537850179999227,"score_spread":0.2385026822144219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410947088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1301356,0.0006998718,0.8668545,0.00007930437,0.00027200268,0.00014481999,0.000009994644,0.00015202779,0.0016519042],"genre_scores_gemma":[0.9966935,0.000013389091,0.0029961905,0.000027916058,0.000014529674,0.000004178133,0.0000033043875,0.000006014196,0.00024097938],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996191,0.000017968308,0.000100869926,0.0001154091,0.000045380704,0.00010124883],"domain_scores_gemma":[0.9997702,0.00004920017,0.000019113673,0.00012365363,0.000016301175,0.000021511842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000038312184,0.00008010067,0.00020267745,0.00009575712,0.000033847024,0.0000075584944,0.00006396387,0.000014185623,0.0000033859344],"category_scores_gemma":[0.000008846313,0.000075822165,0.00005122552,0.0004550217,0.000029344812,0.00006759268,0.00003214887,0.00006531678,0.0000012832196],"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":[0.00007926131,0.00004649219,0.00045788355,0.00031999763,0.0026465207,0.00000836615,0.0013512238,0.47683027,0.48359826,0.006600218,0.004888556,0.02317295],"study_design_scores_gemma":[0.0001659738,0.00002438443,0.0031506713,0.000050370254,0.0006298612,1.4295313e-7,0.00007540093,0.86999226,0.12237241,0.0022302025,0.0011612546,0.0001470513],"about_ca_topic_score_codex":0.000002280433,"about_ca_topic_score_gemma":4.9122406e-7,"teacher_disagreement_score":0.8665579,"about_ca_system_score_codex":0.0000104803685,"about_ca_system_score_gemma":0.000003388159,"threshold_uncertainty_score":0.30919382},"labels":[],"label_agreement":null},{"id":"W4410950036","doi":"10.1063/5.0262605","title":"Electro–optic coupling modulation on persistent photoconductivity and memristive states in thin-film devices with MoOx/ZnO heterostructured electrodes","year":2025,"lang":"en","type":"article","venue":"APL Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Materials science; Photoconductivity; Thin film; Optoelectronics; Electrode; Coupling (piping); Modulation (music); Nanotechnology; Composite material","score_opus":0.00830385683061792,"score_gpt":0.22836704960802826,"score_spread":0.22006319277741032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410950036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99817985,0.00012541097,0.0009397795,0.000019353745,0.00013010102,0.0004241645,0.000007116259,0.00012270144,0.000051510277],"genre_scores_gemma":[0.9994988,0.000020716137,0.00033090735,0.000035168767,0.000029052064,0.000040604045,0.000012902539,0.000018030707,0.00001384267],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992376,0.00003349145,0.00018323872,0.00023523037,0.00007280846,0.00023760812],"domain_scores_gemma":[0.99969727,0.00010474157,0.000046082092,0.00010627305,0.00002149439,0.000024151777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013988552,0.00017971848,0.00024573784,0.00008826617,0.000078686455,0.000063164356,0.00005551679,0.000048202342,0.000008427512],"category_scores_gemma":[0.000020296107,0.00015277952,0.00001746939,0.000119524455,0.0000248626,0.0001660648,0.000015363243,0.00010643354,9.897931e-7],"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":[0.0001690205,0.0000063500183,0.00016865873,0.000102794205,0.000027212056,0.0000029185478,0.00013804005,0.24577689,0.7533861,0.00012851776,0.0000021003323,0.00009138259],"study_design_scores_gemma":[0.00036653783,0.00013244855,0.0061828736,0.00014058556,0.000016491718,0.0000060042394,0.00008570776,0.040700953,0.95135957,0.00083300716,0.000010126929,0.00016566899],"about_ca_topic_score_codex":0.000017311773,"about_ca_topic_score_gemma":0.000015643978,"teacher_disagreement_score":0.20507595,"about_ca_system_score_codex":0.000076965356,"about_ca_system_score_gemma":0.00000855049,"threshold_uncertainty_score":0.62301683},"labels":[],"label_agreement":null},{"id":"W4411031303","doi":"10.3390/microelectronics1010001","title":"Microelectronics—An Open-Access Journal for Advancing Microelectronics Technologies","year":2025,"lang":"en","type":"article","venue":"Microelectronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Microelectronics; Engineering; Computer science; Systems engineering; Engineering physics; Electrical engineering","score_opus":0.019248942616318914,"score_gpt":0.35196888419421857,"score_spread":0.33271994157789964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411031303","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49549988,0.042264648,0.45281464,0.001227285,0.0020626336,0.0021280067,0.000033300974,0.00276643,0.0012031978],"genre_scores_gemma":[0.974525,0.005418575,0.017176064,0.0008141425,0.00025074653,0.00019703749,0.00007146411,0.00025852464,0.0012884897],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962321,0.00006272368,0.00072316994,0.0006722643,0.00014431255,0.0021654111],"domain_scores_gemma":[0.9986004,0.00019160725,0.00017436375,0.00068608834,0.00022464305,0.00012285249],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006320908,0.0005852654,0.0006460646,0.00043878358,0.0009242374,0.0010546842,0.0034298485,0.00028698755,0.000012810035],"category_scores_gemma":[0.00014263229,0.0006263908,0.00017771558,0.0009435817,0.00008737951,0.0016848645,0.000670695,0.0015454999,0.0000065633208],"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":[0.00015748883,0.00009107532,0.000028410861,0.00017060936,0.00017396241,0.000009990818,0.000063376305,0.025890276,0.8534333,0.005305332,0.008724137,0.10595204],"study_design_scores_gemma":[0.0012301817,0.00051019277,0.0000046236028,0.0001057644,0.0000744234,0.00017525225,0.0001103673,0.013168514,0.713119,0.02423666,0.24665366,0.0006114037],"about_ca_topic_score_codex":0.0000054086604,"about_ca_topic_score_gemma":0.0001900463,"teacher_disagreement_score":0.4790251,"about_ca_system_score_codex":0.0014065632,"about_ca_system_score_gemma":0.00070609985,"threshold_uncertainty_score":0.9999823},"labels":[],"label_agreement":null},{"id":"W4411270794","doi":"10.1109/icjece.2025.3570443","title":"Energy-Efficient Hybrid STT-MTJ/CMOS Circuit for Machine Learning-Assisted Neuromorphic Computing Applications","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Science and Technology, Government of Kerala","keywords":"Neuromorphic engineering; CMOS; Computer architecture; Computer science; Electronic engineering; Optoelectronics; Materials science; Electrical engineering; Engineering; Artificial intelligence; Artificial neural network","score_opus":0.010421922683131777,"score_gpt":0.18601949691236053,"score_spread":0.17559757422922875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411270794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07781931,0.0024111497,0.91913795,0.000046145913,0.00035251077,0.00010880763,0.00000399753,0.00007800966,0.000042094038],"genre_scores_gemma":[0.9975192,0.000024063129,0.002023949,0.000101590835,0.0002722206,0.000005159747,0.000004529113,0.000025354117,0.000023956045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990019,0.000016159329,0.0003634078,0.00015616998,0.00007210661,0.0003902332],"domain_scores_gemma":[0.9991618,0.00028707474,0.000060402566,0.0000829081,0.00008811274,0.0003197244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111786,0.00018124716,0.00027584302,0.00037869788,0.00018848128,0.000067322835,0.0001777356,0.00004547094,0.0000017079403],"category_scores_gemma":[0.00003237375,0.00018951528,0.000093565395,0.00040326847,0.000016900702,0.00004690171,0.00001674799,0.00041026477,3.2067462e-7],"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":[0.0000039057168,0.0000085467545,0.000115611,0.00007014732,0.000052093234,0.00003980039,0.00002127469,0.8828412,0.0016469428,0.0023466537,0.0001098148,0.112743996],"study_design_scores_gemma":[0.00033989505,0.00009129394,0.0005512597,0.00007679229,0.000027375221,0.00025212392,0.0000013122755,0.9777883,0.0014745196,0.00013983561,0.01908217,0.00017507155],"about_ca_topic_score_codex":0.000017264667,"about_ca_topic_score_gemma":0.000014919717,"teacher_disagreement_score":0.91969985,"about_ca_system_score_codex":0.00010489797,"about_ca_system_score_gemma":0.00008833856,"threshold_uncertainty_score":0.7728209},"labels":[],"label_agreement":null},{"id":"W4411406701","doi":"10.1109/tnse.2025.3580705","title":"Quantum Deep Deterministic Policy Gradient for Digital Twin-Enabled Semantic IoV Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea","keywords":"Computer science; Theoretical computer science; Algorithm","score_opus":0.008278547988853852,"score_gpt":0.22789446833811122,"score_spread":0.21961592034925737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411406701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06409104,0.0001928752,0.9335049,0.000029447221,0.0014965134,0.00021848863,0.0000026177818,0.0003282819,0.00013586621],"genre_scores_gemma":[0.9987813,0.000085467575,0.000758529,0.00006931448,0.00019237693,0.000039765357,6.6324975e-7,0.000023406938,0.00004913794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987386,0.000003442011,0.00021749387,0.00028526472,0.00012657871,0.00062864716],"domain_scores_gemma":[0.99942654,0.0002038302,0.000016748689,0.00017990395,0.000043493703,0.00012950283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017907246,0.00020679549,0.00019252606,0.00026356537,0.00043811888,0.00014944808,0.00017983375,0.000054440523,9.471146e-7],"category_scores_gemma":[0.000022021188,0.00021237721,0.00006174889,0.0011580788,0.000104766106,0.00032601526,0.0000036906024,0.00021688553,0.0000013977428],"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":[0.000008821087,0.000007860344,0.0000027114818,0.00006556766,0.000012966601,0.0000024871476,0.00003534094,0.9747063,0.0011384869,0.0005761645,0.000019518126,0.023423776],"study_design_scores_gemma":[0.00021129851,0.000053326214,0.00005580571,0.00014941147,0.000019760677,0.000012593413,0.000017990302,0.99679863,0.001667432,0.00023762062,0.0005647347,0.00021137099],"about_ca_topic_score_codex":9.953847e-7,"about_ca_topic_score_gemma":0.0000021420474,"teacher_disagreement_score":0.9346903,"about_ca_system_score_codex":0.00010832149,"about_ca_system_score_gemma":0.000043542906,"threshold_uncertainty_score":0.8660491},"labels":[],"label_agreement":null},{"id":"W4411415376","doi":"10.3390/electronics14122476","title":"A Systematic Review and Classification of HPC-Related Emerging Computing Technologies","year":2025,"lang":"en","type":"review","venue":"Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Iran Telecommunication Research Center","keywords":"Computer science; Scalability; Data science; Cloud computing; Scope (computer science); Supercomputer; Context (archaeology); Emerging technologies; Big data; End-user computing; Domain (mathematical analysis); Utility computing; Artificial intelligence; Database; Parallel computing; Cloud computing security; Data mining","score_opus":0.016408341533263843,"score_gpt":0.2930471395821251,"score_spread":0.27663879804886127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411415376","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000027128622,0.99680555,0.001301513,0.000008004775,0.00006762421,0.0010520762,0.0000023555913,0.0005852774,0.00017486927],"genre_scores_gemma":[0.00008026192,0.9996523,0.00013533876,0.0000044685603,0.0000052843284,0.00003258288,0.000015479522,0.000030161504,0.000044108725],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9984883,0.00007192483,0.00088738004,0.00021957525,0.00007744697,0.00025539193],"domain_scores_gemma":[0.99898964,0.00029606972,0.0003457587,0.00032339923,0.000030859344,0.000014244587],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026696562,0.00028814308,0.001891538,0.00016678683,0.000055133096,0.000009175763,0.0002301637,0.00018716569,8.2263915e-7],"category_scores_gemma":[0.00025504196,0.00024834153,0.00015933684,0.00065050216,0.000024491854,0.000041432366,0.00007384822,0.00058293116,0.0000028036768],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.7529068e-8,0.0000017992842,1.6448006e-8,0.7719925,0.00009699623,5.6572486e-7,0.0000063041925,0.00003691469,0.0000069970697,0.00047989082,0.0000121993835,0.22736576],"study_design_scores_gemma":[0.000043204414,0.000020282305,4.1042735e-8,0.96743643,0.0027235113,0.00004670336,0.000017191389,0.0018535309,0.00003603734,0.00011582988,0.027393447,0.00031376554],"about_ca_topic_score_codex":2.9141535e-8,"about_ca_topic_score_gemma":2.4539762e-7,"teacher_disagreement_score":0.22705199,"about_ca_system_score_codex":0.00011325209,"about_ca_system_score_gemma":0.000046154295,"threshold_uncertainty_score":0.9999969},"labels":[],"label_agreement":null},{"id":"W4411468011","doi":"10.1007/978-3-031-85288-6_1","title":"The Effects of Domain Size and Electrode Placement on Electrical Excitability in the Bidomain Model","year":2025,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Bidomain model; Electrode; Domain (mathematical analysis); Materials science; Neuroscience; Physics; Psychology; Mathematics; Mathematical analysis","score_opus":0.006901571054263567,"score_gpt":0.22804698531281117,"score_spread":0.2211454142585476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411468011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26834658,0.0064818566,0.4219369,0.0003283411,0.0010110217,0.0124231,0.00020571484,0.0006125114,0.28865397],"genre_scores_gemma":[0.5631098,0.009111821,0.401902,0.00037473964,0.0003713625,0.00059427216,0.000015444402,0.0005664506,0.023954112],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834913,0.000011476124,0.00063690933,0.00028036797,0.0003510302,0.0003710977],"domain_scores_gemma":[0.9957936,0.0037426269,0.00016743978,0.00021067623,0.00005184325,0.000033846594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007496016,0.00036714275,0.00046967363,0.00011593844,0.00009026201,0.000044172317,0.00031843354,0.00015407824,0.0000012339576],"category_scores_gemma":[0.00082906784,0.00026186244,0.000045901143,0.000094098075,0.00010664047,0.00003252231,0.0000875279,0.0009069894,9.074908e-7],"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":[0.000050516333,0.00006534428,0.0000123034415,0.0044669197,0.000045478326,0.000016310689,0.0020244906,0.0015374081,0.0021630705,0.98650837,0.00024722907,0.0028625682],"study_design_scores_gemma":[0.00045981962,0.00016762076,0.00004789715,0.0011227495,0.000051586732,0.000005172521,0.000080171725,0.11039182,0.0010509052,0.8858904,0.00037897678,0.0003528529],"about_ca_topic_score_codex":4.5955795e-7,"about_ca_topic_score_gemma":0.0000064278343,"teacher_disagreement_score":0.29476324,"about_ca_system_score_codex":0.00017728949,"about_ca_system_score_gemma":0.00003151858,"threshold_uncertainty_score":0.9999834},"labels":[],"label_agreement":null},{"id":"W4411509350","doi":"10.1038/s41467-026-74466-2","title":"Neuromorphic hierarchical modular reservoirs","year":2025,"lang":"en","type":"preprint","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"","keywords":"Modularity (biology); Modular design; Computer science; Hierarchical organization; Reservoir computing; Function (biology); Theoretical computer science; Artificial neural network; Distributed computing; Artificial intelligence; Recurrent neural network; Biology","score_opus":0.04474530021486626,"score_gpt":0.3078932318969529,"score_spread":0.2631479316820866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411509350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42511708,0.24922296,0.042132497,0.04040504,0.017859852,0.004985905,0.0016409977,0.014219523,0.20441616],"genre_scores_gemma":[0.9834123,0.0025538648,0.012853542,0.00034486243,0.00012577359,0.000065085216,0.00029465806,0.000035890076,0.00031402492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887866,0.0001527182,0.00029977746,0.0002835558,0.00015035234,0.00023491136],"domain_scores_gemma":[0.99585295,0.00043796794,0.00005166621,0.003488981,0.00009107753,0.000077359364],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00013863007,0.00026331475,0.00028365775,0.00017148328,0.00025253327,0.000045309007,0.0024433243,0.00072419894,0.000007280041],"category_scores_gemma":[0.0001951702,0.00029313273,0.00014204817,0.000294961,0.000099185214,0.000055348493,0.002720933,0.007912291,0.000010906277],"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":[0.000018087168,0.00013335701,0.00020423405,0.0007508501,0.0002386777,0.000024851735,0.00039276545,0.92860705,0.0046752547,0.03873594,0.0151156355,0.011103279],"study_design_scores_gemma":[0.00039609786,0.000024295736,0.0020757117,0.00092893105,0.00014335553,0.000022380367,0.00002913209,0.59564084,0.0034566782,0.030925736,0.3652058,0.0011510081],"about_ca_topic_score_codex":0.0000020452142,"about_ca_topic_score_gemma":0.000034992565,"teacher_disagreement_score":0.55829525,"about_ca_system_score_codex":0.00007391931,"about_ca_system_score_gemma":0.00005453042,"threshold_uncertainty_score":0.9999521},"labels":[],"label_agreement":null},{"id":"W4411531690","doi":"10.3389/fnins.2025.1610766","title":"Signal-to-event encoding parameter selection for multiple event classification with spiking neural networks","year":2025,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerstwo Edukacji i Nauki; Narodowe Centrum Nauki","keywords":"Encoding (memory); Computer science; Pattern recognition (psychology); Event (particle physics); Artificial intelligence; Encoder; Artificial neural network; SIGNAL (programming language); Classifier (UML); Data mining","score_opus":0.01865477616808232,"score_gpt":0.2581647693233517,"score_spread":0.23950999315526939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411531690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26284054,0.00003663974,0.7352692,0.000048288206,0.0013275623,0.0003482539,6.320902e-7,0.000100063364,0.000028822064],"genre_scores_gemma":[0.98747396,0.0000075444227,0.012074707,0.00025730324,0.000045183908,0.00007398955,0.000001051912,0.000014246727,0.00005199655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989758,0.000025571786,0.00020247775,0.0003491988,0.00010311245,0.00034380512],"domain_scores_gemma":[0.9996701,0.00010482338,0.00003795923,0.00011316735,0.000022467791,0.000051494673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015286783,0.00013362538,0.00013638109,0.00018987115,0.00016360045,0.000044942964,0.00017786222,0.00003652499,3.1644157e-7],"category_scores_gemma":[0.00010211815,0.0001298322,0.000033652694,0.00079337927,0.000032198375,0.00020763608,0.0000275897,0.00018761937,1.4667431e-7],"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":[0.000033801138,0.000009677604,0.013311587,0.00002070137,0.0000011387151,0.0000011234958,0.00003580804,0.9290589,0.03817582,0.000027589882,0.00016655035,0.019157294],"study_design_scores_gemma":[0.00021543926,0.0000747713,0.0076733767,0.000062353116,0.00000471831,0.0000025479685,0.00003469333,0.97617674,0.01488566,0.00007239013,0.0006637122,0.00013361582],"about_ca_topic_score_codex":0.0000011355163,"about_ca_topic_score_gemma":0.000005593319,"teacher_disagreement_score":0.72463346,"about_ca_system_score_codex":0.00011514927,"about_ca_system_score_gemma":0.000011489705,"threshold_uncertainty_score":0.52944034},"labels":[],"label_agreement":null},{"id":"W4411591558","doi":"10.1007/s13042-025-02714-w","title":"Immunization of binarized deep neural networks against model replication attacks based on stochastic magnetoresistive RAMs","year":2025,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Computational intelligence; Replication (statistics); Artificial neural network; Computer science; Artificial intelligence; Neuroscience; Computational biology; Virology; Biology","score_opus":0.00616272046983831,"score_gpt":0.254836463840408,"score_spread":0.24867374337056966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411591558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3054808,0.0005206524,0.6932033,0.00012609127,0.00026190144,0.00004431841,0.0000016628301,0.00002679555,0.0003345334],"genre_scores_gemma":[0.9980401,0.00011143341,0.0015942021,0.00008393816,0.000064692285,8.8695276e-7,0.000017053744,0.00001340533,0.00007430515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992509,0.000048184458,0.0003459264,0.00009477868,0.00017586329,0.00008433789],"domain_scores_gemma":[0.99923706,0.00021919777,0.00020626895,0.000080657104,0.00022550822,0.000031296197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018255207,0.000105476916,0.00015353716,0.0001639982,0.0000455053,0.000023036711,0.00015079892,0.00004838708,0.00000326624],"category_scores_gemma":[0.00024154497,0.000100229474,0.000052671912,0.00010042937,0.00003003098,0.000055498786,0.00002996581,0.00039810484,1.7385446e-7],"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":[0.00016417213,0.000020950598,0.0006387274,0.000010972797,0.000034701647,0.0000033258411,0.000050478415,0.9535973,0.0020547304,0.00016510896,0.000006817499,0.043252707],"study_design_scores_gemma":[0.0007060433,0.00010286187,0.0010584326,0.00013665084,0.000023389535,0.0000065356285,0.000015666476,0.99706703,0.0005934719,0.00017104663,0.000045888053,0.00007299228],"about_ca_topic_score_codex":0.000001301282,"about_ca_topic_score_gemma":7.8984135e-7,"teacher_disagreement_score":0.6925593,"about_ca_system_score_codex":0.000044031443,"about_ca_system_score_gemma":0.000011039744,"threshold_uncertainty_score":0.40872392},"labels":[],"label_agreement":null},{"id":"W4411612109","doi":"10.63282/3050-9246.ijetcsit-v4i1p101","title":"Low-Power VLSI Architectures for Edge Computing: Advancing Energy-Efficient AI Inference at the Device Level","year":2023,"lang":"en","type":"article","venue":"International Journal of Emerging Trends in Computer Science and Information Technology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Very-large-scale integration; Computer science; Inference; Enhanced Data Rates for GSM Evolution; Computer architecture; Edge computing; Power (physics); Edge device; Computer engineering; Parallel computing; Computational science; Embedded system; Artificial intelligence; Operating system; Physics; Cloud computing","score_opus":0.017224980065188748,"score_gpt":0.30510401400532305,"score_spread":0.2878790339401343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411612109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5511206,0.000039312123,0.44579023,0.0016600633,0.0012225504,0.000029032664,0.0000032124792,0.0000825189,0.000052530824],"genre_scores_gemma":[0.99516493,0.000021051559,0.0042395857,0.0004759624,0.00008406949,0.0000023545997,0.0000024758895,0.0000047190347,0.0000048777456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882656,0.000008407634,0.0004760758,0.0000995386,0.0003382207,0.00025121012],"domain_scores_gemma":[0.9991274,0.00016315606,0.00018100302,0.00010201388,0.0003887385,0.000037686736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061458,0.00010689386,0.00012736552,0.0017231501,0.00018109889,0.00007385538,0.0006204704,0.000042958014,0.0000033159315],"category_scores_gemma":[0.00015096283,0.00008229011,0.000038616196,0.0011796858,0.00016705286,0.00044172927,0.00030022522,0.00021698084,0.0000017395049],"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":[0.000006768903,0.0000038398234,0.00007455644,0.0000049342316,0.000006691184,0.0000023658238,0.0005205047,0.5985302,0.0007164558,0.0007988866,0.00017913074,0.39915568],"study_design_scores_gemma":[0.0003775744,0.00006860937,0.00128304,0.00010065216,0.0000021834012,0.00010486318,0.00010439648,0.97402304,0.017686967,0.00084669294,0.0052887257,0.00011325786],"about_ca_topic_score_codex":8.3513777e-7,"about_ca_topic_score_gemma":0.0000045008123,"teacher_disagreement_score":0.44404435,"about_ca_system_score_codex":0.00012620007,"about_ca_system_score_gemma":0.000037163372,"threshold_uncertainty_score":0.33556935},"labels":[],"label_agreement":null},{"id":"W4411618786","doi":"10.1002/adma.202505150","title":"Heterojunction‐Driven Stochasticity: Bi‐Heterojunction Noise‐Enhanced Negative Transconductance Transistor in Image Generation","year":2025,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Materials science; Transconductance; Heterojunction; Optoelectronics; Transistor; Noise (video); Image (mathematics); Electrical engineering; Computer science; Artificial intelligence","score_opus":0.013598277191532136,"score_gpt":0.24631144460866677,"score_spread":0.23271316741713463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411618786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6652545,0.00007700901,0.33150226,0.000040151393,0.0021625916,0.0004072821,0.000021605143,0.00027593804,0.00025870977],"genre_scores_gemma":[0.99371237,0.00007827364,0.005571347,0.00012272983,0.00017502782,0.00015204704,0.000031173346,0.00004222498,0.00011479065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846846,0.000072496485,0.0005331532,0.0004169569,0.00014645557,0.0003624665],"domain_scores_gemma":[0.9994914,0.00006856677,0.00007294058,0.0002334759,0.00008529749,0.00004835392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012839175,0.00030749186,0.0004208263,0.0002006883,0.00012248606,0.000048717786,0.00012449427,0.00010476034,0.00006197846],"category_scores_gemma":[0.00006963584,0.00033408808,0.0000634075,0.0003598907,0.000053240896,0.0007501325,0.000014500298,0.0001698066,0.000019059515],"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":[0.00010399375,0.000017214299,0.0000011796379,0.00010288309,0.000015776628,0.000003478118,0.00018808915,0.17582081,0.8203429,0.00005743575,0.000024087105,0.003322172],"study_design_scores_gemma":[0.001215568,0.000058901718,0.00018034046,0.00016317247,0.000020239342,0.000002471883,0.00005128224,0.003420804,0.99389386,0.0005357325,0.00016409071,0.00029352598],"about_ca_topic_score_codex":0.000006017394,"about_ca_topic_score_gemma":0.00006384094,"teacher_disagreement_score":0.32845792,"about_ca_system_score_codex":0.0002547909,"about_ca_system_score_gemma":0.000020700169,"threshold_uncertainty_score":0.9999111},"labels":[],"label_agreement":null},{"id":"W4411727262","doi":"10.1109/iscas56072.2025.11043947","title":"Spiking Auto-Encoder for Static and Spatio-Temporal Neuromorphic Pattern Reconstruction","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Neuromorphic engineering; Computer science; Encoder; Autoencoder; Spiking neural network; Artificial intelligence; Computer graphics (images); Computer architecture; Artificial neural network; Operating system","score_opus":0.02167390222023392,"score_gpt":0.24134754178960383,"score_spread":0.21967363956936992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411727262","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.539668,0.000032186206,0.45919,0.000073030606,0.0003622632,0.00011584492,0.000001619672,0.00017412668,0.0003829145],"genre_scores_gemma":[0.99479854,0.000008511024,0.0048297835,0.00014672488,0.00003520796,0.000009159616,0.0000046755167,0.000010317386,0.00015710472],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995938,0.00000782399,0.00013346429,0.000118101154,0.000028177426,0.000118630625],"domain_scores_gemma":[0.9998016,0.0000812145,0.000016130109,0.000064617285,0.000014781064,0.000021658941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047962207,0.000079253325,0.0000914932,0.000052494797,0.00006886275,0.000021963524,0.000029596717,0.000024464916,0.000014793975],"category_scores_gemma":[0.000016889913,0.00007864969,0.000018123059,0.0000673095,0.000013330006,0.000117119256,0.0000140214925,0.00007323309,0.0000011759425],"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":[0.00002050067,0.0000097583925,0.014329419,0.0007018902,0.00003750401,0.000007580593,0.00016654484,0.047180377,0.013194546,0.0007665754,0.00056981534,0.9230155],"study_design_scores_gemma":[0.00046229322,0.0000377038,0.00240545,0.000106945925,0.000016475731,0.000023220513,0.00006867178,0.97501266,0.016543752,0.0035113168,0.0016384363,0.00017305904],"about_ca_topic_score_codex":0.0000034076495,"about_ca_topic_score_gemma":0.000015568205,"teacher_disagreement_score":0.9278323,"about_ca_system_score_codex":0.00001351679,"about_ca_system_score_gemma":0.0000054340035,"threshold_uncertainty_score":0.32072413},"labels":[],"label_agreement":null},{"id":"W4411883862","doi":"10.1038/s41467-025-61623-2","title":"Author Correction: ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers","year":2025,"lang":"en","type":"erratum","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Neuromorphic engineering; Ising model; Condensed matter physics; Computer science; Neuroscience; Physics; Materials science; Psychology; Artificial intelligence; Artificial neural network","score_opus":0.051590002549488624,"score_gpt":0.32568174609637845,"score_spread":0.2740917435468898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411883862","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032135553,0.20024966,0.0067265052,0.007836179,0.45284206,0.0022966526,0.00053695764,0.00660903,0.3196894],"genre_scores_gemma":[0.5257074,0.017344551,0.020783352,0.005315978,0.010434492,0.00020346079,0.005442865,0.0011084389,0.41365942],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998246,0.00019953886,0.000479457,0.0004236785,0.0002508231,0.0004004804],"domain_scores_gemma":[0.9963173,0.00069683505,0.00017708556,0.0025241121,0.00017736995,0.00010725044],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001687648,0.00054081244,0.00051043474,0.0004372694,0.0009793082,0.00010537972,0.001577886,0.0011257747,0.000022228907],"category_scores_gemma":[0.00036931093,0.0005955394,0.00022353344,0.00095185096,0.00011404121,0.00016123572,0.000446226,0.008556248,0.000018201226],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000942601,0.000043554035,0.0000146419525,0.00013943137,0.000096223484,0.000009904089,0.00009027443,0.08534899,0.00028575622,0.00024681387,0.8992438,0.014471197],"study_design_scores_gemma":[0.00010186897,0.000020543337,0.00009217079,0.0007682376,0.00009136486,0.000033912882,0.000021463735,0.34086448,0.000112638365,0.000083338,0.65740067,0.000409302],"about_ca_topic_score_codex":0.000007301304,"about_ca_topic_score_gemma":0.00011956982,"teacher_disagreement_score":0.5224939,"about_ca_system_score_codex":0.00024814307,"about_ca_system_score_gemma":0.00013695625,"threshold_uncertainty_score":0.9996496},"labels":[],"label_agreement":null},{"id":"W4411950038","doi":"10.1109/vnc64509.2025.11054099","title":"Poster: Multi-Receiver Physical Layer Security Using Reconfigurable Intelligent Surfaces","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"CODE","keywords":"Computer science; Physical layer; Layer (electronics); Computer architecture; Embedded system; Computer network; Telecommunications; Materials science; Wireless; Nanotechnology","score_opus":0.037164271975608204,"score_gpt":0.2986839302021105,"score_spread":0.26151965822650225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411950038","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9746741,0.00016875901,0.01477836,0.000023910061,0.00045873615,0.000106899,0.0000020416273,0.00032315077,0.009464039],"genre_scores_gemma":[0.9982204,0.00001811506,0.0008601899,0.000081068225,0.00004173264,0.0000013376448,0.0000013558713,0.000012717707,0.00076308433],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994163,0.000018008268,0.00013596221,0.00016273354,0.000052479947,0.00021452658],"domain_scores_gemma":[0.9997263,0.00007277879,0.000013220537,0.000121361634,0.000026262154,0.000040064737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050098468,0.00013291996,0.00015622195,0.000044345306,0.00007442552,0.000024382776,0.00009467086,0.00003978464,0.000057437388],"category_scores_gemma":[0.000012649935,0.00012199489,0.000055090204,0.00015167659,0.000017231783,0.00014385073,0.00002791974,0.00017659662,0.00003679669],"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":[0.000019367213,0.00008474386,0.0005320387,0.00017498042,0.00007728825,0.00001248343,0.00080775097,0.469647,0.51481307,0.00046680204,0.00035504994,0.013009458],"study_design_scores_gemma":[0.00010850075,0.00000681811,0.00010131256,0.000041510164,0.000008186654,0.00000203408,0.000067985384,0.39649832,0.6017921,0.00030235312,0.0009501196,0.000120763696],"about_ca_topic_score_codex":0.000013981406,"about_ca_topic_score_gemma":0.000011636884,"teacher_disagreement_score":0.08697903,"about_ca_system_score_codex":0.000047682966,"about_ca_system_score_gemma":0.000007494805,"threshold_uncertainty_score":0.49748072},"labels":[],"label_agreement":null},{"id":"W4412375742","doi":"10.1109/tcsi.2025.3583777","title":"Event-Triggered Multi-Kernel Learning-Based Stochastic MPC With Applications in Building Climate Control","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Event (particle physics); Kernel (algebra); Control (management); Control theory (sociology); Control engineering; Engineering; Mathematics; Artificial intelligence; Physics","score_opus":0.008914564970642998,"score_gpt":0.2290068902517019,"score_spread":0.2200923252810589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412375742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07736635,0.00041570462,0.92102,0.000016707156,0.00019425506,0.00065568526,0.000013185851,0.00023310176,0.00008500484],"genre_scores_gemma":[0.999511,0.000021775499,0.000069821595,0.00002846247,0.000013619572,0.00020340811,0.0000020785023,0.00003032311,0.00011952607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894947,0.00006692426,0.00028655466,0.0002879377,0.00011385016,0.00029527987],"domain_scores_gemma":[0.9994669,0.00019320354,0.000045667824,0.00017631936,0.000028522714,0.00008938876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015647181,0.00021156004,0.0002905274,0.00021657444,0.000278233,0.000043980068,0.000079125704,0.00008798337,0.0000022748175],"category_scores_gemma":[0.0000051530665,0.0001932249,0.0000574051,0.00034018932,0.000042576485,0.00007103642,5.262657e-7,0.0003198413,0.0000017331632],"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":[0.000015385158,0.000033382992,0.00004830981,0.00020457154,0.000041679326,0.0000038479498,0.00005944134,0.9658605,0.029115494,0.00010107303,5.5936755e-7,0.0045157447],"study_design_scores_gemma":[0.0033385353,0.000097473465,0.0003638187,0.0008764027,0.00009619674,0.000012888657,0.00056373107,0.9873479,0.00653889,0.000009409491,0.00034294458,0.00041180165],"about_ca_topic_score_codex":0.000008234751,"about_ca_topic_score_gemma":0.000019727333,"teacher_disagreement_score":0.92214465,"about_ca_system_score_codex":0.00008968969,"about_ca_system_score_gemma":0.000028012477,"threshold_uncertainty_score":0.78794825},"labels":[],"label_agreement":null},{"id":"W4412419922","doi":"10.3390/biomedicines13071718","title":"Spike Timing-Dependent Plasticity and Random Inputs Shape Interspike Interval Regularity of Model STN Neurons","year":2025,"lang":"en","type":"article","venue":"Biomedicines","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuroscience; Synaptic plasticity; Refractory period; Spike (software development); Spike-timing-dependent plasticity; Neuron; Neuroplasticity; Electrophysiology; Biology; Physics; Psychology; Biological system; Computer science; Medicine; Internal medicine","score_opus":0.023987347986111927,"score_gpt":0.2682890938334706,"score_spread":0.24430174584735867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412419922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7963751,0.00026399092,0.20226878,0.00014356313,0.00044546928,0.000116246396,0.0000064083347,0.00012414929,0.00025629048],"genre_scores_gemma":[0.9987903,0.00004234549,0.00079796236,0.00012084115,0.00006269259,0.0000037384325,0.0000027091144,0.0000117051495,0.00016770686],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992612,0.000016425385,0.00027375124,0.00018132482,0.00010186775,0.00016544058],"domain_scores_gemma":[0.99962074,0.00013010945,0.000038531503,0.00011911924,0.000034401226,0.0000570746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009638339,0.00014935523,0.0002827304,0.0001595473,0.00004346756,0.0000078165585,0.00012105063,0.000054750348,0.0000068200984],"category_scores_gemma":[0.000096853495,0.00012932433,0.000039483162,0.00015556335,0.00011626879,0.000075306736,0.0001168297,0.00015241702,7.756519e-7],"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":[0.0006898668,0.00014101612,0.0021599766,0.0020339328,0.00020883893,0.00005812055,0.0018911414,0.10658775,0.81707835,0.0007027313,0.0018145964,0.06663365],"study_design_scores_gemma":[0.0014094862,0.00006961654,0.001072452,0.00031954108,0.00004248741,0.000010152382,0.00005103116,0.9625028,0.03387894,0.00033418613,0.00018140957,0.00012793417],"about_ca_topic_score_codex":0.0000035046178,"about_ca_topic_score_gemma":0.0000069315233,"teacher_disagreement_score":0.855915,"about_ca_system_score_codex":0.000019311792,"about_ca_system_score_gemma":0.000011376147,"threshold_uncertainty_score":0.52736926},"labels":[],"label_agreement":null},{"id":"W4412493241","doi":"10.1021/acsnano.5c05850","title":"Electroforming Kinetics in HfO<sub><i>x</i></sub>/Ti RRAM: Mechanisms behind Compositional and Thermal Engineering","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":8,"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":"HORIZON EUROPE Digital, Industry and Space; Staatssekretariat für Bildung, Forschung und Innovation; Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Electroforming; Resistive random-access memory; Materials science; Stack (abstract data type); Electrical conductor; Voltage; Nanotechnology; Kinetic Monte Carlo; Scaling; Optoelectronics; Thermal; Engineering physics; Monte Carlo method; Chemical physics; Computer science; Layer (electronics); Composite material; Electrical engineering; Thermodynamics; Chemistry; Physics","score_opus":0.00375863758349718,"score_gpt":0.18943535384238744,"score_spread":0.18567671625889026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412493241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98004955,0.00033636062,0.01874307,0.00003999885,0.00017422713,0.00009797551,0.000001822899,0.0001588614,0.00039815812],"genre_scores_gemma":[0.99766666,0.000039695533,0.0020871705,0.00011144701,0.00004615579,0.00000851017,0.000006775242,0.000021755553,0.000011846523],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992835,0.000009033441,0.00018199232,0.00015266961,0.00008008628,0.0002927278],"domain_scores_gemma":[0.99974734,0.00008910906,0.00001647396,0.00009579507,0.00001369004,0.000037567766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006840593,0.00015176913,0.00014710474,0.0001276003,0.000055873403,0.00002242668,0.00008699649,0.0000704411,0.0000018726957],"category_scores_gemma":[0.000013772409,0.00017077435,0.000024215855,0.00016219806,0.0000115471885,0.000121337915,0.000053983058,0.00022495321,0.000002789847],"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":[0.00000645058,0.000009066973,0.000053697335,0.000044969165,0.000010709625,0.000011272974,0.000050275812,0.030805532,0.96370363,0.0017337356,0.0000122246565,0.0035584334],"study_design_scores_gemma":[0.00031611446,0.000025464155,0.0017357583,0.000098615186,0.0000072457947,0.000015876154,0.000008271483,0.02199093,0.97486275,0.0006496379,0.00012108208,0.00016826439],"about_ca_topic_score_codex":5.999097e-7,"about_ca_topic_score_gemma":0.0000027165077,"teacher_disagreement_score":0.01761712,"about_ca_system_score_codex":0.000052445168,"about_ca_system_score_gemma":0.000007792574,"threshold_uncertainty_score":0.6963976},"labels":[],"label_agreement":null},{"id":"W4412508475","doi":"10.1021/acsami.5c10123","title":"Spatially Correlated Oxygen Vacancies, Electrons and Conducting Paths in TiO<sub>2</sub> Thin Films","year":2025,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Materials science; Thin film; Oxygen; Electron; Condensed matter physics; Engineering physics; Chemical physics; Nanotechnology; Physics","score_opus":0.01230789374680549,"score_gpt":0.2211801205897182,"score_spread":0.2088722268429127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412508475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997079,0.00047261183,0.00024847273,0.000030099649,0.0006648292,0.0003114608,0.000012271975,0.00031205022,0.0008691655],"genre_scores_gemma":[0.99938864,0.00016271595,0.0001987164,0.00008959848,0.00004257122,0.000039877687,0.00001592719,0.000032853386,0.000029090434],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988037,0.000031518983,0.00041800522,0.00029717945,0.000075261596,0.00037437797],"domain_scores_gemma":[0.99960387,0.00011115767,0.00007267356,0.00016083887,0.00001942825,0.00003202045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022356014,0.00024778303,0.00034027867,0.000110411434,0.00009329594,0.000089049594,0.00015797451,0.00012835616,0.00002145708],"category_scores_gemma":[0.000035464618,0.00025255186,0.000008697208,0.00018185597,0.00004138114,0.00012855865,0.000128801,0.00024216031,0.000015829126],"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":[0.00004619522,0.000008401382,0.000011413825,0.00010411106,0.000028115812,0.0000045766446,0.00032261675,0.010096806,0.9861106,0.0005229165,0.00011861316,0.0026256004],"study_design_scores_gemma":[0.00036683035,0.000023985107,0.00012769768,0.00013293959,0.00001587918,0.000004263212,0.00018030874,0.00018616284,0.99773824,0.0009454548,0.000047033864,0.0002311766],"about_ca_topic_score_codex":0.000015151504,"about_ca_topic_score_gemma":0.00003650515,"teacher_disagreement_score":0.011627635,"about_ca_system_score_codex":0.000053337353,"about_ca_system_score_gemma":0.000022315842,"threshold_uncertainty_score":0.99999267},"labels":[],"label_agreement":null},{"id":"W4412530417","doi":"10.1371/journal.pcbi.1013224","title":"Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates","year":2025,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Spiking neural network; Artificial intelligence; Artificial neural network; Computer science","score_opus":0.026529463361160226,"score_gpt":0.31540291022993217,"score_spread":0.2888734468687719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412530417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6510541,0.00069708546,0.34749147,0.000024170948,0.00029073632,0.00021947271,0.0000025912504,0.0001243714,0.00009602651],"genre_scores_gemma":[0.9962088,0.000007777312,0.0033341262,0.0000970395,0.00023847278,0.000013409212,0.000055208497,0.000023735833,0.000021429752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987034,0.00008570217,0.00047473187,0.0003111532,0.000071380295,0.00035359198],"domain_scores_gemma":[0.99861825,0.001011464,0.00013053475,0.00009978453,0.00008810559,0.000051880637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016205985,0.00023826642,0.00045521997,0.00011166191,0.00020252893,0.000045013137,0.0001418637,0.00012663653,0.0000059407903],"category_scores_gemma":[0.000056750287,0.00022078522,0.00005559841,0.00026228998,0.0001547186,0.00012367441,0.000083391984,0.0003856034,3.095543e-7],"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":[0.00096145476,0.000022029246,0.013916034,0.00006023799,0.000069099224,0.0000033019853,0.00006361075,0.9743845,0.003572839,0.00096842914,0.000005158115,0.005973323],"study_design_scores_gemma":[0.003796376,0.00023322475,0.0046191886,0.00011605262,0.00002819585,0.0000054243847,0.000015878326,0.98754597,0.0018508128,0.0015711534,0.000011693626,0.0002060065],"about_ca_topic_score_codex":0.000001406211,"about_ca_topic_score_gemma":0.0000064911997,"teacher_disagreement_score":0.3451547,"about_ca_system_score_codex":0.00002800609,"about_ca_system_score_gemma":0.000016419164,"threshold_uncertainty_score":0.900336},"labels":[],"label_agreement":null},{"id":"W4412803247","doi":"10.1109/pn66844.2025.11097191","title":"Optoelectronic InGaN Neuromorphic Synapse for Artificial Intelligence Applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Neuromorphic engineering; Synapse; Materials science; Computer science; Optoelectronics; Artificial neural network; Artificial intelligence; Computer architecture; Nanotechnology; Neuroscience; Biology","score_opus":0.030100977546364562,"score_gpt":0.2783287541877946,"score_spread":0.24822777664143006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412803247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03635825,0.000114633396,0.96127075,0.00012541645,0.00011087388,0.0003115582,0.0000014296414,0.00037423417,0.0013328601],"genre_scores_gemma":[0.997249,0.0000147938445,0.002240552,0.000112324946,0.00005831839,0.000110767396,0.0000041354438,0.000010296295,0.00019978744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952567,0.0000047205667,0.00014004718,0.00012590915,0.000025716681,0.00017796972],"domain_scores_gemma":[0.99969935,0.00012458289,0.000009471865,0.0001244421,0.000019949906,0.000022219892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004657827,0.000075100244,0.00007585324,0.00005230581,0.000096628355,0.000015260639,0.00010860611,0.000027302902,0.000010086525],"category_scores_gemma":[0.000019913137,0.000077217876,0.00003218206,0.00022146633,0.000014712516,0.00004749394,0.000017818762,0.00010865407,0.000011847229],"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":[0.00001446213,0.000027686367,0.000006550921,0.00013545236,0.000024060128,9.643536e-7,0.000031758234,0.20615685,0.16407155,0.41379473,0.00021614859,0.21551982],"study_design_scores_gemma":[0.00003705445,0.000036740155,0.000010446514,0.000014691989,0.000013261259,0.0000030841015,0.00003386516,0.21230073,0.7232241,0.056562316,0.0076109893,0.00015273537],"about_ca_topic_score_codex":3.1181116e-7,"about_ca_topic_score_gemma":0.0000036654496,"teacher_disagreement_score":0.96089077,"about_ca_system_score_codex":0.000023850309,"about_ca_system_score_gemma":0.0000120751965,"threshold_uncertainty_score":0.31488535},"labels":[],"label_agreement":null},{"id":"W4412945717","doi":"10.21203/rs.3.rs-7265912/v1","title":"Defect-Aware Extreme Device Scaling Limits of 2D Memristive Technologies","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Memristor; Miniaturization; Scaling; Computer science; Leverage (statistics); Characterization (materials science); Probabilistic logic; Electronic engineering; Nanotechnology; Materials science; Artificial intelligence; Engineering; Mathematics","score_opus":0.1481189052549798,"score_gpt":0.3999361507291881,"score_spread":0.25181724547420825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412945717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8738121,0.042310987,0.0481696,0.00083925517,0.0019710497,0.0039103236,0.0006081049,0.007235227,0.021143384],"genre_scores_gemma":[0.9970994,0.001223658,0.0012502614,0.000009244296,0.00007286153,0.00009243453,0.00003350438,0.000041519485,0.00017711597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976383,0.0001618146,0.00040900728,0.00055010896,0.00058105943,0.0006597287],"domain_scores_gemma":[0.9975827,0.0010219831,0.00007136025,0.0007335082,0.0005225348,0.00006796543],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006660922,0.0003213849,0.0005058106,0.0006623739,0.0001827154,0.000042975833,0.00074839394,0.00042592382,0.000014699584],"category_scores_gemma":[0.00087372,0.00032599212,0.000217793,0.0008112172,0.00016524189,0.000077521174,0.0013486849,0.0027057282,0.00001592773],"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":[0.00016216243,0.00015389334,0.0019370586,0.029819854,0.0006441748,0.00031743,0.0015256196,0.6917013,0.029803423,0.0014708379,0.0043455237,0.23811872],"study_design_scores_gemma":[0.0008103255,0.00024985036,0.0014277099,0.023536567,0.00010098414,0.000015429325,0.0061543104,0.11214435,0.8286516,0.011800822,0.013417247,0.0016907783],"about_ca_topic_score_codex":0.000015120166,"about_ca_topic_score_gemma":0.000014982129,"teacher_disagreement_score":0.7988482,"about_ca_system_score_codex":0.0002371848,"about_ca_system_score_gemma":0.000129916,"threshold_uncertainty_score":0.99991924},"labels":[],"label_agreement":null},{"id":"W4413074920","doi":"10.1109/tnse.2025.3589594","title":"Intent-Driven Cognitive xDFC Bridge in Endogenous Intelligent IIoT: A Systematic Review and S$^{2}$Croft Architecture With Bayesian-CRO-Fuzzy Synergy","year":2025,"lang":"en","type":"review","venue":"IEEE Transactions on Network Science and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Bridge (graph theory); Bayesian probability; Fuzzy logic; Computer science; Cognitive architecture; Architecture; Fuzzy cognitive map; Fuzzy control system; Artificial intelligence; Cognition; Neuroscience; Neuro-fuzzy; Biology; Geography","score_opus":0.021383090031484427,"score_gpt":0.2507659565607459,"score_spread":0.2293828665292615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413074920","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012700212,0.6969289,0.30148417,0.000004398686,0.00024413307,0.0011099424,0.000011678761,0.00014487152,0.00005920458],"genre_scores_gemma":[0.011778536,0.9873528,0.0004184978,0.00005838063,0.000043387623,0.00026692473,0.0000022383201,0.000052095387,0.000027122964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"systematic_review","domain_scores_codex":[0.99795747,0.000050027887,0.00059093913,0.0005539337,0.0002386018,0.0006090503],"domain_scores_gemma":[0.99891573,0.00049746374,0.00008449827,0.00026253986,0.000063105974,0.00017663259],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036964315,0.0005685127,0.0015855471,0.0004830817,0.00020606414,0.000078964746,0.00027148702,0.0001234779,0.0000023013952],"category_scores_gemma":[0.000043149743,0.0004561783,0.00012018038,0.0018378019,0.00012506952,0.00016519241,0.000010140344,0.0009019994,0.0000016732034],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035924697,0.0000129205755,8.0592166e-8,0.42252284,0.00010993948,0.00005429063,0.00011161495,0.447856,0.000008574037,0.000012518939,0.000003300058,0.12930433],"study_design_scores_gemma":[0.0001869343,0.00012564124,7.0852775e-7,0.97802895,0.0017131697,0.0008294269,0.00003032944,0.016760815,0.00007821235,0.0000069423986,0.0013163507,0.00092249556],"about_ca_topic_score_codex":0.0000033613705,"about_ca_topic_score_gemma":0.000014137572,"teacher_disagreement_score":0.55550617,"about_ca_system_score_codex":0.00016855974,"about_ca_system_score_gemma":0.00010823046,"threshold_uncertainty_score":0.999789},"labels":[],"label_agreement":null},{"id":"W4413101999","doi":"10.1002/advs.202502291","title":"Brain‐Inspired Polymer Dendrite Networks for Morphology‐Dependent Computing Hardware","year":2025,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"European Research Council; Région Hauts-de-France; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Context (archaeology); Computer science; Dendrite (mathematics); Process (computing); Nanotechnology; Nonlinear system; Materials science; Distributed computing; Topology (electrical circuits); Electrical engineering; Physics; Mathematics; Geology; Engineering","score_opus":0.011297624398123807,"score_gpt":0.27304036367124757,"score_spread":0.26174273927312375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413101999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31311858,0.0011562532,0.68188226,0.00016575541,0.0018175582,0.00029399057,0.0000043199434,0.00046607622,0.0010952309],"genre_scores_gemma":[0.99041885,0.000016373233,0.007979823,0.0008755393,0.00008967993,0.000016540514,0.0000034681957,0.000019670088,0.0005800332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984189,0.000013352352,0.00026978282,0.00047017567,0.00015661608,0.00067112496],"domain_scores_gemma":[0.99914765,0.0003074072,0.00004915862,0.0003120817,0.000084374566,0.000099338984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029744193,0.00019507746,0.00021414968,0.00015941983,0.00056272704,0.000052071053,0.0004995123,0.000056415884,0.0000043124314],"category_scores_gemma":[0.00016082068,0.00020403542,0.00006335232,0.0008014646,0.00021402544,0.0004081307,0.0001446568,0.00022397167,0.0000046385635],"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":[0.000013157947,0.0000068389495,0.00009621374,0.000027941156,0.0000060057505,0.0000068999607,0.000052511514,0.68070346,0.25580436,0.0014135902,0.00011592796,0.061753083],"study_design_scores_gemma":[0.00088834524,0.00005581687,0.00088141323,0.00015929586,0.000012146215,0.000021990862,0.00011670008,0.5467024,0.4476726,0.0007375591,0.002336853,0.00041488078],"about_ca_topic_score_codex":9.836581e-7,"about_ca_topic_score_gemma":0.0000031526713,"teacher_disagreement_score":0.6773003,"about_ca_system_score_codex":0.000102183025,"about_ca_system_score_gemma":0.000039829938,"threshold_uncertainty_score":0.83203226},"labels":[],"label_agreement":null},{"id":"W4413114452","doi":"10.1016/j.mseb.2025.118683","title":"N-doped HfOx-based memristors for high-density neuromorphic systems: Crystallization and conductive filament growth","year":2025,"lang":"en","type":"article","venue":"Materials Science and Engineering B","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Crystallization; Electrical conductor; Doping; Materials science; Protein filament; Memristor; Neuromorphic engineering; Nanotechnology; Optoelectronics; Chemical engineering; Composite material; Computer science; Electronic engineering; Artificial intelligence; Engineering; Artificial neural network","score_opus":0.014771572517425337,"score_gpt":0.20822561027994396,"score_spread":0.19345403776251863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413114452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9320798,0.00008525146,0.066284835,0.00003211549,0.0010747425,0.00024800556,0.000010876125,0.00017269707,0.000011670008],"genre_scores_gemma":[0.99883705,0.000015524081,0.0010291827,0.00003094338,0.00003460601,0.000025175586,0.0000040653863,0.000011267101,0.000012206447],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993336,0.000007856619,0.00015123161,0.00021166133,0.00009489224,0.00020072787],"domain_scores_gemma":[0.9997046,0.000059945087,0.000021893677,0.00008520601,0.00007881551,0.000049561342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028165965,0.0001242427,0.00016687007,0.00012802663,0.00015860546,0.00012217743,0.00007492546,0.000030558014,0.0000012735363],"category_scores_gemma":[0.00011458249,0.00012219611,0.00000767557,0.00020875463,0.00007118254,0.00019486374,0.000032855667,0.000038316583,3.0195997e-7],"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":[0.0000069270372,0.000002485995,0.000007797097,0.0003624225,0.000003519536,0.0000013654846,0.00003316572,0.075670995,0.9220509,0.0017952261,0.000023361608,0.000041837695],"study_design_scores_gemma":[0.0002755349,0.000039328817,0.0010562162,0.000119185504,0.000013680918,0.000004229976,0.00002505643,0.07582469,0.9222346,0.00011111368,0.0001353559,0.00016098787],"about_ca_topic_score_codex":0.000011583736,"about_ca_topic_score_gemma":3.908281e-7,"teacher_disagreement_score":0.06675722,"about_ca_system_score_codex":0.00005957856,"about_ca_system_score_gemma":0.00002077727,"threshold_uncertainty_score":0.4983013},"labels":[],"label_agreement":null},{"id":"W4413147428","doi":"10.1109/cvpr52734.2025.02271","title":"Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Nova; National Natural Science Foundation of China","keywords":"Computer science; Inference; Scale (ratio); Artificial intelligence; Machine learning","score_opus":0.019137341364813286,"score_gpt":0.2619413902339544,"score_spread":0.24280404886914111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413147428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8602084,0.000030636522,0.13695133,0.00004484692,0.000069961636,0.00023653617,0.000003156556,0.000109211745,0.0023459233],"genre_scores_gemma":[0.99687845,0.000026559916,0.0027640597,0.000078635356,0.000010004169,0.00003530021,0.0000057348134,0.0000043054874,0.0001969431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965864,0.0000030268532,0.00010241924,0.00010243207,0.000023174616,0.000110280795],"domain_scores_gemma":[0.99980116,0.000073617695,0.0000082770875,0.00008197966,0.000015468386,0.000019524101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000036697158,0.0000633441,0.00008607853,0.00004674192,0.00006063925,0.000007645409,0.000050020324,0.0000293481,0.000010846912],"category_scores_gemma":[0.0000041058506,0.000062084866,0.000008452585,0.00012300312,0.000027277436,0.000092302005,0.000028151046,0.00007005267,0.0000034162047],"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":[0.00033277352,0.00037760602,0.089773305,0.0059160586,0.00009715549,0.000003898308,0.0013100894,0.3483329,0.17997867,0.14142564,0.004680256,0.22777164],"study_design_scores_gemma":[0.0022267012,0.00009539321,0.09868778,0.00023846768,0.000013135004,0.0000016735943,0.0002125802,0.5605162,0.3177851,0.010535614,0.009163843,0.0005235048],"about_ca_topic_score_codex":0.0000022150323,"about_ca_topic_score_gemma":0.0000091251695,"teacher_disagreement_score":0.22724813,"about_ca_system_score_codex":0.000018530322,"about_ca_system_score_gemma":0.0000047552976,"threshold_uncertainty_score":0.25317475},"labels":[],"label_agreement":null},{"id":"W4413179504","doi":"10.1109/isscs66034.2025.11105678","title":"Low-Cost Spiking Networks on FPGA for Event-Based Gesture Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Field-programmable gate array; Gesture; Event (particle physics); Embedded system; Real-time computing; Artificial intelligence","score_opus":0.008922363065813628,"score_gpt":0.24773721478151037,"score_spread":0.23881485171569675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413179504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06505898,0.000040103616,0.93248963,0.00003538975,0.0007821262,0.00024475995,5.820776e-7,0.00037031167,0.0009780915],"genre_scores_gemma":[0.9986811,0.000002485522,0.0005323442,0.00037398594,0.0001482239,0.000027636672,0.0000036655094,0.0000142141025,0.00021633906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956554,0.000006221371,0.000103171435,0.00012034485,0.000034410004,0.00017032174],"domain_scores_gemma":[0.9997292,0.00012319164,0.000012090938,0.00009867102,0.000014545537,0.000022310107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052888452,0.00009692881,0.000082422455,0.000049178085,0.00009784592,0.00001481875,0.00005070225,0.00006066086,0.0000052781566],"category_scores_gemma":[0.00002009419,0.00009139372,0.000051014056,0.00013565444,0.0000042713277,0.00004089903,0.000007313945,0.00014956595,0.0000024890774],"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":[0.000021123604,0.0000051211314,0.00001064041,0.00003826015,0.000004383927,7.309093e-7,0.0000026169018,0.88563186,0.006478222,0.000080402104,0.00012592551,0.107600704],"study_design_scores_gemma":[0.00025980172,0.000018999259,0.0001541259,0.00007930785,0.0000053332237,2.9177292e-7,0.000006079095,0.63983434,0.35738117,0.00008143129,0.0020970032,0.00008207763],"about_ca_topic_score_codex":3.8770042e-7,"about_ca_topic_score_gemma":0.000010878846,"teacher_disagreement_score":0.9336221,"about_ca_system_score_codex":0.000045673296,"about_ca_system_score_gemma":0.000003917272,"threshold_uncertainty_score":0.37269276},"labels":[],"label_agreement":null},{"id":"W4413213705","doi":"10.1063/5.0275293","title":"Hardware implementation of tunable fractional-order capacitors by morphogenesis of conducting polymer dendrites","year":2025,"lang":"en","type":"article","venue":"APL Electronic Devices","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"European Research Council; Agence Nationale de la Recherche","keywords":"Electronics; Capacitor; Materials science; Topology (electrical circuits); Nanotechnology; Computer science; Voltage; Electrical engineering; Optoelectronics; Engineering","score_opus":0.009575432937371313,"score_gpt":0.2696780375061167,"score_spread":0.2601026045687454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413213705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98876506,0.0072293878,0.003272863,0.000040495946,0.00017132347,0.000090795846,0.000011286306,0.00006490122,0.00035390892],"genre_scores_gemma":[0.999412,0.00009532482,0.00016259501,0.000044718177,0.00002824379,0.000009301887,0.000020137406,0.000013574208,0.0002140858],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923575,0.000018624669,0.0002638834,0.00012993158,0.000098686825,0.00025309811],"domain_scores_gemma":[0.9996119,0.00011974123,0.00008988051,0.000097130964,0.000063932006,0.000017414404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078234814,0.00010965197,0.00016915453,0.00008476524,0.000062461215,0.0000066483562,0.00009239492,0.00003830076,0.00010563671],"category_scores_gemma":[0.000012196034,0.00011702372,0.000042203945,0.0003263052,0.000019830915,0.00019261039,0.000016534797,0.00011090687,0.0000015607002],"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":[0.000007708737,0.000016444425,0.0032640328,0.00026343824,0.00017551263,5.040693e-7,0.00012361545,0.0037558759,0.98199254,0.0011963822,0.00061302417,0.0085909255],"study_design_scores_gemma":[0.00020402958,0.000025578192,0.00019640221,0.000037291855,0.000035083263,0.0000025296665,0.000853938,0.0005851979,0.9956015,0.00017604482,0.0021867387,0.00009569555],"about_ca_topic_score_codex":0.00012288943,"about_ca_topic_score_gemma":0.0000711656,"teacher_disagreement_score":0.013608935,"about_ca_system_score_codex":0.000056060362,"about_ca_system_score_gemma":0.000052148003,"threshold_uncertainty_score":0.47720888},"labels":[],"label_agreement":null},{"id":"W4413267486","doi":"10.1109/tsipn.2025.3592314","title":"Resilient Output Containment of Heterogeneous Multi-Agent Systems Against Byzantine Attacks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Information Processing over Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Containment (computer programming); Byzantine architecture; Computer science; Byzantine fault tolerance; Computer security; Distributed computing; History; Fault tolerance; Ancient history","score_opus":0.010443576903315516,"score_gpt":0.23012889435113262,"score_spread":0.2196853174478171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413267486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06900585,0.00060273457,0.9292279,0.000008112956,0.00043937258,0.0001985702,0.000006966672,0.00012442886,0.0003860836],"genre_scores_gemma":[0.9993344,0.00019731808,0.00015424028,0.00017691434,0.000019552903,0.00001806358,0.0000055598553,0.000007900766,0.000086057975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999149,0.00001547193,0.0004463745,0.000094778436,0.00012324483,0.00017109246],"domain_scores_gemma":[0.99968195,0.000037155016,0.00008862552,0.000082975246,0.00006058155,0.00004871669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090211244,0.0001512949,0.00018295765,0.00012660191,0.00017256383,0.00005802544,0.000059735692,0.00007607043,0.000004215487],"category_scores_gemma":[0.0000010693474,0.00014067328,0.00004606026,0.00018151606,0.00003351254,0.0005659993,0.0000019347194,0.00019388573,0.0000017131309],"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":[0.000040852407,0.000020721822,0.00000768937,0.0002558587,0.00002595214,6.224538e-7,0.00013368062,0.916825,0.00036134184,0.000010826375,0.000034945053,0.0822825],"study_design_scores_gemma":[0.00056471454,0.000044171367,0.00006370972,0.00040008174,0.000021337488,0.0000037383923,0.00008482112,0.97974306,0.017351154,0.0000020083544,0.0015885247,0.00013267182],"about_ca_topic_score_codex":0.0000017199654,"about_ca_topic_score_gemma":6.8166315e-7,"teacher_disagreement_score":0.93032855,"about_ca_system_score_codex":0.00003747422,"about_ca_system_score_gemma":0.000014196652,"threshold_uncertainty_score":0.573649},"labels":[],"label_agreement":null},{"id":"W4413278482","doi":"10.1109/icfpt64416.2024.11113460","title":"GraphNoC: Graph Neural Networks for Application-Specific FPGA NoC Performance Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Field-programmable gate array; Computer science; Artificial neural network; Computer architecture; Graph; Parallel computing; Embedded system; Artificial intelligence; Theoretical computer science","score_opus":0.01218642828372513,"score_gpt":0.21616486055003817,"score_spread":0.20397843226631304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413278482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1250158,0.0018653309,0.8693342,0.000026820118,0.0011664098,0.00029364406,0.0000053913523,0.00152768,0.00076473766],"genre_scores_gemma":[0.9978139,0.000379883,0.0009953241,0.000031196418,0.0005078406,0.00009751049,0.000027352162,0.000033205313,0.00011380145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936086,0.0000035123053,0.00016494925,0.00019759426,0.000059962094,0.0002131159],"domain_scores_gemma":[0.999724,0.00006831593,0.000009139201,0.000137493,0.000017913559,0.00004318241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006223539,0.00012141565,0.000084452826,0.000077534416,0.00010744286,0.00004418543,0.000083215564,0.000050720144,0.000011198843],"category_scores_gemma":[0.0000012138299,0.00011199757,0.000069386304,0.00030723802,0.000016862308,0.00025564767,0.000011540515,0.00016158792,0.000008944588],"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":[0.000007744393,0.0000033498015,0.00007901751,0.0001036522,0.000011848052,7.4125137e-7,0.000025872403,0.83296233,0.0028073916,0.0013908445,0.0017293971,0.16087784],"study_design_scores_gemma":[0.000083672574,0.00003235395,0.00033165244,0.00002062642,0.000006398017,0.000010330241,0.000009063192,0.9753323,0.005787242,0.00037205065,0.017895227,0.000119041404],"about_ca_topic_score_codex":2.2527375e-7,"about_ca_topic_score_gemma":6.1544984e-7,"teacher_disagreement_score":0.8727981,"about_ca_system_score_codex":0.00001827538,"about_ca_system_score_gemma":0.0000016179863,"threshold_uncertainty_score":0.45671284},"labels":[],"label_agreement":null},{"id":"W4413466396","doi":"10.1016/j.neunet.2025.108024","title":"MSFI: Multi-timescale spatio-temporal features integration in spiking neural networks","year":2025,"lang":"en","type":"review","venue":"Neural Networks","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; National Key Research and Development Program of China; China Association for Science and Technology","keywords":"Computer science; Spiking neural network; Artificial neural network; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.027760929850200317,"score_gpt":0.29589573751661097,"score_spread":0.2681348076664106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413466396","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009957365,0.95596707,0.03810935,0.000011723193,0.003747408,0.0011330748,0.000009082997,0.000692069,0.00023062891],"genre_scores_gemma":[0.021777721,0.97415626,0.00075601035,0.00013351043,0.0017030884,0.0001424649,0.0007482492,0.00019057583,0.00039212278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99659556,0.0002836639,0.0011647313,0.00078148127,0.00021206027,0.0009625242],"domain_scores_gemma":[0.9984281,0.00055739994,0.00027850815,0.00053677574,0.00004461973,0.00015456416],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00022867759,0.0010515946,0.0019040687,0.00038201755,0.0001816661,0.0001538698,0.00058807194,0.0008550379,0.000021533158],"category_scores_gemma":[0.00006830396,0.00095293985,0.00058411504,0.001225878,0.000057363883,0.00034287246,0.00021090361,0.0032237882,0.0000044688727],"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":[0.0000067497886,0.000010074102,0.000014581198,0.0010806136,0.000014734328,0.000048256894,0.00001106191,0.4734469,1.923441e-7,0.0000073870356,0.0003725082,0.5249869],"study_design_scores_gemma":[0.0003077605,0.000035808975,0.000032724263,0.009180548,0.00014358279,0.000052111773,0.00000741586,0.95270973,0.000002648407,0.000008807384,0.036719892,0.0007989766],"about_ca_topic_score_codex":0.000015540732,"about_ca_topic_score_gemma":0.00025399157,"teacher_disagreement_score":0.524188,"about_ca_system_score_codex":0.00021940237,"about_ca_system_score_gemma":0.000026557329,"threshold_uncertainty_score":0.99929214},"labels":[],"label_agreement":null},{"id":"W4413799283","doi":"10.1002/adfm.202514949","title":"Dual‐Mode Optoelectronic Neuromorphic Memory for Complex Edge Detection and Recognition","year":2025,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Neuromorphic engineering; Materials science; Dual mode; Enhanced Data Rates for GSM Evolution; Optoelectronics; Mode (computer interface); Dual (grammatical number); Artificial intelligence; Electronic engineering; Artificial neural network; Computer science; Human–computer interaction","score_opus":0.032291691782761894,"score_gpt":0.24931431558499104,"score_spread":0.21702262380222914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413799283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8958525,0.00016365755,0.101026066,0.000044215634,0.0018923257,0.0003980082,0.00007284575,0.00034705628,0.00020333183],"genre_scores_gemma":[0.99800813,0.000054626264,0.0011634722,0.00014514127,0.0002359834,0.00013402611,0.000086361746,0.000027285303,0.00014498566],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919295,0.00002750097,0.00024028578,0.0002435457,0.000061220235,0.00023447203],"domain_scores_gemma":[0.99959505,0.0001701325,0.000042557396,0.000098692646,0.00005915336,0.00003444379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009289005,0.00016416125,0.00019325723,0.00009404383,0.0001846883,0.000029006593,0.000034029854,0.0000591893,0.00006459507],"category_scores_gemma":[0.000057083595,0.00018064969,0.00003329494,0.00011754166,0.0000279683,0.00016555023,0.000022615628,0.00008496559,0.000009948478],"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":[0.0001755117,0.000007234827,6.920072e-7,0.00012449441,0.000019180217,7.4064263e-7,0.000006316231,0.024375582,0.9414879,0.00014594987,0.000047627058,0.03360876],"study_design_scores_gemma":[0.00084048155,0.0001005912,0.00077243376,0.000048449336,0.000028988936,0.000026554064,0.000013852508,0.004740631,0.9806878,0.011496395,0.0010468478,0.00019699133],"about_ca_topic_score_codex":0.0000010040129,"about_ca_topic_score_gemma":0.000004054306,"teacher_disagreement_score":0.10215562,"about_ca_system_score_codex":0.000059825852,"about_ca_system_score_gemma":0.000011422209,"threshold_uncertainty_score":0.73666805},"labels":[],"label_agreement":null},{"id":"W4413802164","doi":"10.1088/2634-4386/ae006b","title":"Unsupervised sparse coding-based spiking neural network for real-time spike sorting","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"European Research Council; Alliance de recherche numérique du Canada; Ministère de l'Économie, de l’Innovation et des Exportations du Québec","keywords":"Spike sorting; Neuromorphic engineering; Computer science; Spike (software development); Spiking neural network; Neural coding; Neural decoding; Decoding methods; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Perceptron; Sorting; Algorithm","score_opus":0.018749368157921224,"score_gpt":0.227288867786439,"score_spread":0.20853949962851778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413802164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8491814,0.00027046207,0.14703232,0.00007899851,0.0011714828,0.00033810656,0.000004064929,0.0016651917,0.0002579418],"genre_scores_gemma":[0.99013865,0.000021428157,0.00909349,0.00015157579,0.0004498627,0.000011825708,0.000014520404,0.0000865487,0.000032109067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812204,0.000025984402,0.0005070868,0.0004488322,0.00012295684,0.00077313255],"domain_scores_gemma":[0.99872905,0.00077071704,0.00006587934,0.00025795127,0.00004670053,0.00012969026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003273666,0.00040101112,0.00045953912,0.00017438445,0.0003502187,0.000104351326,0.00019905237,0.00010582988,0.000003709112],"category_scores_gemma":[0.00013142555,0.00046677992,0.000121838275,0.00045848626,0.000030298335,0.0000988108,0.00009527073,0.000402431,0.0000020942016],"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":[0.000015634369,0.000007002485,0.00038527805,0.00040249812,0.000021701964,0.000019659017,0.000037805992,0.9395185,0.052358504,0.0002821943,0.00014135907,0.006809858],"study_design_scores_gemma":[0.0007167415,0.0000527743,0.001575174,0.00047859547,0.00003983742,0.000018965133,0.0000074270642,0.9920814,0.0039600297,0.00006954121,0.00060692854,0.00039259347],"about_ca_topic_score_codex":0.000003922596,"about_ca_topic_score_gemma":9.376356e-7,"teacher_disagreement_score":0.1409572,"about_ca_system_score_codex":0.000045535973,"about_ca_system_score_gemma":0.00001807171,"threshold_uncertainty_score":0.9997784},"labels":[],"label_agreement":null},{"id":"W4413811100","doi":"10.1039/d5tc02381e","title":"Application and challenges of organic electrochemical transistors in neuromorphic computing: bionic synapse and multi-mode integration","year":2025,"lang":"en","type":"article","venue":"Journal of Materials Chemistry C","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"Beijing Municipal Natural Science Foundation; Beijing Institute of Graphic Communication; National Research Foundation of Korea; Ministry of Education; Beijing Municipal Education Commission; Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Neuromorphic engineering; Materials science; Synapse; Transistor; Mode (computer interface); Electrochemistry; Nanotechnology; Computer architecture; Neuroscience; Computer science; Artificial intelligence; Artificial neural network; Human–computer interaction; Engineering; Electrical engineering; Physics; Electrode; Psychology; Voltage","score_opus":0.017271228873303512,"score_gpt":0.235079885225079,"score_spread":0.2178086563517755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413811100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99097395,0.0016609627,0.007171709,0.00008660888,0.000046638637,0.000039271275,7.795753e-7,0.000012650032,0.00000744733],"genre_scores_gemma":[0.99844396,0.0010922238,0.00041848255,0.0000049751516,0.000030626186,5.2901726e-7,0.0000012515821,0.000006628428,0.0000013291602],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994753,0.000014816415,0.00031872359,0.00007688351,0.000040774536,0.000073511656],"domain_scores_gemma":[0.9997557,0.00004525041,0.00009820014,0.000049610444,0.000028451706,0.000022817545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012106544,0.00008021436,0.000205087,0.000036165904,0.0000130480075,0.0000070561046,0.000052680945,0.00006028539,0.000001815212],"category_scores_gemma":[0.000037216527,0.000076636046,0.0000143652105,0.00005836896,0.000024052051,0.00004570434,0.0000118719,0.000115385345,4.8528484e-8],"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":[0.000027888109,0.00001577728,0.000026921369,0.00041811337,0.000008041812,0.0000018454845,0.00008356673,0.00021709755,0.99836344,0.000010464697,8.9349976e-7,0.0008259605],"study_design_scores_gemma":[0.000355612,0.000015436348,0.000605854,0.00016732329,0.000012074332,0.000059242007,0.000044037788,0.001715463,0.99687743,0.00008824137,0.0000069536113,0.00005233954],"about_ca_topic_score_codex":3.58301e-7,"about_ca_topic_score_gemma":7.8995987e-7,"teacher_disagreement_score":0.007470022,"about_ca_system_score_codex":0.000027605669,"about_ca_system_score_gemma":0.000010645123,"threshold_uncertainty_score":0.31251273},"labels":[],"label_agreement":null},{"id":"W4414056929","doi":"10.1016/j.apsusc.2025.164564","title":"High-performance reconfigurable synaptic transistor enabled by coupled interface and ferroelectricity based on SnS2/dual-Al2O3/Hf0.5Zr0.5O2","year":2025,"lang":"en","type":"article","venue":"Applied Surface Science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Beijing Municipal Science and Technology Commission; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; Canadian Anesthesiologists' Society","keywords":"Neuromorphic engineering; Ferroelectricity; Transistor; Conductance; Synaptic weight; Non-volatile memory; MNIST database","score_opus":0.006869843206015595,"score_gpt":0.21129465059265942,"score_spread":0.20442480738664381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414056929","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97330284,0.00023755069,0.02083742,0.00010910588,0.00033780184,0.00036533253,0.0000047470267,0.00038880977,0.0044163717],"genre_scores_gemma":[0.9982429,0.00005477464,0.0011284391,0.00024161984,0.000010511872,0.000014547297,0.0000024694568,0.000022647726,0.00028209592],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981401,0.000019639723,0.0002935372,0.0006074126,0.00029086843,0.0006484597],"domain_scores_gemma":[0.99915004,0.00026047393,0.0000510162,0.0003537519,0.000049034326,0.00013567753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005587269,0.00029720983,0.00032886417,0.00014035837,0.0004878164,0.000086542284,0.00034807378,0.00007751502,0.000024858824],"category_scores_gemma":[0.00004432024,0.00029149238,0.000026017908,0.0012408209,0.00025662262,0.00024830698,0.000028899676,0.0004034355,0.000020859852],"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":[0.000051084502,0.000015995052,0.000015557816,0.0000647082,0.000006274693,0.0000011123828,0.00003903357,0.421459,0.57663226,0.00013191011,0.00011975651,0.0014633095],"study_design_scores_gemma":[0.00041736793,0.00006463456,0.00015607015,0.000055999455,0.000009763598,0.0000013380463,0.000022130638,0.4409366,0.5578574,0.000050647042,0.0002075476,0.00022048502],"about_ca_topic_score_codex":0.0000100776,"about_ca_topic_score_gemma":0.0000032427922,"teacher_disagreement_score":0.024940034,"about_ca_system_score_codex":0.00020007657,"about_ca_system_score_gemma":0.000090368725,"threshold_uncertainty_score":0.99995375},"labels":[],"label_agreement":null},{"id":"W4414129225","doi":"10.1007/978-3-032-04558-4_12","title":"Efficient Learning in Spiking Neural Networks - Introducing Feedback Alignment to the Reinforced Liquid State Machine","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"MNIST database; Neuromorphic engineering; Feed forward; Reservoir computing; Spiking neural network; Key (lock); State (computer science); Artificial neural network","score_opus":0.00723575068993434,"score_gpt":0.21914052133414658,"score_spread":0.21190477064421223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414129225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012429709,0.0005178447,0.9835911,0.00033050193,0.0020021347,0.00048448512,0.0000012873296,0.00020127717,0.00044167886],"genre_scores_gemma":[0.9895134,0.000029917383,0.008588163,0.00097015913,0.0005435249,0.000008555253,0.0000049863243,0.00004870641,0.00029256064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750644,0.000034265737,0.00053625135,0.0007825745,0.00040612434,0.000734348],"domain_scores_gemma":[0.9987863,0.0004396728,0.00010194806,0.0005256448,0.00004825395,0.00009822315],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077038567,0.00046584193,0.00042257152,0.0004815385,0.00026499457,0.00014967479,0.00085162115,0.0001192293,0.000006962865],"category_scores_gemma":[0.00010218041,0.00038243033,0.00007804583,0.0006497153,0.00011520804,0.00007568598,0.0007402262,0.0016291796,0.000005016144],"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":[0.000019532436,0.0000014517641,0.0000046919517,0.000030021149,0.000003974086,0.000027191805,0.0004967094,0.83836484,0.00040230475,0.00008675019,0.0000031589648,0.1605594],"study_design_scores_gemma":[0.00015079611,0.00010710493,0.000018400751,0.00062959304,0.0000046739533,0.00001337206,5.7633963e-7,0.99605066,0.002100257,0.00013309803,0.00040807354,0.00038338563],"about_ca_topic_score_codex":0.000010894498,"about_ca_topic_score_gemma":0.000044711887,"teacher_disagreement_score":0.97708374,"about_ca_system_score_codex":0.00040412368,"about_ca_system_score_gemma":0.000047808313,"threshold_uncertainty_score":0.9998628},"labels":[],"label_agreement":null},{"id":"W4414257900","doi":"10.1016/j.mtnano.2025.100681","title":"Perspectives on CMOS-compatible biomolecular computing","year":2025,"lang":"en","type":"article","venue":"Materials Today Nano","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education and Science of the Russian Federation; Ministry of Science and Higher Education of the Russian Federation; Canadian Meteorological and Oceanographic Society","keywords":"Scalability; Nanoelectronics; Computation; Logic gate; Integrated circuit; Electronic circuit; Signal processing; Synthetic biology; Information processing","score_opus":0.00788787862031617,"score_gpt":0.24542133546494505,"score_spread":0.23753345684462887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414257900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98146003,0.00026481063,0.011338208,0.000052781223,0.0011844313,0.00014296739,0.0000061493874,0.0005094425,0.0050411867],"genre_scores_gemma":[0.9986207,0.000014069069,0.00094937324,0.000105659965,0.00011914428,0.000003716874,0.000005272466,0.000022972197,0.00015907448],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991966,0.000038451424,0.00021569747,0.0002198127,0.00007780173,0.00025162115],"domain_scores_gemma":[0.9996552,0.000054795622,0.000028277216,0.00020801561,0.000023657372,0.000030052746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121406374,0.00016940206,0.00022280292,0.00011332672,0.00012144501,0.000059274334,0.00013854602,0.000052663414,0.000071820585],"category_scores_gemma":[0.000028463437,0.00016471194,0.00003919053,0.00020868165,0.000023695535,0.00005743109,0.000051329014,0.000071033275,0.000065433735],"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":[0.000014348942,0.000015846012,0.000006942806,0.00006414263,0.000019481606,0.000015294463,0.00023712561,0.00844409,0.9857299,0.003798445,0.0002529463,0.0014014902],"study_design_scores_gemma":[0.00022588154,0.000030492662,0.00011898705,0.00013695468,0.0000070189385,0.0000035096307,0.0001451486,0.00076282036,0.99599266,0.0005269129,0.0018842552,0.00016534119],"about_ca_topic_score_codex":0.000003355382,"about_ca_topic_score_gemma":2.6670568e-7,"teacher_disagreement_score":0.017160695,"about_ca_system_score_codex":0.000054811473,"about_ca_system_score_gemma":0.000007915181,"threshold_uncertainty_score":0.67167574},"labels":[],"label_agreement":null},{"id":"W4414377900","doi":"10.1021/acsomega.5c07277","title":"Effect of Reduced Graphene Oxide Film Thickness on a Chemiresistor’s Response to Volatile Organic Compounds and Warfare Agents","year":2025,"lang":"en","type":"article","venue":"ACS Omega","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Chemiresistor; Graphene; Oxide; Coating; Layer (electronics); Dimethyl methylphosphonate; Analyte; Raman spectroscopy","score_opus":0.008130418144831179,"score_gpt":0.25445022868957934,"score_spread":0.24631981054474816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414377900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987072,0.00015470787,0.00015688426,0.00006690288,0.00018881648,0.0001891573,0.000004981068,0.00014491554,0.0003864593],"genre_scores_gemma":[0.99943715,0.0000070953297,0.00010654153,0.00010510518,0.000013835535,0.000011638928,0.000003057689,0.000021181522,0.0002943981],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930173,0.00007071887,0.0001684128,0.00018793788,0.00009689162,0.00017431448],"domain_scores_gemma":[0.9991026,0.00054691866,0.000025732517,0.00024180124,0.000023447734,0.000059488302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023178049,0.00015549781,0.00023646267,0.00013584731,0.000076382414,0.000010752421,0.00012976187,0.00006065469,0.0000030937033],"category_scores_gemma":[0.0002521302,0.00014870345,0.00003495666,0.0003920358,0.000020761427,0.00005244613,0.000067812885,0.00015000126,0.0000041992703],"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":[0.00072457304,0.000007470467,0.00019510214,0.00020255444,0.000024966683,0.000006356111,0.00016058779,0.0021787812,0.99493486,0.000013693215,0.00073168374,0.00081937993],"study_design_scores_gemma":[0.0005370439,0.00016906962,0.0068964534,0.0002477505,0.000019585688,0.0000035673545,0.000016018987,0.0005420147,0.99061525,0.00005116461,0.00076280563,0.00013924824],"about_ca_topic_score_codex":0.0000020923592,"about_ca_topic_score_gemma":0.0000017479149,"teacher_disagreement_score":0.0067013516,"about_ca_system_score_codex":0.000052692936,"about_ca_system_score_gemma":0.000010725906,"threshold_uncertainty_score":0.606395},"labels":[],"label_agreement":null},{"id":"W4414539234","doi":"10.1109/icc52391.2025.11161931","title":"Collaborative Knowledge Sharing-Empowered Effective Semantic Rate Maximization for Two-Tier Semantic-Bit Communication Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Maximization; Overhead (engineering); Task (project management); Enhanced Data Rates for GSM Evolution; Integer programming; Transmission (telecommunications); Edge device; Knowledge sharing; Semantics (computer science)","score_opus":0.007872864892917908,"score_gpt":0.2770716050001818,"score_spread":0.2691987401072639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414539234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050963048,0.0013917229,0.9346549,0.00008224658,0.0005972011,0.001452667,0.000004287771,0.0005602769,0.010293635],"genre_scores_gemma":[0.99557537,0.00007442022,0.0025700028,0.00008390249,0.00006333096,0.00015559907,0.00005287909,0.000034275767,0.0013902003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909645,0.00008095095,0.00026949475,0.00025265833,0.00004008476,0.00026034753],"domain_scores_gemma":[0.99880046,0.0005582265,0.00005328042,0.0003421496,0.00020529852,0.000040571627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027485628,0.00020498956,0.0002556108,0.000108222994,0.00025834228,0.00006180795,0.00020883317,0.000082607796,0.000012118575],"category_scores_gemma":[0.000089882466,0.00020653945,0.000057249672,0.00063066575,0.000033145374,0.00021987045,0.000108188746,0.00018932391,0.000009331265],"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":[0.00008641845,0.000037637572,0.00020200014,0.00022655624,0.00012139801,9.759706e-7,0.00055611733,0.9732641,0.0056973738,0.008902062,0.0012609909,0.009644349],"study_design_scores_gemma":[0.0010177572,0.00003607349,0.00047792646,0.00020901611,0.000048074056,7.302205e-7,0.000112936876,0.95972806,0.03454926,0.0022100473,0.0013798758,0.00023023627],"about_ca_topic_score_codex":0.000002391037,"about_ca_topic_score_gemma":0.00005153199,"teacher_disagreement_score":0.9446123,"about_ca_system_score_codex":0.000098032535,"about_ca_system_score_gemma":0.000015846774,"threshold_uncertainty_score":0.84224343},"labels":[],"label_agreement":null},{"id":"W4414539639","doi":"10.1109/icc52391.2025.11161225","title":"Stochastic Device Scheduling and Power Control in Federated Learning with Energy Harvesting","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Scheduling (production processes); Lyapunov optimization; Wireless; Energy harvesting; Efficient energy use; Power control; Dynamic priority scheduling; Overhead (engineering)","score_opus":0.0062644493042365,"score_gpt":0.21279897885950555,"score_spread":0.20653452955526905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414539639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46846965,0.00020765235,0.5289863,0.000019529047,0.00003149783,0.000034844772,6.3548185e-8,0.00017883327,0.0020716311],"genre_scores_gemma":[0.9978814,0.0000021282835,0.00165746,0.00015845678,0.000009848662,0.0000028553982,5.898703e-7,0.00001304123,0.00027422493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994928,0.000019524925,0.00012694375,0.00013667575,0.0000387874,0.00018526356],"domain_scores_gemma":[0.99970126,0.0001958668,0.000014216264,0.00003819291,0.000019749516,0.000030736155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006951055,0.00010729229,0.00013343304,0.000078277575,0.00009847586,0.000043652326,0.000029668927,0.000031326403,0.000004461199],"category_scores_gemma":[0.000060891347,0.000095683914,0.000007621762,0.00021243034,0.000012654748,0.00012037687,0.000015529662,0.0001995475,6.7198823e-7],"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":[0.000016244563,0.0000025652178,0.0006601377,0.000020475909,0.000010990057,0.000009652648,0.00003871916,0.98075175,0.01332866,0.00048672155,7.507579e-7,0.0046733157],"study_design_scores_gemma":[0.0008115667,0.000029298282,0.00068810664,0.00024119536,0.000006534413,0.000009441052,0.00022859829,0.99394745,0.003744703,0.000057602974,0.00008509785,0.000150398],"about_ca_topic_score_codex":0.000014145935,"about_ca_topic_score_gemma":0.00007281725,"teacher_disagreement_score":0.52941173,"about_ca_system_score_codex":0.000020535885,"about_ca_system_score_gemma":0.000010028829,"threshold_uncertainty_score":0.39018768},"labels":[],"label_agreement":null},{"id":"W4414547521","doi":"10.1002/adom.202502227","title":"Light Intensity‐Controlled Photoconductance Polarity Switching for Neuromorphic and Logic Applications","year":2025,"lang":"en","type":"article","venue":"Advanced Optical Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Western Economic Diversification Canada; British Columbia Knowledge Development Fund; CMC Microsystems; Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; Canada Foundation for Innovation","keywords":"Polarity (international relations); Heterojunction; Photoconductivity; Modulation (music); Neuromorphic engineering; Light intensity; Logic gate; AND gate","score_opus":0.016997352421036963,"score_gpt":0.26359363285991433,"score_spread":0.24659628043887738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414547521","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76073366,0.00039035265,0.2343165,0.00044367815,0.0006053645,0.002467971,0.000013608507,0.00041870418,0.0006101815],"genre_scores_gemma":[0.98422104,0.000040645813,0.014497191,0.00041318272,0.000089338326,0.0006675069,0.000005070988,0.000022142165,0.000043861528],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900764,0.000017823968,0.00035057342,0.0002882011,0.000050214414,0.00028555797],"domain_scores_gemma":[0.9993377,0.00028721258,0.000044042026,0.00020459868,0.00006122001,0.00006524303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014328062,0.00019135617,0.00047481118,0.00005554657,0.00016009109,0.00005772556,0.00011660763,0.00007326106,0.000007636366],"category_scores_gemma":[0.0001877313,0.00017441143,0.000046658377,0.00011546905,0.000025508933,0.00017319614,0.00005931754,0.00012712787,0.000004370972],"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":[0.0001968224,0.000012573016,0.0000034816146,0.00014915335,0.00001828654,0.000002240647,0.000008586294,0.00075989176,0.97447544,0.022288632,0.000005458854,0.0020794352],"study_design_scores_gemma":[0.002030591,0.000045536737,0.0001164276,0.000075701595,0.00004047865,0.000011269698,0.000015973408,0.0008811616,0.95336056,0.04151318,0.0016890039,0.00022011038],"about_ca_topic_score_codex":3.7151773e-7,"about_ca_topic_score_gemma":7.787584e-7,"teacher_disagreement_score":0.22348742,"about_ca_system_score_codex":0.000024129424,"about_ca_system_score_gemma":0.0000075052026,"threshold_uncertainty_score":0.71122915},"labels":[],"label_agreement":null},{"id":"W4414707664","doi":"10.1002/adfm.202520432","title":"Ultrafast Light‐Modulated Sliding Ferroelectric Tunnel Junctions for Synaptic in In‐Memory Computing","year":2025,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Neuromorphic engineering; Ultrashort pulse; Conductance; Femtosecond; Memristor; Synaptic weight; Polarization (electrochemistry); Robustness (evolution)","score_opus":0.011761155351708024,"score_gpt":0.23503015716006959,"score_spread":0.22326900180836157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414707664","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9504359,0.00016406187,0.044712745,0.000072864634,0.0033657514,0.00050191977,0.000009889856,0.00034842157,0.000388478],"genre_scores_gemma":[0.99857384,0.000022845934,0.00079347007,0.00009903854,0.00015537572,0.000088124725,0.000043030446,0.00003747974,0.0001868068],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984593,0.000042804706,0.0005784562,0.00035806166,0.00009379569,0.00046758377],"domain_scores_gemma":[0.9993546,0.00033167136,0.000062618674,0.0001563875,0.000053884232,0.000040850955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024405883,0.00024770875,0.0003715311,0.0004700042,0.00015495636,0.0000338756,0.000108543594,0.00010590288,0.000029831086],"category_scores_gemma":[0.00017274021,0.00027571584,0.0000570393,0.00078239234,0.000013583935,0.0002740168,0.00003236274,0.0001906975,0.00001175436],"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":[0.000093614144,0.00002090173,0.00006683056,0.00011004755,0.000018014909,0.0000041481417,0.00002866097,0.39461872,0.6029468,0.000536135,0.000020509442,0.0015356163],"study_design_scores_gemma":[0.0021913354,0.00007129547,0.016055837,0.0004732484,0.000023100007,0.000020619991,0.00014047301,0.025022043,0.95163935,0.0036050582,0.0002935639,0.00046409946],"about_ca_topic_score_codex":0.000003557276,"about_ca_topic_score_gemma":0.000014267454,"teacher_disagreement_score":0.36959666,"about_ca_system_score_codex":0.0002490199,"about_ca_system_score_gemma":0.00002479858,"threshold_uncertainty_score":0.9999695},"labels":[],"label_agreement":null},{"id":"W4414731898","doi":"10.1021/acs.nanolett.5c02634","title":"Multilevel Nanoarray Spin–Orbit Torque Device for Process-in-Memory Applications","year":2025,"lang":"en","type":"article","venue":"Nano Letters","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Nanosecond; Spin-transfer torque; Current (fluid); Overhead (engineering); Limiting; Energy consumption; Torque; Pulse-width modulation","score_opus":0.01156917835131096,"score_gpt":0.28292382395932847,"score_spread":0.27135464560801753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414731898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8088355,0.00025138556,0.18759729,0.00081212865,0.00038984066,0.0008837665,0.000008395292,0.00035634203,0.00086532225],"genre_scores_gemma":[0.99300766,0.0000052584965,0.004032462,0.0022377747,0.00011441844,0.0004182672,0.0000075248677,0.000024640467,0.00015197835],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992572,0.000007305002,0.0002027117,0.0002156559,0.000057864894,0.00025926414],"domain_scores_gemma":[0.99963677,0.00011094945,0.000024024637,0.00017212046,0.00002479377,0.000031326523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067835615,0.00013627116,0.00014252363,0.00010630414,0.00008526209,0.000014342642,0.0001661957,0.000053994147,0.000005760968],"category_scores_gemma":[0.000024604675,0.00014818732,0.0000471948,0.00023889964,0.000020155152,0.00010994922,0.00001547403,0.000112796304,0.000011182225],"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":[0.00000996941,0.000015348085,0.00013048341,0.0004450299,0.000012380597,0.000002438023,0.00014893054,0.079048544,0.89520836,0.00014377137,0.00088626327,0.023948455],"study_design_scores_gemma":[0.0010982953,0.000014480116,0.0007165717,0.0002602461,0.000020268104,0.0000037206655,0.00010076619,0.01061472,0.9502456,0.00040720953,0.03607013,0.0004479492],"about_ca_topic_score_codex":0.0000014759779,"about_ca_topic_score_gemma":0.0000061186206,"teacher_disagreement_score":0.18417215,"about_ca_system_score_codex":0.00007747442,"about_ca_system_score_gemma":0.000016288926,"threshold_uncertainty_score":0.60429037},"labels":[],"label_agreement":null},{"id":"W4414919775","doi":"10.1101/2025.10.05.680523","title":"Rapidly Reconfigurable Dynamic Computing in Neural Networks with Fixed Synaptic Connectivity","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Creative Destruction Lab; University of Calgary","funders":"Alliance de recherche numérique du Canada","keywords":"Robustness (evolution); Neocortex; Artificial neural network; Biological neural network; Feed forward; Recurrent neural network; Synaptic weight; Set (abstract data type)","score_opus":0.00855030328580212,"score_gpt":0.20740075560168664,"score_spread":0.19885045231588452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414919775","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94517815,0.0019487715,0.048891384,0.000034684293,0.0017141249,0.00078632287,0.00003757366,0.0013709242,0.000038076334],"genre_scores_gemma":[0.99697965,0.00010899582,0.0024470019,0.00009465875,0.00017666646,0.000058518377,5.4976323e-7,0.00013031063,0.000003639807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970243,0.00017284192,0.00065203494,0.001012039,0.00020599106,0.0009328204],"domain_scores_gemma":[0.9981217,0.00037363963,0.00023263249,0.0009323438,0.00015563372,0.00018409113],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043509327,0.00078464096,0.0009315286,0.00036797658,0.00017763919,0.00014816756,0.0005390518,0.00049810245,0.000010616427],"category_scores_gemma":[0.00011038445,0.00086102786,0.00012726213,0.00083744555,0.00008555788,0.00020611394,0.00024114216,0.0021560974,0.000005428022],"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":[0.00004606516,0.00003941244,0.0024566923,0.0007042205,0.00014298632,0.00014318275,0.000008933131,0.95037526,0.04598822,0.000035175603,0.00001802073,0.00004181899],"study_design_scores_gemma":[0.000713012,0.000051189243,0.018608777,0.0014761429,0.00006680197,1.13455684e-7,0.0000041905378,0.95401025,0.023956388,0.0000013659735,0.000068838664,0.0010429308],"about_ca_topic_score_codex":0.000019478484,"about_ca_topic_score_gemma":0.000023036328,"teacher_disagreement_score":0.05180152,"about_ca_system_score_codex":0.00047231582,"about_ca_system_score_gemma":0.00014143717,"threshold_uncertainty_score":0.99938405},"labels":[],"label_agreement":null},{"id":"W4415275482","doi":"10.1002/adma.70827","title":"Heterojunction‐Driven Stochasticity: Bi‐Heterojunction Noise‐Enhanced Negative Transconductance Transistor in Image Generation (Adv. Mater. 41/2025)","year":2025,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Transconductance; Transistor; Entropy (arrow of time); Noise (video); Field-effect transistor; Entropy estimation","score_opus":0.016468335092657438,"score_gpt":0.2548688625468189,"score_spread":0.23840052745416146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415275482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71768063,0.00010180692,0.27791002,0.0000570979,0.0029702005,0.00056077336,0.000038688082,0.00038093817,0.00029984763],"genre_scores_gemma":[0.9929173,0.00011586335,0.006052448,0.0001594504,0.00021231316,0.00020816231,0.000052324674,0.000060815495,0.0002213533],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797803,0.00009966912,0.00070279575,0.00054424844,0.00019210813,0.0004831744],"domain_scores_gemma":[0.99933827,0.0000777247,0.00010464582,0.0003064956,0.000107400156,0.00006547849],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001651629,0.0004141161,0.0005539586,0.0002524139,0.00015896598,0.00007607449,0.00017024213,0.00014124451,0.00011239992],"category_scores_gemma":[0.00007028412,0.00044745626,0.00008339418,0.00041860153,0.00006809014,0.00094066514,0.000023398605,0.00020880197,0.000036634898],"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":[0.00016058284,0.00002435613,0.0000022320562,0.0001816077,0.000024356777,0.0000064311384,0.0002366583,0.14547639,0.8511932,0.00006350365,0.000039794446,0.002590919],"study_design_scores_gemma":[0.0014331484,0.00007711772,0.00024413693,0.00024240496,0.00002992454,0.0000036384836,0.00006754602,0.0030760032,0.99365103,0.0005692803,0.00021448437,0.00039129247],"about_ca_topic_score_codex":0.000009337264,"about_ca_topic_score_gemma":0.000060034734,"teacher_disagreement_score":0.27523664,"about_ca_system_score_codex":0.00033060505,"about_ca_system_score_gemma":0.000026249374,"threshold_uncertainty_score":0.9997977},"labels":[],"label_agreement":null},{"id":"W4415481241","doi":"10.1109/tce.2025.3625081","title":"ZTID-IoV: Zero-Trust Intrusion Detection in IoV Using Neurosymbolic AI Approach With Federated Meta-Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Advanced Memory and Neural Computing","field":"Engineering","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":"Brandon University","funders":"","keywords":"Intrusion detection system; Cluster analysis; Transparency (behavior); Transformer; Architecture; Edge computing; Artificial neural network; The Internet; Adaptation (eye)","score_opus":0.016355527094351514,"score_gpt":0.24266926677490555,"score_spread":0.22631373968055404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415481241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39088166,0.0005538243,0.6076886,0.000014209051,0.00018927976,0.00021810355,0.0000013346385,0.0003260644,0.00012691237],"genre_scores_gemma":[0.99872726,0.00026036016,0.00066622725,0.000110008594,0.000012314112,0.00004589262,0.0000023165605,0.00005931328,0.000116313866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984794,0.00012048884,0.00032278092,0.00039409794,0.00015228544,0.0005309007],"domain_scores_gemma":[0.9995431,0.00010361922,0.000046121426,0.00019292648,0.00005470501,0.00005957971],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013848233,0.0003327687,0.00038667393,0.00039474317,0.0004494475,0.000071498245,0.00010463173,0.00014216978,0.0000112153175],"category_scores_gemma":[0.0000067265314,0.00032175513,0.00011134147,0.0011032874,0.00004017855,0.00023634295,0.0000020453824,0.0015135346,0.0000045547617],"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":[0.00010405321,0.000054452034,0.000018905548,0.000051683324,0.00024121856,0.0000054193674,0.00004779536,0.88717985,0.08720996,0.000016650589,0.0000017421685,0.02506826],"study_design_scores_gemma":[0.00062206376,0.000098961085,0.000020120184,0.00003830828,0.0003514704,0.000044751734,0.00002976213,0.5841555,0.41368407,0.00004518414,0.0006309337,0.00027890422],"about_ca_topic_score_codex":0.000019761861,"about_ca_topic_score_gemma":0.00014526794,"teacher_disagreement_score":0.6078456,"about_ca_system_score_codex":0.0002826672,"about_ca_system_score_gemma":0.00008284129,"threshold_uncertainty_score":0.99992347},"labels":[],"label_agreement":null},{"id":"W4415871439","doi":"10.1021/acsaelm.5c01550","title":"Molybdenum Oxide Artificial Synapse: Enabling Cognitive Learning, Image Recognition, and Denoising","year":2025,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Indian Institute of Space Science and Technology","keywords":"Neuromorphic engineering; Image quality; Noise (video); Pattern recognition (psychology); Noise reduction; Image processing; Convolutional neural network","score_opus":0.009695196428042374,"score_gpt":0.23061454751104432,"score_spread":0.22091935108300195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415871439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914042,0.00043324268,0.007225438,0.0000196428,0.00007167888,0.0002831096,0.000007572034,0.0004206069,0.0001345427],"genre_scores_gemma":[0.9993455,0.00022400454,0.000009772604,0.00009218437,0.00011908902,0.000048468322,0.00005141193,0.00003925108,0.000070314505],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987033,0.000041547853,0.0003218664,0.00030321776,0.00008093767,0.00054914714],"domain_scores_gemma":[0.9996164,0.0001444869,0.000062366176,0.00009616496,0.000039469807,0.000041124596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029344027,0.00022253055,0.00028067883,0.00011148845,0.00023052776,0.00012780867,0.00008373436,0.000101237,0.000059867503],"category_scores_gemma":[0.000078503006,0.00024884002,0.000019096646,0.00019022435,0.00004691225,0.00012239847,0.00006393813,0.00029105577,0.00004764459],"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":[0.000058312395,0.000007088594,0.0000020236168,0.00010384672,0.000044251323,0.000004528322,0.00006936696,0.00034292517,0.99398506,0.0011356879,0.000023797727,0.004223115],"study_design_scores_gemma":[0.000281616,0.000022739334,0.000033412834,0.00009210862,0.00004686079,0.000011993959,0.00013589211,0.0000014236462,0.99061596,0.008312185,0.00020494836,0.00024083028],"about_ca_topic_score_codex":0.0000040574673,"about_ca_topic_score_gemma":0.0000032634566,"teacher_disagreement_score":0.007941337,"about_ca_system_score_codex":0.0000763888,"about_ca_system_score_gemma":0.000030920182,"threshold_uncertainty_score":0.99999636},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"agree"},{"id":"W4416055450","doi":"10.1093/cercor/bhaf295","title":"Building on models—a perspective for computational neuroscience","year":2025,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":2,"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":"European Research Council; Engineering and Physical Sciences Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Forschungszentrum Jülich; Bundesministerium für Bildung und Forschung; Fundação de Amparo à Pesquisa do Estado de São Paulo; Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; RWTH Aachen University; European Commission; Government of Ontario","keywords":"Computational neuroscience; Computational model; Neuromorphic engineering; Perspective (graphical); Systems neuroscience; Block (permutation group theory); Correctness; Benchmark (surveying)","score_opus":0.02463626378916327,"score_gpt":0.2947754391518078,"score_spread":0.27013917536264453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416055450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20356716,0.000063463376,0.7887516,0.00012531267,0.0006509355,0.0001921895,0.0000071706068,0.00028286327,0.006359297],"genre_scores_gemma":[0.9941099,0.0000014484112,0.005194782,0.00045534337,0.0000535302,0.000010116019,0.0000016602476,0.000011671574,0.0001615326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994282,0.000006400432,0.000101763486,0.0002090157,0.000072291885,0.00018230235],"domain_scores_gemma":[0.99971867,0.00010297209,0.000014322096,0.00008409492,0.000046818026,0.000033102933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037375168,0.00009993723,0.00009464905,0.00007292685,0.00014423425,0.000023305352,0.00011702853,0.000025324634,0.0000023528662],"category_scores_gemma":[0.000031587693,0.00010284348,0.000047453435,0.00018008315,0.000026571235,0.00013557215,0.00002436423,0.00011456938,0.000002131602],"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":[0.000010046448,0.0000055872756,0.0000075045923,0.00001346064,0.0000026849423,8.818708e-7,0.000026011723,0.7868719,0.0040934198,0.20689256,0.00020418847,0.0018717535],"study_design_scores_gemma":[0.00022443153,0.000039892253,0.0008340134,0.000027851904,0.000004357195,0.0000019261895,0.00004091297,0.8649229,0.0033230358,0.13012707,0.0003495598,0.0001040854],"about_ca_topic_score_codex":7.4556266e-7,"about_ca_topic_score_gemma":5.363253e-7,"teacher_disagreement_score":0.7905427,"about_ca_system_score_codex":0.00007616959,"about_ca_system_score_gemma":0.00001590001,"threshold_uncertainty_score":0.41938356},"labels":[],"label_agreement":null},{"id":"W4416402924","doi":"10.1002/aisy.202500327","title":"Spikoder: Dual‐Mode Graphene Neuron Circuit for Hardware Intelligence","year":2025,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Scalability; Spiking neural network; Realization (probability); Encoder; Artificial neural network; Encoding (memory); Efficient energy use","score_opus":0.02673545954498137,"score_gpt":0.2830981653412295,"score_spread":0.25636270579624815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416402924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015543233,0.0048094164,0.9691153,0.00003134912,0.0052521285,0.0011776047,0.000043224994,0.00091646984,0.003111269],"genre_scores_gemma":[0.9963859,0.00056365156,0.00090562156,0.00010578178,0.000195251,0.00020392447,0.00002545475,0.00007618553,0.0015382462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99790615,0.000038322807,0.00071513455,0.00052617706,0.00017848812,0.0006357469],"domain_scores_gemma":[0.99874955,0.00037461607,0.000093835064,0.0005261588,0.000137663,0.00011817367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016010541,0.00039578232,0.00046277867,0.000227027,0.0002043155,0.000064831234,0.00034050312,0.00011429512,0.000012003692],"category_scores_gemma":[0.00012381897,0.00041291703,0.00019355906,0.00047250456,0.000047416383,0.00027551354,0.000060055725,0.00028223815,0.000038690687],"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":[0.00003376815,0.000024299354,0.000043661137,0.0006790802,0.000056906265,0.00000935863,0.00012819255,0.86973774,0.031680763,0.020490415,0.00035880916,0.07675702],"study_design_scores_gemma":[0.0004476257,0.00022698613,0.000051396346,0.0014903562,0.00007763499,0.000041775416,0.00086923834,0.27637258,0.5966053,0.017759342,0.10492758,0.0011301519],"about_ca_topic_score_codex":0.00000482249,"about_ca_topic_score_gemma":0.0000059634262,"teacher_disagreement_score":0.98084265,"about_ca_system_score_codex":0.00015341854,"about_ca_system_score_gemma":0.000021319018,"threshold_uncertainty_score":0.9998323},"labels":[],"label_agreement":null},{"id":"W4416594271","doi":"10.1002/adma.202511018","title":"Transistor‐Level Activation Functions via Two‐Gate Designs: From Analog Sigmoid and Gaussian Control to Real‐Time Hardware Demonstrations","year":2025,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Seoul National University; National Research Foundation","keywords":"Sigmoid function; Activation function; Transistor; Neuromorphic engineering; Gaussian; Multilayer perceptron; Transconductance; Artificial neural network; Controllability","score_opus":0.015632973714026093,"score_gpt":0.24678832867899764,"score_spread":0.23115535496497155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416594271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46248567,0.000045672226,0.5354731,0.00015053534,0.00033588518,0.0002965984,0.00019866589,0.00035512928,0.00065873156],"genre_scores_gemma":[0.99153274,0.000031582254,0.0076818843,0.00015093056,0.00009865879,0.00008279986,0.00008212865,0.000029419814,0.00030985216],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989843,0.000044377204,0.0003403386,0.0002952868,0.00008871538,0.00024701553],"domain_scores_gemma":[0.9994196,0.00018143503,0.000049223043,0.0002042924,0.000057107813,0.00008829876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009527085,0.00021546066,0.00031080085,0.000114403934,0.00022501635,0.000051749073,0.00008317052,0.000066366636,0.00011631905],"category_scores_gemma":[0.000047889316,0.0002290944,0.000033602395,0.00018323521,0.000026272877,0.00036953334,0.000015073019,0.00008111418,0.000026936605],"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":[0.00007705119,0.000008055287,0.000007664366,0.000027380527,0.000037213675,0.0000021480919,0.00006205552,0.09175242,0.9007918,0.00013579369,0.000046148103,0.007052243],"study_design_scores_gemma":[0.0011955232,0.000047270627,0.0017970933,0.00019029061,0.00007605399,0.000002633766,0.000050014023,0.0022159796,0.98991364,0.003034947,0.0011306122,0.00034594515],"about_ca_topic_score_codex":0.000028253642,"about_ca_topic_score_gemma":0.000024919807,"teacher_disagreement_score":0.5290471,"about_ca_system_score_codex":0.00009047674,"about_ca_system_score_gemma":0.000020410373,"threshold_uncertainty_score":0.93421984},"labels":[],"label_agreement":null},{"id":"W4416697451","doi":"10.1101/2025.11.23.690009","title":"Direct Training of Networks of Morris-Lecar Neurons with Backprop","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Spiking neural network; Artificial neural network; Backpropagation; Process (computing); Feature (linguistics); Function (biology); Replicate; Subnetwork","score_opus":0.015802795055446225,"score_gpt":0.21053375011228787,"score_spread":0.19473095505684163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416697451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89254266,0.005102813,0.096176885,0.000046077745,0.002979454,0.0017407566,0.0003493343,0.00078453514,0.00027748945],"genre_scores_gemma":[0.98780197,0.00069630024,0.010699233,0.000053171174,0.00042285898,0.000074056195,3.2302276e-7,0.00023968992,0.000012417513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99468297,0.00027387304,0.0017454696,0.0015125748,0.00056084903,0.0012242944],"domain_scores_gemma":[0.99513745,0.00052214455,0.0011425017,0.001996904,0.0008175828,0.0003834045],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00073164375,0.0013178404,0.0022068187,0.0005450529,0.00023219222,0.000067910456,0.0010773529,0.00071819767,0.000036232774],"category_scores_gemma":[0.0002961452,0.0014317391,0.00052122446,0.0019030343,0.00037238898,0.0002795365,0.000619483,0.0019267439,0.000003836236],"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":[0.00015840979,0.000111760506,0.0008304014,0.002395051,0.0005839157,0.00006762888,0.00006304929,0.5145364,0.4808454,0.00035178693,0.00001597631,0.000040209743],"study_design_scores_gemma":[0.0012534746,0.000363513,0.010628017,0.0064295656,0.00078435405,8.449355e-8,0.000022441574,0.06957515,0.90835994,0.0000011480772,0.00088313833,0.0016991829],"about_ca_topic_score_codex":0.000025350006,"about_ca_topic_score_gemma":0.000002130307,"teacher_disagreement_score":0.44496125,"about_ca_system_score_codex":0.00022619966,"about_ca_system_score_gemma":0.0007187877,"threshold_uncertainty_score":0.9999573},"labels":[],"label_agreement":null},{"id":"W4416714563","doi":"10.1109/tetci.2025.3631624","title":"Membrane Potential-Driven Adaptive Threshold Plasticity for SNNs: A Bio-Inspired Mechanism Combining Inverse Depolarization Rate and Proportional Membrane Potential Dynamics","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Depolarization; Membrane potential; Neuromorphic engineering; Neurophysiology; Artificial neural network; Control theory (sociology); Threshold model; Negative feedback; Plasticity","score_opus":0.023444806509039188,"score_gpt":0.2739581079785814,"score_spread":0.2505133014695422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416714563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095561594,0.000054731518,0.8998927,0.00045981442,0.002660492,0.0010243396,0.0001414406,0.0001645893,0.000040292674],"genre_scores_gemma":[0.9647927,0.00023406601,0.034347408,0.00017595678,0.00011107907,0.000083395265,0.00006683724,0.000050352253,0.00013817831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691415,0.000114266244,0.0011638184,0.0008543349,0.00039631102,0.00055711175],"domain_scores_gemma":[0.99855715,0.0005546502,0.00025050106,0.0001868316,0.00031455056,0.00013634106],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003231149,0.0005483737,0.0005226865,0.0007099294,0.00081639574,0.00012558578,0.0002875753,0.00030725324,0.000038185855],"category_scores_gemma":[0.00004387507,0.0006870999,0.00019765389,0.00085412676,0.00020492631,0.0004896774,0.00001820094,0.0008847278,0.00000289618],"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":[0.0003580947,0.00022670903,0.000008158875,0.00032293182,0.00019810676,0.000024247638,0.00037634777,0.95607734,0.0057553393,0.027233562,0.000002384309,0.009416793],"study_design_scores_gemma":[0.00079189293,0.00019550313,0.000066145396,0.00063391955,0.00016067189,0.0000184358,0.00033541222,0.89871913,0.068479806,0.030096563,0.000004793699,0.00049772067],"about_ca_topic_score_codex":0.00002968466,"about_ca_topic_score_gemma":0.00013841048,"teacher_disagreement_score":0.86923116,"about_ca_system_score_codex":0.0004131534,"about_ca_system_score_gemma":0.00020018485,"threshold_uncertainty_score":0.99955803},"labels":[],"label_agreement":null},{"id":"W4416725701","doi":"10.1109/mwscas53549.2025.11244595","title":"A Lightweight and Accurate Cordic-Based Digital Implementation of the Hindmarsh-Rose Neuron","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Implementation; Digital electronics; Circuit design; Field-programmable gate array; Key (lock); Resource (disambiguation)","score_opus":0.011033030835071395,"score_gpt":0.27138952229187535,"score_spread":0.26035649145680395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416725701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9807965,0.0003148049,0.015005479,0.0005555141,0.00076067913,0.00043664925,0.00002686734,0.00007132165,0.0020321724],"genre_scores_gemma":[0.99909127,0.00003128262,0.00012026159,0.00024358065,0.000033988,0.000004401833,0.0000040694695,0.00001689256,0.00045427502],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99886084,0.000038960632,0.0004504394,0.00025968242,0.00012546679,0.00026462035],"domain_scores_gemma":[0.9992862,0.00023599534,0.00011653122,0.00026816822,0.00004349335,0.000049631148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007506234,0.00022611367,0.00022222627,0.000079458805,0.00017153962,0.000091676164,0.00017438327,0.000055866272,0.000058107027],"category_scores_gemma":[0.000037401154,0.00017239782,0.000093167284,0.00039497332,0.00006900989,0.0003410217,0.00016779613,0.00021283574,0.0000021700532],"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":[0.00031821738,0.00019077226,0.033218496,0.0021857808,0.00028038616,0.000026978998,0.0010923092,0.072015524,0.2545067,0.009204046,0.0010211583,0.6259396],"study_design_scores_gemma":[0.002249964,0.00019225189,0.039580643,0.00034403527,0.000119643075,0.0000056938356,0.00048402132,0.0652231,0.88679516,0.0009611529,0.0036410373,0.00040328098],"about_ca_topic_score_codex":0.000009125286,"about_ca_topic_score_gemma":0.000013958824,"teacher_disagreement_score":0.63228846,"about_ca_system_score_codex":0.00003504306,"about_ca_system_score_gemma":0.000057173434,"threshold_uncertainty_score":0.7030179},"labels":[],"label_agreement":null},{"id":"W4416726779","doi":"10.1109/mwscas53549.2025.11244482","title":"MAKAN: Memristive Accelerated Kolmugruv-Arnold Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Memristor; Scalability; Flexibility (engineering); Artificial neural network; Overhead (engineering); Energy consumption; Function (biology); Efficient energy use","score_opus":0.01850478071085201,"score_gpt":0.2682913160264803,"score_spread":0.2497865353156283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416726779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0416228,0.0063901497,0.733266,0.0006715008,0.0061543304,0.0007582103,0.000007382786,0.0013472585,0.20978236],"genre_scores_gemma":[0.9695801,0.00064354937,0.0011936247,0.00081613066,0.00043366806,0.000013542818,0.000010560493,0.000057448215,0.027251337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975234,0.00007777699,0.00069507374,0.00064183894,0.00015167883,0.0009102269],"domain_scores_gemma":[0.9987824,0.00035550492,0.000084863874,0.00047893278,0.00013035086,0.00016792772],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018359849,0.0005635729,0.0005630005,0.0001946101,0.00038738616,0.000157333,0.00040685275,0.0003376974,0.0009452593],"category_scores_gemma":[0.00007288424,0.00059665105,0.00017858508,0.0013678585,0.00007636648,0.00031033618,0.00029394036,0.0010620632,0.000114557806],"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":[0.000096978336,0.00005463863,0.0001342564,0.00018334632,0.00032989448,0.000117082665,0.00009349584,0.9076523,0.0039572204,0.0065729837,0.009557186,0.07125059],"study_design_scores_gemma":[0.0011329323,0.000084324405,0.0007950543,0.0005341052,0.00017130347,0.000014119976,0.00022732872,0.9065016,0.06469826,0.0010587145,0.023902293,0.0008799488],"about_ca_topic_score_codex":0.0000127755975,"about_ca_topic_score_gemma":0.000012001959,"teacher_disagreement_score":0.92795736,"about_ca_system_score_codex":0.00017278688,"about_ca_system_score_gemma":0.000050835926,"threshold_uncertainty_score":0.999968},"labels":[],"label_agreement":null},{"id":"W4416728370","doi":"10.1109/mwscas53549.2025.11244495","title":"Reconfigurable Analog Neural Networks: Architecture, Design, and Performance Evaluation","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"MNIST database; Artificial neural network; Converters; Neuromorphic engineering; Measure (data warehouse); Power (physics); Software; Analog computer; Energy (signal processing); Field-programmable gate array","score_opus":0.02569760544434045,"score_gpt":0.26104913361725207,"score_spread":0.2353515281729116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416728370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.335324,0.00606273,0.6466091,0.0001080059,0.001108651,0.0007343015,5.1575654e-7,0.00017178748,0.009880904],"genre_scores_gemma":[0.9942661,0.00073040515,0.003678546,0.00027514054,0.00015146204,0.000024029223,0.00000481103,0.000024681229,0.00084484974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982146,0.00019462308,0.00044226233,0.00045338625,0.0001653396,0.00052976387],"domain_scores_gemma":[0.9990617,0.00039991515,0.00006578943,0.00027975507,0.0000939121,0.00009890417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008059032,0.0003402928,0.00031687415,0.00018604746,0.000357219,0.00009977316,0.00015458903,0.0001390629,0.00016023195],"category_scores_gemma":[0.00008261177,0.00033593475,0.00005696587,0.00055742555,0.00006227267,0.00029780949,0.00005058732,0.00055221043,0.000005065245],"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":[0.000051038114,0.0000053578674,0.0001118755,0.0000752388,0.000027985683,0.0000012767418,0.000039906943,0.61503744,0.0006895349,0.000064107735,0.0000778296,0.38381842],"study_design_scores_gemma":[0.0005812793,0.00014478066,0.002047026,0.00020825306,0.000100719415,0.000017623077,0.000027103806,0.98261696,0.013066591,0.000655569,0.00022141213,0.00031269103],"about_ca_topic_score_codex":0.0000028189702,"about_ca_topic_score_gemma":0.0000028884772,"teacher_disagreement_score":0.6589421,"about_ca_system_score_codex":0.00008283737,"about_ca_system_score_gemma":0.00003917863,"threshold_uncertainty_score":0.9999093},"labels":[],"label_agreement":null},{"id":"W4416746178","doi":"10.1016/j.parco.2025.103165","title":"Butterfly factorization for vision transformers on multi-IPU systems","year":2025,"lang":"en","type":"article","venue":"Parallel Computing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Butterfly; Memory footprint; Transformer; Factorization; Computation","score_opus":0.022138228545259982,"score_gpt":0.2953664193748585,"score_spread":0.27322819082959854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416746178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10883948,0.0002034833,0.8877846,0.000030897485,0.001322174,0.00043804754,0.0000036085964,0.00043897456,0.000938706],"genre_scores_gemma":[0.9952077,0.000011772752,0.0043452857,0.0000686771,0.0001306005,0.000010643484,0.000017528924,0.000026632746,0.00018114995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907684,0.000022734292,0.00029112882,0.00022667304,0.00008344954,0.00029918758],"domain_scores_gemma":[0.9995359,0.00024128896,0.000037434485,0.0001078632,0.000035871766,0.00004162121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012991052,0.00018180236,0.00020234103,0.00010806131,0.00021796983,0.00004948065,0.000112315916,0.000068848996,9.2631336e-7],"category_scores_gemma":[0.000020013049,0.00018198203,0.00007329116,0.00017240724,0.000010981583,0.00009654484,0.000015656296,0.00015480207,0.000007539858],"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":[0.000022306835,0.000012118295,0.000062491374,0.00024681972,0.00002611559,9.906117e-7,0.00016541641,0.97330284,0.010422021,0.0006837048,0.00013898333,0.01491617],"study_design_scores_gemma":[0.000779792,0.000060572314,0.0004424994,0.00027753302,0.000013050629,0.0000013621635,0.0000848399,0.9890133,0.006896189,0.0000746138,0.0021577452,0.00019846225],"about_ca_topic_score_codex":0.0000024325661,"about_ca_topic_score_gemma":8.3378274e-7,"teacher_disagreement_score":0.8863682,"about_ca_system_score_codex":0.00007123905,"about_ca_system_score_gemma":0.000009202425,"threshold_uncertainty_score":0.7421012},"labels":[],"label_agreement":null},{"id":"W4416873489","doi":"10.1109/aann66429.2025.11257731","title":"Cutting FLOPs Overhead with SNT and LReSuMe: A Noise-Tolerant Spiking Classifier","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"FLOPS; Robustness (evolution); Inference; Spiking neural network; Spurious relationship; Flicker; Salient; Artificial neural network","score_opus":0.008318569796166844,"score_gpt":0.23117795257679485,"score_spread":0.222859382780628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416873489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79613864,0.003245908,0.13764334,0.00044363202,0.0010156191,0.0005197775,0.000003804559,0.00046058535,0.060528688],"genre_scores_gemma":[0.9884608,0.00018714523,0.008095468,0.00047530048,0.00021782336,0.000011243873,0.0000019078414,0.000062640844,0.0024876827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975027,0.000054042142,0.0006167573,0.0007375757,0.00024029432,0.00084862445],"domain_scores_gemma":[0.99889004,0.00033664567,0.00009935282,0.00041082452,0.00007286827,0.0001902533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024931927,0.0005649322,0.0005801928,0.00021350285,0.00053320057,0.00020208633,0.00018935428,0.00018407861,0.000087025925],"category_scores_gemma":[0.00006296813,0.0005011702,0.00009298836,0.00061708683,0.000113650414,0.00041231685,0.0002413861,0.000819152,0.000012794062],"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":[0.0006754217,0.00016783168,0.009549968,0.0040924875,0.0007401575,0.00054898515,0.0025883978,0.3018965,0.10071194,0.015849948,0.0006916192,0.5624867],"study_design_scores_gemma":[0.0044088904,0.00033189138,0.0110231675,0.0073551596,0.00037393114,0.00018738431,0.0017127681,0.86603993,0.08787437,0.0017025517,0.01697483,0.002015113],"about_ca_topic_score_codex":0.000018137445,"about_ca_topic_score_gemma":0.000027685475,"teacher_disagreement_score":0.5641434,"about_ca_system_score_codex":0.00012453835,"about_ca_system_score_gemma":0.00007426713,"threshold_uncertainty_score":0.999744},"labels":[],"label_agreement":null},{"id":"W4416893202","doi":"10.1016/j.nanoen.2025.111633","title":"Energy-efficient Ising solver implementations in forming-free memristor crossbar arrays for combinatorial optimization problems","year":2025,"lang":"en","type":"article","venue":"Nano Energy","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Solver; Crossbar switch; Memristor; Ising model; Resistive random-access memory; Simulated annealing; Entropy (arrow of time); Conductance; Scalability","score_opus":0.010738608006571589,"score_gpt":0.24831403134079566,"score_spread":0.23757542333422407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416893202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015526247,0.00027876193,0.98068833,0.000037555983,0.0020289468,0.00016807792,0.000020547217,0.00017745413,0.0010740773],"genre_scores_gemma":[0.9903077,0.0000114872,0.008948478,0.000084201085,0.00013323907,0.00019140428,0.00006787717,0.000031626616,0.00022400363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903655,0.000019181374,0.00031964973,0.00020548132,0.000111661495,0.00030744856],"domain_scores_gemma":[0.9995897,0.00009124348,0.00004917737,0.00018489789,0.0000518837,0.00003311389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011370334,0.00014576846,0.00016253737,0.00015090723,0.00027509505,0.00003199699,0.0001739545,0.000056212706,0.0000067999977],"category_scores_gemma":[0.000025112753,0.00016819892,0.00006117782,0.0003525224,0.00001607865,0.000099222394,0.00007667196,0.000071909875,1.6521533e-7],"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":[0.000017735656,0.000031693497,0.0000054511006,0.000033697692,0.000011035381,6.345913e-7,0.00008620842,0.95252436,0.011688924,0.032606717,0.0005678522,0.0024256872],"study_design_scores_gemma":[0.0018196611,0.000044247136,0.0000030786966,0.000051291077,0.000013122719,0.0000010748935,0.00006790557,0.8755164,0.09844351,0.0064197406,0.017415617,0.00020437174],"about_ca_topic_score_codex":0.000027508702,"about_ca_topic_score_gemma":0.0000438044,"teacher_disagreement_score":0.97478145,"about_ca_system_score_codex":0.00024440995,"about_ca_system_score_gemma":0.000038326343,"threshold_uncertainty_score":0.6858953},"labels":[],"label_agreement":null},{"id":"W4417102796","doi":"10.1109/iccv51701.2025.00646","title":"Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Event (particle physics); Object (grammar); Object detection; Bounding overwatch; Frame (networking); RGB color model; Filter (signal processing); Minimum bounding box; Dimension (graph theory)","score_opus":0.005256647624677588,"score_gpt":0.2281531288389257,"score_spread":0.2228964812142481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417102796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52933764,0.0000779196,0.46895105,0.00003298883,0.00038093136,0.00023262053,0.0000019905708,0.00013853084,0.0008463111],"genre_scores_gemma":[0.9978092,0.0000024675828,0.0011012546,0.000044255747,0.000070379225,0.000009776322,0.0000017695116,0.000010716942,0.00095021],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950784,0.000018206383,0.00018794886,0.000095251824,0.000053766875,0.00013699956],"domain_scores_gemma":[0.99971724,0.000098366494,0.0000318867,0.00010943289,0.000029020095,0.000014063577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012219002,0.00008564878,0.00012864795,0.000039954688,0.00009038651,0.000013390623,0.00008339288,0.00003266259,0.000013125364],"category_scores_gemma":[0.000026499265,0.00006395093,0.0000735,0.00009417076,0.000014750817,0.000072077724,0.000025159563,0.00009293457,0.0000025789814],"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":[0.000078457415,0.000016426473,0.00010470224,0.00015785435,0.00008717041,0.000001687632,0.00012518134,0.21589671,0.6564242,0.00013757864,0.00022648642,0.12674351],"study_design_scores_gemma":[0.0005210728,0.00007817233,0.00048041175,0.000068344554,0.000040487736,0.000005458192,0.00019332758,0.32555702,0.6706627,0.00040446752,0.0018450894,0.00014343849],"about_ca_topic_score_codex":0.000011027052,"about_ca_topic_score_gemma":0.0000122284455,"teacher_disagreement_score":0.46847153,"about_ca_system_score_codex":0.000023546558,"about_ca_system_score_gemma":0.000007696609,"threshold_uncertainty_score":0.26078433},"labels":[],"label_agreement":null},{"id":"W4417161975","doi":"10.1109/iccv51701.2025.00730","title":"TESPEC: Temporally-Enhanced Self-Supervised Pretraining for Event Cameras","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feed forward; Leverage (statistics); Event (particle physics); ENCODE; Monocular; Discriminative model; Noise (video); Deep learning","score_opus":0.00976999849713157,"score_gpt":0.2594428188344185,"score_spread":0.24967282033728694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417161975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31515387,0.00017475347,0.6586021,0.00008824038,0.00052986265,0.00044657954,0.0000026740463,0.0013886761,0.023613228],"genre_scores_gemma":[0.9554986,0.0000106954985,0.04279198,0.000201889,0.00008204279,0.000041634717,0.0000047505378,0.000021473514,0.0013469473],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928164,0.0000089442765,0.00021226086,0.0001819602,0.00005890613,0.0002562713],"domain_scores_gemma":[0.99960756,0.00014748695,0.000015912357,0.0001519065,0.000033859364,0.00004329039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000920709,0.0001415293,0.00016162133,0.000061659295,0.00009765569,0.000020040243,0.000116163996,0.000049924543,0.00003319695],"category_scores_gemma":[0.000038323305,0.00013679483,0.00007035693,0.00017887681,0.000008214333,0.000109374465,0.00002599844,0.00011979724,0.000006855885],"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":[0.0000668067,0.00007802393,0.00026797783,0.0009241603,0.00023427935,0.0000073999777,0.0015727058,0.26230663,0.49045315,0.005443442,0.00433778,0.23430765],"study_design_scores_gemma":[0.00095653377,0.00006674758,0.00010201166,0.00012021479,0.000023418568,0.0000016583423,0.0002935224,0.5138087,0.47728136,0.0013713494,0.0056410213,0.00033344407],"about_ca_topic_score_codex":7.33778e-7,"about_ca_topic_score_gemma":0.0000026774019,"teacher_disagreement_score":0.64034474,"about_ca_system_score_codex":0.0000489929,"about_ca_system_score_gemma":0.000018612087,"threshold_uncertainty_score":0.55783314},"labels":[],"label_agreement":null},{"id":"W4417169843","doi":"10.1109/icecs66544.2025.11270823","title":"A Versatile Time-Domain Winner-Take-All Spike Decoder for Neuromorphic Systems","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Spike (software development); CMOS; Block (permutation group theory); Converters; Computation; Power (physics); Decoding methods; Spiking neural network","score_opus":0.026121550187352977,"score_gpt":0.2520205143396941,"score_spread":0.2258989641523411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417169843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2147712,0.0034352741,0.73964715,0.00080866355,0.008021496,0.0032288092,0.000083579274,0.0011648017,0.028839016],"genre_scores_gemma":[0.969975,0.000057489866,0.005670727,0.00078467716,0.00052056037,0.00010759983,0.000029946057,0.00013318265,0.022720845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971993,0.00009429857,0.00082598167,0.0007231735,0.00019816101,0.0009590723],"domain_scores_gemma":[0.9981488,0.00082142866,0.00012342758,0.00060373644,0.000107519416,0.0001950806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036193305,0.0005699439,0.0006945877,0.00023054042,0.0003129047,0.00016330842,0.00040794912,0.00024224931,0.0002284216],"category_scores_gemma":[0.00014178795,0.0006073551,0.00027392647,0.00049191376,0.00006057317,0.00028476916,0.00015355294,0.00043566432,0.00022466069],"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":[0.00033837053,0.00016777,0.000057543966,0.003151577,0.00056204863,0.00011230508,0.00051764655,0.7999184,0.14306073,0.01595509,0.025929224,0.010229282],"study_design_scores_gemma":[0.0023723547,0.00026802195,0.000044929595,0.00061857165,0.00020198281,0.00003757025,0.00026204917,0.8410487,0.018178133,0.0014247089,0.134702,0.0008409836],"about_ca_topic_score_codex":0.000010603158,"about_ca_topic_score_gemma":0.0000050531703,"teacher_disagreement_score":0.7552038,"about_ca_system_score_codex":0.00016797482,"about_ca_system_score_gemma":0.00007581491,"threshold_uncertainty_score":0.9996378},"labels":[],"label_agreement":null},{"id":"W4417337576","doi":"10.1109/ispcs66324.2025.11281787","title":"Mitigating Imprecise Timing in Spiking Neural Networks through Offset-Aware Training","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Science Foundation","keywords":"Skew; Neuromorphic engineering; Jitter; Asynchronous communication; Spiking neural network; Offset (computer science); Artificial neural network; Unavailability","score_opus":0.04072325526712451,"score_gpt":0.2929873663411023,"score_spread":0.2522641110739778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417337576","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5120031,0.002882354,0.46571425,0.00027071987,0.0034152386,0.00057615584,0.0000030514962,0.0005921728,0.014542961],"genre_scores_gemma":[0.99139214,0.00013062824,0.0067674983,0.00067451457,0.0005349734,0.000020577605,0.000011146515,0.000091210844,0.0003773143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958262,0.00014353388,0.0013804745,0.0008865332,0.00023137586,0.0015318823],"domain_scores_gemma":[0.9982254,0.00093973085,0.00017916741,0.0004585475,0.000059166763,0.00013802265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048006067,0.0007211789,0.00085248065,0.0002781206,0.0005388216,0.00018496078,0.0004571257,0.00036917796,0.0001187859],"category_scores_gemma":[0.00019160299,0.0008342116,0.00025606915,0.0015378448,0.00009225809,0.000968512,0.00036709374,0.0019013322,0.0000062759127],"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":[0.000023619394,0.000017064385,0.0006021109,0.0002184203,0.00003503098,0.00009298911,0.0021423083,0.7722895,0.0018243344,0.00064606295,0.000033898843,0.22207463],"study_design_scores_gemma":[0.0009974259,0.00004020097,0.0004527413,0.0022999325,0.0000462431,0.000024605206,0.0035565945,0.98500216,0.0057813707,0.000839091,0.00023416955,0.00072546303],"about_ca_topic_score_codex":0.000044127857,"about_ca_topic_score_gemma":0.00006518102,"teacher_disagreement_score":0.47938904,"about_ca_system_score_codex":0.0002768131,"about_ca_system_score_gemma":0.00006027861,"threshold_uncertainty_score":0.99941087},"labels":[],"label_agreement":null},{"id":"W4417537031","doi":"10.1007/s00034-025-03434-w","title":"Guest Editorial: Low Power Computing: Devices, Circuits &amp; Systems for Signal Processing","year":2025,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Signal processing; Electronic circuit; Power (physics); Integrated circuit; SIGNAL (programming language); Digital signal processing","score_opus":0.020791952349028607,"score_gpt":0.26143163114365303,"score_spread":0.24063967879462442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417537031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085817695,0.059041414,0.8129635,0.000028596807,0.035629593,0.0019176395,0.000033894677,0.001578186,0.0029895133],"genre_scores_gemma":[0.9828005,0.000021716665,0.000060280916,0.00004638991,0.016642144,0.00006488158,0.000023871871,0.00010046414,0.00023978768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970183,0.00007736791,0.00095472304,0.0007135396,0.00040866548,0.00082743383],"domain_scores_gemma":[0.99852705,0.00030140826,0.00028701944,0.00020766183,0.00046059364,0.00021628667],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075212435,0.0005445696,0.00078579446,0.0002630916,0.0008943776,0.0008830738,0.00031600954,0.00033363124,0.0000018037086],"category_scores_gemma":[0.000042666652,0.00054353906,0.000101028636,0.0005669297,0.00008683793,0.00062764285,0.00006486128,0.00048124042,0.000004614599],"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":[0.00006950845,0.00022866933,0.0017555658,0.06962946,0.00037115504,0.00005283082,0.0040050675,0.47736445,0.17070396,0.0009516239,0.02066201,0.25420567],"study_design_scores_gemma":[0.0028821267,0.00019303725,0.00026877658,0.02037839,0.0002567429,0.00024482518,0.0019781587,0.7076923,0.0034879667,0.00028681708,0.25983498,0.0024958283],"about_ca_topic_score_codex":0.000012932908,"about_ca_topic_score_gemma":0.0000033556187,"teacher_disagreement_score":0.8969828,"about_ca_system_score_codex":0.00015389052,"about_ca_system_score_gemma":0.0001777384,"threshold_uncertainty_score":0.9997016},"labels":[],"label_agreement":null},{"id":"W6885872122","doi":"10.1371/journal.pone.0273205.s004","title":"Validation of Ontario Health Insurance Plan (OHIP) billing diagnostic code-only case episodes.","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Health plan; Health insurance; Data collection; MEDLINE; Health care","score_opus":0.07045566143740138,"score_gpt":0.2752384256410823,"score_spread":0.2047827642036809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6885872122","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9679563,0.00018906541,0.00009160423,0.00001976856,0.00015227127,0.00021696043,0.030679122,0.0005000849,0.00019479718],"genre_scores_gemma":[0.98714745,0.000008819285,0.00018249395,0.00002854796,0.000059739235,0.00002186822,0.012473467,0.000025382511,0.00005225015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993032,0.000015908632,0.0002241291,0.00014014941,0.000090254,0.00022633589],"domain_scores_gemma":[0.99925524,0.00044484524,0.00006358492,0.00014190142,0.000028522783,0.00006593422],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000051789946,0.00011007358,0.0001581248,0.00006665152,0.000093178416,0.000011627198,0.00007929858,0.00003932546,0.0016527835],"category_scores_gemma":[0.00038586013,0.00011685615,0.000036032045,0.0002498412,0.0000027896945,0.00012216,0.000032775584,0.00016782132,0.00019523597],"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":[0.0000060576995,0.000013028043,0.0023670753,0.0012254134,0.00001775258,0.00076082523,0.002156207,0.9638798,0.0005623033,0.000008189523,0.011229289,0.01777402],"study_design_scores_gemma":[0.0047836294,0.001028843,0.1181143,0.038095906,0.00004795072,0.005472161,0.0009919367,0.27722254,0.45185652,0.000958919,0.096874446,0.0045528337],"about_ca_topic_score_codex":0.00015167338,"about_ca_topic_score_gemma":0.0018502945,"teacher_disagreement_score":0.6866573,"about_ca_system_score_codex":0.00012818993,"about_ca_system_score_gemma":0.000081376755,"threshold_uncertainty_score":0.9992598},"labels":[],"label_agreement":null},{"id":"W6889189236","doi":"10.25384/sage.c.6782841","title":"Enhancing involvement of people with multiple sclerosis in clinical trial design","year":2023,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Clinical trial; Clinical study design; Multiple sclerosis; Alternative medicine; Research design; Public health; Clinical Practice; Adaptive design","score_opus":0.22489413027793637,"score_gpt":0.35222718437197936,"score_spread":0.127333054094043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889189236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04865124,0.04787049,0.87946147,0.000079138736,0.0088663,0.0057508014,0.0021646018,0.0028042803,0.0043516867],"genre_scores_gemma":[0.3967012,0.33407453,0.20121649,0.00033557464,0.017099358,0.00025037557,0.00069452246,0.0124271205,0.037200842],"study_design_codex":"design_other","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99828047,0.00012941734,0.0007800199,0.00030296773,0.00024040055,0.00026674438],"domain_scores_gemma":[0.9983177,0.00058577163,0.00029857244,0.0007075079,0.000011853162,0.000078571444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014392607,0.00021676836,0.00059305027,0.00021581174,0.000024681225,0.000021463402,0.0005478931,0.00015680167,0.0002737858],"category_scores_gemma":[0.00032982585,0.00018680752,0.000055997683,0.0003072074,0.00002098553,0.00014027787,0.0002166783,0.0005811032,0.00001601294],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012007162,0.00093585096,0.0060955184,0.002463664,0.002090272,0.0006282394,0.0011584237,0.11998842,0.022496305,0.0000017969029,0.15226358,0.6798708],"study_design_scores_gemma":[0.3602464,0.006915542,0.019098237,0.3179468,0.0016558168,0.00010101555,0.0027441927,0.1293119,0.078644656,0.00034977507,0.07334507,0.009640635],"about_ca_topic_score_codex":0.000049697108,"about_ca_topic_score_gemma":0.002908358,"teacher_disagreement_score":0.67824495,"about_ca_system_score_codex":0.000033119595,"about_ca_system_score_gemma":0.000038747246,"threshold_uncertainty_score":0.76177895},"labels":[],"label_agreement":null},{"id":"W6892597408","doi":"10.5281/zenodo.11441015","title":"GlycoCare: Complete Overview of Blood Sugar Management Benefits, Ingredients, Pros, Cons, and Price?","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Blood sugar; Insulin; Sugar; Diabetes mellitus; Insulin sensitivity; Sugar production","score_opus":0.04421679733306264,"score_gpt":0.24257746730001611,"score_spread":0.19836066996695348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892597408","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009092467,0.033251442,0.0008170448,0.00007148216,0.000448927,0.0012127903,0.0010347479,0.0034274952,0.95882684],"genre_scores_gemma":[0.23096007,0.1470786,0.010437782,0.0006089667,0.0032691192,0.0000015080794,0.012427722,0.11637412,0.47884208],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889404,0.00005464835,0.00023256114,0.00034422358,0.00021777903,0.00025676077],"domain_scores_gemma":[0.9994487,0.000009019757,0.00007866071,0.00030810782,0.0000637034,0.00009177005],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015859751,0.00020729014,0.00023494867,0.00025838488,0.00020402715,0.00014924935,0.00039480854,0.00007607463,0.002453131],"category_scores_gemma":[0.000032441472,0.00022165528,0.000039582046,0.0003206058,0.00007937149,0.000052146086,0.00085666764,0.0002591727,0.0010424984],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019550009,0.0000721348,0.0000011381649,0.00970422,0.000568727,0.00008743826,0.00040097037,0.00032378684,0.0013322368,0.016103845,0.8774646,0.09392135],"study_design_scores_gemma":[0.00028557103,0.00007174755,0.000014884182,0.00090440025,0.00007960545,0.00005774304,0.000040181807,0.0001603313,0.00020918803,0.00009206686,0.99787587,0.00020840923],"about_ca_topic_score_codex":0.0000022731024,"about_ca_topic_score_gemma":1.9477179e-7,"teacher_disagreement_score":0.47998473,"about_ca_system_score_codex":0.000033231743,"about_ca_system_score_gemma":7.290513e-7,"threshold_uncertainty_score":0.9997353},"labels":[],"label_agreement":null},{"id":"W6893077940","doi":"10.5281/zenodo.13685285","title":"Dataset for the Rab10 antibody screening study","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Immunofluorescence; Immunoprecipitation; Antibody; Western blot; Indirect immunofluorescence; Blot","score_opus":0.052136556578602326,"score_gpt":0.3055819242164625,"score_spread":0.2534453676378602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893077940","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007684998,0.00034360428,0.008226268,0.00007662361,0.00046795752,0.0011088423,0.9887724,0.00070875004,0.00021870904],"genre_scores_gemma":[0.0010088604,0.00018509061,0.00008259046,0.00006300121,0.00053691457,1.6604034e-7,0.99660164,0.0014510388,0.00007072195],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99837035,0.00013480865,0.00030783948,0.0004716229,0.00030468323,0.00041067248],"domain_scores_gemma":[0.9987474,0.00013127249,0.000065142194,0.00082124886,0.000133711,0.00010122379],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007097399,0.00027710857,0.00022793662,0.00019093833,0.0020039873,0.00087784894,0.0015495613,0.00008718537,0.0015255411],"category_scores_gemma":[0.00034639088,0.00023503003,0.000070412796,0.0004309702,0.000071417104,0.00016747894,0.0013848168,0.00078481995,0.0063766446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025054529,0.00003968429,3.169446e-8,0.00027082468,0.00012570803,0.000031545875,0.00008839895,0.0030383405,0.00024715197,0.000007820505,0.9812799,0.014845509],"study_design_scores_gemma":[0.0002853506,0.00018702766,0.0000052338996,0.00007512594,0.00010893418,0.000059603695,0.00023945396,0.0028017664,0.000060831502,0.000016606851,0.9959017,0.00025836402],"about_ca_topic_score_codex":0.000004780828,"about_ca_topic_score_gemma":7.1692307e-7,"teacher_disagreement_score":0.014621771,"about_ca_system_score_codex":0.000062895066,"about_ca_system_score_gemma":0.0000018578188,"threshold_uncertainty_score":0.9993872},"labels":[],"label_agreement":null},{"id":"W6905971324","doi":"10.15468/dl.q2p9fx","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arctic; Download; Marine mammal; Mammal; Ursus maritimus; Range (aeronautics)","score_opus":0.013399266333625993,"score_gpt":0.21434823620518872,"score_spread":0.20094896987156274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6905971324","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005826965,0.000084691885,0.00001832567,0.000018228688,0.001467502,0.00014719088,0.9970619,0.00048638327,0.00013309476],"genre_scores_gemma":[0.00004767091,0.000087016655,0.0000012689554,0.000107433414,0.0000029156708,0.0000014414501,0.99975216,1.9166606e-8,5.030618e-8],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913245,0.000013263034,0.0002628281,0.00014094224,0.00021658646,0.00023391224],"domain_scores_gemma":[0.99951464,0.000013557811,0.00004997842,0.0002684491,0.00005462409,0.000098730976],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000864095,0.00024726734,0.00019202621,0.00006505521,0.00011658591,0.00010061605,0.00027904287,0.00021106531,0.00037664772],"category_scores_gemma":[0.000030219888,0.000258268,0.000105272375,0.000252337,0.000053176227,0.0006446302,0.00017404104,0.000399284,0.4117145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066320144,0.0000033031824,0.000012725162,0.0005151258,0.000019977422,0.00000500868,0.000018364657,0.0013675219,2.5508442e-7,3.095571e-8,0.996055,0.001996027],"study_design_scores_gemma":[0.00008461569,0.00001131601,0.0000030781314,0.0000069160847,0.000033039287,0.000007401113,0.0000335917,0.000005273451,0.000028331977,0.0000017674352,0.9995327,0.00025196758],"about_ca_topic_score_codex":0.000009382286,"about_ca_topic_score_gemma":0.0000020615144,"teacher_disagreement_score":0.41133785,"about_ca_system_score_codex":0.00021680267,"about_ca_system_score_gemma":0.00002552822,"threshold_uncertainty_score":0.99998695},"labels":[],"label_agreement":null},{"id":"W6920403072","doi":"10.60692/m80ve-75853","title":"Correction to: Frailty, nutrition-related parameters, and mortality across the adult age spectrum","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"","keywords":"Table (database); Spectrum (functional analysis); Population; Broad spectrum","score_opus":0.030501940516889335,"score_gpt":0.24460251581796322,"score_spread":0.21410057530107388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920403072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97297037,0.0000022171237,0.02318286,0.000021018786,0.001931846,0.0003333447,0.000024617904,0.00043226974,0.0011014569],"genre_scores_gemma":[0.9995939,2.3882566e-7,0.000102690305,0.000097918375,0.000113894486,0.000026875328,0.0000073254873,0.000008521939,0.00004861857],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99923915,0.000023896977,0.00032783407,0.00009158114,0.000114463364,0.00020305898],"domain_scores_gemma":[0.9996064,0.000013828189,0.000066821194,0.00019761529,0.00005488873,0.000060456838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014388484,0.00012485424,0.00012152716,0.00004649029,0.00025367358,0.00013122603,0.00008291992,0.000053263062,0.000004061643],"category_scores_gemma":[0.00002008221,0.00009436085,0.00003187407,0.00019696189,0.000038514983,0.00040301945,0.00003638271,0.00012197404,0.00021644658],"study_design_candidate":"qualitative","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":[0.0003125001,0.000013948763,0.11141814,0.0029339364,0.00051879074,0.000039692408,0.7938881,0.0664438,0.00055937644,0.0010368279,0.005274849,0.017560024],"study_design_scores_gemma":[0.0037444944,0.0004935168,0.42532238,0.0019851062,0.00010330593,0.00060569035,0.05744245,0.40959984,0.096870944,0.000102077196,0.0020968765,0.0016333094],"about_ca_topic_score_codex":0.000006963741,"about_ca_topic_score_gemma":0.000002930991,"teacher_disagreement_score":0.73644567,"about_ca_system_score_codex":0.000051544765,"about_ca_system_score_gemma":0.0000023969285,"threshold_uncertainty_score":0.38479236},"labels":[],"label_agreement":null},{"id":"W6920479254","doi":"10.60692/bfg29-hd855","title":"Roadmap on emerging hardware and technology for machine learning","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Von Neumann architecture; Neuromorphic engineering; Artificial neural network; Applications of artificial intelligence; Architecture; Efficient energy use; Unconventional computing; Reconfigurable computing; Emerging technologies","score_opus":0.026243367642311777,"score_gpt":0.2040163267182287,"score_spread":0.17777295907591692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920479254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7670493,0.000022701272,0.22959454,0.00023716793,0.00022470909,0.0003536401,0.000028304115,0.0016513005,0.0008383259],"genre_scores_gemma":[0.999381,2.0547795e-7,0.00042350878,0.00008591883,0.00005557248,0.00001936533,0.000007838082,0.000011122774,0.000015443004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951786,0.000006559608,0.00021014857,0.00007481638,0.000055953067,0.0001346476],"domain_scores_gemma":[0.9998029,0.0000065127297,0.000051623123,0.00006290208,0.000030111138,0.00004594504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051159484,0.00010277698,0.00012592747,0.00010302576,0.00011098176,0.000032855503,0.00005372312,0.000050398387,0.0000019168508],"category_scores_gemma":[0.00002653036,0.00009349505,0.0000197728,0.00013403331,0.000006304633,0.0002186303,0.000023692688,0.00012101966,0.000046215122],"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":[0.00023335448,0.0000013313403,0.046085555,0.006618938,0.00014574116,0.0000137947745,0.12863739,0.7610994,0.00082480017,0.001833057,0.00024020029,0.05426646],"study_design_scores_gemma":[0.0010678369,0.00015406622,0.00035415357,0.00019594823,0.000014099107,0.000028016868,0.0073495945,0.9643713,0.018738536,0.0000028484992,0.0074206833,0.00030291567],"about_ca_topic_score_codex":6.580256e-8,"about_ca_topic_score_gemma":8.625207e-9,"teacher_disagreement_score":0.23233172,"about_ca_system_score_codex":0.000019110088,"about_ca_system_score_gemma":0.0000018917176,"threshold_uncertainty_score":0.38126177},"labels":[],"label_agreement":null},{"id":"W6920750241","doi":"10.6084/m9.figshare.14188271.v1","title":"2D Resistive Switching Based on Amorphous Zinc–Tin Oxide Schottky Diodes","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"Fundação para a Ciência e a Tecnologia; European Commission; Trent University; Nottingham Trent University","keywords":"Schottky diode; Amorphous solid; Oxide; Resistive touchscreen; Diode","score_opus":0.028374567313926256,"score_gpt":0.24183509767341985,"score_spread":0.21346053035949358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920750241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8226015,0.004237037,0.004699115,0.0004190151,0.0012508781,0.0011827549,0.10168046,0.007418976,0.05651026],"genre_scores_gemma":[0.9930688,0.0000019641157,0.0010604868,0.00032565818,0.00014898888,0.00002842804,0.0051716603,0.000050271785,0.00014370901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917156,0.00003047985,0.00014777997,0.00024460646,0.00014121387,0.00026436566],"domain_scores_gemma":[0.9992919,0.00029671518,0.00003138945,0.0002480181,0.00004971723,0.00008225451],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000028830214,0.00017100363,0.00015532083,0.00004533479,0.00013210336,0.000048185197,0.00012322066,0.000061598854,0.008193975],"category_scores_gemma":[0.0007444558,0.00018078832,0.00007379256,0.00018950179,0.000002042642,0.000109010754,0.00004998202,0.00033660082,0.00063044514],"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":[0.000054054344,0.000051442366,0.000069844595,0.000746125,0.000041066756,0.0015632645,0.00013761793,0.763585,0.14110442,0.00003349127,0.076765254,0.01584845],"study_design_scores_gemma":[0.00046954758,0.00003628244,0.002411993,0.0038730411,0.000010364225,0.000022731834,0.0000555828,0.071363054,0.88495356,0.00008211731,0.036174633,0.00054708554],"about_ca_topic_score_codex":6.644482e-7,"about_ca_topic_score_gemma":0.0000041843555,"teacher_disagreement_score":0.74384916,"about_ca_system_score_codex":0.00006162579,"about_ca_system_score_gemma":0.000025443225,"threshold_uncertainty_score":0.9927127},"labels":[],"label_agreement":null},{"id":"W6926285796","doi":"10.20382/jocg.v13i1a13","title":"Recognizing weighted and seeded disk graphs","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Geometry (Carleton University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Unit disk graph; Intersection graph; Vertex (graph theory); Planar graph; Intersection (aeronautics); Graph; Stars","score_opus":0.008482291990980738,"score_gpt":0.18264390229779687,"score_spread":0.17416161030681612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926285796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9750182,0.0002455269,0.023260582,0.000069443864,0.0003602118,0.000040114428,0.0000084168405,0.000046763635,0.0009507072],"genre_scores_gemma":[0.99632984,0.000040405,0.0033933502,0.000052454994,0.000053104453,5.9530546e-8,0.0000048135867,0.000011414032,0.000114553455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993031,0.000046097673,0.00019021414,0.00008940913,0.00024340213,0.000127799],"domain_scores_gemma":[0.99944884,0.00020024065,0.00012686975,0.000041450177,0.00009158528,0.00009101701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014187323,0.00009521967,0.00016551718,0.00073393225,0.00024656925,0.000016232132,0.00014341202,0.000021349253,0.000056781104],"category_scores_gemma":[0.000011915066,0.00011067699,0.00008085352,0.00086749595,0.00002644513,0.00026168814,0.000085552056,0.00034762043,8.8831956e-7],"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":[0.00007794915,0.00002643542,0.000985311,0.00002579464,0.00010610318,0.00025869536,0.00015770967,0.9841976,0.001934941,0.0014170244,0.00028645803,0.010525948],"study_design_scores_gemma":[0.01842266,0.0031302085,0.062314548,0.00042365023,0.00080718717,0.00806209,0.014283368,0.48973584,0.010948358,0.10176829,0.2864581,0.0036457162],"about_ca_topic_score_codex":6.483261e-7,"about_ca_topic_score_gemma":2.096444e-7,"teacher_disagreement_score":0.4944618,"about_ca_system_score_codex":0.000103308725,"about_ca_system_score_gemma":0.000025525555,"threshold_uncertainty_score":0.45132768},"labels":[],"label_agreement":null},{"id":"W6931115339","doi":"10.5281/zenodo.4598673","title":"Macrocorsinae Vondracek 1963","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Subfamily; Monophyly; Taxonomy (biology); Synonym (taxonomy); Genus","score_opus":0.026625186252058654,"score_gpt":0.2289627814002716,"score_spread":0.20233759514821295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931115339","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43012574,0.0009833325,0.063307784,0.00071051245,0.0007659452,0.00046667742,0.00014193066,0.007027562,0.4964705],"genre_scores_gemma":[0.9955078,0.00010736219,0.0008984296,0.00012275498,0.00018882155,1.18341195e-8,0.0004306914,0.0012621632,0.0014819715],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991394,0.00007982596,0.00014261928,0.00021721676,0.00014530364,0.00027559622],"domain_scores_gemma":[0.99941415,0.000017012422,0.000020970581,0.00024990985,0.00018885187,0.0001091387],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013470768,0.00009921581,0.00009560631,0.000065696775,0.0009553262,0.00024576462,0.00030968257,0.000034934474,0.0049193557],"category_scores_gemma":[0.00016374225,0.00011646731,0.000035925277,0.00038790467,0.000039771014,0.00017359384,0.00037565018,0.0002324081,0.0040417532],"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":[0.000033657456,0.00009292516,0.0000028957186,0.0002465632,0.000060818824,0.0003166311,0.0013468912,0.017559357,0.370943,0.0046954537,0.14032635,0.46437544],"study_design_scores_gemma":[0.00020956097,0.00003538428,0.0001000897,0.000022529575,0.000005071956,0.00026134457,0.00013531651,0.0020102093,0.04296413,0.00021293106,0.9538967,0.00014672315],"about_ca_topic_score_codex":4.3423825e-7,"about_ca_topic_score_gemma":7.6978665e-8,"teacher_disagreement_score":0.8135704,"about_ca_system_score_codex":0.00006742935,"about_ca_system_score_gemma":0.0000012330307,"threshold_uncertainty_score":0.9967337},"labels":[],"label_agreement":null},{"id":"W6931414225","doi":"10.5281/zenodo.5060670","title":"Palaeictops Matthew 1899","year":2016,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sagittal plane; Foramen; Crest; Condyle; Mastoid process; Fossa; Confusion","score_opus":0.027824297183115864,"score_gpt":0.22107874181008258,"score_spread":0.19325444462696673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931414225","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39842373,0.0002903867,0.18025176,0.0013293969,0.0006890547,0.00068503415,0.0001883288,0.010759832,0.4073825],"genre_scores_gemma":[0.99758387,0.000072752526,0.00023256302,0.000058009253,0.00014757825,1.1367097e-8,0.000051344756,0.0009783576,0.000875526],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992039,0.0000618849,0.00013973114,0.00018521164,0.00013554955,0.00027370872],"domain_scores_gemma":[0.99949896,0.00002629872,0.00002336621,0.000251025,0.0000953734,0.000104970364],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017270794,0.000098955235,0.00008533267,0.00008034243,0.00066484103,0.00012905315,0.00041422126,0.000032341988,0.005417482],"category_scores_gemma":[0.00014259283,0.00008113994,0.000030518946,0.00019842257,0.000052910575,0.00019174199,0.00028942528,0.00011394479,0.008277383],"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":[0.000025003437,0.000029573093,0.0000036801853,0.000074943986,0.000030065086,0.000024015251,0.00036550927,0.00085574924,0.24064311,0.004248843,0.08776389,0.6659356],"study_design_scores_gemma":[0.00029107806,0.000051110612,0.00019523322,0.00004293816,0.00000363644,0.00007484965,0.000033198372,0.00033804897,0.017510839,0.0004117191,0.980897,0.00015031049],"about_ca_topic_score_codex":2.8141037e-7,"about_ca_topic_score_gemma":3.4435786e-8,"teacher_disagreement_score":0.89313316,"about_ca_system_score_codex":0.00006616733,"about_ca_system_score_gemma":4.8312876e-7,"threshold_uncertainty_score":0.9954917},"labels":[],"label_agreement":null},{"id":"W6966954787","doi":"10.48550/arxiv.2202.06367","title":"Information Density in Multi-Layer Resistive Memories","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Resistive touchscreen; Encoding (memory); Series (stratigraphy); Selection (genetic algorithm); Resistive random-access memory; Information theory; Simple (philosophy)","score_opus":0.08759476002226692,"score_gpt":0.19869233758539695,"score_spread":0.11109757756313003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6966954787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9294961,0.000035825848,0.06778883,0.000005729608,0.0006024064,0.00020832216,0.00002044774,0.00030573495,0.0015366189],"genre_scores_gemma":[0.99919987,0.00008211724,0.000340739,0.000028096681,0.000025746265,0.0000010596289,0.000044840108,0.000015020749,0.00026251303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926984,0.000054743097,0.00017639987,0.0002283517,0.00005009885,0.00022053628],"domain_scores_gemma":[0.99945164,0.000067007364,0.00007955341,0.00030413372,0.000042189087,0.000055476226],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000115916075,0.00021045777,0.00022554751,0.00022764337,0.000118623546,0.000021154083,0.0002757819,0.0001291339,0.000050221104],"category_scores_gemma":[0.000041072304,0.00028067146,0.00007934476,0.0003225388,0.000033914926,0.00040483102,0.00059478916,0.0008262705,0.000025389523],"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":[0.00003691117,0.0000108915165,0.0016183285,0.00011018508,0.000019660978,0.00010418057,0.000493595,0.9962735,0.00009307935,0.0009776596,0.000040108214,0.00022188411],"study_design_scores_gemma":[0.0012009707,0.000035701407,0.029160438,0.00014691515,0.00006706018,0.0000058436976,0.0021890295,0.95418364,0.004858108,0.005222145,0.0018797867,0.0010503714],"about_ca_topic_score_codex":0.00003037108,"about_ca_topic_score_gemma":0.000075625874,"teacher_disagreement_score":0.06970379,"about_ca_system_score_codex":0.00037006236,"about_ca_system_score_gemma":0.000030649528,"threshold_uncertainty_score":0.99996454},"labels":[],"label_agreement":null},{"id":"W7019737871","doi":"","title":"Implementation of spiking neural network on field-programmable gate array for image classification","year":2023,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Contextual image classification; Pattern recognition (psychology); Image (mathematics); Degree (music); Image processing; Spiking neural network; Statistical classification; Feature extraction","score_opus":0.021702938684434965,"score_gpt":0.2693499929365914,"score_spread":0.24764705425215644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019737871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.954977,0.00008694219,0.028780302,0.0135645475,0.0011127486,0.0007242618,0.000020708707,0.00031673888,0.00041675623],"genre_scores_gemma":[0.9086534,0.00077431847,0.03626509,0.00003264289,0.0006936172,0.000012576793,0.0020645186,0.000276479,0.051227372],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994109,0.000016870916,0.000030329595,0.000193716,0.00012388092,0.00022429672],"domain_scores_gemma":[0.9993539,0.00010930911,0.00024356201,0.00016668292,0.00009116274,0.000035394438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009981094,0.00015098095,0.00024603776,0.00011943387,0.00012673574,0.000008833343,0.00015272395,0.00011655815,0.000002701132],"category_scores_gemma":[0.000011227428,0.00020693631,0.00013028215,0.00023050551,0.0000152182765,0.00013900739,0.000009288459,0.00016905506,0.0000032951557],"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":[0.0009104639,0.00003432595,0.00039325762,0.0056138514,0.00041655274,0.000042017207,0.0038975212,0.24295029,0.23263693,0.0023190444,0.25421703,0.25656873],"study_design_scores_gemma":[0.0055380054,0.0026553136,0.0110947555,0.0036586227,0.0011437188,0.000008508796,0.17503802,0.098184295,0.31490242,0.000642251,0.38437355,0.0027605242],"about_ca_topic_score_codex":0.0000058036126,"about_ca_topic_score_gemma":0.000370509,"teacher_disagreement_score":0.2538082,"about_ca_system_score_codex":0.00004760635,"about_ca_system_score_gemma":0.00001756799,"threshold_uncertainty_score":0.84386176},"labels":[],"label_agreement":null},{"id":"W7033551687","doi":"","title":"Politique institutionnelle d'évaluation des apprentissages de l'École supérieure de danse du Québec","year":2001,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Power (physics); Politics; Context (archaeology); Identity (music)","score_opus":0.012479621871034225,"score_gpt":0.22858879758619727,"score_spread":0.21610917571516305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033551687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5763254,0.014828677,0.1610592,0.007058313,0.00042356868,0.00097479805,0.00022704255,0.0006809132,0.23842208],"genre_scores_gemma":[0.7156529,0.00455748,0.010565967,0.001079839,0.0017616671,0.00027393454,0.00006942333,0.0004998639,0.26553893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958397,0.0005409462,0.00088694936,0.0008697283,0.00084290473,0.0010197615],"domain_scores_gemma":[0.992864,0.0056007057,0.0004666772,0.0005229172,0.00008866839,0.00045702918],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055216084,0.0009676249,0.00068706594,0.0009521489,0.00072068605,0.0003507225,0.00086963136,0.00030718415,0.013198387],"category_scores_gemma":[0.0033180905,0.0010937442,0.00040665394,0.0008888768,0.00073769945,0.0007835231,0.00037544197,0.0009321969,0.00044092792],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00033650297,0.0012198654,0.2727528,0.0023793676,0.0011231855,0.0004449358,0.025151568,0.51879895,0.027689662,0.056188017,0.0754332,0.018481972],"study_design_scores_gemma":[0.0027044392,0.00029195956,0.21356793,0.0021998514,0.00042181983,0.0010605723,0.0008776234,0.04161963,0.010802151,0.0036250926,0.7203922,0.0024367045],"about_ca_topic_score_codex":0.38830173,"about_ca_topic_score_gemma":0.9657522,"teacher_disagreement_score":0.64495903,"about_ca_system_score_codex":0.013212003,"about_ca_system_score_gemma":0.039266657,"threshold_uncertainty_score":0.9991513},"labels":[],"label_agreement":null},{"id":"W7034355337","doi":"","title":"Uncharted Paths: The Use of Traditional, Complementary and Alternative Medicine (TCAM) among Sub-Saharan Africans living in the Greater Toronto Area (GTA)","year":2019,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ethnic group; Residence; Context (archaeology); Sociocultural evolution; Identity (music); Immigration; Population; Country of origin","score_opus":0.03184337627175945,"score_gpt":0.20168751630313667,"score_spread":0.1698441400313772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034355337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99608594,0.00011872587,0.00013657798,0.0012849679,0.00024327003,0.0005271628,0.00011754798,0.00008814341,0.0013976874],"genre_scores_gemma":[0.9963238,0.00078477996,0.000076312645,0.00009210053,0.000078576406,0.0000020386358,0.00046070537,0.000036572597,0.0021450762],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9989132,0.00016511169,0.00019232144,0.0002714609,0.00023425891,0.0002236358],"domain_scores_gemma":[0.99880457,0.00072057836,0.00014211319,0.0002539481,0.000025549121,0.0000532373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005856181,0.00028561987,0.00033174813,0.0001308125,0.00013413045,0.000026931446,0.0003504787,0.000092546405,0.00015344263],"category_scores_gemma":[0.000015198804,0.00021412042,0.000072370596,0.00020717645,0.000099980934,0.0012125578,0.000052424613,0.00037133013,9.92228e-7],"study_design_candidate":"observational","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":[0.0019562545,0.0006665894,0.11554541,0.0044468483,0.0028606593,0.0017010003,0.35764045,0.079936944,0.005829257,0.0075618243,0.41677627,0.0050785015],"study_design_scores_gemma":[0.0030492477,0.0008843312,0.8124141,0.006206055,0.00067888515,0.0000055439577,0.12524961,0.008026677,0.00849298,0.00045274236,0.032374866,0.0021649634],"about_ca_topic_score_codex":0.002333476,"about_ca_topic_score_gemma":0.0008618236,"teacher_disagreement_score":0.6968687,"about_ca_system_score_codex":0.00008440398,"about_ca_system_score_gemma":0.000020579882,"threshold_uncertainty_score":0.87315774},"labels":[],"label_agreement":null},{"id":"W7036175604","doi":"","title":"Avg ((1-888-879-0163)) customer service Avg tech support phone number","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phone; Service (business); Customer service; Toll; Customer satisfaction","score_opus":0.01174598998018955,"score_gpt":0.25399633830632284,"score_spread":0.2422503483261333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036175604","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008717504,0.000005785072,0.005133694,0.00008333752,0.001446676,0.0008758063,0.00006700496,0.002078553,0.9894374],"genre_scores_gemma":[0.0018374549,0.00032236506,0.0015154078,0.0002506307,0.00061922695,0.00019054666,0.000047903348,0.00092541176,0.99429107],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955225,0.00020151977,0.00077362696,0.0019915772,0.0005369965,0.0009737652],"domain_scores_gemma":[0.9958517,0.0002918122,0.00033966367,0.0030183534,0.00010984523,0.00038860703],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010249633,0.0009073976,0.0008564513,0.0003092347,0.00015019205,0.0000846279,0.0014073955,0.00079046993,0.9060238],"category_scores_gemma":[0.00024503795,0.00093682855,0.00030271758,0.00044364968,0.00011477255,0.00024665572,0.0011070232,0.0011294131,0.98935497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004045949,0.00010749223,0.00027430526,0.0006798881,0.0003055285,0.000121428086,0.0002545048,0.0017907037,0.009348816,0.00013041572,0.97782093,0.009125521],"study_design_scores_gemma":[0.0006573487,2.481827e-7,0.000064715416,0.0004215618,0.00009565723,0.00014648189,0.000021557054,0.00024892716,0.015046986,0.00036190482,0.9818818,0.0010527989],"about_ca_topic_score_codex":0.000082463084,"about_ca_topic_score_gemma":0.000047584155,"teacher_disagreement_score":0.08333115,"about_ca_system_score_codex":0.00032943723,"about_ca_system_score_gemma":0.00009949031,"threshold_uncertainty_score":0.9993082},"labels":[],"label_agreement":null},{"id":"W7037445951","doi":"","title":"The Electronic Device with Resistance Switching Behavior based on Different Mechanisms","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Neuromorphic engineering; Memristor; Von Neumann architecture; Miniaturization; Energy consumption; Electronics; Resistive random-access memory; Resistive touchscreen","score_opus":0.007481300065932274,"score_gpt":0.19123672834157257,"score_spread":0.1837554282756403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037445951","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99814796,0.00005977441,0.00080656755,0.00007423898,0.00024284548,0.00025112953,0.00000560094,0.00028059512,0.00013127991],"genre_scores_gemma":[0.91588223,0.00007759146,0.0002790554,0.000007956492,0.000021639737,0.000002159173,0.00010044437,0.00007185385,0.08355709],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990625,0.000028250655,0.00008482147,0.00023970749,0.00023735697,0.00034741414],"domain_scores_gemma":[0.9994345,0.00010877516,0.00009595743,0.00026724636,0.000045236367,0.000048297068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000709948,0.00024139482,0.00023406702,0.000105065956,0.00038626115,0.000018779765,0.00029614806,0.00011038953,0.000007439346],"category_scores_gemma":[0.000004662683,0.00020159263,0.000075748045,0.00018503405,0.000015168328,0.0000650876,0.000016010295,0.00045633872,0.000011411552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009799603,0.0004445557,0.00029451321,0.006328121,0.0012361959,0.0013723337,0.11607606,0.2818948,0.506773,0.012149086,0.0014584935,0.062173232],"study_design_scores_gemma":[0.0076602,0.0034474216,0.030812146,0.00924313,0.0025321578,0.00001197646,0.35422143,0.08557223,0.49345517,0.00440891,0.002410107,0.0062251026],"about_ca_topic_score_codex":0.00019913241,"about_ca_topic_score_gemma":0.05477919,"teacher_disagreement_score":0.23814538,"about_ca_system_score_codex":0.00012868976,"about_ca_system_score_gemma":0.000028166918,"threshold_uncertainty_score":0.9624686},"labels":[],"label_agreement":null},{"id":"W7043072113","doi":"","title":"Report on the Residence Satisfaction Survey","year":2001,"lang":"en","type":"article","venue":"WinnSpace (University of Winnipeg)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Residence; Strengths and weaknesses; Survey research; Survey data collection; Survey instrument; Survey methodology","score_opus":0.027986078458669357,"score_gpt":0.21773492878920117,"score_spread":0.18974885033053182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043072113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886566,0.000034112163,0.0070015574,0.0007174836,0.0001627787,0.00008110235,0.0000029152284,0.00014623468,0.003197265],"genre_scores_gemma":[0.9980569,0.000059088703,0.00024718346,0.000027264177,0.00003374835,5.9456717e-8,0.000003004291,0.000009001066,0.001563754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99945444,0.00005231638,0.00006951785,0.00013976752,0.0001399259,0.00014402055],"domain_scores_gemma":[0.99938935,0.00019756684,0.000055676875,0.00027557148,0.000043842847,0.000038004262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024606753,0.000087536064,0.000110629226,0.00005065239,0.00017429088,0.0000057972966,0.00013586904,0.00004369747,0.00007572952],"category_scores_gemma":[0.00007003792,0.00008751455,0.000041978907,0.00028522406,0.000060140595,0.00017544853,0.00003136576,0.00017258614,0.000034404246],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071832055,0.00008805026,0.46318412,0.00016110059,0.00023698031,0.003364601,0.0035190408,0.3442161,0.08368078,0.00292046,0.048378944,0.0495315],"study_design_scores_gemma":[0.00021193692,0.000057619432,0.98747504,0.00008035162,0.000013050769,0.000100806545,0.00068508036,0.0030716462,0.003185066,0.00018995494,0.004722482,0.00020693788],"about_ca_topic_score_codex":0.0002973077,"about_ca_topic_score_gemma":0.00068387395,"teacher_disagreement_score":0.5242909,"about_ca_system_score_codex":0.000038933184,"about_ca_system_score_gemma":0.000009216209,"threshold_uncertainty_score":0.35687396},"labels":[],"label_agreement":null},{"id":"W7043405718","doi":"","title":"Stabilité du gradient dans les réseaux de neurones récurrents, à décharges et conventionnels","year":2024,"lang":"fr","type":"dissertation","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"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":"CHIST-ERA; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Domain (mathematical analysis); Stabiliser; Agrégation","score_opus":0.01501669880676029,"score_gpt":0.22414672404078134,"score_spread":0.20913002523402105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043405718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91215116,0.01665329,0.06377585,0.00013554964,0.0031843458,0.0003688805,0.000059627324,0.00010216035,0.0035691378],"genre_scores_gemma":[0.99342704,0.0023965703,0.0021178387,0.000005060923,0.00012831234,0.0000011780712,0.00008741072,0.00003644311,0.0018001667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99832696,0.00017951048,0.0004341268,0.00042430297,0.00030610562,0.00032899046],"domain_scores_gemma":[0.99906874,0.0001760582,0.00014990891,0.0002880173,0.00019279607,0.0001245031],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020077125,0.00041362192,0.0004544956,0.00017526285,0.0006304049,0.000022235796,0.0006915814,0.0002486042,0.000054462067],"category_scores_gemma":[0.00008950726,0.00042803944,0.0005827709,0.00033428374,0.00048492,0.00026327174,0.00023971316,0.0006202267,0.00003756779],"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":[0.00014067157,0.0004067609,0.0054576485,0.022918433,0.00037171695,0.00004180888,0.03805376,0.6248459,0.24067013,0.06449384,0.00022955646,0.0023697412],"study_design_scores_gemma":[0.0014158098,0.00025172794,0.11576459,0.029835043,0.0014938667,0.00013875711,0.0060113743,0.27267218,0.55018777,0.0034364841,0.017656354,0.0011360762],"about_ca_topic_score_codex":0.0010324036,"about_ca_topic_score_gemma":0.009908686,"teacher_disagreement_score":0.35217378,"about_ca_system_score_codex":0.0012756466,"about_ca_system_score_gemma":0.00023309384,"threshold_uncertainty_score":0.99981713},"labels":[],"label_agreement":null},{"id":"W7084753658","doi":"10.82161/4j6c-mc05","title":"Impact of Limb Loading Asymmetry during Walking on Hip and Knee Function in Community-Dwelling Older Adults: A Prospective Cohort Study","year":2025,"lang":"en","type":"other","venue":"World Physiotherapy Congress Archive","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"WOMAC; Osteoarthritis; Prospective cohort study; Biofeedback; Cohort study; Foot (prosody); Lower limb; Gait","score_opus":0.005046486462745633,"score_gpt":0.26544761783800996,"score_spread":0.2604011313752643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084753658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9639571,0.0012234551,0.0004622478,8.5521077e-7,0.00046194514,0.0022206719,0.000048523707,0.00035524473,0.03126998],"genre_scores_gemma":[0.9947018,0.00016316792,0.000101644255,0.00001122273,0.00017180256,0.000081714155,0.0000168718,0.00030765575,0.0044441074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984073,0.00024939908,0.0003722422,0.00042575467,0.0001866153,0.0003586737],"domain_scores_gemma":[0.99879587,0.00046616746,0.00020631515,0.00044520522,0.000025166186,0.000061279614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014102778,0.0005219754,0.00076396525,0.0010774494,0.00016011053,0.00003259977,0.00019583371,0.00008171508,0.000071287315],"category_scores_gemma":[0.000015857462,0.00050570874,0.00014434058,0.0004865013,0.00007027799,0.000079624755,0.000083630104,0.0012032381,0.0000020251925],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007066592,0.0031568427,0.741084,0.007651629,0.007342373,0.0001317414,0.02392073,0.08864007,0.027894242,0.0003999862,0.0012852456,0.091426566],"study_design_scores_gemma":[0.0053744265,0.0010555817,0.9695263,0.0146118095,0.0000962836,0.0000023374548,0.001059565,0.0033988624,0.0032126827,0.0004955789,0.000078546705,0.0010880053],"about_ca_topic_score_codex":0.00037735642,"about_ca_topic_score_gemma":0.00045924133,"teacher_disagreement_score":0.22844234,"about_ca_system_score_codex":0.00015780043,"about_ca_system_score_gemma":0.00002036586,"threshold_uncertainty_score":0.99973947},"labels":[],"label_agreement":null},{"id":"W7091599669","doi":"10.1016/j.mtphys.2025.101890","title":"A bio-inspired tactile sensor for artificial tactile synapses","year":2025,"lang":"en","type":"article","venue":"Materials Today Physics","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Guangdong Provincial Pearl River Talents Program; National Natural Science Foundation of China; Royal Society of Canada","keywords":"Neuromorphic engineering; Tactile sensor; Piezoresistive effect; Memristor; Artificial neuron; Tactile perception; Haptic technology; Artificial neural network","score_opus":0.018863567257044706,"score_gpt":0.25862270865627895,"score_spread":0.23975914139923424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7091599669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9627607,0.000030829542,0.033014912,0.000048085803,0.0023876578,0.00036702972,0.000121990466,0.00056151603,0.0007073162],"genre_scores_gemma":[0.99778324,0.0000063237344,0.0011111257,0.00009569212,0.0006519955,0.00005989493,0.00003859239,0.000037145528,0.00021601586],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991772,0.000019210434,0.00025148224,0.00020158068,0.00006159199,0.00028889932],"domain_scores_gemma":[0.9995683,0.00011441317,0.00004599736,0.00020295521,0.000035453773,0.000032887747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006535293,0.00018534342,0.00027568734,0.00003475591,0.00013569611,0.000069326015,0.00010512771,0.00006643812,0.000034025827],"category_scores_gemma":[0.00003444885,0.00018865122,0.00006376294,0.00013559517,0.000022152026,0.00014240168,0.000035339734,0.00006161935,0.000029360444],"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":[0.000048040747,0.00002261002,0.0000028569846,0.00020152015,0.000030336072,0.000002516739,0.000046333324,0.006961703,0.9841781,0.0018189262,0.000849385,0.0058376263],"study_design_scores_gemma":[0.0001720077,0.000025455241,0.000025704374,0.00005157573,0.00002496528,0.0000010044089,0.00003313208,0.0018403655,0.98704034,0.0049327724,0.0056658555,0.00018683042],"about_ca_topic_score_codex":0.0000029069886,"about_ca_topic_score_gemma":8.1518704e-7,"teacher_disagreement_score":0.03502255,"about_ca_system_score_codex":0.000036536236,"about_ca_system_score_gemma":0.000014473419,"threshold_uncertainty_score":0.7692973},"labels":[],"label_agreement":null},{"id":"W7094948774","doi":"10.1007/s00348-025-04127-5","title":"An evaluation of event-based cameras for particle image velocimetry","year":2025,"lang":"en","type":"article","venue":"Experiments in Fluids","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Particle image velocimetry; Particle tracking velocimetry; Velocimetry; Dynamic range; High dynamic range; Laser; Temporal resolution; Image resolution; Image processing","score_opus":0.03265849366607397,"score_gpt":0.37946946609823856,"score_spread":0.3468109724321646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7094948774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92378014,0.0005491524,0.07485196,0.000007452634,0.00024071115,0.00024321205,0.0000022087615,0.000051398554,0.00027373692],"genre_scores_gemma":[0.9953262,0.0000010423581,0.0045199464,0.000035797108,0.000018403807,0.00006708999,0.000006089427,0.000009985663,0.000015402851],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942756,0.000037621732,0.00018618272,0.00011068912,0.00010969916,0.0001282352],"domain_scores_gemma":[0.9997344,0.00004226594,0.000013114736,0.00014612082,0.00004433115,0.000019748357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027323412,0.000071490846,0.00009852194,0.000054209828,0.000027520118,0.000006182851,0.00007414456,0.000027808906,0.000022578235],"category_scores_gemma":[0.00004349196,0.000079069134,0.000025716532,0.00015958301,0.000015368232,0.00010601086,0.0000085281235,0.00004125468,0.0000012890829],"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":[0.000019729718,0.00005046613,0.0003285349,0.00002458081,0.000006767476,2.559413e-7,0.00017920067,0.09554196,0.8959061,0.00013905713,0.00004201274,0.0077613606],"study_design_scores_gemma":[0.00060524384,0.000026000984,0.00045847995,0.000022348344,0.00000508888,3.556317e-8,0.000076920915,0.38845852,0.6101566,0.00011640213,0.00003183288,0.00004254711],"about_ca_topic_score_codex":0.0000021262072,"about_ca_topic_score_gemma":7.205377e-7,"teacher_disagreement_score":0.29291657,"about_ca_system_score_codex":0.00010380287,"about_ca_system_score_gemma":0.000032777487,"threshold_uncertainty_score":0.32243457},"labels":[],"label_agreement":null},{"id":"W7098970132","doi":"","title":"Advanced clone-analysis to support object-oriented system refactoring","year":2000,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Code refactoring; clone (Java method); Software maintenance; Code (set theory); Source code; Interpretation (philosophy); Cloning (programming); Legacy system","score_opus":0.010167678755028067,"score_gpt":0.24031068016532672,"score_spread":0.23014300141029864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098970132","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9276675,0.000027380755,0.03244949,0.0000081720955,0.00027580193,0.00014221486,0.0000040613195,0.0013518155,0.038073584],"genre_scores_gemma":[0.99341595,0.0000069578286,0.003345482,0.000046794903,0.000081534075,0.000011081963,0.000008663545,0.000027858785,0.003055678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894446,0.000013276582,0.00028210212,0.00025850246,0.00015214898,0.00034949803],"domain_scores_gemma":[0.9994576,0.000036220947,0.00001617835,0.00030272148,0.00002465215,0.00016265539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007705263,0.00017486177,0.00028491832,0.00014695482,0.00008657767,0.000017246413,0.00012093908,0.000041843847,0.0005348134],"category_scores_gemma":[0.000008213957,0.0001666775,0.00012233922,0.0009254074,0.000005307809,0.00016288208,0.000020676398,0.0001250389,0.0003154954],"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":[0.000024767867,0.0000071890818,0.00019950718,0.000044137167,0.00021807273,0.000035027675,0.0002107133,0.9331375,0.027810331,0.0002101675,0.00008642509,0.038016167],"study_design_scores_gemma":[0.001618091,0.00045440803,0.005387982,0.00015746629,0.0009740371,0.000076322845,0.0013892073,0.1530343,0.72859323,0.00002433251,0.105879635,0.0024109983],"about_ca_topic_score_codex":0.0000049735113,"about_ca_topic_score_gemma":0.00000953153,"teacher_disagreement_score":0.7801032,"about_ca_system_score_codex":0.000111232766,"about_ca_system_score_gemma":0.000004556753,"threshold_uncertainty_score":0.67969114},"labels":[],"label_agreement":null},{"id":"W7099267769","doi":"","title":"CANADIAN JOURNAL OF DIABETES. 2009;33(4):363-374. COST-EFFECTIVENESS OF ATORVASTATIN IN DIABETES | 363","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Atorvastatin; Type 2 diabetes; Cohort; Christian ministry; Diabetes mellitus; Hazard ratio; Blood pressure","score_opus":0.026265018229642247,"score_gpt":0.24802177160628164,"score_spread":0.2217567533766394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099267769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99697345,0.0005262535,0.0009592166,0.0000097575385,0.00039346798,0.00016079498,0.000012342225,0.000024613151,0.0009401073],"genre_scores_gemma":[0.99934435,0.0000035445553,0.00053274736,0.000020313533,0.000050121736,0.0000044809017,0.0000022361687,0.000021371752,0.000020829046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990307,0.00008556939,0.00032692018,0.0000917356,0.000115568066,0.00034951195],"domain_scores_gemma":[0.9991703,0.00026519084,0.00006671305,0.000110605426,0.00011670022,0.00027047354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060835853,0.00013075049,0.00030968722,0.00020443843,0.000021295144,0.000012041496,0.00014754712,0.000054351614,0.000020609968],"category_scores_gemma":[0.00019118757,0.0001223015,0.000047535123,0.0002793262,0.000032311542,0.00019644518,0.00001536071,0.00021172724,0.000004390655],"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":[0.000021591115,0.00003999509,0.370439,0.0005091857,0.00007656893,0.000047267207,0.00067992386,0.5368166,0.07014193,0.00022281356,0.0008207309,0.02018441],"study_design_scores_gemma":[0.0025101893,0.00035797563,0.086122565,0.00075316516,0.000029412919,0.000007425316,0.00039848912,0.026565682,0.87858784,0.0019253299,0.0021503458,0.00059156195],"about_ca_topic_score_codex":0.00032141458,"about_ca_topic_score_gemma":0.0021318512,"teacher_disagreement_score":0.80844593,"about_ca_system_score_codex":0.00017705251,"about_ca_system_score_gemma":0.00014425605,"threshold_uncertainty_score":0.49873102},"labels":[],"label_agreement":null},{"id":"W7099778553","doi":"","title":"Canadian Jewish News, a question","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.008947721571386898,"score_gpt":0.21148167276935587,"score_spread":0.20253395119796896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099778553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8065302,0.00023645669,0.095897265,0.0014790472,0.0008911815,0.00011785665,0.0000043312175,0.0012289571,0.09361475],"genre_scores_gemma":[0.9979213,0.000011497213,0.0005248267,0.00010497136,0.00006962369,0.000001005878,3.3576012e-7,0.0000070439382,0.0013594049],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997636,0.0000032874561,0.00004119622,0.000049011975,0.000019773723,0.00012314961],"domain_scores_gemma":[0.99983746,0.000014695807,0.0000024692572,0.00005734476,0.0000057023876,0.00008233865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000017791206,0.00003739652,0.000029988914,0.0000256098,0.000021434576,0.000004267854,0.000029971618,0.000017316033,0.00008457347],"category_scores_gemma":[0.000010331737,0.000025513958,0.000009207991,0.000034633806,0.000003099544,0.000078000485,0.0000031611553,0.00002323262,0.000107784144],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001521988,0.00000176465,0.0019660953,0.000011268549,0.000006866886,0.000022455006,0.00002688996,0.0022596184,0.088434175,0.010338304,0.0059880633,0.890943],"study_design_scores_gemma":[0.0011229746,0.00009526158,0.010282202,0.0003045726,0.000014370882,0.00009484504,0.000051357445,0.009712009,0.5225926,0.03298449,0.4214941,0.0012512229],"about_ca_topic_score_codex":0.0015031453,"about_ca_topic_score_gemma":0.04665742,"teacher_disagreement_score":0.88969177,"about_ca_system_score_codex":0.00006643063,"about_ca_system_score_gemma":0.0000076014703,"threshold_uncertainty_score":0.9707386},"labels":[],"label_agreement":null},{"id":"W7105687559","doi":"10.1109/ijcnn64981.2025.11228328","title":"Learning With Spiking Neural Assembly-Based State Machine","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Robustness (evolution); Probabilistic logic; Sequence (biology); State (computer science); Spiking neural network; Process (computing); Types of artificial neural networks","score_opus":0.007979452959009033,"score_gpt":0.23868716355036043,"score_spread":0.2307077105913514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105687559","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.567734,0.00061639247,0.41747814,0.00029299446,0.0005909917,0.00027406993,0.0000019777444,0.0007499307,0.012261508],"genre_scores_gemma":[0.9902671,0.000027907985,0.00460571,0.0004285333,0.00007456375,0.0000066978223,0.0000076100105,0.00007284116,0.004509037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978332,0.000103976854,0.00048518868,0.00054636266,0.00022862114,0.0008026556],"domain_scores_gemma":[0.99901426,0.00035150634,0.000104121675,0.00031806444,0.00007308997,0.0001389885],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022982621,0.00051875395,0.00045697275,0.0002599797,0.00047073927,0.0001529424,0.00024490562,0.00008764298,0.0001070076],"category_scores_gemma":[0.000064851636,0.00046776605,0.00011574675,0.0008391838,0.000055235418,0.00029921,0.000113972186,0.0012393218,0.000021923792],"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":[0.00013710752,0.00002421659,0.002467764,0.000335411,0.00006195393,0.000105385945,0.00007891189,0.91298383,0.01562654,0.00018923361,0.000015552056,0.06797407],"study_design_scores_gemma":[0.001211537,0.00029419176,0.0009868373,0.00049327215,0.00006087787,0.000009732338,0.000080852835,0.89693415,0.0973431,0.000059291986,0.0020154472,0.0005107043],"about_ca_topic_score_codex":0.000022690405,"about_ca_topic_score_gemma":0.00003002431,"teacher_disagreement_score":0.42253312,"about_ca_system_score_codex":0.0001142183,"about_ca_system_score_gemma":0.00007037531,"threshold_uncertainty_score":0.9997774},"labels":[],"label_agreement":null},{"id":"W7107950260","doi":"10.5281/zenodo.17765155","title":"Quantization Effects on Neural Operator Conditioning","year":2025,"lang":"","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Quantization (signal processing); Artificial neural network; Software deployment; Conditioning; Operator (biology); Stability (learning theory); Software","score_opus":0.029693714575190683,"score_gpt":0.26481433448187597,"score_spread":0.23512061990668529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7107950260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4132862,0.0015493124,0.27446282,0.0019777776,0.011030954,0.008371661,0.0023126155,0.014525739,0.27248293],"genre_scores_gemma":[0.9898397,0.0002998413,0.00047063146,0.0004018108,0.0008009762,2.2928853e-7,0.004627231,0.0025175074,0.0010421118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99583447,0.00088238873,0.0007320249,0.001186417,0.00055250584,0.0008122029],"domain_scores_gemma":[0.99753916,0.0002497203,0.0002499002,0.0009342737,0.00071667176,0.00031028528],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006735724,0.00063699397,0.0005507534,0.00062478846,0.004973333,0.0016265303,0.0015511254,0.00032570452,0.0033578465],"category_scores_gemma":[0.0015251379,0.0007759813,0.00019102953,0.00096220855,0.00014041275,0.00041666356,0.0027411268,0.0018369703,0.003980877],"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":[0.00026526855,0.0002208986,0.0000026810565,0.0032960547,0.00029717316,0.00011609078,0.0018698513,0.57186836,0.033464506,0.012843019,0.02983714,0.34591898],"study_design_scores_gemma":[0.0024249598,0.0010992835,0.00044590925,0.003584013,0.00019279038,0.00016707127,0.00032550114,0.17616738,0.07728561,0.00072558795,0.73572206,0.0018597986],"about_ca_topic_score_codex":0.0000040968994,"about_ca_topic_score_gemma":9.095929e-8,"teacher_disagreement_score":0.70588493,"about_ca_system_score_codex":0.0005227126,"about_ca_system_score_gemma":0.000009349622,"threshold_uncertainty_score":0.9994691},"labels":[],"label_agreement":null},{"id":"W7107956368","doi":"10.5281/zenodo.17765154","title":"Quantization Effects on Neural Operator Conditioning","year":2025,"lang":"","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Quantization (signal processing); Artificial neural network; Software deployment; Conditioning; Operator (biology); Stability (learning theory); Software","score_opus":0.029693714575190683,"score_gpt":0.26481433448187597,"score_spread":0.23512061990668529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7107956368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4132862,0.0015493124,0.27446282,0.0019777776,0.011030954,0.008371661,0.0023126155,0.014525739,0.27248293],"genre_scores_gemma":[0.9898397,0.0002998413,0.00047063146,0.0004018108,0.0008009762,2.2928853e-7,0.004627231,0.0025175074,0.0010421118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99583447,0.00088238873,0.0007320249,0.001186417,0.00055250584,0.0008122029],"domain_scores_gemma":[0.99753916,0.0002497203,0.0002499002,0.0009342737,0.00071667176,0.00031028528],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006735724,0.00063699397,0.0005507534,0.00062478846,0.004973333,0.0016265303,0.0015511254,0.00032570452,0.0033578465],"category_scores_gemma":[0.0015251379,0.0007759813,0.00019102953,0.00096220855,0.00014041275,0.00041666356,0.0027411268,0.0018369703,0.003980877],"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":[0.00026526855,0.0002208986,0.0000026810565,0.0032960547,0.00029717316,0.00011609078,0.0018698513,0.57186836,0.033464506,0.012843019,0.02983714,0.34591898],"study_design_scores_gemma":[0.0024249598,0.0010992835,0.00044590925,0.003584013,0.00019279038,0.00016707127,0.00032550114,0.17616738,0.07728561,0.00072558795,0.73572206,0.0018597986],"about_ca_topic_score_codex":0.0000040968994,"about_ca_topic_score_gemma":9.095929e-8,"teacher_disagreement_score":0.70588493,"about_ca_system_score_codex":0.0005227126,"about_ca_system_score_gemma":0.000009349622,"threshold_uncertainty_score":0.9994691},"labels":[],"label_agreement":null},{"id":"W7111087712","doi":"10.1109/icjece.2025.3628528","title":"Design and Implementation of a Low-Power Memristor-Based Piccolo-80 Lightweight Encryption Algorithm Using VTM Logic Gates","year":2025,"lang":"","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Encryption; Cryptography; Energy consumption; Hardware security module; Stateful firewall; Implementation; CMOS; Efficient energy use; Key (lock)","score_opus":0.009043555704101109,"score_gpt":0.2249391735203,"score_spread":0.21589561781619887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111087712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21798235,0.0071424446,0.77407366,0.000064589534,0.0005486026,0.00016846304,0.00000250898,0.000013994516,0.0000033551266],"genre_scores_gemma":[0.92641646,0.00011023946,0.0732089,0.000072840514,0.00016290387,0.000001520505,0.0000010329269,0.00002326861,0.0000028579861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835044,0.00005794548,0.0007487118,0.00022620182,0.00011988183,0.0004968156],"domain_scores_gemma":[0.99884456,0.00029030014,0.0002009635,0.00009398011,0.00015204256,0.00041815784],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027787036,0.00030351194,0.00051199115,0.000788036,0.00014640226,0.0000819291,0.00014525041,0.00013807527,0.00001456709],"category_scores_gemma":[0.000027778842,0.00031999688,0.000097214586,0.0005926714,0.000041732186,0.0002269467,0.0000213725,0.0004602502,2.0388536e-7],"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":[0.000032475487,0.000025570038,0.00051006227,0.00046920028,0.00024553336,0.00021550471,0.0004162288,0.9018628,0.026295278,0.001161439,0.000064001564,0.0687019],"study_design_scores_gemma":[0.00085005595,0.0005065107,0.001573828,0.0005767039,0.000093712304,0.00012826305,0.000017643104,0.9649966,0.030452063,0.00029126374,0.00021428271,0.00029907652],"about_ca_topic_score_codex":0.000074672644,"about_ca_topic_score_gemma":0.000014550639,"teacher_disagreement_score":0.7084341,"about_ca_system_score_codex":0.000302194,"about_ca_system_score_gemma":0.00036892938,"threshold_uncertainty_score":0.9999252},"labels":[],"label_agreement":null},{"id":"W7111180194","doi":"10.1021/acsaelm.5c01550.s001","title":"Molybdenum OxideArtificial Synapse: Enabling CognitiveLearning, Image Recognition, and Denoising","year":2025,"lang":"","type":"article","venue":"Figshare","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neuromorphic engineering; Image quality; Noise (video); Pattern recognition (psychology); Noise reduction; Image processing; Convolutional neural network","score_opus":0.04039431714665039,"score_gpt":0.2694873142129842,"score_spread":0.22909299706633382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111180194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78761286,0.023894101,0.0036609492,0.00028778586,0.001202667,0.0021296658,0.17336518,0.0019394713,0.0059073204],"genre_scores_gemma":[0.976258,0.000116755364,0.000228043,0.00031372876,0.0006040515,0.000072523646,0.021685556,0.00011077648,0.00061057927],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99784416,0.00011484092,0.00053468486,0.00064996205,0.00017545362,0.00068091386],"domain_scores_gemma":[0.9984783,0.00069396454,0.00014798237,0.00021423353,0.00030343462,0.00016208552],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00012971909,0.0004425755,0.00040631197,0.0002767938,0.0006530326,0.00036391584,0.00018153914,0.00025444978,0.027649246],"category_scores_gemma":[0.0035095236,0.00056136085,0.0001137575,0.0006211951,0.000029826562,0.00057914294,0.00031159914,0.0009354414,0.001270567],"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":[0.0002278587,0.0001399066,0.00006273641,0.009139179,0.00049638224,0.00092402013,0.0019578817,0.006581113,0.43912074,0.00003769446,0.048064243,0.49324825],"study_design_scores_gemma":[0.0010427425,0.000094150884,0.0008076277,0.027883045,0.00015152023,0.00009491762,0.0007420548,0.0032636211,0.93927306,0.00086345273,0.024558643,0.0012251791],"about_ca_topic_score_codex":0.0000039158317,"about_ca_topic_score_gemma":0.000004408547,"teacher_disagreement_score":0.5001523,"about_ca_system_score_codex":0.00008852678,"about_ca_system_score_gemma":0.00008025499,"threshold_uncertainty_score":0.9996838},"labels":[],"label_agreement":null},{"id":"W7117167236","doi":"10.1002/adma.202514620","title":"A Retina‐Inspired Organic Iono‐Optoelectronic Synapse","year":2025,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Neuromorphic engineering; Synapse; Ionic bonding; Polymer; Coupling (piping); Bioelectronics; Transistor; Acceptor","score_opus":0.0036201241587937244,"score_gpt":0.21669504736812312,"score_spread":0.2130749232093294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117167236","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98729837,0.0006917004,0.007971525,0.00007973562,0.0010906996,0.00025277838,0.000007116492,0.00091539737,0.0016926528],"genre_scores_gemma":[0.998005,0.00020811263,0.0009801176,0.00015486998,0.00008543112,0.000034642602,0.000011411622,0.000039297724,0.0004811158],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988897,0.000032599426,0.00031914457,0.0002522438,0.00008872039,0.00041759096],"domain_scores_gemma":[0.9995059,0.00006112404,0.000044471264,0.00030107237,0.00004271534,0.000044754415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001384648,0.00021235061,0.0002955238,0.00008109991,0.00010818812,0.000033150147,0.00018311881,0.00007403574,0.00019462696],"category_scores_gemma":[0.000104352584,0.0002182321,0.000038095022,0.0002689934,0.000032538086,0.00019077047,0.00004366547,0.000117802774,0.00007952718],"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":[0.0000328037,0.0000071513073,0.000002349451,0.00010004186,0.000018354884,0.0000073799238,0.000020427973,0.003569974,0.9914895,0.001506929,0.00013729051,0.0031077643],"study_design_scores_gemma":[0.00038924138,0.00003797399,0.00007434454,0.00010157893,0.000014401509,0.0000074792288,0.00001478941,0.00006226257,0.9909838,0.0035489888,0.004555837,0.00020931622],"about_ca_topic_score_codex":7.053597e-7,"about_ca_topic_score_gemma":0.0000027464025,"teacher_disagreement_score":0.010706606,"about_ca_system_score_codex":0.0001460851,"about_ca_system_score_gemma":0.000027634658,"threshold_uncertainty_score":0.8899247},"labels":[],"label_agreement":null},{"id":"W7124174525","doi":"10.1109/biocas67066.2025.00147","title":"Utilizing the Silent-State in Spiking Neurons: A Modified Rulkov Model","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Scalability; Computation; Key (lock); Spiking neural network; Artificial neural network; Spike (software development); Energy (signal processing); Computational model","score_opus":0.03304333562199795,"score_gpt":0.27634882307207675,"score_spread":0.2433054874500788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124174525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58965236,0.0018330651,0.3572344,0.0005204467,0.0009992496,0.0006618629,0.0000037969799,0.00033905983,0.048755772],"genre_scores_gemma":[0.99526423,0.00040384728,0.0010300482,0.000835879,0.000052782187,0.000023492928,0.0000010503156,0.00004974811,0.0023389286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974418,0.00010484507,0.00078776444,0.0005754626,0.00019501499,0.0008951021],"domain_scores_gemma":[0.9987434,0.00047709473,0.00007622304,0.0005882727,0.000034911056,0.000080079684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040069135,0.0004573972,0.00041503512,0.00026662098,0.00033992153,0.00012902389,0.0005265356,0.00011153576,0.000018616636],"category_scores_gemma":[0.00013536625,0.00038924752,0.00016146389,0.00090493716,0.00007316452,0.0003510792,0.00038862604,0.0011244252,0.000015140484],"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":[0.0000401276,0.000029126324,0.00004475212,0.00016714481,0.000022566765,0.000029183957,0.0007745674,0.93974066,0.012216567,0.011382093,0.000034876535,0.035518356],"study_design_scores_gemma":[0.00061176164,0.000016772068,0.00014584085,0.00046164868,0.00002599605,0.000004375771,0.0004504669,0.96874535,0.015160848,0.013892186,0.0001475504,0.00033722084],"about_ca_topic_score_codex":0.000039099636,"about_ca_topic_score_gemma":0.00005308869,"teacher_disagreement_score":0.40561184,"about_ca_system_score_codex":0.00014988512,"about_ca_system_score_gemma":0.00007377848,"threshold_uncertainty_score":0.99985594},"labels":[],"label_agreement":null},{"id":"W7126271076","doi":"10.18280/isi.301201","title":"Resistive Random-Access Memory Design for Image Enhancement in Edge Devices","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Resistive touchscreen; Enhanced Data Rates for GSM Evolution; Image (mathematics); Image enhancement; Image processing; Edge detection; Power (physics)","score_opus":0.023723134425847763,"score_gpt":0.28348312604353876,"score_spread":0.259759991617691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126271076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07475831,0.0016683722,0.90934753,0.000055119745,0.0015833207,0.0028443853,0.000028341727,0.00018948338,0.009525153],"genre_scores_gemma":[0.98357064,0.0002896234,0.014751457,0.00026319714,0.00012932812,0.00061683764,0.000070612754,0.00003195858,0.0002763381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969055,0.0001659058,0.0016511482,0.0003110908,0.00022414261,0.0007421904],"domain_scores_gemma":[0.99784684,0.0009189077,0.0004423572,0.00033521574,0.0003654332,0.00009122331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012484051,0.00050261046,0.0006708921,0.00073621026,0.0005116144,0.0005495786,0.00049313327,0.0002227179,0.000040034796],"category_scores_gemma":[0.0008444152,0.0005590269,0.00016129104,0.00100967,0.00014585028,0.006417749,0.00018690719,0.0003404935,0.000034055265],"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":[0.004289024,0.00013155742,0.00015690208,0.019200895,0.00038307827,0.000018391162,0.01704109,0.47767314,0.013045216,0.0020945438,0.0020497881,0.46391636],"study_design_scores_gemma":[0.010492269,0.00035807447,0.0022048638,0.0073621697,0.00021487534,0.00001302249,0.004184624,0.41775942,0.53991395,0.011008723,0.0049818475,0.0015061458],"about_ca_topic_score_codex":0.0000295069,"about_ca_topic_score_gemma":0.000022038439,"teacher_disagreement_score":0.90881234,"about_ca_system_score_codex":0.00096658303,"about_ca_system_score_gemma":0.00019899305,"threshold_uncertainty_score":0.9996861},"labels":[],"label_agreement":null},{"id":"W7127291350","doi":"10.1109/ccece64018.2025.11364440","title":"FPGA Implementation of SNN Based Multiple Input Multiple Output Neurons Using LIF Model","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Field-programmable gate array; Artificial neural network; Spiking neural network; Key (lock); MIMO","score_opus":0.04736781667258541,"score_gpt":0.31405201939244115,"score_spread":0.2666842027198557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127291350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4524785,0.000102872305,0.54590726,0.00008195337,0.0004928707,0.0004389887,0.00003944987,0.00014406502,0.00031403822],"genre_scores_gemma":[0.95716655,0.000018745257,0.04203937,0.00035124444,0.00006238711,0.000010584927,0.00002051051,0.00006170743,0.00026890406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974189,0.00008696044,0.0010412103,0.00055719796,0.00024574946,0.0006499838],"domain_scores_gemma":[0.99844724,0.00056346995,0.00020620109,0.0005054609,0.0001461233,0.00013152167],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002056657,0.00046433727,0.00052545645,0.00036715003,0.000290528,0.00004561597,0.0002815567,0.00015032102,0.00007107542],"category_scores_gemma":[0.0001682649,0.0005363405,0.00023881294,0.0006322221,0.00006200726,0.00039896957,0.0002111682,0.0004019582,0.000005296573],"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":[0.000049581595,0.0000595919,0.0021668773,0.0003304865,0.000044832443,0.0000035446562,0.0001945936,0.8183847,0.15702416,0.000082395505,0.000057990263,0.02160126],"study_design_scores_gemma":[0.0015100314,0.000038693717,0.00048748034,0.00012307076,0.00006565281,8.7057134e-7,0.00024712068,0.6178197,0.37928423,0.00007517632,0.0000945883,0.00025335752],"about_ca_topic_score_codex":0.00014161237,"about_ca_topic_score_gemma":0.00010186338,"teacher_disagreement_score":0.504688,"about_ca_system_score_codex":0.00017136669,"about_ca_system_score_gemma":0.00019958147,"threshold_uncertainty_score":0.99970883},"labels":[],"label_agreement":null},{"id":"W7127371321","doi":"10.1109/ccece64018.2025.11364354","title":"Energy-Efficient Spike Encoding for Implementing Spiking Neural Networks on FPGA","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Spiking neural network; Encoding (memory); Artificial neural network; Spike (software development); Field-programmable gate array; Energy consumption; Power consumption","score_opus":0.0234047208710011,"score_gpt":0.27878127446025597,"score_spread":0.2553765535892549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127371321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08205336,0.0010329791,0.8886186,0.0001893773,0.00483655,0.0005983274,0.0000062236054,0.00048717583,0.022177443],"genre_scores_gemma":[0.99482006,0.000052455,0.0018012522,0.0009097922,0.0010211362,0.00005082222,0.000013425785,0.000083347586,0.0012476955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611956,0.000058000605,0.0010120687,0.00085722754,0.0002248003,0.0017283228],"domain_scores_gemma":[0.9982097,0.00090051664,0.00018101145,0.00046006206,0.00008919489,0.00015947122],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005593205,0.0006494302,0.00057028997,0.00033683176,0.0011224623,0.00021572095,0.0004011516,0.00019019641,0.000088541005],"category_scores_gemma":[0.00008751225,0.00069172814,0.00039051624,0.0007282048,0.000039221966,0.00015396373,0.00036527918,0.0005623013,0.000002861626],"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":[0.00005628676,0.0000363553,0.000034713867,0.00017205761,0.000059220358,0.000009706156,0.00006747316,0.7824912,0.004518878,0.020590965,0.0001989201,0.1917642],"study_design_scores_gemma":[0.0009131535,0.00013899108,0.000030197887,0.00050602114,0.000082334096,0.0000043910713,0.00024714516,0.92305475,0.06535935,0.00028979324,0.008796328,0.00057753647],"about_ca_topic_score_codex":0.000010479886,"about_ca_topic_score_gemma":0.000013677009,"teacher_disagreement_score":0.9127667,"about_ca_system_score_codex":0.00025373054,"about_ca_system_score_gemma":0.000028168462,"threshold_uncertainty_score":0.9995534},"labels":[],"label_agreement":null},{"id":"W7129082084","doi":"10.1109/ccmcc67628.2025.11380559","title":"Fast and Scalable MAGIC-Based Wallace Tree Multiplier for In-Memory Computing","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Scalability; Von Neumann architecture; Computation; Multiplier (economics); Tree (set theory); Logic synthesis; Domino logic; Unconventional computing","score_opus":0.009607783063057073,"score_gpt":0.2413114465301456,"score_spread":0.23170366346708854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129082084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63420326,0.00023107535,0.35270748,0.00014558324,0.00022247828,0.0003022826,0.000001435173,0.00029772543,0.011888686],"genre_scores_gemma":[0.9772775,0.0000031289587,0.020698018,0.000222519,0.00003114485,0.0000048265156,0.0000022489844,0.0000152831,0.0017453023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938464,0.000008024223,0.00016302429,0.00017503653,0.000036026184,0.00023322574],"domain_scores_gemma":[0.9995695,0.0002684894,0.000011806341,0.00010132558,0.000015739264,0.000033192315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009677152,0.00011766179,0.00014984728,0.00009113329,0.000059168877,0.00001930166,0.00006515689,0.000043126067,0.000006525458],"category_scores_gemma":[0.000028337385,0.00011635043,0.00002778394,0.00015263744,0.000014024235,0.000060920564,0.000033441465,0.00010834371,0.000002882719],"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":[0.000034218505,0.000020258416,0.001459521,0.00036339773,0.000010736793,0.0000049156756,0.000092773065,0.79156715,0.043635,0.00029793946,0.0004545297,0.16205956],"study_design_scores_gemma":[0.0011028359,0.000014392247,0.0015893338,0.00009150118,0.000004610061,9.280121e-7,0.00008047251,0.91180235,0.08435437,0.00020860451,0.0006068087,0.00014378742],"about_ca_topic_score_codex":0.000004528212,"about_ca_topic_score_gemma":0.00004602788,"teacher_disagreement_score":0.3430743,"about_ca_system_score_codex":0.000047742553,"about_ca_system_score_gemma":0.000008465206,"threshold_uncertainty_score":0.47446328},"labels":[],"label_agreement":null},{"id":"W7130693762","doi":"10.1109/swc65939.2025.00087","title":"Exploring Neuromorphic Computing for UAV Navigation","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Neuromorphic engineering; Artificial neural network; Spiking neural network; Process (computing); Computation; Spike (software development); Efficient energy use","score_opus":0.15572972089406995,"score_gpt":0.29391486965484503,"score_spread":0.1381851487607751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130693762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50419545,0.00031966149,0.4881844,0.00017233044,0.0032853652,0.00048083317,0.0000027474716,0.0004209392,0.002938249],"genre_scores_gemma":[0.9910424,0.00012790019,0.0074890857,0.00019693596,0.0004372463,0.000028691717,0.000013256051,0.000051591498,0.0006128916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981372,0.00003892005,0.0006129636,0.00048879324,0.000119114055,0.00060301786],"domain_scores_gemma":[0.99854904,0.00088801957,0.000081301696,0.0002931838,0.00009971338,0.00008874378],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002609691,0.00034735727,0.00036044553,0.00016731846,0.0004904633,0.00009742953,0.00022532372,0.00008462148,0.0000109572175],"category_scores_gemma":[0.00015401642,0.00040953737,0.00017324237,0.0005875935,0.000034366927,0.0005345079,0.00013919915,0.0004069223,0.000018698318],"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":[0.000054752032,0.000041129355,0.00015113653,0.0015265893,0.00008649348,0.000015488005,0.00039584382,0.62756205,0.065013416,0.01925393,0.00015845425,0.28574073],"study_design_scores_gemma":[0.00095293706,0.00010096687,0.00072536175,0.0011138855,0.000076942495,0.000010969051,0.00026361106,0.7722893,0.21617524,0.0016775813,0.006119841,0.0004933926],"about_ca_topic_score_codex":0.0000029967098,"about_ca_topic_score_gemma":9.4463354e-7,"teacher_disagreement_score":0.48684692,"about_ca_system_score_codex":0.000101750134,"about_ca_system_score_gemma":0.000030635267,"threshold_uncertainty_score":0.99983567},"labels":[],"label_agreement":null},{"id":"W7131131876","doi":"10.1109/iccvw69036.2025.00497","title":"OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Hopfield network; Pipeline (software); Energy (signal processing); Artificial neural network; Efficient energy use; Deep learning","score_opus":0.01582405560429916,"score_gpt":0.2617229095793162,"score_spread":0.24589885397501704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131131876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034710873,0.00064284535,0.91447765,0.0007020292,0.004811035,0.0006274442,0.000010901464,0.00042489552,0.043592338],"genre_scores_gemma":[0.9882873,0.00013310206,0.005199921,0.0010314485,0.00075553235,0.00005896219,0.000016545484,0.00005224711,0.0044649034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977646,0.000053133273,0.0006081834,0.00064675335,0.00017517283,0.00075216376],"domain_scores_gemma":[0.998313,0.0008249068,0.000108883265,0.00053174456,0.00009249009,0.00012899065],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024063885,0.00042296233,0.0003913534,0.00015309894,0.0004448367,0.00009123581,0.00027203467,0.00024501802,0.00008248471],"category_scores_gemma":[0.00012288646,0.00042968005,0.00021937017,0.0006481571,0.000051792944,0.00008575547,0.00009458078,0.0003143933,0.00002655826],"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":[0.00012250163,0.000057048175,0.0000129781065,0.00016050105,0.000055598113,0.00000303627,0.00003893907,0.840405,0.0136870695,0.091079205,0.009594098,0.044784054],"study_design_scores_gemma":[0.0006736854,0.00017233293,0.000118724274,0.0002957431,0.00005667811,0.0000015973769,0.00008884378,0.8662596,0.08754637,0.0017722704,0.04256629,0.00044789168],"about_ca_topic_score_codex":0.000005615897,"about_ca_topic_score_gemma":0.000006656752,"teacher_disagreement_score":0.95357645,"about_ca_system_score_codex":0.00016844361,"about_ca_system_score_gemma":0.000046808826,"threshold_uncertainty_score":0.9998155},"labels":[],"label_agreement":null},{"id":"W7132980480","doi":"","title":"Compensation of delta-sigma modulators: stabilization, signal restoration and integrated circuits","year":2003,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada","funders":"University of Toronto; CMC Microsystems","keywords":"CMOS; Compensation (psychology); Integrated circuit; Modulation (music); Offset (computer science); Bandwidth (computing); Chip; Signal processing; Frequency modulation","score_opus":0.02537983896039097,"score_gpt":0.2920996745032703,"score_spread":0.2667198355428793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132980480","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86252767,0.0012688951,0.13423355,0.000020819054,0.0005917316,0.0006171418,0.000012885479,0.00009505146,0.0006322784],"genre_scores_gemma":[0.99698997,0.0002214138,0.00090417534,0.000012258546,0.000083118764,0.000014620028,0.0008920503,0.000094874515,0.00078753004],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99771476,0.0002088436,0.00085915986,0.0005413246,0.0003428678,0.0003330644],"domain_scores_gemma":[0.99818164,0.00024288784,0.0005653453,0.00030120803,0.0005754826,0.00013344124],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028550997,0.000533585,0.0006195674,0.0003004661,0.00023334751,0.000059134392,0.00012630706,0.00041649767,0.00019719037],"category_scores_gemma":[0.00026179125,0.0006262379,0.00008394874,0.000736249,0.00006795979,0.00039179254,0.000017316313,0.00049428263,0.0000052855403],"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":[0.000093471084,0.000069218455,0.00075888203,0.0015819805,0.00009832099,0.000003963739,0.011035847,0.42809287,0.5470625,0.002132626,0.000047052887,0.009023267],"study_design_scores_gemma":[0.0013910664,0.000404735,0.0038795914,0.0016825577,0.00027528926,0.00001899105,0.008123869,0.30481958,0.67634326,0.0013574472,0.00036564405,0.0013379509],"about_ca_topic_score_codex":0.000053194213,"about_ca_topic_score_gemma":0.00009040914,"teacher_disagreement_score":0.13446231,"about_ca_system_score_codex":0.00017959563,"about_ca_system_score_gemma":0.0001239264,"threshold_uncertainty_score":0.9996189},"labels":[],"label_agreement":null},{"id":"W7134177373","doi":"10.1109/cmsda68297.2025.11414386","title":"Evaluation of Spiking Neural Network Architecture for Image Classification Using Binary Encoding","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Pattern recognition (psychology); Encoding (memory); Contextual image classification; Artificial neural network; Binary number; Image (mathematics); Image processing; Feature extraction","score_opus":0.0853083905497196,"score_gpt":0.35006868271417146,"score_spread":0.26476029216445185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134177373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4396725,0.0013255286,0.55430096,0.00009566252,0.0013721107,0.00090893137,0.000003582066,0.00009192283,0.0022288049],"genre_scores_gemma":[0.9544432,0.000021661894,0.044917975,0.000046460456,0.00046113253,0.000021353715,0.0000104815535,0.00003643112,0.00004126977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977563,0.0002059277,0.0007373597,0.00042820038,0.00037726987,0.00049495115],"domain_scores_gemma":[0.99844515,0.00048215676,0.0002403014,0.00032611485,0.00045534028,0.00005095373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018024422,0.0003064422,0.0003747745,0.00022782155,0.00036925648,0.00005033378,0.00019709126,0.00015132416,0.00003328912],"category_scores_gemma":[0.00035786474,0.00033311354,0.00019398537,0.00075458706,0.000059184367,0.0002963504,0.000095623756,0.0003344747,6.5130064e-7],"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":[0.000031728116,0.000011709015,0.000036994374,0.00030650708,0.00004166311,4.5929445e-7,0.00009566153,0.5970559,0.3206977,0.0007005916,0.0000242197,0.08099684],"study_design_scores_gemma":[0.0007003111,0.000055536155,0.0005254399,0.00066499034,0.0004981402,0.0000047815533,0.000195851,0.93267196,0.059341248,0.0050049988,0.00008681461,0.00024991762],"about_ca_topic_score_codex":0.0000036150261,"about_ca_topic_score_gemma":0.000004202187,"teacher_disagreement_score":0.51477075,"about_ca_system_score_codex":0.00027315685,"about_ca_system_score_gemma":0.00012219697,"threshold_uncertainty_score":0.9999121},"labels":[],"label_agreement":null},{"id":"W7139418828","doi":"","title":"Étude des mémoires non volatiles émergentes à plusieurs niveaux pour le calcul en mémoire et les réseaux neuronaux analogiques (SNN) basés sur la technologie FD-SOI","year":2024,"lang":"en","type":"dissertation","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Von Neumann architecture; Bottleneck; Block (permutation group theory); Efficient energy use; Artificial neural network; Non-volatile memory; Component (thermodynamics); In-Memory Processing; Energy (signal processing)","score_opus":0.012976826113310483,"score_gpt":0.24426181840619063,"score_spread":0.23128499229288013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139418828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8986556,0.008131292,0.053416,0.0018167566,0.00045654536,0.0005431332,0.00008669107,0.002515358,0.03437864],"genre_scores_gemma":[0.96534973,0.003844198,0.017598504,0.000034548677,0.000051603027,0.00007746419,0.00091142417,0.00020677937,0.011925751],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99589,0.0016794043,0.00066402345,0.0008303338,0.000371579,0.00056461775],"domain_scores_gemma":[0.995697,0.002112351,0.00026029992,0.00097441336,0.00079639856,0.0001595386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016473844,0.00067259744,0.0005668785,0.0004390044,0.0004836495,0.00031679453,0.0011250722,0.0005810819,0.000037298792],"category_scores_gemma":[0.0014124378,0.00070733076,0.00031003947,0.0006928384,0.00020540413,0.00039098255,0.0003399631,0.0014474698,0.000031298525],"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":[0.000090934656,0.0009131434,0.002206575,0.0042492896,0.0007180086,0.0002657367,0.026469767,0.043779466,0.33058584,0.0926835,0.0073179235,0.49071983],"study_design_scores_gemma":[0.0008171518,0.000005919291,0.015725017,0.007849884,0.0002430661,0.00005505358,0.0041241953,0.23754194,0.70330507,0.009850955,0.018390719,0.0020910478],"about_ca_topic_score_codex":0.00091384543,"about_ca_topic_score_gemma":0.0065075387,"teacher_disagreement_score":0.48862877,"about_ca_system_score_codex":0.00018345681,"about_ca_system_score_gemma":0.00033110086,"threshold_uncertainty_score":0.99953777},"labels":[],"label_agreement":null},{"id":"W7160094643","doi":"10.1109/iccv51701.2025.00878","title":"From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Pose; Event (particle physics); Motion blur; Domain (mathematical analysis); Adaptation (eye); Motion (physics); Pattern recognition (psychology)","score_opus":0.07054788909406257,"score_gpt":0.32329887178189204,"score_spread":0.2527509826878295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160094643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45131624,0.00013287146,0.5463635,0.00023097615,0.0007802696,0.0008774669,0.000021973727,0.00016903307,0.00010769206],"genre_scores_gemma":[0.8568296,0.0000052319656,0.14188854,0.0003112114,0.00037335514,0.00003890691,0.00015762812,0.00006356069,0.00033196973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974534,0.000100120815,0.00090461236,0.0007282237,0.0002549553,0.0005586885],"domain_scores_gemma":[0.99884915,0.0002785164,0.00013774473,0.00040275714,0.00016421384,0.0001675865],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029051924,0.0004830495,0.0004382261,0.0003393351,0.0006160352,0.00014856241,0.0002102236,0.0002067036,0.00017953469],"category_scores_gemma":[0.00008607307,0.0005681527,0.00019756737,0.0005651017,0.000024352637,0.000604769,0.00010465175,0.00025740225,0.000022505572],"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":[0.00006446501,0.000054626693,0.000014682935,0.00013415988,0.000065936096,0.0000018740046,0.0010308129,0.70336545,0.27159557,0.0024768666,0.000029234263,0.021166293],"study_design_scores_gemma":[0.0014615215,0.00010325043,0.00091162376,0.0006118296,0.00015846525,0.0000016647875,0.0014458803,0.91691566,0.053166423,0.024613446,0.00009886676,0.0005113541],"about_ca_topic_score_codex":0.00023976063,"about_ca_topic_score_gemma":0.0001240176,"teacher_disagreement_score":0.40551335,"about_ca_system_score_codex":0.0006347395,"about_ca_system_score_gemma":0.000059516053,"threshold_uncertainty_score":0.999677},"labels":[],"label_agreement":null},{"id":"W7161136033","doi":"10.1109/ispdc67428.2025.00019","title":"FNNS-EC: Federated Non-performing-node-resilient Neural Selector with Energy Constraints","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Energy (signal processing); Artificial neural network; Field (mathematics); Feature (linguistics); Energy consumption; Constraint (computer-aided design)","score_opus":0.006149365833820038,"score_gpt":0.22041548127766397,"score_spread":0.21426611544384394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161136033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69752187,0.00053693284,0.25879672,0.00023893062,0.001583033,0.00044491142,0.000010287014,0.0006166902,0.040250637],"genre_scores_gemma":[0.99063927,0.0000537204,0.001479648,0.00081382616,0.00018094154,0.000021462332,0.000015151902,0.00007658896,0.0067194207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996921,0.00006359341,0.00073825667,0.00081250456,0.00029193002,0.0011727231],"domain_scores_gemma":[0.9987811,0.00024124258,0.00011215434,0.00039254056,0.00019764986,0.00027532052],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001256579,0.00077134575,0.00066871784,0.00025368022,0.0006903217,0.00021743751,0.00030836003,0.00022589939,0.00053130876],"category_scores_gemma":[0.00004342598,0.00068612443,0.00014040612,0.0010550327,0.00023189002,0.00037792398,0.00014998818,0.0007070103,0.000029142506],"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":[0.0005344771,0.00019675159,0.0010498001,0.00058087567,0.0005243161,0.00029833976,0.00025193937,0.7134705,0.14322118,0.002887373,0.0013523017,0.13563211],"study_design_scores_gemma":[0.0015921204,0.00027585673,0.00034508854,0.00043586583,0.00008472159,0.00008399328,0.00020650683,0.58797574,0.40686777,0.000045611323,0.001346926,0.0007397763],"about_ca_topic_score_codex":0.000045905686,"about_ca_topic_score_gemma":0.00007332653,"teacher_disagreement_score":0.29311737,"about_ca_system_score_codex":0.00021296178,"about_ca_system_score_gemma":0.00021884099,"threshold_uncertainty_score":0.999559},"labels":[],"label_agreement":null}]}