{"meta":{"query_hash":"068e000b28d5","filters":{"venue":"Neuromorphic Computing and Engineering"},"cohort_total":24,"direct_labels_cover":0,"predictions_cover":24,"exported":24,"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/068e000b28d5","api":"https://metacan.xera.ac/api/v1/cohort?venue=Neuromorphic+Computing+and+Engineering"},"results":[{"id":"W3086434542","doi":"10.1088/2634-4386/ac6533","title":"P-CRITICAL: a reservoir autoregulation plasticity rule for neuromorphic hardware","year":2022,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","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; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Compute Canada; Agence Nationale de la Recherche","keywords":"Neuromorphic engineering; Computer science; Artificial neural network; Artificial intelligence; Reservoir computing; Computer architecture; Preprocessor; Task (project management); Key (lock); Machine learning; Recurrent neural network; Engineering","score_opus":0.027552909021120824,"score_gpt":0.23351575522830786,"score_spread":0.20596284620718705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086434542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08598786,0.0003600764,0.9024063,0.0003399287,0.0001379379,0.00007999054,0.00009324484,0.0014698242,0.009124871],"genre_scores_gemma":[0.9052514,0.00010725506,0.091664255,0.000115119474,0.000022197233,0.000074108146,0.000039859002,0.00008724316,0.0026384732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997844,0.000039499017,0.000022971122,0.00005295755,0.00007015642,0.00002999773],"domain_scores_gemma":[0.99952424,0.00017135174,0.00005223028,0.000110968394,0.00010393782,0.000037409296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039033513,0.00026090807,0.00029495655,0.00025014716,0.00025893372,0.00057459855,0.0010675051,0.0004558153,0.0016010795],"category_scores_gemma":[0.0013854997,0.0001383555,0.00021875874,0.00013334777,0.00051275495,0.00069238164,0.0005341323,0.0006670268,0.00029621483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041229016,0.0001941855,0.0021115527,0.00032028314,0.000082116116,0.00087807263,0.00015350676,0.34426546,0.18229704,0.1338318,0.007935718,0.32751793],"study_design_scores_gemma":[0.000017944009,0.00008647618,0.00021377153,0.0000143637235,0.000012297972,0.00025554767,0.000011990827,0.9359371,0.037444163,0.023022411,0.002968554,0.000015266747],"about_ca_topic_score_codex":0.0006010964,"about_ca_topic_score_gemma":0.0008938825,"teacher_disagreement_score":0.0016010795,"about_ca_system_score_codex":0.00028924688,"about_ca_system_score_gemma":0.00036010548,"threshold_uncertainty_score":0},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87093496,0.0008229022,0.0862455,0.0005499469,0.00049260125,0.00029477483,0.0005461393,0.012869294,0.027243825],"genre_scores_gemma":[0.97189045,0.00010427039,0.021794643,0.00013164392,0.000013231005,0.00008240982,0.00026689554,0.0001554973,0.0055610267],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971265,0.000031350966,0.00001792714,0.000061313236,0.00010251636,0.000074269796],"domain_scores_gemma":[0.9995135,0.00011419013,0.00004134151,0.00010580903,0.0001546918,0.00007050304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002997792,0.00052160176,0.00040096813,0.0003035473,0.00023108216,0.0007025637,0.0020452617,0.00052623724,0.008995965],"category_scores_gemma":[0.0008889146,0.00013066946,0.000174847,0.0002715929,0.00029973892,0.00096737896,0.00042813827,0.00048568423,0.0011260638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039274725,0.0011896631,0.0038442204,0.0020631077,0.00030870727,0.0011884028,0.00039231507,0.21364109,0.391861,0.009887149,0.020958064,0.35073885],"study_design_scores_gemma":[0.00028438767,0.0033714336,0.0039928625,0.00006616469,0.000094625,0.00034014764,0.00021303524,0.5865078,0.38837314,0.0015432325,0.015126258,0.00008689939],"about_ca_topic_score_codex":0.0017948587,"about_ca_topic_score_gemma":0.0022615583,"teacher_disagreement_score":0.008995965,"about_ca_system_score_codex":0.00058540684,"about_ca_system_score_gemma":0.0005685236,"threshold_uncertainty_score":0.030094564},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17376737,0.001163535,0.8095675,0.0010020939,0.0001501594,0.000088523026,0.00014109656,0.0010407028,0.013078981],"genre_scores_gemma":[0.93962157,0.0002619795,0.056575168,0.00018536637,0.00002754298,0.000038666272,0.000050638137,0.000034421533,0.0032046482],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999,0.000024137926,0.0000063875386,0.00002679296,0.00002562524,0.00001703277],"domain_scores_gemma":[0.99986994,0.000043703487,0.000015675012,0.000020233985,0.00003864238,0.000011712989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021911925,0.00028009398,0.00021544946,0.00020801794,0.0001142501,0.00056098314,0.0006526333,0.0005108353,0.0020511125],"category_scores_gemma":[0.00070119783,0.00008522354,0.00017253059,0.00019514331,0.00019857574,0.00049798447,0.0003648079,0.0003114215,0.00037943068],"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":[0.00033877263,0.00028446622,0.0025911978,0.00019551368,0.00009339314,0.00024951718,0.000075887976,0.37106138,0.13547924,0.011991771,0.0023550706,0.47528368],"study_design_scores_gemma":[0.0000075054572,0.00011722061,0.00079128024,0.000017987579,0.000015484833,0.00009408383,0.000019628538,0.9712947,0.021874428,0.004218439,0.0015414421,0.000007694098],"about_ca_topic_score_codex":0.00085811736,"about_ca_topic_score_gemma":0.0010822327,"teacher_disagreement_score":0.0020511125,"about_ca_system_score_codex":0.00027964517,"about_ca_system_score_gemma":0.00027573435,"threshold_uncertainty_score":0.0068616867},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9120862,0.00014378935,0.083248764,0.0003150806,0.000025239655,0.000013906198,0.00006192907,0.00016245076,0.003942709],"genre_scores_gemma":[0.9963425,0.000013809663,0.003137517,0.000012277204,0.0000018951464,0.000005511444,0.000012013821,0.0000057214493,0.0004687818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993014,0.000018506671,0.000004914403,0.000013353814,0.00001757862,0.000015522639],"domain_scores_gemma":[0.999255,0.0004403385,0.00009693814,0.00006495606,0.00008677843,0.000055996315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004346852,0.00017313688,0.00025703316,0.00022233771,0.00027427732,0.000601811,0.0006383565,0.00055836607,0.0012385937],"category_scores_gemma":[0.0019946613,0.0001720791,0.00023686145,0.00012054672,0.0005304777,0.0007237663,0.0003697447,0.00035877322,0.000107116015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028972462,0.00005232831,0.0041964334,0.00006714789,0.000039518956,0.00019958166,0.00010119593,0.92758095,0.03453168,0.027246118,0.00031917382,0.005376211],"study_design_scores_gemma":[0.00000323435,0.000011802228,0.0001803077,0.0000027869667,0.0000023337554,0.0000145507665,0.000005570731,0.9963438,0.0016853096,0.0017107283,0.00003735209,0.0000021542787],"about_ca_topic_score_codex":0.0014069987,"about_ca_topic_score_gemma":0.0010247115,"teacher_disagreement_score":0.0014069987,"about_ca_system_score_codex":0.00067288784,"about_ca_system_score_gemma":0.0002436978,"threshold_uncertainty_score":0.0048822165},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32878622,0.0013321536,0.5885344,0.0024374593,0.00090330007,0.00018333305,0.0004386776,0.0022351604,0.07514924],"genre_scores_gemma":[0.88625854,0.00042118592,0.10415966,0.00013461603,0.00005784001,0.00008264886,0.00008766937,0.00006703765,0.008730846],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999304,0.000017172139,0.0000060098255,0.000014111197,0.000025086882,0.000007386701],"domain_scores_gemma":[0.9998902,0.000022108119,0.000012773082,0.000016727057,0.000044534416,0.000013625927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009367039,0.00012822478,0.00016372927,0.00018951818,0.0003664007,0.00048428326,0.0005023475,0.000423625,0.00249836],"category_scores_gemma":[0.0002740337,0.00010764149,0.000116808464,0.00020123237,0.00030691447,0.00051873643,0.00027077383,0.00019458646,0.000605224],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009398721,0.00008913776,0.0005980527,0.00024242498,0.000036053847,0.00042247967,0.000109083856,0.022900496,0.70040685,0.23895238,0.003913444,0.032235544],"study_design_scores_gemma":[0.0000679742,0.00046411576,0.0011561175,0.00009221545,0.000078778176,0.0007989716,0.000096658194,0.60684246,0.23410924,0.08991991,0.06630884,0.00006470531],"about_ca_topic_score_codex":0.0005399702,"about_ca_topic_score_gemma":0.0015719022,"teacher_disagreement_score":0.00249836,"about_ca_system_score_codex":0.00031358536,"about_ca_system_score_gemma":0.0003304799,"threshold_uncertainty_score":0.008357823},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967347,0.000104113366,0.0020971103,0.00004705372,0.00001684651,0.000009826294,0.00004763612,0.00004926078,0.0008933984],"genre_scores_gemma":[0.998579,0.000069374924,0.00064765295,0.000020310552,0.0000032210517,0.000008075897,0.000033374057,0.000011816934,0.00062706915],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999211,0.0000074746586,0.0000037122343,0.00001975092,0.000025274896,0.000022684226],"domain_scores_gemma":[0.9998467,0.000050212144,0.000036626316,0.000013078174,0.000021444286,0.00003193075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009689858,0.00015099182,0.00013434223,0.0001154078,0.00009881905,0.0003152944,0.0002903053,0.00025143335,0.0010022827],"category_scores_gemma":[0.00042217394,0.00010787661,0.00011256733,0.00008241578,0.0002944362,0.00031081846,0.00023168862,0.00035370202,0.00016928917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062663385,0.000010523491,0.00017513549,0.0000166777,0.000002295347,0.00008120156,0.0000259618,0.00027587463,0.99825376,0.00013069117,0.00002220175,0.0009430387],"study_design_scores_gemma":[0.000019581741,0.0004103253,0.0054451753,0.000012220329,0.000010004294,0.00025600038,0.000082493876,0.011150209,0.9814843,0.00038689538,0.00073270715,0.000010062336],"about_ca_topic_score_codex":0.00031574108,"about_ca_topic_score_gemma":0.00018969786,"teacher_disagreement_score":0.0010022827,"about_ca_system_score_codex":0.00014659669,"about_ca_system_score_gemma":0.00013850084,"threshold_uncertainty_score":0.00335294},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5965859,0.000857411,0.39295226,0.0004669507,0.00013447064,0.00006321558,0.0007405315,0.0049463655,0.0032528571],"genre_scores_gemma":[0.94684017,0.00015969142,0.050511017,0.00007977132,0.000017758895,0.000034911878,0.0008458648,0.000073584226,0.0014371921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998061,0.000037635302,0.000014281425,0.00005375242,0.000050899485,0.000037327394],"domain_scores_gemma":[0.9996327,0.000115938165,0.00004635716,0.000072127004,0.00011095578,0.00002195985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042579405,0.00064953475,0.00040230437,0.00041496972,0.00018234401,0.00045218633,0.0010050088,0.00052842725,0.00051185157],"category_scores_gemma":[0.0012726779,0.00026496212,0.000553507,0.00036559298,0.00031052224,0.0005771346,0.0004485567,0.00075721357,0.0002084571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014151976,0.000081913946,0.002306567,0.00007160186,0.00009456576,0.00011321775,0.000029405865,0.90414387,0.019407935,0.0015927398,0.0017663318,0.07025032],"study_design_scores_gemma":[0.000002145861,0.000010180501,0.00019453042,0.0000019970596,0.0000031235793,0.000008337595,0.00000210582,0.995824,0.0034398637,0.00037648718,0.0001351617,0.0000021022502],"about_ca_topic_score_codex":0.011852557,"about_ca_topic_score_gemma":0.011765213,"teacher_disagreement_score":0.011852557,"about_ca_system_score_codex":0.000622776,"about_ca_system_score_gemma":0.0006223577,"threshold_uncertainty_score":0.02356714},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7114906,0.00010660679,0.26886585,0.0003660792,0.00011548695,0.00010032418,0.0001629637,0.001970421,0.016821701],"genre_scores_gemma":[0.9837856,0.000013437682,0.01487738,0.000030024139,0.000001618454,0.000026877506,0.000026857211,0.000014667162,0.0012235598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994516,0.000009023533,0.0000027186582,0.000009981599,0.000019845687,0.00001325568],"domain_scores_gemma":[0.9998913,0.00003262695,0.000012733033,0.000018317129,0.000028687547,0.000016325623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001495803,0.00018493597,0.00013769965,0.00010466078,0.00013882543,0.00021085933,0.00060221064,0.00023837028,0.0020274268],"category_scores_gemma":[0.00037923252,0.00006887772,0.00014794747,0.000066071734,0.00024121418,0.00015759323,0.00026198218,0.00023444633,0.00016158615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023839746,0.00011896814,0.0020473632,0.000116220435,0.000048124988,0.00028242392,0.00013174149,0.8571031,0.08271017,0.00507792,0.0011833803,0.05094214],"study_design_scores_gemma":[0.000016290167,0.00017155225,0.0006959909,0.0000066808498,0.000008864998,0.000031897274,0.000019019506,0.9763798,0.020593936,0.0008146249,0.0012536658,0.000007623268],"about_ca_topic_score_codex":0.001810678,"about_ca_topic_score_gemma":0.0022049965,"teacher_disagreement_score":0.0020274268,"about_ca_system_score_codex":0.00028516128,"about_ca_system_score_gemma":0.00044042003,"threshold_uncertainty_score":0.0067824125},"labels":[],"label_agreement":null},{"id":"W4378469847","doi":"10.1088/2634-4386/acd952","title":"Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory","year":2023,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","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":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Neuromorphic engineering; ENCODE; Encoding (memory); Spike (software development); Coding (social sciences); Neural coding; Speech recognition; Efficient energy use; Decoding methods; Artificial intelligence; Artificial neural network; Algorithm; Mathematics","score_opus":0.03124161040200353,"score_gpt":0.23713232752709473,"score_spread":0.20589071712509122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378469847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17859691,0.0016451209,0.8130747,0.00033949746,0.00004680003,0.0001469586,0.00017852559,0.00081532827,0.0051561403],"genre_scores_gemma":[0.84558004,0.00037058006,0.15250428,0.00004846578,0.000017424516,0.00012602744,0.000206919,0.00012869353,0.0010175599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985827,0.00046965934,0.00013011879,0.00010665315,0.0006127704,0.00009805737],"domain_scores_gemma":[0.98835284,0.008521953,0.0009050094,0.0007512268,0.001314656,0.00015442686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028083138,0.00063773955,0.00057881454,0.002207059,0.000361892,0.0011099873,0.00088966754,0.00066815736,0.0011234976],"category_scores_gemma":[0.014846942,0.0002092014,0.0004007075,0.0014189564,0.0009415712,0.0024459267,0.001008224,0.00067905936,0.00022077785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006002357,0.00028004154,0.0040551187,0.00040178958,0.00017016355,0.00009899867,0.00014804014,0.6815354,0.042122673,0.07870353,0.0016935464,0.19019037],"study_design_scores_gemma":[0.00001036631,0.00013790134,0.0010504236,0.000020186528,0.000019160874,0.00007954605,0.000023198947,0.9620872,0.022312516,0.013758503,0.0004727021,0.000028160983],"about_ca_topic_score_codex":0.0010702463,"about_ca_topic_score_gemma":0.0007475263,"teacher_disagreement_score":0.0028083138,"about_ca_system_score_codex":0.0016406572,"about_ca_system_score_gemma":0.00064896425,"threshold_uncertainty_score":0.014851987},"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":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025079018,0.009274315,0.00010407384,0.02448006,0.9634902,0.000023214085,0.000038394894,0.00003701289,0.0025277503],"genre_scores_gemma":[0.00035134298,0.0057249013,0.00009079939,0.026731716,0.95102894,0.000033748307,0.000029513776,0.000036481822,0.015972534],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99446964,0.0007605833,0.00045112133,0.0006580932,0.0032156024,0.00044505828],"domain_scores_gemma":[0.9876258,0.0042811055,0.0009861601,0.00029691152,0.004395475,0.0024145884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006311616,0.0040411027,0.003826993,0.004121036,0.0038083997,0.009036145,0.0036282714,0.02423715,0.021261945],"category_scores_gemma":[0.017695896,0.0015953074,0.0030927,0.0015800889,0.0027564412,0.004619419,0.0024790012,0.01902413,0.015227342],"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":[0.000029366465,0.000009722741,0.000008627807,0.00015910342,0.000012267241,0.00007004434,0.0000051891966,0.000016627613,0.00007672695,0.00018927056,0.9958805,0.0035424782],"study_design_scores_gemma":[0.00007279881,0.000029537203,0.00014132378,0.00033820016,0.000043738957,0.00018320687,0.000017456823,0.00012182487,0.00012557878,0.0009075568,0.99800235,0.000016364924],"about_ca_topic_score_codex":0.0021253151,"about_ca_topic_score_gemma":0.00756527,"teacher_disagreement_score":0.02423715,"about_ca_system_score_codex":0.003912433,"about_ca_system_score_gemma":0.003647688,"threshold_uncertainty_score":0.07112837},"labels":[],"label_agreement":null},{"id":"W4396883304","doi":"10.1088/2634-4386/ad4b5b","title":"Optical spike amplitude weighting and neuromimetic rate coding using a joint VCSEL-MRR neuromorphic photonic system","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Photonic and Optical Devices","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":"Queen's University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; European Commission; H2020 Future and Emerging Technologies; UK Research and Innovation","keywords":"Neuromorphic engineering; Spike (software development); Photonics; Amplitude; Joint (building); Weighting; Computer science; Coding (social sciences); Vertical-cavity surface-emitting laser; Electronic engineering; Physics; Optoelectronics; Optics; Artificial intelligence; Laser; Engineering; Acoustics; Mathematics; Artificial neural network; Statistics","score_opus":0.02613475955735328,"score_gpt":0.21113758433024293,"score_spread":0.18500282477288965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396883304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9576978,0.00012310574,0.039109904,0.00016128221,0.00004304899,0.000027486949,0.000044652712,0.00032724923,0.0024655925],"genre_scores_gemma":[0.9795426,0.000027952707,0.019589933,0.00003139135,0.0000089109235,0.000014754083,0.000008183739,0.0000125888,0.00076381076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998173,0.000025605752,0.000014071357,0.00004787066,0.000068048306,0.000027078744],"domain_scores_gemma":[0.99963534,0.00010010279,0.000114105416,0.000049147653,0.0000652918,0.000035910823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030552657,0.0001895769,0.00017921191,0.00012730254,0.00016888755,0.00042178456,0.0005833199,0.0003096186,0.00077972264],"category_scores_gemma":[0.0004722452,0.00015712941,0.00013920892,0.00009783335,0.00036499696,0.0004298632,0.00040201854,0.00022608165,0.00018175086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029947296,0.000024368257,0.00023176672,0.000015802789,0.0000061614837,0.000045586505,0.000020819058,0.0015117262,0.99400085,0.0010738192,0.000050051134,0.00298907],"study_design_scores_gemma":[0.00002651274,0.00030499784,0.0011232733,0.0000067592237,0.000014991702,0.00017583488,0.000026011692,0.1002461,0.8959597,0.00069495983,0.0014035469,0.00001746514],"about_ca_topic_score_codex":0.0002515127,"about_ca_topic_score_gemma":0.00048547666,"teacher_disagreement_score":0.00077972264,"about_ca_system_score_codex":0.00025053622,"about_ca_system_score_gemma":0.0001849857,"threshold_uncertainty_score":0.0026084185},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08126874,0.00026378667,0.91172165,0.00024574308,0.00007483805,0.000042869528,0.0003260246,0.0032435842,0.0028127625],"genre_scores_gemma":[0.7000413,0.00022731528,0.29426783,0.00018278573,0.000032922824,0.00006496619,0.00064289797,0.00012778025,0.0044122003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997516,0.00003138618,0.000017767874,0.00004600091,0.0001242162,0.000029018387],"domain_scores_gemma":[0.99942446,0.00017394598,0.000050985655,0.00013344713,0.00019007966,0.00002710277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042260857,0.00037256113,0.00037125038,0.00029822587,0.00017109566,0.00048482316,0.0008174703,0.0004003927,0.0014818806],"category_scores_gemma":[0.0014530662,0.00019763564,0.000276666,0.00032127908,0.0002595274,0.0006729997,0.00059969275,0.0007143803,0.00053393934],"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":[0.00031936,0.00022613838,0.0033750911,0.00016338697,0.00010110775,0.00018106707,0.00009497587,0.22617003,0.11920527,0.014767099,0.005902588,0.6294939],"study_design_scores_gemma":[0.0000050253934,0.000033768203,0.00032685846,0.00000637932,0.000008920717,0.000057535457,0.000006601441,0.9535309,0.043069586,0.001782487,0.0011653593,0.000006631533],"about_ca_topic_score_codex":0.0017130809,"about_ca_topic_score_gemma":0.0042931116,"teacher_disagreement_score":0.0017130809,"about_ca_system_score_codex":0.00032817698,"about_ca_system_score_gemma":0.0007245109,"threshold_uncertainty_score":0.0049574375},"labels":[],"label_agreement":null},{"id":"W4400672809","doi":"10.1088/2634-4386/ad63c6","title":"Tissue-like interfacing of planar electrochemical organic neuromorphic devices","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Conducting polymers and applications","field":"Materials Science","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":"Interfacing; Neuromorphic engineering; Planar; Materials science; Computer science; Nanotechnology; Optoelectronics; Artificial intelligence; Computer graphics (images); Computer hardware; Artificial neural network","score_opus":0.018267960814415635,"score_gpt":0.22882118251904923,"score_spread":0.2105532217046336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400672809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9564777,0.0013379948,0.037175015,0.00014407918,0.00011549144,0.000046751135,0.000216933,0.00030882302,0.00417714],"genre_scores_gemma":[0.9693815,0.0007672369,0.02498936,0.00010968199,0.000017394243,0.000044789704,0.00012737015,0.000026819427,0.0045358227],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999248,0.000007542253,0.0000041437142,0.000023406885,0.000025740748,0.000014401007],"domain_scores_gemma":[0.9998876,0.000031093394,0.000030640364,0.000011620004,0.000025830604,0.000013218788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007524383,0.0002456883,0.00013642292,0.00012699568,0.000097811055,0.0002780938,0.00036302727,0.00034809634,0.0008969607],"category_scores_gemma":[0.0002680235,0.00010548488,0.00009630166,0.000117110816,0.00018809509,0.00036039107,0.00025898014,0.00023700148,0.00027878958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010276967,0.0000050261165,0.000047108562,0.000043099375,0.0000020560403,0.00006970173,0.000014172223,0.00021432269,0.9974017,0.00016925693,0.000042278538,0.001981148],"study_design_scores_gemma":[0.0000046466353,0.00007768078,0.00046525864,0.0000069882167,0.0000037236025,0.00014741054,0.000023881574,0.0022241087,0.9939069,0.0000958434,0.0030389146,0.000004721264],"about_ca_topic_score_codex":0.00021345483,"about_ca_topic_score_gemma":0.0003660767,"teacher_disagreement_score":0.0008969607,"about_ca_system_score_codex":0.00017600719,"about_ca_system_score_gemma":0.0001104377,"threshold_uncertainty_score":0.003000617},"labels":[],"label_agreement":null},{"id":"W4401977444","doi":"10.1088/2634-4386/ad5d0f","title":"Kernel heterogeneity improves sparseness of natural images representations","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Canadian Institutes of Health Research; Agence Nationale de la Recherche","keywords":"Kernel (algebra); Artificial intelligence; Natural (archaeology); Computer science; Pattern recognition (psychology); Mathematics; Machine learning; Statistics; Geography; Archaeology; Combinatorics","score_opus":0.01679178309519349,"score_gpt":0.26612297877573204,"score_spread":0.24933119568053855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401977444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3761048,0.00016444892,0.62088406,0.00027909086,0.000025831938,0.000023388078,0.00009790295,0.0005652525,0.0018551742],"genre_scores_gemma":[0.975935,0.00005951381,0.02313715,0.000061448096,0.000012598755,0.000012652541,0.000091880946,0.000046224304,0.000643622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971527,0.000060220624,0.000016896436,0.000075676355,0.0000930071,0.000038904876],"domain_scores_gemma":[0.9985915,0.0006539591,0.00019559021,0.00030966185,0.00016371609,0.00008557238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060027343,0.0003720123,0.00040293668,0.00039002043,0.00017147537,0.0006227197,0.0006002514,0.00049877306,0.0009825189],"category_scores_gemma":[0.0041473624,0.000189561,0.0002826982,0.00030084487,0.0007145549,0.0011394921,0.0011072641,0.00073713384,0.00015996057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046435924,0.00015525025,0.0032744734,0.0001721421,0.00008187865,0.00024475803,0.00015522375,0.7191257,0.15482423,0.042772032,0.00139219,0.07733788],"study_design_scores_gemma":[0.000008325713,0.000061136365,0.0006349708,0.0000048229613,0.0000083240375,0.00006425129,0.000014326532,0.9747629,0.015339004,0.008720086,0.00037387948,0.000007949877],"about_ca_topic_score_codex":0.0010394007,"about_ca_topic_score_gemma":0.00083105726,"teacher_disagreement_score":0.0010394007,"about_ca_system_score_codex":0.00058877346,"about_ca_system_score_gemma":0.0003953733,"threshold_uncertainty_score":0.004271865},"labels":[],"label_agreement":null},{"id":"W4403857108","doi":"10.1088/2634-4386/ad8c78","title":"Unsupervised end-to-end training with a self-defined target","year":2024,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Grand Équipement National De Calcul Intensif; Agence Nationale de la Recherche; Canadian Institute for Advanced Research","keywords":"MNIST database; Computer science; Artificial intelligence; Unsupervised learning; Semi-supervised learning; Perceptron; Machine learning; Supervised learning; Deep learning; Labeled data; Hebbian theory; Pattern recognition (psychology); Multilayer perceptron; Artificial neural network","score_opus":0.019522746746219156,"score_gpt":0.21647302414908098,"score_spread":0.19695027740286183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403857108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07955808,0.00006185529,0.9126316,0.00017461042,0.00004574885,0.000050979124,0.00007205201,0.0035804627,0.0038246473],"genre_scores_gemma":[0.7885085,0.00003652657,0.2054595,0.00022647002,0.000024215733,0.00014623132,0.00022846574,0.00027488134,0.00509525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953175,0.00007213472,0.000023514991,0.00015987021,0.00015930815,0.000053535827],"domain_scores_gemma":[0.998835,0.00033951673,0.00012234654,0.0003256873,0.00032150422,0.000055871616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006277616,0.0005014863,0.00036941178,0.00021283295,0.0002740823,0.0006404403,0.0015301453,0.0006558911,0.0020579011],"category_scores_gemma":[0.0020835795,0.00027050907,0.00024328042,0.00023785743,0.00061394187,0.0011834342,0.00096510054,0.001161,0.0011415342],"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":[0.00077849603,0.0005892536,0.0041966103,0.0001787434,0.00010858387,0.00017395163,0.00024286321,0.32080358,0.29183397,0.021093015,0.00873113,0.35126987],"study_design_scores_gemma":[0.000008656969,0.000085585154,0.0005664164,0.0000071618715,0.0000059554786,0.000043259508,0.000014059342,0.9178184,0.07664073,0.0034069084,0.0013936694,0.000009126723],"about_ca_topic_score_codex":0.0007157841,"about_ca_topic_score_gemma":0.0012605637,"teacher_disagreement_score":0.0020579011,"about_ca_system_score_codex":0.0005232899,"about_ca_system_score_gemma":0.0005912272,"threshold_uncertainty_score":0.006884396},"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":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038175493,0.094972074,0.5656866,0.03420458,0.012746929,0.0008547123,0.0031351773,0.007978858,0.24224563],"genre_scores_gemma":[0.4650024,0.049544364,0.40731874,0.0103982985,0.0052792816,0.0018342676,0.0114255315,0.00667919,0.042517807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9877893,0.0033380243,0.0008472371,0.0012026535,0.0061526666,0.0006701219],"domain_scores_gemma":[0.9774736,0.007922517,0.00088548084,0.0032887717,0.009369067,0.0010605897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008850099,0.0018545267,0.0017723292,0.0029588619,0.0012292484,0.0068073026,0.0046996255,0.002034122,0.010187904],"category_scores_gemma":[0.044567954,0.00055040827,0.000830159,0.0049702143,0.0020171404,0.010313245,0.0036385613,0.0049573053,0.0047675283],"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":[0.00038900904,0.00034571806,0.0025313024,0.0029132583,0.00013021735,0.0002613725,0.00038614045,0.033089824,0.012772568,0.5027385,0.09657497,0.34786704],"study_design_scores_gemma":[0.00006956601,0.0006258858,0.0015234277,0.0025275317,0.00006366615,0.0005511972,0.0006450472,0.061957393,0.027916439,0.41577184,0.488219,0.00012907904],"about_ca_topic_score_codex":0.0013434029,"about_ca_topic_score_gemma":0.0013462546,"teacher_disagreement_score":0.010187904,"about_ca_system_score_codex":0.0031588057,"about_ca_system_score_gemma":0.0025263978,"threshold_uncertainty_score":0.04680437},"labels":[],"label_agreement":null},{"id":"W4406256348","doi":"10.1088/2634-4386/ada8d4","title":"Maximizing information in neuron populations for neuromorphic spike encoding","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","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":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Spike (software development); Encoding (memory); Computer science; Artificial intelligence; Neuroscience; Biology; Artificial neural network; Software engineering","score_opus":0.031261405762644155,"score_gpt":0.24092333094432417,"score_spread":0.20966192518168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406256348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10891197,0.00041847222,0.8877687,0.0002768743,0.000021595624,0.00005068764,0.000030751202,0.0002476868,0.0022732185],"genre_scores_gemma":[0.881193,0.00011993116,0.1173089,0.00009683567,0.000027159595,0.00008087899,0.00003448698,0.000037676342,0.0011012459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995777,0.00015961386,0.000029309127,0.000068712994,0.00011345843,0.000051227635],"domain_scores_gemma":[0.9990778,0.000535839,0.00013677955,0.000077078985,0.00012929973,0.00004314772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087296125,0.00053449895,0.00057047635,0.0004686713,0.00023069163,0.00059337326,0.00074213085,0.0006230174,0.0008525647],"category_scores_gemma":[0.0028532762,0.00025339785,0.00030170337,0.00045983665,0.0005086896,0.0009108898,0.0008639445,0.0005166014,0.0001929179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018056316,0.00014936698,0.0010563289,0.00011173123,0.00007921367,0.00007462123,0.00011409543,0.72198313,0.058303777,0.01779382,0.0008937975,0.19925961],"study_design_scores_gemma":[0.000008234884,0.000060350027,0.00020689775,0.000008653144,0.000012527178,0.00003998869,0.000012249644,0.98265153,0.011044938,0.0055168183,0.00043081652,0.0000070619913],"about_ca_topic_score_codex":0.00045902753,"about_ca_topic_score_gemma":0.0005707548,"teacher_disagreement_score":0.00087296125,"about_ca_system_score_codex":0.00068410125,"about_ca_system_score_gemma":0.00038797103,"threshold_uncertainty_score":0.004963517},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04579769,0.00013459566,0.9513203,0.00021352264,0.00005440044,0.00006052797,0.000035120484,0.0012571618,0.0011266724],"genre_scores_gemma":[0.5532785,0.00008317146,0.44301152,0.00022524844,0.000026816359,0.00018533805,0.00011849909,0.00015285774,0.0029180064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997818,0.000048338625,0.000019208788,0.000051171093,0.00007421185,0.000025287865],"domain_scores_gemma":[0.9991353,0.00035582442,0.0001054101,0.00013647729,0.00021608008,0.000050936596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007003426,0.0004748502,0.00035524112,0.00040325813,0.0002450153,0.00049511075,0.0015041005,0.00081037416,0.0016736583],"category_scores_gemma":[0.002400245,0.0002509388,0.00026938642,0.0003279374,0.00046572884,0.00067666423,0.0008373257,0.001040703,0.00029956977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028136853,0.0001330586,0.0017630882,0.0000988635,0.000082464314,0.00008925911,0.00009499343,0.6630923,0.032333214,0.011203387,0.0023708895,0.28845707],"study_design_scores_gemma":[0.000006725696,0.000019335703,0.00006762733,0.0000034298725,0.0000022564209,0.000012848751,0.0000030839205,0.9946655,0.0036071008,0.0013445615,0.00026509055,0.0000023720133],"about_ca_topic_score_codex":0.0013051479,"about_ca_topic_score_gemma":0.0018169776,"teacher_disagreement_score":0.0016736583,"about_ca_system_score_codex":0.000584738,"about_ca_system_score_gemma":0.00069187966,"threshold_uncertainty_score":0.0055989623},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21072105,0.0010406859,0.7689065,0.00068889,0.00037389365,0.00005896084,0.00030880267,0.009461683,0.00843962],"genre_scores_gemma":[0.7595298,0.0003986378,0.23496935,0.0001833427,0.00004730811,0.000076673095,0.0004202375,0.0002820257,0.0040926826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991226,0.000010134325,0.000005550822,0.000020557669,0.000037147533,0.000014211093],"domain_scores_gemma":[0.99977106,0.00007961052,0.000026366126,0.000042632277,0.00006117652,0.000019240448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002681858,0.00039919026,0.00026527076,0.0001894179,0.00013956912,0.00037339833,0.0008724224,0.00031627313,0.0023798915],"category_scores_gemma":[0.0010480918,0.00014896285,0.00026017777,0.00022633097,0.00021389367,0.00061688724,0.00044826968,0.0006102939,0.0005738942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002796772,0.00011104587,0.0022139624,0.00019439541,0.00010870184,0.00021569878,0.00008629485,0.6544664,0.062930346,0.009508494,0.008187888,0.26169708],"study_design_scores_gemma":[0.0000088371435,0.00003222778,0.00014398423,0.000005371471,0.000006822892,0.000023223387,0.0000067691885,0.9838435,0.012491434,0.0018933618,0.0015401454,0.000004326038],"about_ca_topic_score_codex":0.0027576762,"about_ca_topic_score_gemma":0.0038544661,"teacher_disagreement_score":0.0027576762,"about_ca_system_score_codex":0.00037644422,"about_ca_system_score_gemma":0.00045698127,"threshold_uncertainty_score":0.007961512},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12631936,0.00043412618,0.8687594,0.000270281,0.000069281625,0.000028041015,0.0000372825,0.0011417496,0.002940491],"genre_scores_gemma":[0.9130231,0.00015433828,0.08454256,0.00008561235,0.00002602321,0.000028037695,0.00004863506,0.00008061311,0.002011088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998299,0.000037031576,0.000011590006,0.00003666079,0.00005854032,0.00002628064],"domain_scores_gemma":[0.9995153,0.00020080975,0.000076922806,0.00006154082,0.00011137728,0.000033962257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005751854,0.00039216495,0.00034302994,0.00019956447,0.00014098536,0.00044767352,0.00085761095,0.0005212598,0.0011413705],"category_scores_gemma":[0.002343234,0.00015607475,0.00025582247,0.00023438687,0.00032162998,0.0006846465,0.00070064835,0.0006236697,0.0002620089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016104014,0.00014475662,0.0008229241,0.000108384,0.000059640624,0.0001227181,0.0000645307,0.7600038,0.060716163,0.012854358,0.0013740886,0.16356756],"study_design_scores_gemma":[0.0000024097453,0.00002286128,0.000050038503,0.0000024741755,0.000003207952,0.000013967972,0.0000022592837,0.9940211,0.00408416,0.0015678387,0.00022777116,0.000001961281],"about_ca_topic_score_codex":0.0010720366,"about_ca_topic_score_gemma":0.0017300682,"teacher_disagreement_score":0.0011413705,"about_ca_system_score_codex":0.0003661528,"about_ca_system_score_gemma":0.00035646767,"threshold_uncertainty_score":0.0038182735},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05270318,0.001828649,0.015096532,0.0009277895,0.00060027314,0.00046468445,0.91072184,0.010804531,0.0068524624],"genre_scores_gemma":[0.049768668,0.00043751384,0.022270538,0.00027577393,0.00006806902,0.00078476814,0.92322165,0.0005617633,0.0026113077],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992093,0.00014176672,0.00012112404,0.00019166902,0.00025447705,0.000081701364],"domain_scores_gemma":[0.998437,0.00042529314,0.00014842144,0.00040574136,0.00041993966,0.00016364706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069802516,0.0013939495,0.0006044985,0.0022130085,0.00072853867,0.0011655765,0.0019088383,0.0017197798,0.006208234],"category_scores_gemma":[0.005010127,0.00030247986,0.0011792133,0.0021883994,0.0004973884,0.0010972176,0.0016608681,0.0012809004,0.0063709673],"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":[0.0009955235,0.00064808875,0.014925287,0.0032680556,0.00035129397,0.00079111767,0.00027370793,0.030290198,0.015187424,0.006245321,0.8278522,0.09917184],"study_design_scores_gemma":[0.00070151,0.00095395255,0.03901741,0.0006453304,0.00017066726,0.001839089,0.000678136,0.13104878,0.031073144,0.018360903,0.775198,0.00031313492],"about_ca_topic_score_codex":0.0070256274,"about_ca_topic_score_gemma":0.016672222,"teacher_disagreement_score":0.0070256274,"about_ca_system_score_codex":0.00079991284,"about_ca_system_score_gemma":0.0011604698,"threshold_uncertainty_score":0.020768642},"labels":[],"label_agreement":null},{"id":"W4410632802","doi":"10.1088/2634-4386/addc90","title":"Wandering around: a bioinspired approach to visual attention through object motion sensitivity","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sensitivity (control systems); Computer vision; Object (grammar); Motion (physics); Visual attention; Artificial intelligence; Computer science; Cognitive psychology; Psychology; Engineering; Neuroscience; Perception","score_opus":0.0203339267509768,"score_gpt":0.2494724945119773,"score_spread":0.2291385677610005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410632802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20921995,0.0013365399,0.77943933,0.000698355,0.00014063569,0.00005266882,0.0000994396,0.0011917216,0.007821355],"genre_scores_gemma":[0.9575808,0.00023014954,0.039343037,0.00017863706,0.000024372297,0.000023309187,0.0000358849,0.00004171591,0.0025419863],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999859,0.00002232238,0.000006144517,0.00006117168,0.00003207624,0.000019204117],"domain_scores_gemma":[0.9998097,0.000059105718,0.000032110715,0.00002978554,0.000047168553,0.000022159657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029420078,0.00024150759,0.00021450473,0.0002216967,0.00017525477,0.0004577621,0.00091435225,0.00049124163,0.0013187138],"category_scores_gemma":[0.0007715185,0.00013463976,0.00029787183,0.00015191549,0.0004244201,0.0004930689,0.00054535875,0.00043836783,0.0001547318],"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":[0.00022510934,0.00016203677,0.0023519844,0.0002373735,0.00016570116,0.0003410541,0.00026534585,0.26996964,0.50583506,0.029703444,0.0023662231,0.18837695],"study_design_scores_gemma":[0.000009738646,0.00013148389,0.0019533844,0.000014368073,0.000031273827,0.00011794,0.000022177648,0.9399466,0.040963333,0.014232447,0.0025624943,0.000014747457],"about_ca_topic_score_codex":0.0017362481,"about_ca_topic_score_gemma":0.0012183553,"teacher_disagreement_score":0.0017362481,"about_ca_system_score_codex":0.00073254947,"about_ca_system_score_gemma":0.00026543648,"threshold_uncertainty_score":0.0053150654},"labels":[],"label_agreement":null},{"id":"W4412565533","doi":"10.1088/2634-4386/adf2d4","title":"End-to-end neuromorphic speech enhancement with PDM microphones <sup>*</sup>","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Neuromorphic engineering; Speech enhancement; Computer science; End-to-end principle; Speech recognition; Artificial intelligence; Artificial neural network","score_opus":0.011205878053274382,"score_gpt":0.20653548206170316,"score_spread":0.19532960400842878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412565533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11980182,0.0010881519,0.856044,0.0007800774,0.00047729127,0.00012205744,0.000764161,0.009048479,0.01187394],"genre_scores_gemma":[0.7443116,0.0004989028,0.23748952,0.00083057024,0.000095963005,0.0000988771,0.00068074086,0.00024688613,0.015746972],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998547,0.000021844193,0.000009561623,0.000034844525,0.00006551117,0.000013570521],"domain_scores_gemma":[0.99979955,0.00005598576,0.000020081603,0.000032895543,0.00007704447,0.000014613981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026015146,0.00038805816,0.00027584686,0.0001452077,0.00013579178,0.00038226784,0.00061406795,0.00044148412,0.0035520447],"category_scores_gemma":[0.00079319073,0.000118992604,0.00021464303,0.00010753727,0.00022568493,0.00043716122,0.0005012918,0.00032576115,0.0014023148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050264515,0.000102470774,0.0016348984,0.00031581664,0.00007548648,0.0004689372,0.00011482521,0.018189155,0.5285904,0.0035122042,0.00929332,0.4371997],"study_design_scores_gemma":[0.000036516525,0.00033654989,0.0020115343,0.000052324714,0.00006434737,0.00088556524,0.00007026034,0.3793088,0.58247554,0.0030502393,0.031671073,0.000037207676],"about_ca_topic_score_codex":0.00042292615,"about_ca_topic_score_gemma":0.0013797843,"teacher_disagreement_score":0.0035520447,"about_ca_system_score_codex":0.00019060423,"about_ca_system_score_gemma":0.00021897034,"threshold_uncertainty_score":0.011882782},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06800144,0.0001886219,0.9271612,0.00024959116,0.000048642138,0.000044941087,0.00014504117,0.0019416439,0.0022188346],"genre_scores_gemma":[0.77386534,0.00012250135,0.22262846,0.00013490194,0.000020687745,0.00007997647,0.00025447007,0.00009519594,0.0027985594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998235,0.000022984772,0.0000097786415,0.0000363574,0.00008392365,0.000023506384],"domain_scores_gemma":[0.999629,0.00011241187,0.00004684907,0.00004173762,0.00014708762,0.00002290945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032913513,0.00033538067,0.00031952086,0.00034907085,0.00020494571,0.00043105113,0.0009469859,0.00040791073,0.0014602059],"category_scores_gemma":[0.001109336,0.00018416243,0.00026618614,0.0004754734,0.00029469965,0.00056259433,0.00046298033,0.0006384474,0.00030905617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002476164,0.00013335496,0.0012330877,0.00010778168,0.00006465281,0.00007512979,0.00007218075,0.68693006,0.06665182,0.011862465,0.0032766017,0.22934525],"study_design_scores_gemma":[0.000003153027,0.000012656774,0.000090134105,0.0000017422883,0.0000024328065,0.000009100028,0.0000025524373,0.99350005,0.0050075054,0.0010899657,0.00027805206,0.000002596745],"about_ca_topic_score_codex":0.004536575,"about_ca_topic_score_gemma":0.008247077,"teacher_disagreement_score":0.004536575,"about_ca_system_score_codex":0.0008354914,"about_ca_system_score_gemma":0.0010102133,"threshold_uncertainty_score":0.0090203285},"labels":[],"label_agreement":null}]}