{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":205,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":205,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"a9e9a836e0ff","filters":{"venue":"Journal of Neural Engineering"}},"results":[{"id":"W2160961804","doi":"10.1088/1741-2560/4/3/s02","title":"An optical neural interface:<i>in vivo</i>control of rodent motor cortex with integrated fiberoptic and optogenetic technology","year":2007,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Photoreceptor and optogenetics research","field":"Neuroscience","cited_by":979,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Stanford Bio-X; National Institutes of Health; Ryerson University; American Psychiatric Institute for Research and Education","keywords":"Optogenetics; Channelrhodopsin; Neuroscience; Excitatory postsynaptic potential; Brain–computer interface; Photostimulation; Millisecond; Computer science; Biological neural network; Interface (matter); In vivo; Inhibitory postsynaptic potential; Biomedical engineering; Biology; Physics; Medicine; Electroencephalography","retraction":null,"screen_n_in":null,"score":{"opus":0.01156798842700325,"gpt":0.2739548556419412,"spread":0.2623868672149379,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027939,0.0001800681,0.000344033,0.0006364722,0.00002281218,0.00003583249,0.0003520247,0.00008946135,0.00001551351],"category_scores_gemma":[0.0002342736,0.0001372577,0.00005026529,0.0004786403,0.0001347419,0.0001604111,0.00004468863,0.0005561697,4.488814e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006196156,"about_ca_system_score_gemma":0.00003412818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005055947,"about_ca_topic_score_gemma":0.000007085398,"domain_scores_codex":[0.9984685,0.00003391289,0.0005158634,0.0002100861,0.0003690237,0.0004025852],"domain_scores_gemma":[0.9991907,0.0001838667,0.000134195,0.0001629245,0.0001093839,0.0002188534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004180287,0.00007164366,0.003350572,0.00002167437,0.000008287016,0.0001874949,0.00005282087,0.03513981,0.9593056,0.00002184895,5.976067e-7,0.001421567],"study_design_scores_gemma":[0.001070239,0.001900018,0.0113682,0.00006525929,0.00001313912,0.0007547538,0.00006258466,0.2158453,0.768769,0.000004359413,0.00002107994,0.0001261015],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916267,0.0002419391,0.00770427,0.00006544549,0.0001586473,0.0001654905,0.000004925389,0.00001794748,0.00001463701],"genre_scores_gemma":[0.9984015,0.00004953969,0.001449695,0.00002116119,0.00004225126,0.000002215689,8.613728e-8,0.00002631278,0.000007215424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1905366,"threshold_uncertainty_score":0.5597207,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145947508","doi":"10.1088/1741-2560/4/2/r03","title":"A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals","year":2007,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":898,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Neil Squire Society; University of British Columbia","funders":"","keywords":"Brain–computer interface; Computer science; Signal processing; SIGNAL (programming language); Electroencephalography; Key (lock); Process (computing); Human–computer interaction; Digital signal processing; Neuroscience; Computer hardware; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.08576177725486432,"gpt":0.3482956464884522,"spread":0.2625338692335878,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001829527,0.0005958678,0.00202189,0.001642948,0.00003342376,0.0001395475,0.0009599808,0.0002738086,0.00001463655],"category_scores_gemma":[0.0007894876,0.0004532258,0.0004662922,0.001436574,0.00005207652,0.0002825652,0.00009692391,0.00171497,0.000002657869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724641,"about_ca_system_score_gemma":0.0002529704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007468883,"about_ca_topic_score_gemma":0.000001534621,"domain_scores_codex":[0.995737,0.0005133538,0.001933896,0.0004692915,0.0007672827,0.0005791875],"domain_scores_gemma":[0.9926932,0.005643825,0.001155345,0.0001868541,0.0001430397,0.0001777035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004451702,0.0001465512,0.000007787657,0.002416887,0.00002096075,0.0003649877,0.00005064114,0.07370465,0.001184793,0.000001392038,0.0002255067,0.9218313],"study_design_scores_gemma":[0.0006241436,0.001526867,0.0001268048,0.02074543,0.00005084116,0.0006354838,0.000001546627,0.948149,0.003337463,0.000002108476,0.02417147,0.0006288243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.006676851,0.9120998,0.07891361,0.0002156833,0.001222393,0.0007110046,0.00003961912,0.000088141,0.00003285284],"genre_scores_gemma":[0.5073647,0.4686401,0.01453568,0.003642468,0.004591777,0.00003968322,0.00002814237,0.0009629271,0.0001945582],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9212025,"threshold_uncertainty_score":0.9997919,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2751178603","doi":"10.1088/1741-2552/aa8b4f","title":"A review on mechanical considerations for chronically-implanted neural probes","year":2017,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":206,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Agence Nationale de la Recherche; University of Alberta","keywords":"Stiffness; Computer science; Brain implant; Buckling; Materials science; Biomedical engineering; Brain tissue; Interface (matter); Nanotechnology; Mechanical engineering; Artificial intelligence; Composite material; Medicine; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.2690860135227386,"gpt":0.4060776335540909,"spread":0.1369916200313523,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004498721,0.0006641331,0.0023138,0.0004012194,0.0002551663,0.0002331945,0.001001935,0.0001956648,0.00001552312],"category_scores_gemma":[0.009041838,0.0004821286,0.001265311,0.0002520618,0.00005830854,0.0004974337,0.0001244057,0.001372529,0.00001064198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001225109,"about_ca_system_score_gemma":0.0002236711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.657617e-7,"about_ca_topic_score_gemma":2.850076e-7,"domain_scores_codex":[0.9966707,0.0001114734,0.001544899,0.0005539551,0.0004955902,0.0006234208],"domain_scores_gemma":[0.9956636,0.002143038,0.001214226,0.0005731063,0.00009964723,0.0003063694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007497879,0.000362568,5.010853e-8,0.1349296,0.0001583212,0.003027343,0.00002527519,0.007017346,0.0338661,0.003783613,0.008501227,0.8082535],"study_design_scores_gemma":[0.0003954324,0.000818352,2.117309e-7,0.05502219,0.000477511,0.009444885,4.139128e-7,0.01039346,0.001963587,0.00002650474,0.9208421,0.0006153836],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004843013,0.9951976,0.0001535172,0.0004351076,0.002649228,0.001333205,0.0000621048,0.00009784863,0.00002295973],"genre_scores_gemma":[0.0007243004,0.9973034,0.0002579013,0.0008083647,0.0006542777,0.00008479406,0.000002092877,0.0001120576,0.00005281508],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9123408,"threshold_uncertainty_score":0.999763,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2922311477","doi":"10.1088/1741-2552/ab0e2e","title":"Regression convolutional neural network for improved simultaneous EMG control","year":2019,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"National Brain Mapping Laboratory","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Support vector machine; Regression; Pattern recognition (psychology); Regression analysis; Usability; Feature extraction; Artificial neural network; Machine learning; Feature (linguistics); Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.006121808715776334,"gpt":0.203645234542997,"spread":0.1975234258272207,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001102002,0.0001745368,0.0003128187,0.0001155724,0.00004411082,0.00002566365,0.000119392,0.00006043488,0.00001451941],"category_scores_gemma":[0.00008303428,0.0001449615,0.0002017585,0.0001523726,0.0000084662,0.0001916026,0.000009104371,0.0002713737,6.945645e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005250697,"about_ca_system_score_gemma":0.000008899979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.594783e-7,"about_ca_topic_score_gemma":3.61923e-7,"domain_scores_codex":[0.9990898,0.000008586329,0.000364608,0.00008656997,0.0001302476,0.0003201712],"domain_scores_gemma":[0.9992437,0.0003492966,0.0001073754,0.00008539393,0.0001303486,0.00008383622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007933332,0.000007027841,0.0001926916,0.00005265976,0.0001156559,0.000004221753,0.0000196226,0.9256786,0.06804071,0.00004381008,0.001169926,0.004595741],"study_design_scores_gemma":[0.001289111,0.000211689,0.00311443,0.00005331937,0.00002610458,0.00006670098,0.00001006356,0.98846,0.0005463735,0.00002121654,0.006037278,0.000163694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533027,0.002227705,0.03966677,0.0004620138,0.003613317,0.0004330345,0.00001439759,0.0002045229,0.00007551751],"genre_scores_gemma":[0.9978513,0.00004579701,0.001302959,0.00008759715,0.0006363782,0.000005890466,0.000002408186,0.0000339827,0.00003369909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06749433,"threshold_uncertainty_score":0.5911356,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2784349096","doi":"10.1088/1741-2552/aa9ee7","title":"Rapid calibration of an intracortical brain–computer interface for people with tetraplegia","year":2018,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":165,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"National Center for Medical Rehabilitation Research; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Institute on Deafness and Other Communication Disorders; Killam Trusts; Rehabilitation Research and Development Service; Massachusetts General Hospital; Craig H. Neilsen Foundation; National Science Foundation; Canadian Institutes of Health Research; U.S. Department of Veterans Affairs; ALS Association; National Institutes of Health","keywords":"Tetraplegia; Brain–computer interface; Calibration; Computer science; Interface (matter); Physical medicine and rehabilitation; Human–computer interaction; Psychology; Medicine; Neuroscience; Spinal cord injury; Spinal cord; Operating system; Electroencephalography; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01762999614824377,"gpt":0.2589287067788008,"spread":0.241298710630557,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001390288,0.0001254732,0.0002357741,0.0001338172,0.00003370691,0.00006233463,0.0002614263,0.0000397906,0.00001355914],"category_scores_gemma":[0.0001359132,0.00009096393,0.00007074044,0.0001630054,0.00004757013,0.0005913897,0.00003072531,0.0001733829,4.148812e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001542642,"about_ca_system_score_gemma":0.00002188036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001149128,"about_ca_topic_score_gemma":0.000001792673,"domain_scores_codex":[0.9990531,0.0000299102,0.0003778599,0.0001504068,0.0001975753,0.0001911409],"domain_scores_gemma":[0.9991874,0.0003113235,0.0001794767,0.0001129948,0.0001062998,0.0001025502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002598785,0.00008735002,0.00008250827,0.00006702285,0.00001912465,0.00001658842,0.00104352,0.03921374,0.9496824,0.0001396477,0.0003965939,0.008991591],"study_design_scores_gemma":[0.0003806259,0.00262285,0.001001178,0.0000595956,0.000008805515,0.0003686927,0.00001722339,0.560595,0.4345908,0.00001074973,0.0002591744,0.00008527115],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7261922,0.00001279497,0.2728032,0.0003470817,0.0005406472,0.00007472431,0.00000238885,0.00002076852,0.000006191292],"genre_scores_gemma":[0.9850279,0.000001786561,0.01405361,0.0001764498,0.0007105254,0.000001241713,2.618855e-7,0.00001969928,0.000008516392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5213813,"threshold_uncertainty_score":0.3709401,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3091943846","doi":"10.1088/1741-2552/abbff2","title":"Supervised machine learning tools: a tutorial for clinicians","year":2020,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Canada Research Chairs","keywords":"Machine learning; Computer science; Artificial intelligence; Deep learning; Domain (mathematical analysis); Key (lock); Big data; Supervised learning; Artificial neural network; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.1008331781126135,"gpt":0.3593270679309971,"spread":0.2584938898183837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007743314,0.0004574473,0.002012609,0.000339087,0.00008975503,0.0002481193,0.001627223,0.000250688,0.000005440995],"category_scores_gemma":[0.003499439,0.0003773093,0.001146574,0.0004965637,0.000006542627,0.0004683206,0.0002333522,0.002575289,0.000006786342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001698254,"about_ca_system_score_gemma":0.0003555835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004229214,"about_ca_topic_score_gemma":4.390351e-7,"domain_scores_codex":[0.9970124,0.0002136276,0.001554222,0.0003539482,0.0004361782,0.00042965],"domain_scores_gemma":[0.9962682,0.001932306,0.001040502,0.0002799249,0.0001855749,0.0002934915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008341995,0.000009860933,0.000001960553,0.007199187,0.00009463493,0.0001687546,0.0001140017,0.01068248,0.000001740518,0.000358947,0.00008539684,0.9812747],"study_design_scores_gemma":[0.0002272571,0.000369833,7.27293e-7,0.002199011,0.00009651839,0.0004085628,0.000001272169,0.3333003,2.393991e-7,0.000004945295,0.6631775,0.0002137486],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004885403,0.9291809,0.06484424,0.0004801212,0.00492407,0.0003963818,0.00001080283,0.0001502933,0.000008295872],"genre_scores_gemma":[0.00008709862,0.9702614,0.02470762,0.00006875232,0.004712645,0.00001936138,0.00001037482,0.0001033799,0.00002940672],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9810609,"threshold_uncertainty_score":0.9998679,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993750289","doi":"10.1088/1741-2560/6/1/016003","title":"Decoding subjective preference from single-trial near-infrared spectroscopy signals","year":2008,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Universität Wien; Sveučilište u Zagrebu","keywords":"Brain–computer interface; Decoding methods; Linear discriminant analysis; Functional near-infrared spectroscopy; Computer science; Preference; Interface (matter); SIGNAL (programming language); Task (project management); Prefrontal cortex; Encoding (memory); Speech recognition; Brain activity and meditation; Artificial intelligence; Pattern recognition (psychology); Electroencephalography; Psychology; Cognition; Statistics; Neuroscience; Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.06847710715580958,"gpt":0.2625035706962142,"spread":0.1940264635404046,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001012514,0.0001965163,0.0003282921,0.0001295646,0.0001143677,0.0001314807,0.000409581,0.0000591456,0.00004308572],"category_scores_gemma":[0.0005571729,0.0001675943,0.0001574565,0.00021427,0.00004700314,0.0005625781,0.00006675588,0.0004700865,0.000008027273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008012654,"about_ca_system_score_gemma":0.00004913201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005658563,"about_ca_topic_score_gemma":4.201855e-7,"domain_scores_codex":[0.9985493,0.00005902085,0.0004828885,0.0002255024,0.0003770011,0.0003063201],"domain_scores_gemma":[0.9987646,0.0006160425,0.0002749021,0.0001397433,0.00006406696,0.0001406913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008279672,0.00005629451,0.0001089417,0.000005246227,0.0000139159,0.0002752363,0.0004948176,0.02236028,0.9753541,0.000007617359,0.0001933603,0.0003021808],"study_design_scores_gemma":[0.003573524,0.0008271759,0.0008303149,0.00009496495,0.00001201309,0.0004673826,0.00001472265,0.06290936,0.9306294,0.00009019883,0.0003518766,0.0001990028],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894692,0.0001176251,0.00830633,0.00008083773,0.001619771,0.0001149593,0.000005823209,0.00006110079,0.0002243165],"genre_scores_gemma":[0.9934711,0.00002536501,0.005575225,0.0001137792,0.0007368515,0.000001495322,2.737704e-7,0.00002309649,0.0000527435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04472468,"threshold_uncertainty_score":0.6834296,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2112686332","doi":"10.1088/1741-2560/7/2/026002","title":"Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy","year":2010,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":159,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"","keywords":"Brain–computer interface; Motor imagery; Functional near-infrared spectroscopy; Computer science; Hidden Markov model; Prefrontal cortex; Mental image; Cognition; Task (project management); Brain activity and meditation; Mental arithmetic; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Cognitive psychology; Psychology; Electroencephalography; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.0269919220164667,"gpt":0.2599957369011586,"spread":0.2330038148846919,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000150453,0.0001311011,0.0002241646,0.000136206,0.00005729997,0.0001021111,0.0001372932,0.00004374461,0.000004929224],"category_scores_gemma":[0.00007114651,0.0001160555,0.00004631727,0.0001111786,0.0000565734,0.0006493262,0.00006886498,0.0003009186,1.446855e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003433714,"about_ca_system_score_gemma":0.00002303324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008995672,"about_ca_topic_score_gemma":0.000003005853,"domain_scores_codex":[0.9991689,0.00003076077,0.0002645092,0.0001706144,0.0001959434,0.0001693259],"domain_scores_gemma":[0.9994814,0.00009939673,0.0001665158,0.0001030289,0.00002895441,0.0001207236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003455032,0.00002458432,0.0001328478,0.00002644893,0.000005666424,0.0000321243,0.000424789,0.000538176,0.9972944,0.00004305644,0.0000108439,0.001432487],"study_design_scores_gemma":[0.0003985178,0.0002318117,0.02320887,0.00007752443,0.00001514086,0.001646353,0.0000409905,0.4745077,0.499245,0.000455104,0.000008187547,0.0001648503],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875922,0.00003489871,0.01156529,0.0001419691,0.0004872637,0.0001081231,0.000007228232,0.00001334415,0.00004966564],"genre_scores_gemma":[0.9439917,0.000004277476,0.05585892,0.00002740551,0.00009794402,8.16023e-7,1.115787e-7,0.00001399527,0.000004812893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4980495,"threshold_uncertainty_score":0.4732605,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3000563309","doi":"10.1088/1741-2552/ab6a67","title":"Comparing user-dependent and user-independent training of CNN for SSVEP BCI","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":158,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Canonical correlation; Computer science; Convolutional neural network; Brain–computer interface; Artificial intelligence; Pattern recognition (psychology); Correlation; Speech recognition; Electroencephalography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0847864117913438,"gpt":0.272686663644103,"spread":0.1879002518527592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001766589,0.0001446151,0.0003576071,0.0001117771,0.000038378,0.0000725822,0.0002992779,0.00003878232,0.000003887381],"category_scores_gemma":[0.0003025902,0.0001259547,0.0001154393,0.0001099182,0.00002033785,0.0003187011,0.00009144888,0.0002862855,5.198239e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969336,"about_ca_system_score_gemma":0.00001827988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001385699,"about_ca_topic_score_gemma":4.871857e-7,"domain_scores_codex":[0.9988011,0.00002144503,0.0004751905,0.000171827,0.000306099,0.0002243237],"domain_scores_gemma":[0.9991786,0.0002822173,0.0002465805,0.00006978751,0.00005188807,0.0001709032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008700466,0.00002004175,0.0006490537,0.0001494082,0.00002358756,0.00006084052,0.001513501,0.137129,0.8582321,0.000248005,0.00007786116,0.001809661],"study_design_scores_gemma":[0.001499031,0.0006982724,0.002375436,0.0001747428,0.0000349662,0.0004994371,0.0002244286,0.4485943,0.5436326,0.00001822854,0.002017801,0.0002308019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799004,0.0001051244,0.0188107,0.0005599362,0.0004609253,0.0001007821,0.000004000305,0.00002696572,0.00003123032],"genre_scores_gemma":[0.9974665,0.00001616835,0.002032561,0.0001964211,0.0002492061,0.000001166785,1.237131e-7,0.00002052041,0.00001733621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3145995,"threshold_uncertainty_score":0.5136282,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1968911628","doi":"10.1088/1741-2560/8/6/066004","title":"Towards a system-paced near-infrared spectroscopy brain–computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state","year":2011,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":152,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"","keywords":"Brain–computer interface; Functional near-infrared spectroscopy; Singing; Mental arithmetic; Computer science; Set (abstract data type); Brain activity and meditation; Interface (matter); Psychology; Control (management); Cognitive psychology; Electroencephalography; Speech recognition; Audiology; Artificial intelligence; Prefrontal cortex; Cognition; Neuroscience; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01295204744613669,"gpt":0.2294016008165424,"spread":0.2164495533704057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002172641,0.0003061871,0.0004292492,0.0001016948,0.0001795708,0.0003703861,0.0004205495,0.00004292067,0.00001675932],"category_scores_gemma":[0.000110151,0.000215478,0.0001406527,0.0001213966,0.00003841041,0.0005229996,0.0002358979,0.0004918834,0.000005985269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001601928,"about_ca_system_score_gemma":0.00002283718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004795425,"about_ca_topic_score_gemma":0.000003389721,"domain_scores_codex":[0.9983384,0.000129624,0.0004474373,0.0003148362,0.000359427,0.0004102809],"domain_scores_gemma":[0.9990381,0.000288771,0.0002665198,0.0001711451,0.0000379665,0.000197475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002695918,0.00005458042,0.0002477674,0.00004062696,0.00007329949,0.0001138126,0.005584699,0.002512395,0.9881507,0.000004771165,0.0001861559,0.002761642],"study_design_scores_gemma":[0.001229274,0.0006794564,0.01733604,0.0003782311,0.00002857706,0.0007302878,0.0001244929,0.6206889,0.3584317,0.000008009264,0.00009123314,0.0002738204],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9375131,0.00006470618,0.05967176,0.0003638217,0.001970124,0.0002735785,0.00003530195,0.0000686015,0.00003897773],"genre_scores_gemma":[0.9926905,0.000002517454,0.006709209,0.0002159101,0.0003201762,0.00000365767,3.374459e-7,0.00003583175,0.00002188598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.629719,"threshold_uncertainty_score":0.8786939,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058727113","doi":"10.1088/1741-2560/10/2/026003","title":"High-resolution measurement of electrically-evoked vagus nerve activity in the anesthetized dog","year":2013,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Vagus Nerve Stimulation Research","field":"Neuroscience","cited_by":147,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Boston Scientific Corporation","keywords":"Vagus nerve; Vagus nerve stimulation; Medicine; Stimulation; Epineurium; Oculocardiac reflex; Anesthesia; Reflex; Anatomy; Sciatic nerve; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.04602622960889594,"gpt":0.2769526994320922,"spread":0.2309264698231962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008065904,0.0001111018,0.0002237693,0.0002791138,0.00003655403,0.00004511879,0.0003675211,0.00004293964,0.00002285488],"category_scores_gemma":[0.00125667,0.00007470605,0.0001076013,0.0006019598,0.00002314543,0.0003345601,0.00002919745,0.0004887962,0.000004423251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001213308,"about_ca_system_score_gemma":0.00003993755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006892538,"about_ca_topic_score_gemma":0.00000167336,"domain_scores_codex":[0.9979472,0.000239905,0.0003996988,0.0001156415,0.001033911,0.0002636517],"domain_scores_gemma":[0.9990108,0.0003424871,0.0002192407,0.0001563237,0.0002021351,0.00006898623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003572666,0.00006487237,0.0002029647,0.00001402037,0.000003207263,0.00003161837,0.00004221524,0.167444,0.831171,0.00007944077,0.00005310894,0.0008577831],"study_design_scores_gemma":[0.001464547,0.0005052389,0.178348,0.00005678727,0.00001194729,0.0002522327,0.00001037666,0.4116131,0.4074198,0.0001191838,0.00005123609,0.000147438],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996678,0.00005860236,0.001464493,0.001403496,0.0001248201,0.0002305756,4.614151e-7,0.00001194689,0.00002760625],"genre_scores_gemma":[0.9996427,0.00000948691,0.000223847,0.00003143326,0.00006439564,0.00000720237,6.351847e-8,0.00001129067,0.000009628272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4237512,"threshold_uncertainty_score":0.3046424,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2946105889","doi":"10.1088/1741-2552/ab2373","title":"Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs","year":2019,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":140,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Stimulus (psychology); Brain–computer interface; Computer science; Speech recognition; Electroencephalography; Psychology; Neuroscience; Cognitive psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.04641674609126999,"gpt":0.3515078405640809,"spread":0.3050910944728109,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005351704,0.0001437285,0.0002614299,0.0001730471,0.00003533628,0.00007918054,0.000236486,0.00005479021,0.00003660106],"category_scores_gemma":[0.0006645502,0.0001235225,0.0001112225,0.0002483841,0.00001626266,0.0004259343,0.00004086452,0.0006125205,0.00001527555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004715963,"about_ca_system_score_gemma":0.0000163237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003292665,"about_ca_topic_score_gemma":6.298317e-7,"domain_scores_codex":[0.9987834,0.0001365035,0.0004154717,0.0001796689,0.0001962861,0.0002886682],"domain_scores_gemma":[0.9989635,0.0006265439,0.0002159281,0.00007678554,0.00004654056,0.00007065601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002173428,0.00002020026,0.0003521593,0.0000255404,0.000002144239,0.00003458884,0.0002450259,0.4356758,0.5541615,4.648518e-7,0.000002758585,0.009458006],"study_design_scores_gemma":[0.0004680463,0.0001810584,0.0008822766,0.0001064795,0.00000187893,0.00005701016,0.00003140208,0.5411861,0.4566352,0.000005166144,0.0003501006,0.00009527137],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363516,0.00009226706,0.06220661,0.0000948115,0.001119884,0.00006982933,0.0000018704,0.00003708691,0.0000260227],"genre_scores_gemma":[0.9453376,0.000009971812,0.05435171,0.0001200112,0.00009739646,0.000001377688,3.626623e-7,0.0000183245,0.00006326305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1055103,"threshold_uncertainty_score":0.5037103,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3133918036","doi":"10.1088/1741-2552/ac1d5b","title":"Towards adaptive deep brain stimulation: clinical and technical notes on a novel commercial device for chronic brain sensing","year":2021,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":134,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Parkinson Schweiz; European Commission; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; York University; Fondazione Grigioni per il Morbo di Parkinson; Centre Hospitalier Universitaire Vaudois; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; School of Medicine, New York University; National Science Foundation","keywords":"Deep brain stimulation; Brain stimulation; Computer science; Neuroscience; Stimulation; Brain implant; Medicine; Artificial intelligence; Psychology; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.145774901933477,"gpt":0.411631789123066,"spread":0.2658568871895891,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003193877,0.0003670867,0.001954895,0.0001549084,0.00004799688,0.00003130891,0.00008211468,0.0003163331,0.000005479779],"category_scores_gemma":[0.001958709,0.000251967,0.0008162192,0.0001799813,0.00004098967,0.00005649411,0.00005993619,0.001003761,6.734039e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122285,"about_ca_system_score_gemma":0.0001803621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001107931,"about_ca_topic_score_gemma":0.000002191038,"domain_scores_codex":[0.9981927,0.00006956115,0.0009642701,0.000281487,0.0002431787,0.0002487655],"domain_scores_gemma":[0.9965637,0.002604349,0.0003653828,0.0001441132,0.0001150005,0.0002074398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001320749,0.0001852254,0.0000115783,0.001587335,0.0003421524,0.0004628767,0.000006444677,0.0009027552,0.00002852659,0.0000545234,0.0001346294,0.9961519],"study_design_scores_gemma":[0.02339778,0.03273284,0.008412529,0.06196985,0.01070635,0.01474241,0.00001938955,0.1172375,0.00001957622,0.0001178558,0.7285194,0.002124518],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001326735,0.9761199,0.01880025,0.002436946,0.0004669956,0.0007824746,0.00001954074,0.00003229662,0.00001484578],"genre_scores_gemma":[0.03053924,0.9419236,0.02133758,0.002796006,0.003112689,0.0000185663,0.00005620237,0.0001950445,0.00002110296],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9940274,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110484872","doi":"10.1088/1741-2560/11/3/035002","title":"An independent SSVEP-based brain–computer interface in locked-in syndrome","year":2014,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":129,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"James S. McDonnell Foundation","keywords":"Brain–computer interface; Covert; Computer science; Interface (matter); Locked-in syndrome; Electroencephalography; Speech recognition; Artificial intelligence; Psychology; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.0136870173780192,"gpt":0.2530650205719384,"spread":0.2393780031939192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004475047,0.0001846625,0.0003127015,0.0005092178,0.00001703531,0.0000960983,0.0005316865,0.0000653682,0.00001073585],"category_scores_gemma":[0.0001507296,0.0001609785,0.00008431378,0.0002725464,0.00001932967,0.0004640009,0.00005471288,0.000606211,0.000005197571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008123428,"about_ca_system_score_gemma":0.00001967839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005688824,"about_ca_topic_score_gemma":0.000005705771,"domain_scores_codex":[0.998484,0.0001199815,0.0005403006,0.0002358561,0.0002984045,0.0003214417],"domain_scores_gemma":[0.9991546,0.0003635927,0.0001529414,0.0001740873,0.00002471893,0.0001300515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002673858,0.00008065993,0.0008355873,0.00002806312,0.00000209273,0.0004029715,0.0002027838,0.783823,0.2121213,0.00002499908,0.00003777419,0.00241403],"study_design_scores_gemma":[0.0008729803,0.0006708029,0.01556074,0.0002510582,0.000002145118,0.0009329354,0.000006376773,0.9063621,0.07474004,0.00002605906,0.0003895311,0.0001852177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535872,0.00002428323,0.044622,0.0008084263,0.0008432779,0.00006759853,6.601521e-7,0.00003398428,0.00001259994],"genre_scores_gemma":[0.9977914,0.000001344042,0.001500695,0.0005224602,0.0001474075,0.000001186765,1.217001e-7,0.00002337387,0.00001202439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1373813,"threshold_uncertainty_score":0.6564512,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067094910","doi":"10.1088/1741-2560/4/3/s05","title":"New functional electrical stimulation approaches to standing and walking","year":2007,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":126,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke","keywords":"Functional electrical stimulation; Microstimulation; Neuroprosthetics; Spinal cord injury; Physical medicine and rehabilitation; Medicine; Rehabilitation; Neuroscience; Lumbosacral joint; Spinal cord; Stimulation; Physical therapy; Psychology; Surgery","retraction":null,"screen_n_in":null,"score":{"opus":0.1509119421711778,"gpt":0.3340950266428975,"spread":0.1831830844717197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004549301,0.00007341611,0.0001579872,0.0003978024,0.00002525669,0.00002233892,0.00003395598,0.0000344365,0.00001813009],"category_scores_gemma":[0.0003149088,0.00006140619,0.00004655231,0.0002900958,0.00000383058,0.0001305809,0.0000189924,0.0003361392,0.000001157953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001320319,"about_ca_system_score_gemma":0.00004101925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001585613,"about_ca_topic_score_gemma":3.393316e-7,"domain_scores_codex":[0.9990969,0.000005752813,0.00025422,0.00007530118,0.0003740819,0.0001937961],"domain_scores_gemma":[0.9994508,0.0001141586,0.00004709309,0.00004412543,0.00005991616,0.000283908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006523604,0.00005869697,0.04057714,0.0001257021,0.0001433091,0.0005095858,0.0002227061,0.05049991,0.5638267,0.0008444597,0.0005611965,0.336107],"study_design_scores_gemma":[0.001951766,0.003975231,0.8480688,0.0001798231,0.0000679934,0.003243491,0.00004931197,0.1277065,0.01278012,0.00004637741,0.001752741,0.0001778566],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8135853,0.000224107,0.1856148,0.0003131441,0.0001265764,0.00006940929,8.697781e-8,0.00001208214,0.00005449356],"genre_scores_gemma":[0.9836259,0.00000454989,0.015528,0.00003661741,0.0007219581,2.428274e-7,3.397959e-7,0.00001299149,0.00006938694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8074916,"threshold_uncertainty_score":0.2504072,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2999007024","doi":"10.1088/1741-2552/ab6aad","title":"Common misconceptions, hidden biases and modern challenges of dMRI tractography","year":2020,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":123,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Tractography; Human Connectome Project; Computer science; Connectomics; Focus (optics); Connectome; Diffusion MRI; Data science; Field (mathematics); Artificial intelligence; Functional connectivity; Neuroscience; Psychology; Medicine; Mathematics; Magnetic resonance imaging; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.2085426723380991,"gpt":0.3999560560535329,"spread":0.1914133837154338,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005243068,0.0002042506,0.001173332,0.0002418288,0.00001496205,0.000007647358,0.0001176987,0.00007689465,0.000002050891],"category_scores_gemma":[0.00006578216,0.0001591097,0.0003708864,0.0001572475,0.00003019999,0.0000738344,0.00003338916,0.0005713631,2.090185e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001745031,"about_ca_system_score_gemma":0.00002901262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.278415e-7,"about_ca_topic_score_gemma":7.299539e-8,"domain_scores_codex":[0.9990233,0.00001581587,0.0005850251,0.0001290062,0.0001439731,0.000102898],"domain_scores_gemma":[0.9990579,0.0001737726,0.000439743,0.0001545449,0.00004838383,0.0001256118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005219075,0.00003335027,0.000003724331,0.00488162,0.00007653816,0.00007389547,0.00003363442,0.00004545715,0.0001886298,0.0001079918,0.0000471135,0.9945028],"study_design_scores_gemma":[0.0003722433,0.0005322111,0.0001429419,0.02373319,0.001440227,0.004712671,0.00002362209,0.00235374,0.00006130284,0.000130717,0.9661923,0.000304837],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000310276,0.9979568,0.0008586184,0.0005416731,0.00004283467,0.0002117881,0.00001401744,0.00004322121,0.00002078342],"genre_scores_gemma":[0.005079224,0.9889999,0.005671601,0.0000180003,0.00017505,0.000007951704,0.000003475277,0.00004203305,0.000002760279],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.994198,"threshold_uncertainty_score":0.6488304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2599340422","doi":"10.1088/1741-2552/aa6a5f","title":"Modeling the response of small myelinated axons in a compound nerve to kilohertz frequency signals","year":2017,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Vagus Nerve Stimulation Research","field":"Neuroscience","cited_by":93,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Fulbright Canada; Triangle Community Foundation; Duke University","keywords":"Electrode; Excitation; Action potential; Materials science; Transmission (telecommunications); Amplitude; Block (permutation group theory); Physics; Biomedical engineering; Acoustics; Electrophysiology; Computer science; Mathematics; Neuroscience; Optics; Geometry; Telecommunications; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.1202160078299198,"gpt":0.3451605235924053,"spread":0.2249445157624855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001144659,0.0001234567,0.0002513373,0.0003558879,0.0001358275,0.0001206038,0.0008805227,0.00004373208,0.00001341339],"category_scores_gemma":[0.006305504,0.00009075447,0.0001115203,0.0002518819,0.00003826885,0.0002871851,0.0001360333,0.0004904746,0.000003089144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000065455,"about_ca_system_score_gemma":0.00006707635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006940864,"about_ca_topic_score_gemma":0.00001161077,"domain_scores_codex":[0.9983976,0.0001859513,0.0005819184,0.0001422213,0.0004159259,0.000276321],"domain_scores_gemma":[0.9983813,0.000736238,0.0002249582,0.0003516682,0.000178747,0.0001271278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001356204,0.00001348011,0.0001896128,0.000007189396,0.000002440139,0.0001161041,0.0001190289,0.5168588,0.4824231,0.00002894977,0.000003191702,0.0001025501],"study_design_scores_gemma":[0.0005059988,0.0002026681,0.01037004,0.0001339674,0.000004975477,0.0001146185,0.00002167315,0.9397525,0.04871058,0.00007586226,0.00001356404,0.00009356704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927133,0.0000504231,0.004588405,0.002228159,0.0002006138,0.0001657596,0.000003759362,0.00001084178,0.00003869474],"genre_scores_gemma":[0.9989361,0.000004663154,0.0008751118,0.00006242401,0.00006483951,0.000002817343,6.102662e-8,0.00001971497,0.00003423651],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4337125,"threshold_uncertainty_score":0.7548733,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3085768000","doi":"10.1088/1741-2552/abb7a7","title":"Thinker invariance: enabling deep neural networks for BCI across more people","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":91,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; St. Michael's Hospital; Vector Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Electronics and Telecommunications Research Institute","keywords":"Generality; Computer science; Brain–computer interface; Artificial intelligence; Machine learning; Classifier (UML); Transfer of learning; Deep neural networks; Invariant (physics); Artificial neural network; Electroencephalography","retraction":null,"screen_n_in":null,"score":{"opus":0.03222141815507383,"gpt":0.2756459686591374,"spread":0.2434245505040636,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001543695,0.0001972855,0.0003191259,0.00004978672,0.0001092475,0.0001989491,0.0004912867,0.00006796041,0.00001070518],"category_scores_gemma":[0.0005653211,0.0001638332,0.0002042269,0.0002829955,0.00001964838,0.0005862258,0.0001142602,0.000547799,0.000001766432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002619572,"about_ca_system_score_gemma":0.00001086127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001541288,"about_ca_topic_score_gemma":9.60993e-7,"domain_scores_codex":[0.9986176,0.0000242825,0.0004531337,0.0002256585,0.000252205,0.0004270421],"domain_scores_gemma":[0.9990058,0.0003870556,0.0002189355,0.0001062374,0.00007180945,0.0002101861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006885664,0.00001192325,0.0000844749,0.00005112469,0.00001020553,0.00007678408,0.001524401,0.8958743,0.09981219,0.00002658524,0.0002631218,0.002195979],"study_design_scores_gemma":[0.0005601604,0.0002288221,0.0004442421,0.00003288715,0.00001121794,0.0003083529,0.0001022893,0.9730532,0.02392361,0.000008113087,0.001159145,0.0001679534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8457506,0.0003076313,0.1489743,0.003158707,0.001575024,0.0001417975,0.000005157879,0.00007917524,0.000007578473],"genre_scores_gemma":[0.9948565,0.00001962645,0.001447439,0.002303602,0.001319303,0.000003041523,4.990861e-7,0.00003563199,0.00001440585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1491059,"threshold_uncertainty_score":0.6680926,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030275344","doi":"10.1088/1741-2560/4/3/s04","title":"Limb-state feedback from ensembles of simultaneously recorded dorsal root ganglion neurons","year":2007,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Microstimulation; Dorsal root ganglion; Sensory system; Functional electrical stimulation; Neuroscience; Neuroprosthetics; Spinal cord; Hindlimb; Computer science; Stimulation; Medicine; Anatomy; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01735532360515485,"gpt":0.2445232533158918,"spread":0.227167929710737,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002012284,0.0001862994,0.0003103919,0.0002457894,0.00003551944,0.00005032918,0.0003661996,0.00005134949,0.00001526159],"category_scores_gemma":[0.0005368991,0.0001569833,0.0001598857,0.0002456208,0.00003038821,0.0002643756,0.00006302677,0.0004104571,0.000004383881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003076185,"about_ca_system_score_gemma":0.0000173427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001527003,"about_ca_topic_score_gemma":0.000008798233,"domain_scores_codex":[0.9984781,0.00003874253,0.0006549672,0.0001819967,0.0003327221,0.00031343],"domain_scores_gemma":[0.9981757,0.001116505,0.0003505214,0.0001464009,0.00008557317,0.0001253246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001500802,0.00004277263,0.000117629,0.00001710349,0.00001041405,0.0006640235,0.0002793422,0.1441077,0.8489715,0.000006646652,0.0000677502,0.005565002],"study_design_scores_gemma":[0.000548821,0.0006361991,0.003689954,0.0001302547,0.00001763114,0.0007016116,0.00002899023,0.08145303,0.9114725,0.00005065735,0.0010758,0.00019457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919295,0.0001418802,0.00616118,0.0001623324,0.001432983,0.00005972956,0.000008972289,0.0000442846,0.00005913532],"genre_scores_gemma":[0.9984586,0.00002431841,0.001077722,0.00009617164,0.0002553183,1.828554e-7,3.3856e-7,0.00002905218,0.00005828102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06265464,"threshold_uncertainty_score":0.6401593,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983195655","doi":"10.1088/1741-2560/9/2/026022","title":"Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy","year":2012,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":87,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hospital for Sick Children; Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"","keywords":"Psychology; Prefrontal cortex; Audiology; Valence (chemistry); Arousal; Active listening; Linear discriminant analysis; Functional near-infrared spectroscopy; Cognition; Cognitive psychology; Developmental psychology; Communication; Neuroscience; Computer science; Artificial intelligence; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.04147558240504323,"gpt":0.2802506101944911,"spread":0.2387750277894479,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003428605,0.0001408531,0.0002395653,0.0002051923,0.00006151523,0.00004860735,0.0001673386,0.00004650803,0.00001801506],"category_scores_gemma":[0.0008642041,0.0001228503,0.0001050697,0.0002662624,0.00002537009,0.0004412865,0.00005198449,0.0002864318,0.00000353134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064063,"about_ca_system_score_gemma":0.00003637555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000218443,"about_ca_topic_score_gemma":3.809456e-7,"domain_scores_codex":[0.9987097,0.0001178212,0.000474835,0.0001116175,0.0002989607,0.0002870676],"domain_scores_gemma":[0.9991763,0.0002767141,0.0001972348,0.00009642703,0.00006707914,0.0001862846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002087084,0.00007177656,0.00003618578,0.00002197624,0.000009046481,0.00002650738,0.0005989717,0.07758548,0.9213033,0.000001020921,0.000006077214,0.0001309785],"study_design_scores_gemma":[0.0002970971,0.0002622136,0.01581009,0.00007520407,0.00001053527,0.0006564287,0.00001935294,0.5218598,0.460922,0.000001308308,0.00001298499,0.00007295467],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9106871,0.00003068789,0.0880842,0.00004198411,0.001012647,0.0001017061,0.000002766245,0.00003686054,0.000002070582],"genre_scores_gemma":[0.9894962,6.728128e-7,0.01023563,0.00006661038,0.0001654534,9.143386e-7,8.532118e-8,0.00002026095,0.00001414532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4603812,"threshold_uncertainty_score":0.5009691,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1980047894","doi":"10.1088/1741-2560/11/3/035001","title":"Performance measurement for brain–computer or brain–machine interfaces: a tutorial","year":2014,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":85,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute on Deafness and Other Communication Disorders; National Institutes of Health","keywords":"Brain–computer interface; Computer science; Field (mathematics); Human–computer interaction; Research center; Data science; Psychology; Electroencephalography; Neuroscience; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.04123954494336098,"gpt":0.2584311937718193,"spread":0.2171916488284583,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007877641,0.0002272368,0.0003385252,0.000175718,0.00007579169,0.0001278038,0.000497447,0.00005364212,0.00001190867],"category_scores_gemma":[0.001057224,0.0001577239,0.0001568291,0.0001436716,0.00002301539,0.0004810622,0.00008014136,0.0003232762,0.000003562299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007767422,"about_ca_system_score_gemma":0.00003187764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.287147e-7,"about_ca_topic_score_gemma":8.320952e-7,"domain_scores_codex":[0.9983427,0.00005947737,0.0005509832,0.0002232734,0.0004827993,0.0003407478],"domain_scores_gemma":[0.9986212,0.0007016039,0.0002473777,0.0001559508,0.000138537,0.0001353173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005079455,0.00008783608,0.00004595529,0.0002533511,0.00004029034,0.00002484263,0.0004976874,0.1805313,0.7683969,0.0001175715,0.008171706,0.04132468],"study_design_scores_gemma":[0.001370056,0.001822577,0.0001690221,0.0002226059,0.00001301181,0.0004306426,0.000003857543,0.7878287,0.1598555,0.0000100929,0.04805487,0.0002191101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8613085,0.0000417074,0.129471,0.002643062,0.006228429,0.0001912781,0.000003289223,0.00007485053,0.00003785438],"genre_scores_gemma":[0.9922861,0.000005866581,0.004068107,0.0009868767,0.00251713,0.000004381507,1.899232e-7,0.00003255202,0.00009878271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6085414,"threshold_uncertainty_score":0.6431794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3085891723","doi":"10.1088/1741-2552/abb7a5","title":"Insights into human cognition from intracranial EEG: A review of audition, memory, internal cognition, and causality","year":2020,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hotchkiss Brain Institute; Ontario Brain Institute; University of Calgary","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; James S. McDonnell Foundation","keywords":"Neurocognitive; Cognition; Electroencephalography; Cognitive psychology; Cognitive science; Psychology; Computer science; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.03630004717495483,"gpt":0.2942436220119581,"spread":0.2579435748370033,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000167564,0.0003818064,0.001506197,0.0002434094,0.00007624157,0.00006127741,0.0002683145,0.0001400566,0.00004597663],"category_scores_gemma":[0.001038074,0.00030832,0.0004232377,0.0003519871,0.00006555939,0.0003912536,0.00009887895,0.0009089867,0.000004459338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007777919,"about_ca_system_score_gemma":0.00006595928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008816519,"about_ca_topic_score_gemma":0.000002064085,"domain_scores_codex":[0.9973735,0.0002049789,0.001473636,0.0003338655,0.0004570063,0.0001569677],"domain_scores_gemma":[0.997653,0.0003976853,0.001425115,0.0001376451,0.0002030006,0.0001835194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00008618513,0.0002122526,0.000001259347,0.147951,0.0004037837,0.001689233,0.0001840956,0.00006769066,0.1255261,0.0003681122,0.00093319,0.7225772],"study_design_scores_gemma":[0.002692966,0.002126204,0.0001727825,0.5061262,0.006941573,0.006346121,0.00002590006,0.007333514,0.00402507,0.002940137,0.4590394,0.00223005],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003355583,0.9938812,0.0008079399,0.0001918387,0.00115957,0.0004383383,0.00008518882,0.00003567708,0.00004470551],"genre_scores_gemma":[0.02040159,0.9784242,0.000103436,0.0002950103,0.0006682486,0.000009369183,0.00004705168,0.00004638097,0.000004732957],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7203471,"threshold_uncertainty_score":0.9999369,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2035537816","doi":"10.1088/1741-2560/5/1/002","title":"A self-paced brain–computer interface system with a low false positive rate","year":2007,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Neil Squire Society; University of British Columbia","funders":"","keywords":"Support vector machine; Computer science; Electroencephalography; Pattern recognition (psychology); Artificial intelligence; Brain–computer interface; Classifier (UML); Interface (matter); Neuroscience; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.007939894789313175,"gpt":0.2287179652030751,"spread":0.2207780704137619,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004506881,0.0002361805,0.0003242842,0.0002503431,0.00005245109,0.0001347203,0.0003401955,0.00005282615,0.000002159373],"category_scores_gemma":[0.00007469273,0.0001689855,0.0001162529,0.000285986,0.0000219033,0.0004301226,0.0000713436,0.0004535872,0.000006667934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001179224,"about_ca_system_score_gemma":0.00002293918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001707443,"about_ca_topic_score_gemma":8.677681e-7,"domain_scores_codex":[0.9985746,0.00006538357,0.0004636892,0.0002220443,0.0002914644,0.0003828035],"domain_scores_gemma":[0.9986953,0.0005945126,0.0002761087,0.0001354146,0.0001097874,0.0001889491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002848286,0.00008242616,0.00004996329,0.0002232191,0.00007019339,0.00234375,0.001457404,0.1707678,0.8226673,0.000251005,0.000194629,0.001607525],"study_design_scores_gemma":[0.001013387,0.0009457255,0.001075359,0.0007911188,0.00002113243,0.006385046,0.00009080564,0.3802741,0.6086645,0.000001550469,0.0004409797,0.0002963502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7810122,0.00004767218,0.2175841,0.0003371336,0.0007386171,0.0001038016,0.000001692461,0.0001130964,0.00006163464],"genre_scores_gemma":[0.9936814,0.000002331123,0.005513565,0.0003138887,0.0004148196,6.963812e-7,8.510116e-8,0.00003321247,0.00004005882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2140028,"threshold_uncertainty_score":0.6891028,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2909338255","doi":"10.1088/1741-2552/ab260c","title":"Deep learning-based electroencephalography analysis: a systematic review","year":2019,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique; InteraXon (Canada); Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Computer science; Artificial intelligence; Convolutional neural network; Raw data; Deep learning; Preprocessor; Machine learning; Pattern recognition (psychology); Psychology; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.03094699724379292,"gpt":0.2966603220450255,"spread":0.2657133248012326,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005530409,0.0004948633,0.004167363,0.001290459,0.00003926983,0.0001221963,0.0009738788,0.0001205336,0.00001534441],"category_scores_gemma":[0.000956712,0.0003281075,0.002768019,0.002174425,0.00001819531,0.0001896335,0.00004801179,0.001316732,0.00001777108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007783794,"about_ca_system_score_gemma":0.00006487109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.018228e-7,"about_ca_topic_score_gemma":1.293102e-7,"domain_scores_codex":[0.9966261,0.0004418177,0.001661569,0.0003296334,0.0005549191,0.0003859013],"domain_scores_gemma":[0.9966143,0.000980037,0.001827641,0.0003457328,0.00009218416,0.0001401328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000001710538,0.00002366351,5.956667e-7,0.9419664,0.0005872738,0.0001639389,0.00001004731,0.05207703,0.0000406634,0.000005618206,0.00002498964,0.005098125],"study_design_scores_gemma":[0.0001577964,0.0004673023,7.207701e-7,0.8193922,0.02245518,0.00148977,0.000002094214,0.1095551,0.00004282724,8.230129e-7,0.04581529,0.0006208539],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001066673,0.9935965,0.005308164,0.00002392702,0.0004369142,0.0005590946,0.00000166464,0.00005302318,0.00001004468],"genre_scores_gemma":[0.0009388272,0.9985328,0.0001178523,0.0001907445,0.0001166363,0.00001790257,0.00000203532,0.00004952506,0.00003367499],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1225741,"threshold_uncertainty_score":0.9999171,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083819547","doi":"10.1088/1741-2560/11/1/016003","title":"Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest","year":2013,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Brain–computer interface; Computer science; Motor imagery; Brain activity and meditation; Modality (human–computer interaction); Prefrontal cortex; Task (project management); Discriminative model; Binary classification; Functional near-infrared spectroscopy; Binary number; Resting state fMRI; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Electroencephalography; Psychology; Support vector machine; Neuroscience; Cognition; Mathematics; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.01181388759378729,"gpt":0.2723305916697639,"spread":0.2605167040759766,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007032281,0.00008890262,0.0002598465,0.0001878531,0.00001370919,0.000007956837,0.00004692801,0.00004621093,0.00001063664],"category_scores_gemma":[0.00006695693,0.0000692018,0.00007776883,0.00007630534,0.00003949225,0.000121888,0.00002147339,0.000217553,9.336693e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002592376,"about_ca_system_score_gemma":0.000007269735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001541722,"about_ca_topic_score_gemma":0.000001242104,"domain_scores_codex":[0.9992166,0.00001172266,0.0004081396,0.00007315182,0.0001915085,0.00009889146],"domain_scores_gemma":[0.9995275,0.00003887007,0.0001712067,0.00007085007,0.0001122832,0.00007927933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002621364,0.0002043387,0.00533937,0.0001071609,0.00004046486,0.000003185797,0.0001260068,0.0001021304,0.9908625,0.000009598485,0.00001061957,0.003168447],"study_design_scores_gemma":[0.0004845444,0.0006857206,0.4294819,0.0002541211,0.00006520918,0.00008698667,0.00009010419,0.5472672,0.02150781,0.000021519,0.000001664865,0.00005327672],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902384,0.0001754664,0.008717285,0.0006492806,0.00007251423,0.0001216362,0.000004679728,0.00001640327,0.000004342709],"genre_scores_gemma":[0.9916462,0.00008230726,0.008205313,0.0000124324,0.00002619395,0.000001773004,0.00000607629,0.00001143252,0.000008234469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9693546,"threshold_uncertainty_score":0.2821968,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089752636","doi":"10.1088/1741-2560/12/2/026011","title":"Defining regions of interest using cross-frequency coupling in extratemporal lobe epilepsy patients","year":2015,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":73,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University Health Network; Toronto Western Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Aeronautics and Space Administration","keywords":"Epilepsy; Temporal lobe; Epilepsy surgery; Lobe; Modulation (music); Electroencephalography; Amplitude; Rhythm; Coupling (piping); Medicine; Physics; Psychology; Audiology; Neuroscience; Anatomy; Internal medicine; Optics; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.112484949138704,"gpt":0.3192373668027248,"spread":0.2067524176640208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000277028,0.0001410333,0.0002743272,0.000279448,0.00002433361,0.0000620218,0.0003149743,0.00005087037,0.000002779175],"category_scores_gemma":[0.0006518273,0.0001248691,0.00008900577,0.0002757433,0.0000401053,0.0006088291,0.000070084,0.000375262,0.000001035064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008648134,"about_ca_system_score_gemma":0.00005226888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001530069,"about_ca_topic_score_gemma":0.000002141914,"domain_scores_codex":[0.9986696,0.00002622308,0.0007079334,0.0001419553,0.0002196465,0.0002346888],"domain_scores_gemma":[0.9990957,0.0001537036,0.0003796077,0.0001099401,0.0001342167,0.0001268411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004544034,0.00008450999,0.08336117,0.00004572519,0.000005965714,0.0001584539,0.0003951465,0.7356028,0.179592,0.0005526473,0.00002542404,0.0001306869],"study_design_scores_gemma":[0.00210509,0.0006964298,0.01831588,0.001018306,0.00001439059,0.0004858545,0.0001200298,0.8425155,0.13398,0.0003323019,0.0000562839,0.000359859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966648,0.0001566179,0.002050713,0.00003195629,0.000987152,0.00004940994,0.000002963106,0.00001554264,0.00004089775],"genre_scores_gemma":[0.9978623,0.000004958392,0.002000652,0.00002966766,0.0000783586,3.837386e-7,2.988354e-7,0.00001910955,0.000004285078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1069128,"threshold_uncertainty_score":0.5092013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107774167","doi":"10.1088/1741-2560/6/5/056005","title":"The design and hardware implementation of a low-power real-time seizure detection algorithm","year":2009,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Ontario Brain Institute","keywords":"Computer science; Computer hardware; Power (physics); Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.0122048804463346,"gpt":0.2577377833105085,"spread":0.2455329028641739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001774926,0.00007619084,0.0001055892,0.00006684551,0.00005453172,0.00005205345,0.000108501,0.00002068196,0.000003675086],"category_scores_gemma":[0.00004987391,0.00005116718,0.00004445795,0.00009685865,0.00001154465,0.0002209256,0.00001299686,0.0001268255,4.345148e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001729198,"about_ca_system_score_gemma":0.000009049601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001252881,"about_ca_topic_score_gemma":1.318356e-7,"domain_scores_codex":[0.9993693,0.00003825411,0.0002459209,0.00007265335,0.0001575034,0.0001163338],"domain_scores_gemma":[0.9995294,0.000179195,0.0001522463,0.00005509372,0.00004370819,0.00004039798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000146854,0.000005933997,0.00000249373,0.000004557407,0.000004107517,0.00001609994,0.0001822711,0.01118803,0.9316454,0.000005902322,0.00005333022,0.05687723],"study_design_scores_gemma":[0.0002696006,0.0005530929,0.001926752,0.00003714428,0.000007342437,0.0004311441,0.0000498767,0.1719799,0.8245033,0.00002967136,0.0001485234,0.00006362104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401668,0.00006522572,0.05910928,0.0002853095,0.0002605858,0.00008458481,0.000001365815,0.00001876367,0.000008088408],"genre_scores_gemma":[0.9973335,0.00005653951,0.002480724,0.0000391822,0.00006923638,4.662664e-7,4.448255e-8,0.00000605776,0.00001427378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1607919,"threshold_uncertainty_score":0.2086537,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2520517651","doi":"10.1088/1741-2560/13/5/056016","title":"Intraspinal microstimulation produces over-ground walking in anesthetized cats","year":2016,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Veterinary Orthopedics and Neurology","field":"Veterinary","cited_by":71,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta; Alberta Innovates","funders":"Institute of Neurosciences, Mental Health and Addiction; National Institute of Neurological Disorders and Stroke; National Institutes of Health; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions","keywords":"Microstimulation; Ankle; Kinematics; Ground reaction force; Spinal cord injury; Physical medicine and rehabilitation; Lumbar; Hindlimb; Medicine; Spinal cord; CATS; Stimulation; Anatomy; Biomedical engineering; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03340162982890559,"gpt":0.2977350511565693,"spread":0.2643334213276637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000270014,0.0001424483,0.0002442295,0.0002582492,0.00002400315,0.000023373,0.0001420003,0.00006071213,0.00003520743],"category_scores_gemma":[0.0001199014,0.0001008743,0.00008417782,0.000126621,0.00001782168,0.0003734802,0.00004101699,0.0002201587,0.000003770752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000532888,"about_ca_system_score_gemma":0.00001679827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006366416,"about_ca_topic_score_gemma":0.000001282943,"domain_scores_codex":[0.9989429,0.00004598601,0.0004626803,0.0001465031,0.0001609683,0.0002410367],"domain_scores_gemma":[0.9994712,0.0001128228,0.0001864912,0.0001132515,0.00004786606,0.00006830784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007052957,0.00004315091,0.005236764,0.00002998775,0.00001358454,0.00261487,0.0001235231,0.00237337,0.9790961,0.00002153314,0.00002059221,0.009721269],"study_design_scores_gemma":[0.004118478,0.005960277,0.9298307,0.0004590348,0.00003572408,0.03509382,0.00001452379,0.01463095,0.004044872,0.00009596621,0.005192993,0.0005227004],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979987,0.0001849352,0.0003402286,0.0007621232,0.0006073283,0.00007238913,0.000001187703,0.00002136507,0.00001179309],"genre_scores_gemma":[0.9992439,0.00004228033,0.0002955155,0.00005342743,0.0003207946,0.000001764314,3.582853e-7,0.00002485149,0.00001715525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9750512,"threshold_uncertainty_score":0.4113534,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2766242384","doi":"10.1088/1741-2552/aa9666","title":"Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization","year":2017,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Shenzhen Peacock Plan","keywords":"Non-negative matrix factorization; Matrix decomposition; Computer science; Constraint (computer-aided design); Pattern recognition (psychology); Matrix (chemical analysis); Artificial intelligence; Control theory (sociology); Control (management); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01456061556117128,"gpt":0.2337637491886185,"spread":0.2192031336274472,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009132438,0.0001186857,0.0002180414,0.0002163644,0.0001282663,0.00003618769,0.00006648213,0.00004792661,0.000004889128],"category_scores_gemma":[0.0002787792,0.0001098474,0.00009461827,0.00009370669,0.00001809618,0.000386635,0.000005323681,0.0001561555,7.521656e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000438281,"about_ca_system_score_gemma":0.000010993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004280281,"about_ca_topic_score_gemma":0.000001937122,"domain_scores_codex":[0.9993307,0.000005140147,0.0003212667,0.00006985492,0.0001350008,0.000138016],"domain_scores_gemma":[0.999195,0.000195746,0.0002665208,0.00007440607,0.0002160087,0.00005227795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000669776,0.00002192358,0.0009849685,0.0001016155,0.0001976939,0.000003659638,0.00006923012,0.8375223,0.1499659,0.00001756591,0.0000777619,0.01097035],"study_design_scores_gemma":[0.0009419256,0.0002432704,0.05932531,0.00003732603,0.00008822625,0.00006731153,0.00002601796,0.9278756,0.01107209,0.00002674723,0.0001609162,0.0001352182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3919831,0.0001147645,0.6071832,0.00008269461,0.0004195159,0.0001628369,0.000009407925,0.00002636199,0.00001818613],"genre_scores_gemma":[0.9982432,0.00007620075,0.001436977,0.000003204827,0.0001961302,0.000008210246,0.000001578937,0.00001880337,0.00001573918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6062601,"threshold_uncertainty_score":0.4479447,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1982787715","doi":"10.1088/1741-2560/8/1/016006","title":"Automated spike sorting algorithmbased on Laplacian eigenmaps and<i>k</i>-means clustering","year":2011,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Trinity College","funders":"","keywords":"Spike sorting; Pattern recognition (psychology); Artificial intelligence; Sorting; Cluster analysis; Computer science; Principal component analysis; Spike (software development); Feature extraction; Classifier (UML); Maximization; Sorting algorithm; Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.01632420669351058,"gpt":0.2087358833935223,"spread":0.1924116767000117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000486743,0.0002286665,0.0002977104,0.0002107215,0.00003587325,0.00001520931,0.0001871645,0.0001068868,0.000005246302],"category_scores_gemma":[0.0001238142,0.0002104126,0.00008403965,0.0001813724,0.00002721167,0.0002187389,0.00003867468,0.0005155744,0.000002767347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007968099,"about_ca_system_score_gemma":0.000002355441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001444754,"about_ca_topic_score_gemma":5.399482e-7,"domain_scores_codex":[0.998921,0.000005197709,0.0004542601,0.0001145951,0.000173863,0.00033108],"domain_scores_gemma":[0.9995346,0.00006040541,0.0001085158,0.000135871,0.00004054778,0.0001200333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002107924,0.00001645978,0.0001540735,0.00008665599,0.00005481426,0.0003579949,0.0002598253,0.6008718,0.389917,0.00002246732,0.0000746481,0.00816318],"study_design_scores_gemma":[0.0003447406,0.0001217999,0.0009618253,0.0001198672,0.00001387614,0.000558949,0.0000545506,0.8409686,0.1563375,0.00002106919,0.0002530542,0.0002441075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802371,0.0003146496,0.01713837,0.0000279826,0.0004728337,0.00006893018,0.00000300224,0.001466546,0.0002705665],"genre_scores_gemma":[0.97428,0.0001293861,0.02539778,0.00002324325,0.0001038252,0.00000185421,5.365767e-7,0.00005825306,0.000005128082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2400968,"threshold_uncertainty_score":0.8580375,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2898529289","doi":"10.1088/1741-2552/aaeb0c","title":"On the parameters used in finite element modeling of compound peripheral nerves","year":2018,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute of General Medical Sciences; Fulbright Canada; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; NIH Office of the Director; Triangle Community Foundation; Duke University","keywords":"Perineurium; Electrical resistivity and conductivity; Fascicle; Materials science; Resistive touchscreen; Finite element method; Axon; Biomedical engineering; Composite material; Mechanics; Anatomy; Peripheral nerve; Physics; Thermodynamics; Electrical engineering; Biology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06479752956133286,"gpt":0.2683777069978259,"spread":0.2035801774364931,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002708189,0.0001582485,0.0002373079,0.0002348213,0.00005379886,0.00004747775,0.0003945595,0.00002777284,0.00001047238],"category_scores_gemma":[0.0006725797,0.0001078826,0.0001163651,0.0004229766,0.00006312387,0.0003030934,0.00004661564,0.0003740417,0.000001446095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000433194,"about_ca_system_score_gemma":0.00001652597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005021041,"about_ca_topic_score_gemma":0.000001217962,"domain_scores_codex":[0.9985889,0.00004681715,0.0005102688,0.0001593113,0.0004013347,0.0002933232],"domain_scores_gemma":[0.9990382,0.0005349654,0.000158078,0.0001624883,0.00003509233,0.00007119471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002227577,0.00001697697,0.0000324365,0.000006936214,0.000001404204,0.00003270742,0.000115215,0.5839813,0.4154302,0.0002839782,0.000003862479,0.00007271353],"study_design_scores_gemma":[0.0002428254,0.0003515279,0.0001176801,0.0000777641,0.000003288193,0.00005741662,0.00002468421,0.7782365,0.2207329,0.00004587619,0.00002433796,0.00008516957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956827,0.00001557011,0.003173463,0.0003351818,0.0006501891,0.00008977629,0.00000133961,0.00001602227,0.00003578766],"genre_scores_gemma":[0.9993876,0.00001501669,0.0002058865,0.000274547,0.00008738364,0.000001961222,4.267704e-8,0.00001870635,0.000008909341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1946972,"threshold_uncertainty_score":0.4399326,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3084261808","doi":"10.1088/1741-2552/abb5be","title":"EEG data augmentation: towards class imbalance problem in sleep staging tasks","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Sleep (system call); Segmentation; Electroencephalography; Sleep Stages; Class (philosophy); Pattern recognition (psychology); Machine learning; Polysomnography; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.0568486807920523,"gpt":0.2884237282564034,"spread":0.2315750474643511,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001555259,0.0001221395,0.0001984381,0.00009085209,0.00002288927,0.00009600815,0.0006721848,0.00002538869,0.00001378752],"category_scores_gemma":[0.0001848755,0.0001063687,0.0000402921,0.000269595,0.00001296058,0.0007577052,0.000182057,0.00040484,0.000003144153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003757747,"about_ca_system_score_gemma":0.00002322127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002935131,"about_ca_topic_score_gemma":4.164711e-7,"domain_scores_codex":[0.998861,0.00003236496,0.0004112058,0.000208371,0.0002797466,0.0002073451],"domain_scores_gemma":[0.9994745,0.00008917414,0.0001577598,0.0001446575,0.00002423757,0.00010968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003425065,0.00002327329,0.000486062,0.00008462856,0.000008024592,0.00034463,0.0007091883,0.3217921,0.6702003,0.00005033112,0.0005705187,0.005696685],"study_design_scores_gemma":[0.0006206745,0.0001614682,0.002222687,0.0001089059,0.00000692619,0.0001808458,0.00006540163,0.9290068,0.06477651,0.00001154367,0.002681853,0.0001564451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811884,0.0001522021,0.0106591,0.006997425,0.0006696344,0.0001045633,0.00001370794,0.00005319361,0.0001617324],"genre_scores_gemma":[0.9959735,0.00001932395,0.002889999,0.0008556687,0.000233579,8.060348e-7,0.000001026925,0.00001492141,0.00001122252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6072147,"threshold_uncertainty_score":0.4337589,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2791945743","doi":"10.1088/1741-2552/aaa904","title":"Cortical visual prostheses: from microstimulation to functional percept","year":2018,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phosphene; Microstimulation; Visual cortex; Percept; Visual prosthesis; Neuroscience; Visual perception; Perception; Macaque; Computer science; Psychology; Stimulation; Transcranial magnetic stimulation","retraction":null,"screen_n_in":null,"score":{"opus":0.09547151028447189,"gpt":0.3457472499285698,"spread":0.2502757396440979,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001963011,0.0005045204,0.001120972,0.0005666051,0.0001143242,0.0001851449,0.0005364672,0.0001819936,0.00007174274],"category_scores_gemma":[0.001753305,0.0003974904,0.000562272,0.0007434154,0.00005271455,0.0004997926,0.0001510282,0.00113136,0.0001247088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001584036,"about_ca_system_score_gemma":0.0001211963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001296559,"about_ca_topic_score_gemma":1.379353e-7,"domain_scores_codex":[0.9971424,0.00009086852,0.001084554,0.0005117151,0.0006914829,0.0004789193],"domain_scores_gemma":[0.9981186,0.0008103599,0.0003852967,0.0002237018,0.00008483013,0.0003771717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001632737,0.0002736476,0.00000722327,0.003236936,0.00008223571,0.001291299,0.0002685236,0.04006599,0.6324284,0.00008565818,0.00142279,0.3206741],"study_design_scores_gemma":[0.0004491032,0.001138437,0.0001449229,0.007133591,0.0003870855,0.002869153,0.000007943402,0.03837393,0.009135215,0.000006506006,0.9392084,0.001145679],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0898392,0.8836629,0.008515588,0.0002001071,0.01549833,0.001720468,0.0001175013,0.0003906935,0.00005516332],"genre_scores_gemma":[0.0361529,0.9554699,0.001252037,0.0006329543,0.005870116,0.00003596603,0.00001128294,0.0003303961,0.0002444128],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9377856,"threshold_uncertainty_score":0.9998477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1541185439","doi":"10.1088/1741-2560/12/5/056003","title":"Comparison of spatial filters and features for the detection and classification of movement-related cortical potentials in healthy individuals and stroke patients","year":2015,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Teknologi og Produktion, Det Frie Forskningsråd","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Neurorehabilitation; Brain–computer interface; Support vector machine; Electroencephalography; Motor imagery; Speech recognition; Psychology; Neuroscience; Rehabilitation","retraction":null,"screen_n_in":null,"score":{"opus":0.0427136954449082,"gpt":0.3027990706766931,"spread":0.2600853752317849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001907791,0.00005546686,0.000155796,0.00009238994,0.0000199874,0.00001887002,0.00005101565,0.00002995049,2.069234e-7],"category_scores_gemma":[0.0002672933,0.00003882047,0.00001799261,0.00004549542,0.00003303654,0.0001071221,0.00002358491,0.0001322749,8.92382e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001018228,"about_ca_system_score_gemma":0.000005917123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008328162,"about_ca_topic_score_gemma":0.000003503884,"domain_scores_codex":[0.9993412,0.00003544202,0.0003399139,0.00006895822,0.0001407237,0.00007368468],"domain_scores_gemma":[0.9993722,0.0002776177,0.0002308962,0.00003241471,0.00004212467,0.00004478571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003139729,0.00008647212,0.05378522,0.00008669195,0.00002012455,7.511022e-7,0.001279305,0.01541662,0.9061967,0.0000370592,0.00001666718,0.02276044],"study_design_scores_gemma":[0.001287108,0.00114854,0.6480467,0.00004628749,0.00001822063,0.00001302334,0.0001305119,0.2474661,0.1017666,0.0000190942,0.00001229882,0.0000454826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963855,0.0001953734,0.002897465,0.0001669026,0.0002191398,0.0001258122,0.000006411358,0.000002507878,9.227197e-7],"genre_scores_gemma":[0.9997776,0.0000197325,0.0001571398,0.0000227362,0.00001666873,0.000001059302,1.780055e-7,0.000003911119,0.000001033335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8044301,"threshold_uncertainty_score":0.1583053,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983649872","doi":"10.1088/1741-2560/10/5/056008","title":"Real-time control of walking using recordings from dorsal root ganglia","year":2013,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; Alberta Innovates; Canadian Institutes of Health Research; Dana Foundation; National Institute for Health and Care Research; Christopher and Dana Reeve Foundation","keywords":"Kinematics; Computer science; Gyroscope; Sensory system; Functional electrical stimulation; Spinal cord; Tilt (camera); Control theory (sociology); Biomedical engineering; Medicine; Stimulation; Mathematics; Neuroscience; Artificial intelligence; Physics; Control (management); Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.006886832332034148,"gpt":0.1930146957642077,"spread":0.1861278634321736,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009267693,0.0001643878,0.000376067,0.0002448354,0.00002677406,0.00003091255,0.0001303356,0.00005385558,0.00007985746],"category_scores_gemma":[0.0000593972,0.0001513082,0.000163997,0.0002200936,0.00001144944,0.0004069968,0.00001307614,0.0002337383,0.000001340202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005716098,"about_ca_system_score_gemma":0.000006953876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000726501,"about_ca_topic_score_gemma":5.071277e-7,"domain_scores_codex":[0.9990019,0.00001240339,0.0004910062,0.00007390494,0.0001857145,0.0002350513],"domain_scores_gemma":[0.9994093,0.000138609,0.0001619833,0.00009228342,0.0001274284,0.00007036833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009500247,0.000007981203,0.0007968565,0.00002962718,0.0001943407,0.000006937626,0.0001429556,0.1151284,0.8768756,0.000005713302,0.000253474,0.006548564],"study_design_scores_gemma":[0.001137677,0.0001409492,0.09927867,0.0002533513,0.0001077476,0.00004398514,0.00007301666,0.8584006,0.03989702,0.00009014638,0.000212079,0.000364708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921136,0.0003794988,0.006753797,0.00004872597,0.0004371824,0.00007298449,0.00000280684,0.00007752944,0.0001139115],"genre_scores_gemma":[0.9973912,0.00005452304,0.002282244,0.00001022166,0.0002200832,0.000001740938,5.592264e-7,0.00003569783,0.000003780904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8369786,"threshold_uncertainty_score":0.6170171,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2409078106","doi":"10.1088/1741-2560/13/2/026024","title":"Pushing the P300-based brain–computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain","year":2016,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Algoma University","funders":"Algoma University","keywords":"Brain–computer interface; Computer science; Artificial intelligence; Flashing; Domain (mathematical analysis); Interface (matter); Sentence; Pattern recognition (psychology); Speech recognition; Electroencephalography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02241871060157112,"gpt":0.265199760152856,"spread":0.2427810495512848,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008887119,0.0002741173,0.0002772355,0.0001794051,0.0002385837,0.0002483051,0.001095769,0.00005785777,0.00003140105],"category_scores_gemma":[0.0003343415,0.000125629,0.0001934454,0.0002599615,0.0002180475,0.0006638898,0.0001780749,0.0005437278,0.00001076262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000933,"about_ca_system_score_gemma":0.00004945396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002894456,"about_ca_topic_score_gemma":0.000001155923,"domain_scores_codex":[0.9980358,0.0001475242,0.0006634319,0.000255792,0.0004680674,0.0004293917],"domain_scores_gemma":[0.9977245,0.001412559,0.000370683,0.0002975611,0.00007371565,0.000120975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000543021,0.00003106924,0.0004236762,0.0000437145,0.00002904182,0.0001174772,0.001426942,0.06585325,0.8772435,0.0003403535,0.001763462,0.05267317],"study_design_scores_gemma":[0.002105016,0.0008028958,0.003100783,0.00122044,0.00003155046,0.003200421,0.0001679468,0.3589,0.6096153,0.0002332657,0.01994646,0.0006759716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9038241,0.00009512776,0.0789526,0.0151402,0.001724482,0.0001335746,0.000001897194,0.00005752999,0.00007047335],"genre_scores_gemma":[0.9948981,0.00001231356,0.002934999,0.001447416,0.0006102303,0.000002986621,9.749521e-8,0.00003223889,0.00006160071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2930467,"threshold_uncertainty_score":0.5123004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2269768844","doi":"10.1088/1741-2560/13/2/021002","title":"Ethical issues in neuroprosthetics","year":2016,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":58,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Neuroprosthetics; Neuroscience; Neural Prosthesis; Neuroethics; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02536236858021491,"gpt":0.2804677588302474,"spread":0.2551053902500325,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001641614,0.00008685805,0.0001484786,0.0001333745,0.00001179552,0.00003087436,0.0002390441,0.00005649923,0.00001142899],"category_scores_gemma":[0.0006290195,0.00005183291,0.00005911929,0.0001166452,0.00002381879,0.0001668934,0.0000430322,0.0004335425,0.000006592983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001943999,"about_ca_system_score_gemma":0.000008144041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.403094e-7,"about_ca_topic_score_gemma":2.051687e-7,"domain_scores_codex":[0.999193,0.00004297673,0.0002893524,0.00009878699,0.0002034743,0.0001723702],"domain_scores_gemma":[0.9994453,0.0003083235,0.00007735537,0.00007953607,0.00002494844,0.00006460132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001471459,0.00001511668,0.0001697167,0.000007884591,0.000001051242,0.0002883279,0.00009640869,0.004624208,0.9896151,0.0003583126,0.0001566169,0.004652504],"study_design_scores_gemma":[0.0007657497,0.0004423824,0.003557192,0.0003054242,0.000003937537,0.001621209,0.000007941566,0.02318239,0.9532686,0.0002123935,0.01643224,0.0002004948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894809,0.00006438781,0.001164093,0.008532226,0.0006698138,0.00002595074,6.939578e-7,0.00002177915,0.0000401228],"genre_scores_gemma":[0.9988375,0.00005692158,0.0003757378,0.0004537427,0.0001828664,3.006696e-7,6.633798e-9,0.00001212804,0.00008078995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0363465,"threshold_uncertainty_score":0.2113685,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070126685","doi":"10.1088/1741-2560/12/1/016007","title":"A comparative study of event-related coupling patterns during an auditory oddball task in schizophrenia","year":2014,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Instituto de Salud Carlos III; Universidad de Valladolid; Ministerio de Economía y Competitividad; Pratt and Whitney Canada; “la Caixa” Foundation","keywords":"Audiology; Psychology; Electroencephalography; Oddball paradigm; Cognition; Stimulus (psychology); Salience (neuroscience); Event-related potential; Neuroscience; Cognitive psychology; Developmental psychology; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.02533377803733395,"gpt":0.2787837142901781,"spread":0.2534499362528441,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002613781,0.0001372909,0.0003094469,0.0003323225,0.0000510275,0.00002958615,0.0002752844,0.00003420281,0.000008376901],"category_scores_gemma":[0.0001425829,0.0001232156,0.00005959105,0.0003050339,0.00002265143,0.0006114077,0.00004468401,0.0004084858,0.000001495489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004821354,"about_ca_system_score_gemma":0.00001691846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008469145,"about_ca_topic_score_gemma":0.000009781476,"domain_scores_codex":[0.9985993,0.00006787101,0.0005583785,0.0002007538,0.0003637466,0.0002099847],"domain_scores_gemma":[0.9993445,0.00008352854,0.0002944706,0.0001397251,0.00004009799,0.00009768316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004143393,0.0001452154,0.0004741558,0.00001089086,0.000001613811,0.00003576413,0.001104966,0.3227536,0.6753562,0.000003572026,8.648494e-7,0.00007169697],"study_design_scores_gemma":[0.00272629,0.00125885,0.40433,0.000170666,0.00001703019,0.0001951814,0.0006208171,0.5436623,0.04679462,0.000006130826,0.000007808921,0.0002103389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986801,0.000004009111,0.0002308062,0.00001287421,0.0009229624,0.0001220996,0.000001119938,0.00001998467,0.000006053694],"genre_scores_gemma":[0.9997905,0.000006983805,0.00002591003,0.00001661629,0.0001400606,0.00000201473,1.241824e-7,0.00001267853,0.000005144976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6285616,"threshold_uncertainty_score":0.5024585,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2005215079","doi":"10.1088/1741-2560/12/1/016018","title":"A hybrid feature selection approach for the early diagnosis of Alzheimer’s disease","year":2015,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"University of Victoria","keywords":"Feature selection; Electroencephalography; Feature (linguistics); Computer science; Set (abstract data type); Artificial intelligence; Cognitive impairment; Disease; Task (project management); Selection (genetic algorithm); Pattern recognition (psychology); Cognition; Machine learning; Medicine; Internal medicine; Psychiatry","retraction":null,"screen_n_in":null,"score":{"opus":0.05132745616237037,"gpt":0.2675445776123231,"spread":0.2162171214499527,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001530642,0.00009007162,0.0001380772,0.00007416389,0.00003202949,0.00004418954,0.0002191653,0.00001693167,8.085226e-7],"category_scores_gemma":[0.0003496985,0.00005648535,0.0001253465,0.0001091594,0.0000162891,0.0002211455,0.00002573926,0.0001933583,2.180991e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001500594,"about_ca_system_score_gemma":0.00002232086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001788616,"about_ca_topic_score_gemma":4.660842e-8,"domain_scores_codex":[0.999357,0.00002147189,0.0001882977,0.00009087839,0.0002113257,0.0001310593],"domain_scores_gemma":[0.9993576,0.0002228866,0.000147683,0.00007328792,0.00008445116,0.0001141069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002640096,0.0001454634,0.001611987,0.00007782508,0.00007990831,0.00003241529,0.0004741039,0.8995711,0.08276992,0.0001496632,0.008676372,0.006147198],"study_design_scores_gemma":[0.0005563609,0.0004266039,0.00410403,0.00004903584,0.0001090928,0.0002557836,0.00002002149,0.7320376,0.2600767,0.00003448107,0.002204061,0.0001262083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9704259,0.0009545026,0.02684973,0.0008956218,0.0006566881,0.0001746653,0.00000830993,0.00002202025,0.00001258032],"genre_scores_gemma":[0.997933,0.00001568865,0.001616142,0.00008354445,0.0003109491,0.000008931839,1.761355e-7,0.000012732,0.00001883864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1773068,"threshold_uncertainty_score":0.2303406,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2891352015","doi":"10.1088/1741-2552/aae4b9","title":"Online classification of imagined speech using functional near-infrared spectroscopy signals","year":2018,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Task (project management); Speech processing; Task analysis; Pattern recognition (psychology); Voice activity detection","retraction":null,"screen_n_in":null,"score":{"opus":0.03871090785143159,"gpt":0.3281086917368706,"spread":0.289397783885439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001870873,0.0001352725,0.0003530059,0.0002063956,0.00003323067,0.00002705209,0.00008349829,0.00005394538,0.00008841891],"category_scores_gemma":[0.0002149552,0.0001133499,0.0001441625,0.0002577076,0.00009133999,0.0001960205,0.00001815731,0.0003450798,0.000001747119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048305,"about_ca_system_score_gemma":0.00009531444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004510996,"about_ca_topic_score_gemma":1.46813e-7,"domain_scores_codex":[0.9988149,0.00001320582,0.0005311957,0.0001104621,0.0003350437,0.0001951282],"domain_scores_gemma":[0.9989836,0.00004925331,0.0002347924,0.0001488397,0.000456383,0.0001271135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001501125,0.0001224799,0.001237199,0.00004234959,0.00004445187,0.00003180916,0.00002684013,0.0004459805,0.9970836,0.00004418525,0.0004272796,0.0003437085],"study_design_scores_gemma":[0.0006986171,0.0009057258,0.02854302,0.0002558901,0.0001224833,0.0009396431,0.00002439467,0.4205046,0.5474201,0.0000938101,0.0003738207,0.0001178952],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.93959,0.0001271808,0.05900798,0.000595134,0.00031022,0.00008111616,0.000003675852,0.00005771278,0.0002270147],"genre_scores_gemma":[0.8257666,0.0000119849,0.1733503,0.00007265175,0.0007133508,3.692913e-7,0.000003115731,0.00002313254,0.00005858058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4496635,"threshold_uncertainty_score":0.4622273,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3010798784","doi":"10.1088/1741-2552/ab8131","title":"Artificial intelligence in glioma imaging: challenges and advances","year":2020,"lang":"en","type":"review","venue":"Journal of Neural Engineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University; University of British Columbia; Kingston General Hospital; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Neuroimaging; Artificial intelligence; Medical physics; Magnetic resonance imaging; Medical imaging; Deep learning; Brain tumor; Machine learning; Data science; Medicine; Radiology; Pathology","retraction":null,"screen_n_in":null,"score":{"opus":0.1117737587357624,"gpt":0.3322835630726941,"spread":0.2205098043369317,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001439223,0.0001886239,0.0005835427,0.0003510742,0.00001862777,0.00004379767,0.0002060014,0.00005001381,0.000003568051],"category_scores_gemma":[0.0006304162,0.0001565961,0.0001322419,0.0003194644,0.00002686342,0.0002712342,0.00003373138,0.000576124,0.000003901475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005243567,"about_ca_system_score_gemma":0.00002901955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.14512e-7,"about_ca_topic_score_gemma":3.258453e-7,"domain_scores_codex":[0.9987776,0.00006665658,0.0006322106,0.0002054387,0.0001773698,0.0001407799],"domain_scores_gemma":[0.9991863,0.0002650331,0.0003619828,0.00007766538,0.00001723436,0.00009176116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003614952,0.000008461109,3.95977e-8,0.001145527,0.000001910554,0.0002510236,0.00005219421,0.0001096035,0.0008649069,0.0004736257,8.668858e-7,0.9970883],"study_design_scores_gemma":[0.0000452006,0.0001053911,0.000008531868,0.003519291,0.00005569304,0.005507254,0.00007722128,0.01864902,0.001406964,0.0002250867,0.9700637,0.0003365892],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005318866,0.9984371,0.0004082933,0.0003765285,0.0005708585,0.00009195818,0.000001106588,0.00002292464,0.00003801398],"genre_scores_gemma":[0.008772817,0.9908447,0.00009036659,0.00001727902,0.0002458004,0.000004172703,1.003025e-7,0.00002314204,0.000001599683],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9967517,"threshold_uncertainty_score":0.6385803,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3010843813","doi":"10.1088/1741-2552/ab8113","title":"Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; École de Technologie Supérieure; Concordia University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Savoy Foundation","keywords":"Magnetoencephalography; Epilepsy; Ictal; Stereoelectroencephalography; Resting state fMRI; Epilepsy surgery; Cortex (anatomy); Neuroscience; Electroencephalography; Epileptic spasms; Psychology; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.03490066141138105,"gpt":0.265558420875113,"spread":0.230657759463732,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002233038,0.0001533993,0.0003856646,0.0002584798,0.00003409894,0.00001628705,0.0002125102,0.00003106455,0.000009853034],"category_scores_gemma":[0.007989315,0.0001357309,0.0001374184,0.0005335651,0.0000531504,0.000353276,0.00009808618,0.000458785,0.000001547583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003468334,"about_ca_system_score_gemma":0.00001760681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001163439,"about_ca_topic_score_gemma":0.000001448861,"domain_scores_codex":[0.9984467,0.0001117079,0.0005944156,0.0002140705,0.000412698,0.0002204107],"domain_scores_gemma":[0.9962472,0.003093701,0.0003872169,0.0000762718,0.00007769305,0.0001178743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001569934,0.0001014067,0.9425917,0.00008800711,0.00001411337,0.0002748971,0.00071382,0.03030902,0.02463823,0.0000603499,0.0000229659,0.001028507],"study_design_scores_gemma":[0.001384658,0.0009761222,0.9751463,0.00009066567,0.000009524964,0.00007936701,0.00006991201,0.008216126,0.01359624,0.00005881852,0.0001829608,0.0001892927],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976159,0.00004014424,0.0002107422,0.001688943,0.0002658819,0.0001003819,0.00001452294,0.00002085327,0.00004267987],"genre_scores_gemma":[0.9994872,0.00003142895,0.0001006114,0.0002917917,0.00006930687,0.000002317178,2.326125e-7,0.0000147446,0.000002350116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03255462,"threshold_uncertainty_score":0.9564534,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3082119294","doi":"10.1088/1741-2552/abb417","title":"Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Kurtosis; Pattern recognition (psychology); Brain–computer interface; Support vector machine; Artificial intelligence; Computer science; Finger tapping; Skewness; Linear discriminant analysis; Feature extraction; Feature (linguistics); Independent component analysis; Mathematics; Speech recognition; Electroencephalography; Statistics; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.04207844733730386,"gpt":0.3504407696763284,"spread":0.3083623223390245,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102299,0.0001280792,0.0004951576,0.0002351207,0.00002014571,0.00002525421,0.00009964659,0.0000549246,0.00002836841],"category_scores_gemma":[0.0006255712,0.0001078464,0.0002846462,0.0005182533,0.00001917828,0.000163715,0.00001282444,0.0002511371,3.106348e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007069032,"about_ca_system_score_gemma":0.00005282232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001622588,"about_ca_topic_score_gemma":3.400747e-7,"domain_scores_codex":[0.9989563,0.00001869984,0.0004864619,0.0001218215,0.0002748855,0.0001418442],"domain_scores_gemma":[0.9990932,0.0001566191,0.0002967089,0.0001305412,0.0001518927,0.0001710631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001853819,0.00006392155,0.0006914928,0.00005700169,0.0002399018,0.00004866071,0.000114495,0.009752707,0.9882187,0.000009317553,0.00002818825,0.0005902158],"study_design_scores_gemma":[0.0006519083,0.0002745056,0.009164996,0.000120252,0.0007498563,0.00001324082,0.00003296794,0.4668939,0.5219971,0.000002274327,0.00002868919,0.00007032705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8488945,0.0002790185,0.149264,0.001371688,0.00006728961,0.00005837198,0.000005183208,0.00005094198,0.000009054366],"genre_scores_gemma":[0.9550306,0.00001324378,0.04451576,0.0001663252,0.0002484684,5.345528e-7,0.000006166682,0.00001739936,0.000001536931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4662217,"threshold_uncertainty_score":0.4397851,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097220887","doi":"10.1088/1741-2560/7/6/064001","title":"Designing a somatosensory neural prosthesis: percepts evoked by different patterns of thalamic stimulation","year":2010,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Somatosensory system; Thalamus; Neuroscience; Stimulation; Neural Prosthesis; Neuroprosthetics; Sensation; Somatosensory evoked potential; Deep brain stimulation; Psychology; Functional electrical stimulation; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01840528356502033,"gpt":0.238946377970032,"spread":0.2205410944050117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001532984,0.0002859895,0.0003991685,0.0003050904,0.00007298185,0.00006931683,0.0004333259,0.00008170927,0.00001785803],"category_scores_gemma":[0.0005264154,0.0002325616,0.0002012717,0.000242631,0.00004094143,0.0006840774,0.00005945969,0.0007588491,0.000001554276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002825645,"about_ca_system_score_gemma":0.00001625091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002096172,"about_ca_topic_score_gemma":4.117644e-7,"domain_scores_codex":[0.9980494,0.00005078708,0.00066216,0.0002655844,0.0005638047,0.0004082968],"domain_scores_gemma":[0.9989097,0.0002813473,0.0003581738,0.0002121925,0.00005891323,0.000179668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002349706,0.00005133909,0.002069377,0.00005600081,0.000003429055,0.00006184354,0.0001246183,0.02854362,0.9683949,0.00002512123,0.000008914109,0.0006373891],"study_design_scores_gemma":[0.0003922095,0.0002004456,0.01341679,0.00006701089,0.00001691867,0.0005176286,0.00001830855,0.2067761,0.7783876,0.000005557727,0.00001958273,0.0001817834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971188,0.00002929646,0.001400959,0.00008726507,0.001115337,0.000164132,0.000006850651,0.00006925605,0.000008044252],"genre_scores_gemma":[0.9994347,0.00002434207,0.0002327735,0.00006621177,0.0001530112,0.000003835847,4.386822e-7,0.00004833875,0.00003634339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1900072,"threshold_uncertainty_score":0.9483587,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4315432772","doi":"10.1088/1741-2552/acb1d7","title":"A geometric approach to quantifying the neuromodulatory effects of persistent inward currents on individual motor unit discharge patterns","year":2023,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"National Institute of Neurological Disorders and Stroke; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Excitatory postsynaptic potential; Inhibitory postsynaptic potential; Motor unit; Neuromodulation; Neuroscience; Physics; Chemistry; Biology; Central nervous system","retraction":null,"screen_n_in":null,"score":{"opus":0.04791551165697589,"gpt":0.2461777630446065,"spread":0.1982622513876306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001768378,0.00017336,0.0002569492,0.001015146,0.00005210541,0.00002909404,0.0002588622,0.00003339245,0.000001353141],"category_scores_gemma":[0.0001420603,0.0001274365,0.0002775163,0.001280279,0.000007827682,0.0001190038,0.00004862501,0.0003793009,0.000001187915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000278132,"about_ca_system_score_gemma":0.000004814645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001505818,"about_ca_topic_score_gemma":7.147481e-8,"domain_scores_codex":[0.9988797,0.00002375696,0.0003201703,0.00009716317,0.0004057838,0.0002734376],"domain_scores_gemma":[0.999441,0.0002034072,0.00007798115,0.0001319755,0.00004846956,0.00009719127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001762642,0.00006900456,0.005922671,0.0006843614,0.0005129079,0.00001154518,0.000918682,0.9691309,0.01193127,0.0000270665,0.0004498697,0.01032405],"study_design_scores_gemma":[0.0006933805,0.0005569407,0.7557095,0.0002964092,0.0001154987,0.00003414321,0.0002212675,0.2378241,0.003747286,0.000001167667,0.0005054792,0.0002947729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972263,0.0003324696,0.001289394,0.00005800662,0.0008228828,0.0001637823,0.000007029826,0.00008300468,0.00001711567],"genre_scores_gemma":[0.9996513,0.0000912672,0.00005165298,0.00002714758,0.0001280065,0.00001141638,0.000001793296,0.00003309669,0.000004291649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7497869,"threshold_uncertainty_score":0.5196712,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2999245846","doi":"10.1088/1741-2552/ab6cb6","title":"<i>In vivo</i> quantification of excitation and kilohertz frequency block of the rat vagus nerve","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Vagus Nerve Stimulation Research","field":"Neuroscience","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fulbright Canada; Duke University","keywords":"Vagus nerve; In vivo; Excitation; Block (permutation group theory); Pulse (music); Biomedical engineering; Acoustics; Materials science; Neuroscience; Computer science; Medicine; Physics; Optics; Psychology; Mathematics; Biology; Stimulation","retraction":null,"screen_n_in":null,"score":{"opus":0.05281649667565613,"gpt":0.2882981981031241,"spread":0.235481701427468,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001592922,0.0000665281,0.0001500379,0.0001029012,0.00001801198,0.00001315828,0.0001847131,0.00002710743,0.000009601659],"category_scores_gemma":[0.001168235,0.00005000321,0.00005949317,0.0004457799,0.00003117139,0.0002547222,0.00002990508,0.0002037677,3.478685e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001559778,"about_ca_system_score_gemma":0.0000229519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006894655,"about_ca_topic_score_gemma":7.656269e-7,"domain_scores_codex":[0.9989926,0.00006773126,0.0004313271,0.00009133203,0.0003212703,0.00009574439],"domain_scores_gemma":[0.9992781,0.0002389715,0.0002531734,0.00008598604,0.00008970332,0.00005403576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000170923,0.00001137013,0.001037054,0.00005367533,0.000001631032,0.000007205155,0.0001886183,0.1268354,0.8714586,0.0003010959,0.00002894197,0.00005924018],"study_design_scores_gemma":[0.000417659,0.0001357224,0.01497293,0.00005408965,0.000006515202,0.0000438237,0.00002364831,0.2121865,0.7719976,0.00006958575,0.00003707092,0.00005482093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970412,0.0001023243,0.0005835521,0.001959902,0.0001846039,0.00009609582,0.000002704476,0.00000447831,0.00002518838],"genre_scores_gemma":[0.9996132,0.00001528933,0.0002419627,0.00006578659,0.00004295672,7.062076e-7,4.610251e-8,0.000008710148,0.00001130835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09946102,"threshold_uncertainty_score":0.2039072,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3190122749","doi":"10.1088/1741-2552/ac1adc","title":"MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning","year":2021,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Canada Research Chairs","keywords":"Kinematics; Electromyography; Computer science; Biomechanics; Artificial intelligence; Physical medicine and rehabilitation; Machine learning; Computer vision; Medicine; Physics; Anatomy","retraction":null,"screen_n_in":null,"score":{"opus":0.005262899621501619,"gpt":0.1900159367913301,"spread":0.1847530371698285,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001316811,0.0001851581,0.0003139442,0.0003841194,0.00005608197,0.00005770265,0.00009103988,0.00004982205,0.00001391537],"category_scores_gemma":[0.00009764336,0.0001813282,0.0001066952,0.0007444129,0.000006979352,0.0001551298,0.00003166548,0.0003902153,2.806007e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003742852,"about_ca_system_score_gemma":0.000007157774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001749497,"about_ca_topic_score_gemma":0.000001320939,"domain_scores_codex":[0.9990143,0.00002009891,0.0003737822,0.0001199978,0.000178993,0.0002928905],"domain_scores_gemma":[0.999549,0.00009467251,0.00006064697,0.00007916229,0.00007221637,0.0001443451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004831277,0.00001189633,0.00009694097,0.0001425998,0.0001914478,0.00002765888,0.0001131157,0.05068234,0.9279838,0.0000365175,0.0002222914,0.02048662],"study_design_scores_gemma":[0.001146361,0.0004306346,0.00935271,0.0004309849,0.0001038218,0.0008761089,0.0002081098,0.9302136,0.03484473,0.0001108447,0.0214787,0.0008033974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8642893,0.008988062,0.1252399,0.0007034445,0.0004036407,0.00009573695,0.000004490496,0.0001975117,0.00007793157],"genre_scores_gemma":[0.9932497,0.0007826409,0.005775934,0.00006302728,0.00006863099,0.000003816595,0.000003200586,0.0000379008,0.00001515618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.893139,"threshold_uncertainty_score":0.7394348,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2481095539","doi":"10.1088/1741-2560/13/5/056003","title":"Automated detection and labeling of high-density EEG electrodes from structural MR images","year":2016,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"China Scholarship Council; KU Leuven; Fonds Wetenschappelijk Onderzoek; Vlaamse regering; European Commission; Seventh Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Electroencephalography; Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Psychology; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.009089258250926463,"gpt":0.2257610484644783,"spread":0.2166717902135518,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006916727,0.00009974902,0.0001863069,0.000100403,0.00003159679,0.00003291382,0.0001169937,0.00003218072,0.000004352968],"category_scores_gemma":[0.000221705,0.00006367874,0.0000439202,0.00008086268,0.00002492998,0.0003383977,0.00003617983,0.0001348667,4.378245e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002197335,"about_ca_system_score_gemma":0.000006112459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002298696,"about_ca_topic_score_gemma":0.000001868396,"domain_scores_codex":[0.9993101,0.00002829084,0.0002608223,0.0001132317,0.0001488365,0.0001387252],"domain_scores_gemma":[0.9994124,0.0002502428,0.000170808,0.00006403104,0.00005134722,0.00005122088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002701319,0.00000308042,0.0001379379,0.000009431138,0.000008503845,0.00001787964,0.00003702163,0.002451492,0.9919439,0.000008192522,0.000007511214,0.005348013],"study_design_scores_gemma":[0.0003063638,0.0001346931,0.0193473,0.00007440517,0.000009847445,0.0002273702,0.000003362115,0.04475825,0.9349859,0.00007469694,0.000006344913,0.00007145638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960753,0.00009526287,0.003111764,0.0001561524,0.0004573969,0.00002521983,0.000004962961,0.00007264149,0.0000012504],"genre_scores_gemma":[0.9984856,0.00002609978,0.001342048,0.00002042596,0.0001101629,1.576777e-7,5.125962e-8,0.000009875884,0.000005547481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05695802,"threshold_uncertainty_score":0.2596743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1989936650","doi":"10.1088/1741-2560/3/2/008","title":"Different classification techniques considering brain computer interface applications","year":2006,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University; University of Northern British Columbia","funders":"","keywords":"Brain–computer interface; Computer science; Artificial intelligence; Electroencephalography; Pattern recognition (psychology); Bayesian probability; Hidden Markov model; Machine learning; Artificial neural network; Interface (matter); Classifier (UML); Support vector machine; Mutual information; Naive Bayes classifier; Field (mathematics); Linear classifier; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02597771834991354,"gpt":0.2681451687083258,"spread":0.2421674503584123,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009074762,0.0001412827,0.0001866339,0.0001724394,0.00004760217,0.0001032556,0.0002610182,0.00004145885,0.000005874325],"category_scores_gemma":[0.00003820972,0.0001154366,0.00009004871,0.0001277461,0.00002819609,0.0002346616,0.00005654556,0.0002799122,0.000002988828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005246891,"about_ca_system_score_gemma":0.000007918216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001619692,"about_ca_topic_score_gemma":5.128404e-7,"domain_scores_codex":[0.9990302,0.00003057792,0.0004311432,0.0001552561,0.0001816198,0.0001712422],"domain_scores_gemma":[0.9992631,0.0002945071,0.0002103403,0.0001282284,0.00004809947,0.0000557072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005119435,0.00004048176,0.00008087842,0.00002217691,0.000003702385,0.00001147646,0.0000325601,0.01792553,0.9721304,0.0007559233,0.0008987442,0.008092994],"study_design_scores_gemma":[0.0001640469,0.0001153967,0.001638542,0.0000839885,0.000006963671,0.0004887174,0.000007740891,0.1616947,0.8220531,0.000135682,0.01345558,0.0001555434],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5926124,0.00007238784,0.4055504,0.001121012,0.0003024878,0.0001213719,0.00000178427,0.0001148415,0.0001033229],"genre_scores_gemma":[0.9931882,0.000007739545,0.006039413,0.0001727268,0.0005235767,0.000007054912,3.593478e-7,0.00001798687,0.00004295073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4005758,"threshold_uncertainty_score":0.470737,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2917810324","doi":"10.1088/1741-2552/ab08c8","title":"System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation","year":2019,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Motor imagery; Computer science; Brain–computer interface; Artificial intelligence; Pattern recognition (psychology); Spectrogram; Electroencephalography; Speech recognition; Computer vision; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.01996487921524984,"gpt":0.2529208907145466,"spread":0.2329560114992968,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004296541,0.0001819652,0.0003087409,0.0003471599,0.00004367247,0.0001255879,0.000276549,0.00005044687,0.00001100729],"category_scores_gemma":[0.0005625992,0.0001523998,0.0002403946,0.0002276466,0.000008132165,0.0002743571,0.00002523006,0.0002403181,0.00001478099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001506717,"about_ca_system_score_gemma":0.00002992457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.818787e-7,"about_ca_topic_score_gemma":8.301213e-8,"domain_scores_codex":[0.9986082,0.00004697305,0.000469386,0.0002597351,0.0003429474,0.0002728023],"domain_scores_gemma":[0.9982978,0.001086703,0.0001763434,0.0001822214,0.0001262256,0.0001307256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006586301,0.00004232831,0.00003952765,0.0002405114,0.000005322688,0.00002885687,0.0001687786,0.1842044,0.8114718,0.00004347543,0.0003329682,0.002763416],"study_design_scores_gemma":[0.001320915,0.005795181,0.001140295,0.0007995539,0.00001383461,0.0001306705,0.0001040823,0.7695507,0.2163332,0.000005625827,0.004480372,0.0003255422],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775271,0.00002614258,0.01799993,0.0006787535,0.003222158,0.0003704558,0.00001304946,0.00006988927,0.00009252683],"genre_scores_gemma":[0.9930881,0.000001749063,0.00620384,0.000188298,0.0004026075,0.000009198007,3.734025e-7,0.00003501133,0.00007085487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5951385,"threshold_uncertainty_score":0.6214682,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}