{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":744,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":744,"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":"b2df22d3ac47","filters":{"topic":"Blind Source Separation Techniques"}},"results":[{"id":"W1659874285","doi":"10.1201/9781420036756","title":"The Transforms and Applications Handbook, Second Edition","year":2000,"lang":"en","type":"book","venue":"˜The œelectrical engineering handbook/Electrical engineering handbook series/Electrical engineering handbook","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":640,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Library science","retraction":null,"screen_n_in":null,"score":{"opus":0.00396740531704648,"gpt":0.1807244245733031,"spread":0.1767570192562566,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.001658867,0.003565414,0.002924324,0.002071681,0.001710961,0.00244564,0.004601761,0.002627603,0.0001198211],"category_scores_gemma":[0.0004467329,0.002951212,0.001352575,0.003559221,0.0004252163,0.001575766,0.0006349363,0.007775587,0.0002250872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002265073,"about_ca_system_score_gemma":0.001508634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002349947,"about_ca_topic_score_gemma":0.00001881583,"domain_scores_codex":[0.9861376,0.00025015,0.003181687,0.003271357,0.002710613,0.004448631],"domain_scores_gemma":[0.9901659,0.003712393,0.0007059645,0.003090132,0.0005699526,0.001755675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001523773,0.001437344,0.000007616138,0.002536224,0.005693551,0.0007831018,0.00219922,0.187266,0.04201064,0.3346458,0.1720792,0.2498175],"study_design_scores_gemma":[0.001534547,0.001134539,0.00001428863,0.0009366702,0.0003250628,0.0007174349,0.000001508866,0.3918556,0.008719351,0.002114123,0.5895995,0.003047301],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00001336956,0.07536991,0.9103954,0.0004202025,0.0008201929,0.004244548,0.00008246593,0.005139682,0.003514264],"genre_scores_gemma":[0.01068239,0.1422433,0.1256098,0.005335436,0.02180702,0.02814837,0.001999173,0.007852406,0.6563221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7847856,"threshold_uncertainty_score":0.9995887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2143169494","doi":"10.1162/0899766054322964","title":"The Cocktail Party Problem","year":2005,"lang":"en","type":"review","venue":"Neural Computation","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":515,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Auditory scene analysis; Analogy; Computer science; Phenomenon; Process (computing); Cognitive science; Artificial intelligence; Auditory system; Auditory perception; Computational auditory scene analysis; Psychology; Cognitive psychology; Neuroscience; Linguistics","retraction":null,"screen_n_in":null,"score":{"opus":0.06976775862070309,"gpt":0.3699043797292316,"spread":0.3001366211085285,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004965045,0.0002782078,0.0004696785,0.0001064627,0.0003107353,0.0006738682,0.001046058,0.0001586558,0.000001924009],"category_scores_gemma":[0.00002541349,0.0001772529,0.0002365949,0.0004880637,0.00004196712,0.0004117625,0.0002278452,0.0004423709,0.0001299604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008199638,"about_ca_system_score_gemma":0.0001677232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002635769,"about_ca_topic_score_gemma":0.000007329922,"domain_scores_codex":[0.9979185,0.0005130252,0.0005632493,0.0004155641,0.0003470267,0.0002426529],"domain_scores_gemma":[0.9984559,0.0004660238,0.0004613581,0.0004427298,0.0001158621,0.00005809349],"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":[2.6105e-7,0.000008603155,5.145307e-8,0.0001658746,0.000007978659,0.000001160535,0.00005755129,0.00008693707,2.261881e-8,0.01184273,0.003660926,0.9841679],"study_design_scores_gemma":[0.00004063886,0.00004153838,8.174082e-7,0.0003219796,0.00002368536,0.00003189034,0.000001519805,0.02497228,5.645314e-7,0.002057961,0.9723188,0.000188366],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.929875e-7,0.805357,0.1910082,0.0005839229,0.0002025728,0.0006995217,0.00000157272,0.0005854175,0.001561612],"genre_scores_gemma":[0.00002949786,0.9792781,0.01972348,0.0002631823,0.0001484854,0.0001390493,0.00003757359,0.00002378326,0.0003568018],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9839795,"threshold_uncertainty_score":0.7228165,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2027411766","doi":"10.1152/jn.90896.2008","title":"Resolving Precise Temporal Processing Properties of the Auditory System Using Continuous Stimuli","year":2009,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":269,"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":"Auditory system; Computer science; Auditory scene analysis; Speech recognition; Generalizability theory; Noise (video); Auditory masking; Acoustics; Artificial intelligence; Perception; Psychology; Neuroscience; Octave (electronics); Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.03606127629310299,"gpt":0.2672881402956619,"spread":0.2312268640025589,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001699598,0.0001025285,0.0002976173,0.0001121245,0.0001046049,0.00005105934,0.0007997643,0.00005617689,3.228553e-7],"category_scores_gemma":[0.000101814,0.00006256066,0.0001087887,0.000220727,0.00008199459,0.0004096084,0.0001089046,0.0002719047,4.677528e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003533452,"about_ca_system_score_gemma":0.0002252721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002853183,"about_ca_topic_score_gemma":8.960479e-8,"domain_scores_codex":[0.99861,0.0003513853,0.0005306957,0.0001345121,0.0002338884,0.0001394823],"domain_scores_gemma":[0.9983819,0.00003416862,0.0009153172,0.0002722231,0.0003601973,0.00003624443],"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":[0.00004276268,0.00005529662,0.00002717967,0.0000400118,0.000006275511,0.000017691,0.0006519256,0.003489898,0.9916925,0.0002802821,0.00008960224,0.003606565],"study_design_scores_gemma":[0.001852378,0.005204072,0.04933262,0.003367541,0.0001102442,0.002489324,0.0005901416,0.5368634,0.3948614,0.002414248,0.002108816,0.0008057572],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615367,0.0001380332,0.037152,0.0004269483,0.0004956258,0.0001236057,2.191387e-7,0.00005147361,0.00007539833],"genre_scores_gemma":[0.9892955,0.000003740831,0.01024916,0.000227707,0.0001970145,4.958129e-7,2.359895e-8,0.000006441455,0.0000199481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5968311,"threshold_uncertainty_score":0.255115,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138178674","doi":"10.1109/tbme.2003.816076","title":"Temporally constrained ica: an application to artifact rejection in electromagnetic brain signal analysis","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":257,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Montreal Neurological Institute and Hospital; Aston University","keywords":"Independent component analysis; A priori and a posteriori; Artifact (error); SIGNAL (programming language); Computer science; Signal processing; Waveform; Pattern recognition (psychology); Blind signal separation; Artificial intelligence; Magnetoencephalography; Electroencephalography; Set (abstract data type); Speech recognition; Channel (broadcasting); Digital signal processing; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.007024912417257371,"gpt":0.2401210362216599,"spread":0.2330961238044025,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056334,0.0001588126,0.0001982364,0.001198964,0.00005755599,0.00007427017,0.0002598503,0.0001286298,0.00003338814],"category_scores_gemma":[0.00001934991,0.0001711062,0.00008601939,0.003171507,0.00002710604,0.0002733339,0.00000123667,0.0002886801,0.00001685149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170479,"about_ca_system_score_gemma":0.00007632311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004742657,"about_ca_topic_score_gemma":0.00005157434,"domain_scores_codex":[0.9985402,0.0001006763,0.0003436054,0.0004256791,0.0003174638,0.0002723889],"domain_scores_gemma":[0.9992296,0.0001058294,0.00003913671,0.00035355,0.00003790774,0.0002339441],"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":[0.00002089879,0.0007661568,0.00001481487,0.00001590927,0.00013114,0.00001383969,0.001131628,0.3890631,0.5304307,0.008865098,0.0000502349,0.06949644],"study_design_scores_gemma":[0.0003755992,0.0007010367,0.0006533371,0.00001545093,0.00003110508,0.00001743222,0.00002495735,0.9217834,0.07428326,0.0002635205,0.001503292,0.0003475897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02839153,0.000003092079,0.9697667,0.0009374984,0.00006876018,0.0002636751,0.000002623357,0.0004859786,0.00008016248],"genre_scores_gemma":[0.9258238,0.00000203398,0.0736857,0.0003142347,0.00001111578,0.0001211402,0.000004899637,0.00001163799,0.00002544508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8974323,"threshold_uncertainty_score":0.6977507,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2841351641","doi":"10.1016/j.wocn.2018.07.008","title":"Bayesian data analysis in the phonetic sciences: A tutorial introduction","year":2018,"lang":"en","type":"article","venue":"Journal of Phonetics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":249,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge","funders":"National Institutes of Health; University of Lethbridge; National Institute on Deafness and Other Communication Disorders; Ohio State University","keywords":"Computer science; Bayesian probability; Code (set theory); Process (computing); Artificial intelligence; Machine learning; Data mining; Natural language processing; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.04121776555961447,"gpt":0.3316891550027601,"spread":0.2904713894431456,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003706069,0.00007673701,0.0001681306,0.0005062209,0.0001005853,0.0002990384,0.002545518,0.0000446652,0.0000208545],"category_scores_gemma":[0.0002341934,0.00005117026,0.00005860032,0.002634995,0.0001829241,0.0006009669,0.0001938069,0.0002273021,0.000006408527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002943224,"about_ca_system_score_gemma":0.0001615073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002152367,"about_ca_topic_score_gemma":0.00008714279,"domain_scores_codex":[0.9983205,0.0002899848,0.0004385609,0.0002097404,0.0005947697,0.0001464144],"domain_scores_gemma":[0.998538,0.0001101537,0.000335615,0.0007893406,0.0001898395,0.00003705529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003673026,0.003355501,0.01607926,0.000061614,0.001474838,0.0002374161,0.172145,0.01814458,0.02849976,0.1591335,0.4772796,0.1232215],"study_design_scores_gemma":[0.001825002,0.003766832,0.02613188,0.00006319163,0.0005757983,0.0005949471,0.001418274,0.7629414,0.01471499,0.05918168,0.1280653,0.0007206487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02025414,0.00008382405,0.9716936,0.006520347,0.001026608,0.00007009121,0.000001181828,0.00001802929,0.0003321189],"genre_scores_gemma":[0.8642531,0.00003968005,0.1333107,0.0004295969,0.001948652,5.919771e-7,0.000001149963,0.000002848297,0.00001365686],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.843999,"threshold_uncertainty_score":0.4730248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2045125028","doi":"10.1016/j.neuroimage.2005.08.053","title":"Evaluation of EEG localization methods using realistic simulations of interictal spikes","year":2005,"lang":"en","type":"article","venue":"NeuroImage","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":240,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Ictal; Electroencephalography; Computer science; Spurious relationship; Pattern recognition (psychology); Geodesic; Principle of maximum entropy; Entropy (arrow of time); Artificial intelligence; Algorithm; Mathematics; Neuroscience; Machine learning; Physics; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.1189133963909001,"gpt":0.4382427528238568,"spread":0.3193293564329568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112764,0.00007095065,0.0001218754,0.0001709825,0.00003537639,0.00002981649,0.0002400721,0.00003578757,0.00002169039],"category_scores_gemma":[0.0005185419,0.00007427428,0.00003942725,0.0004079452,0.00004540072,0.0004818958,0.00008322509,0.00005522459,0.000001076352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004047266,"about_ca_system_score_gemma":0.00008675028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002797741,"about_ca_topic_score_gemma":0.000008397127,"domain_scores_codex":[0.998345,0.0006409436,0.0003361972,0.0001776599,0.0004242841,0.00007595892],"domain_scores_gemma":[0.9986983,0.0001292484,0.0002336273,0.0003549034,0.0005616638,0.0000222867],"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":[0.000004468453,0.0001345726,0.0001252939,0.00002096797,0.000008405149,3.812553e-7,0.001549946,0.4328346,0.4327501,0.0148578,0.00009113642,0.1176223],"study_design_scores_gemma":[0.0001194153,0.00003976077,0.001074765,0.00001224369,0.00002599313,0.000002394103,0.000005894997,0.9102821,0.08578277,0.002439098,0.0001639069,0.00005169461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04658174,0.00002428368,0.9518749,0.00009856972,0.00005296854,0.0001718812,0.00000375397,0.00007767947,0.001114204],"genre_scores_gemma":[0.6689646,0.000001426097,0.3309373,0.00007120343,0.00001037264,0.000001736202,0.00000319675,0.000005060044,0.000005075596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6223829,"threshold_uncertainty_score":0.3028817,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110692145","doi":"10.1016/j.mri.2006.09.042","title":"CORSICA: correction of structured noise in fMRI by automatic identification of ICA components","year":2006,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Independent component analysis; Artificial intelligence; Noise (video); Computer science; Pattern recognition (psychology); Functional magnetic resonance imaging; Noise reduction; Aliasing; Communication noise; Brain activity and meditation; Prior probability; Electroencephalography; Bayesian probability; Undersampling; Image (mathematics); Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.005566741325366598,"gpt":0.2297228262656513,"spread":0.2241560849402847,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003152029,0.0001045312,0.0001909582,0.0001968908,0.0000306335,0.00005076074,0.0004127046,0.00003356989,0.00001193418],"category_scores_gemma":[0.00004028165,0.0001132706,0.00003643729,0.0005130505,0.00008820742,0.000309646,0.00006785453,0.00009587633,0.000002976501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004276025,"about_ca_system_score_gemma":0.000027275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004475067,"about_ca_topic_score_gemma":0.00001817109,"domain_scores_codex":[0.9985725,0.00011094,0.0006068421,0.000259084,0.0003007169,0.0001499565],"domain_scores_gemma":[0.9990987,0.00006705039,0.0003184615,0.0003976529,0.00009917662,0.00001902111],"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":[0.00001367445,0.0002656809,0.05713211,0.00006707838,0.000001724748,0.000003632671,0.0009748087,0.000461292,0.5142962,0.003873965,0.00798538,0.4149245],"study_design_scores_gemma":[0.0002912702,0.00002663374,0.4622861,0.00006818824,0.00000294764,0.000004282691,0.00001607364,0.4411546,0.09290563,0.002255703,0.0008852227,0.0001033833],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7878653,0.001703236,0.2079401,0.0005252933,0.000297545,0.000418629,0.000007794612,0.0001722485,0.001069805],"genre_scores_gemma":[0.9887744,0.00001250171,0.01088487,0.0000433609,0.000007917158,0.00001788688,0.000009631687,0.000007642875,0.000241747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4406933,"threshold_uncertainty_score":0.4619043,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W110813552","doi":"","title":"THE CO-INFORMATION LATTICE","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Mutual information; Mathematics; Lattice (music); Information theory; Subspace topology; Partially ordered set; Combinatorics; Theoretical computer science; Discrete mathematics; Computer science; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01164127541300171,"gpt":0.2669328433378336,"spread":0.2552915679248319,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004540207,0.00002865129,0.00002086885,0.00001914546,0.0001265329,0.0002705697,0.0002530648,0.00001651357,0.00001173665],"category_scores_gemma":[0.00005522195,0.00001758789,0.0000124949,0.000107554,0.00001180635,0.0008666255,0.00001549062,0.00004082006,0.0002698043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007691412,"about_ca_system_score_gemma":0.00002596783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002707721,"about_ca_topic_score_gemma":0.000001804818,"domain_scores_codex":[0.999648,0.00004945254,0.00008550805,0.00003995552,0.0001066537,0.00007046056],"domain_scores_gemma":[0.9996032,0.0000755081,0.00002799254,0.0002339522,0.00004167088,0.00001765601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[1.164814e-7,0.000001630647,0.00001414678,2.23115e-7,6.379453e-7,3.29943e-8,0.0001961744,0.000001906248,0.00000604398,0.9858037,0.0075824,0.006393014],"study_design_scores_gemma":[0.00004972634,0.00001407615,0.0001793481,5.857057e-7,3.452379e-7,0.000004289424,0.00004631545,0.005189692,0.01393074,0.02492708,0.9556108,0.0000470268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001910211,0.000005487947,0.6754822,0.001164651,0.00003572967,0.00004494519,4.719353e-8,0.000193198,0.3228827],"genre_scores_gemma":[0.7050444,0.00002968268,0.2809263,0.009250785,0.00001207156,0.00002778282,0.000001415952,0.000003506507,0.004704112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9608766,"threshold_uncertainty_score":0.3467878,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2096441936","doi":"10.1109/tsa.2005.851925","title":"A frequency domain method for blind source separation of convolutive audio mixtures","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Blind signal separation; Algorithm; Initialization; Frequency domain; Computer science; Mixing (physics); Joint (building); Bin; Speech recognition; Permutation (music); Spectral density; Mathematics; Acoustics","retraction":null,"screen_n_in":null,"score":{"opus":0.01936232405151699,"gpt":0.3218353127592473,"spread":0.3024729887077303,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005711102,0.0001879464,0.0002533442,0.0002571519,0.0003081117,0.0001520152,0.0002458417,0.0001371898,0.000007340957],"category_scores_gemma":[0.00001043056,0.0001791599,0.00009709202,0.0003839653,0.00007629388,0.0007974389,0.000002764399,0.0002035166,0.000003071596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004925719,"about_ca_system_score_gemma":0.0001512594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001684694,"about_ca_topic_score_gemma":0.00004353561,"domain_scores_codex":[0.9986584,0.0001006001,0.0003630924,0.0004106046,0.0002369522,0.000230329],"domain_scores_gemma":[0.9990964,0.000153355,0.0002215156,0.0002306604,0.0002170922,0.00008099982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001045189,0.0002665482,0.000008043401,0.0001190299,0.00005443492,0.000001196422,0.007389618,0.002340697,0.1243554,0.002313754,0.0002574665,0.8627893],"study_design_scores_gemma":[0.00107204,0.0002582259,0.00001823148,0.0001033319,0.00003337944,0.00003973997,0.0001448738,0.04428999,0.9402277,0.01163945,0.001882826,0.0002902207],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01048324,0.0002310525,0.9865996,0.001404985,0.0000585444,0.0004392623,0.00001121923,0.0002306026,0.0005414636],"genre_scores_gemma":[0.4218474,0.00001803404,0.577472,0.0003388879,0.00003649646,0.00005629658,0.000001226824,0.0000109218,0.0002186728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8624991,"threshold_uncertainty_score":0.7305929,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2019224463","doi":"10.1002/mrm.21187","title":"Correction for geometric distortion and N/2 ghosting in EPI by phase labeling for additional coordinate encoding (PLACE)","year":2007,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Ghosting; Encoding (memory); Distortion (music); Computer science; Phase (matter); Artificial intelligence; Computer vision; Physics; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01942087426703802,"gpt":0.3156871707508114,"spread":0.2962662964837734,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002262976,0.0001126769,0.0001974012,0.0005339954,0.00008320691,0.00002625241,0.0001572167,0.00007273356,0.00002783223],"category_scores_gemma":[0.001898789,0.0001118581,0.00001773475,0.0008955998,0.00006040099,0.0002201025,0.00003068331,0.0001440733,4.223018e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001198991,"about_ca_system_score_gemma":0.00002716062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000632001,"about_ca_topic_score_gemma":0.00009341852,"domain_scores_codex":[0.998703,0.00003782988,0.00044928,0.0003387654,0.0002037154,0.0002674318],"domain_scores_gemma":[0.997925,0.001669921,0.0001317362,0.0001254005,0.00009599254,0.00005197193],"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":[0.0000887184,0.00008419486,0.001179135,0.00003564159,8.241752e-7,0.000003908716,0.0006592261,0.00002526352,0.001519281,0.0007306493,0.03768543,0.9579877],"study_design_scores_gemma":[0.01293496,0.004067018,0.02962766,0.001614419,0.00001655446,0.00004872417,0.0004595538,0.5961186,0.008484266,0.006601938,0.3393442,0.0006820912],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03861661,0.004718839,0.9538229,0.001292977,0.0003689602,0.0007426576,0.00002772885,0.00008159375,0.000327706],"genre_scores_gemma":[0.9175697,0.0003224871,0.0794352,0.0007546124,0.000213991,0.0004190171,0.0001789487,0.00002097147,0.001085088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9573056,"threshold_uncertainty_score":0.4561441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995703890","doi":"10.1190/geo2013-0465.1","title":"Sparse multichannel blind deconvolution","year":2014,"lang":"en","type":"article","venue":"Geophysics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":127,"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":"Academy of Finland","keywords":"Deconvolution; Wavelet; Blind deconvolution; Impulse response; Mathematics; Algorithm; Wiener deconvolution; Noise (video); Impulse (physics); A priori and a posteriori; Computer science; Mathematical analysis; Artificial intelligence; Image (mathematics); Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01625247292943819,"gpt":0.2435172620019322,"spread":0.227264789072494,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002394192,0.00008923898,0.00009336527,0.00005197958,0.0000756697,0.00009509622,0.0004044074,0.00005034728,0.000002817042],"category_scores_gemma":[0.00003229049,0.00009034809,0.00004517475,0.0002115638,0.00002442039,0.0004295337,0.0001203306,0.00009721079,0.0003637731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001621787,"about_ca_system_score_gemma":0.00002586457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002247637,"about_ca_topic_score_gemma":0.000005856493,"domain_scores_codex":[0.9992585,0.00006660083,0.0001240298,0.0002260011,0.0001563853,0.0001684463],"domain_scores_gemma":[0.9992796,0.00005367661,0.00006661662,0.0004653784,0.00007985447,0.00005485708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005504298,0.0001149329,0.0001049946,0.000007956337,0.000009391206,9.976218e-7,0.001353534,0.0003920132,0.002250059,0.917306,0.002061205,0.0763934],"study_design_scores_gemma":[0.0008698482,0.0001831499,0.005516942,0.000018043,0.000008432463,0.000005263493,0.00002024747,0.6523345,0.02338388,0.2895478,0.02765459,0.0004573443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06527093,0.000008125304,0.9300205,0.0004479439,0.0001768861,0.00009784578,5.383448e-7,0.0004061274,0.003571135],"genre_scores_gemma":[0.9336513,0.00000319232,0.06501029,0.0009034618,0.0001199702,0.00001396899,0.000003723533,0.000007186956,0.0002868721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8683804,"threshold_uncertainty_score":0.4675689,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164273299","doi":"","title":"Topmoumoute Online Natural Gradient Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Stochastic gradient descent; Frequentist inference; Generalization; Gradient descent; Computer science; Convergence (economics); Algorithm; Computation; Mathematical optimization; Bayesian probability; Mathematics; Artificial intelligence; Bayesian inference; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01200021456643386,"gpt":0.280624120790552,"spread":0.2686239062241181,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004043284,0.00008321652,0.00007750122,0.0001098994,0.00004881036,0.00007728566,0.0004845632,0.00003888063,0.00001314515],"category_scores_gemma":[0.00001249711,0.0000682012,0.00004472535,0.0002734732,0.00001916381,0.0003195937,0.0001406028,0.0001356936,0.00003512714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000324859,"about_ca_system_score_gemma":0.0000214825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003160186,"about_ca_topic_score_gemma":0.00006920412,"domain_scores_codex":[0.9991802,0.00001909428,0.0001739097,0.0002129218,0.0002042698,0.0002096351],"domain_scores_gemma":[0.9994512,0.00004340358,0.0000385153,0.0003256925,0.00006959266,0.00007164397],"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":[0.000001951085,0.0002006235,0.0000855657,0.000001804414,0.000009336542,0.00002920659,0.0008809987,0.00000424782,0.0006087885,0.3204598,0.004854402,0.6728632],"study_design_scores_gemma":[0.001194054,0.0006362566,0.0324643,0.0000299157,0.00001014339,0.0002559348,0.0003201505,0.4073227,0.180054,0.04404767,0.3322457,0.001419182],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00515666,0.00005148041,0.9846741,0.001383868,0.0002816809,0.00008801138,7.475912e-7,0.0007791116,0.007584347],"genre_scores_gemma":[0.2391272,0.000004287928,0.7547224,0.002923653,0.00006302707,0.000001549101,0.000005286854,0.000004352964,0.003148208],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6714441,"threshold_uncertainty_score":0.2781164,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2142615865","doi":"10.1162/089976606775093936","title":"Topographic Product Models Applied to Natural Scene Statistics","year":2005,"lang":"en","type":"article","venue":"Neural Computation","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"Canada Research Chairs; Gatsby Charitable Foundation; Wellcome Trust","keywords":"Computer science; Artificial intelligence; Statistical model; Relation (database); Gaussian; Mixture model; Pattern recognition (psychology); Machine learning; Contrast (vision); Scale (ratio); Product (mathematics); Mathematics; Data mining; Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.02100555942873878,"gpt":0.2770095159409491,"spread":0.2560039565122103,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001836813,0.0001344077,0.0001231103,0.000211242,0.0001040093,0.0002081709,0.0003680774,0.0000301028,0.000002001693],"category_scores_gemma":[0.00001543254,0.0001336027,0.00002893274,0.0005462414,0.00001874707,0.0005921019,0.0001099243,0.0001604398,0.00003747355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003778945,"about_ca_system_score_gemma":0.0000304801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006794193,"about_ca_topic_score_gemma":0.00001511734,"domain_scores_codex":[0.9987951,0.00006116415,0.0002380358,0.0003829067,0.0003228714,0.0001999554],"domain_scores_gemma":[0.999373,0.00005100641,0.00007922863,0.0002559112,0.0001615509,0.00007935862],"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":[0.00001417096,0.00006095399,0.00001665555,0.000009323709,0.000006711079,0.000001930535,0.00143454,0.2383085,0.002458203,0.2052174,0.005615016,0.5468566],"study_design_scores_gemma":[0.0001500083,0.0000582771,0.0009074119,0.000003901097,0.000002614292,0.000006864119,0.000004971259,0.9687371,0.004763966,0.02450719,0.0006793513,0.0001782944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03857432,0.00003290212,0.956526,0.003077572,0.0001681471,0.0003936154,0.000003178819,0.0006145972,0.0006096231],"genre_scores_gemma":[0.611592,0.000001340038,0.3865764,0.001691606,0.00006397301,0.00001637235,0.00001623158,0.000007007128,0.00003504051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7304287,"threshold_uncertainty_score":0.5448158,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137996830","doi":"10.1109/tsa.2005.851941","title":"A blind channel identification-based two-stage approach to separation and dereverberation of speech signals in a reverberant environment","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec","funders":"","keywords":"Reverberation; Blind signal separation; MIMO; Robustness (evolution); Computer science; Speech recognition; Source separation; Interference (communication); Channel (broadcasting); Signal processing; Acoustics; Algorithm; Telecommunications; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02512904293596091,"gpt":0.2882898073889731,"spread":0.2631607644530122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006145007,0.0001712396,0.0002059579,0.0004026566,0.0001663795,0.0002113772,0.0001564331,0.00008037072,0.000003539718],"category_scores_gemma":[0.000005378446,0.0001769327,0.00003450446,0.0004241631,0.00004510915,0.0007705496,0.000003684506,0.0001654591,0.000008398028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007300876,"about_ca_system_score_gemma":0.00008390567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003596737,"about_ca_topic_score_gemma":0.0000561991,"domain_scores_codex":[0.9985083,0.00009188668,0.0004478434,0.0004747871,0.0002995619,0.0001775746],"domain_scores_gemma":[0.9993989,0.00004681026,0.0001644547,0.0002381,0.00006390897,0.00008785279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001351287,0.0006977023,0.00002930424,0.0001969462,0.00001912435,0.000003052318,0.005741504,0.2892268,0.1502789,0.0001892473,0.00003706545,0.5534452],"study_design_scores_gemma":[0.000768218,0.00008370834,0.0001580366,0.00008474734,0.00001018864,0.00001054338,0.00006542321,0.3341084,0.6641907,0.0002081228,0.0001145023,0.0001973414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08473204,0.0000797886,0.9134051,0.0009650953,0.00001774718,0.000533304,0.00000696039,0.00007016841,0.0001897925],"genre_scores_gemma":[0.8266534,0.00003129181,0.1726519,0.0003417451,0.00001830867,0.00008449358,0.000003395658,0.00001019824,0.0002053197],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7419213,"threshold_uncertainty_score":0.7215106,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055700480","doi":"10.1016/s0933-3657(03)00037-x","title":"Extracting multisource brain activity from a single electromagnetic channel","year":2003,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Montreal Neurological Institute and Hospital; Aston University; Wellcome Trust","keywords":"Computer science; Electroencephalography; Embedding; Independent component analysis; Channel (broadcasting); Pattern recognition (psychology); SIGNAL (programming language); Artificial intelligence; Basis (linear algebra); Matrix (chemical analysis); Brain activity and meditation; Magnetoencephalography; Neurophysiology; Speech recognition; Neuroscience; Mathematics; Psychology; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.07269309130752616,"gpt":0.3342955862209487,"spread":0.2616024949134226,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001332243,0.0002017415,0.0002880823,0.0002711533,0.00009933147,0.00008349705,0.0005630219,0.0001186588,0.0001210553],"category_scores_gemma":[0.002011684,0.0001928443,0.00004233042,0.0009117003,0.000133594,0.0004658446,0.00006192258,0.0004352872,0.00005712749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008860117,"about_ca_system_score_gemma":0.00005838577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009308271,"about_ca_topic_score_gemma":0.0006991589,"domain_scores_codex":[0.9977478,0.0004171182,0.000495812,0.0005539698,0.0003719813,0.0004133689],"domain_scores_gemma":[0.9980484,0.001070802,0.0001731146,0.000517493,0.00007924852,0.0001109829],"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":[0.00002871417,0.000521475,0.0001099975,0.000007688693,0.00001106012,0.00006014742,0.01543123,0.0004073442,0.5221275,0.08294705,0.0003617549,0.377986],"study_design_scores_gemma":[0.0001048923,0.000717514,0.0003874767,0.0001361821,0.000006513718,0.00002153638,0.001172405,0.1472041,0.6455847,0.2027596,0.001507986,0.0003971294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1874027,0.0001300763,0.8027521,0.006697266,0.0002252642,0.0002325151,4.345534e-7,0.0002298957,0.002329664],"genre_scores_gemma":[0.966764,0.00001540379,0.03173943,0.001255579,0.0001005952,0.00002864209,0.000001831073,0.0000147732,0.00007974296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7793612,"threshold_uncertainty_score":0.7863961,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149807144","doi":"10.1109/tnn.2006.885436","title":"Face Recognition Using an Enhanced Independent Component Analysis Approach","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Independent component analysis; Linear discriminant analysis; Pattern recognition (psychology); Eigenface; Facial recognition system; Computer science; Artificial intelligence; Subspace topology; Principal component analysis; Support vector machine; Face (sociological concept); Component analysis; Dimension (graph theory); Unsupervised learning; Speech recognition; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04720915868967798,"gpt":0.2904797751869109,"spread":0.2432706164972329,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005930112,0.0002085558,0.0002355386,0.0004900159,0.0002746045,0.000184751,0.0004407359,0.0001733126,0.00001537635],"category_scores_gemma":[0.000001065944,0.0002131643,0.0001978144,0.001504666,0.00003866231,0.0007439469,0.000003806211,0.0005069802,0.000004696538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009537963,"about_ca_system_score_gemma":0.00001549649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006157256,"about_ca_topic_score_gemma":0.000110298,"domain_scores_codex":[0.9981619,0.0002020654,0.0003789257,0.0005362416,0.0003673429,0.0003535394],"domain_scores_gemma":[0.9989732,0.00008733868,0.0001390714,0.0005357178,0.00009759155,0.0001670997],"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":[0.00002628078,0.0002864974,0.00000351177,0.000001748556,0.00007221226,0.000003015841,0.0003829013,0.9310618,0.001394653,0.00003069669,0.000003312173,0.06673338],"study_design_scores_gemma":[0.0001928397,0.0001125223,0.0002248941,0.000004955491,0.00008804906,0.00001222553,0.0000591987,0.9706004,0.02839626,0.00006664543,0.000006033451,0.0002359217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.125627,0.00001020764,0.873094,0.00004231486,0.0002759183,0.0002656751,0.00000307142,0.0004820823,0.0001997261],"genre_scores_gemma":[0.9430202,0.00001011747,0.05641383,0.0004380115,0.00004929004,0.00001729168,0.00001472097,0.00001510614,0.00002143856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8173932,"threshold_uncertainty_score":0.869259,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137008936","doi":"10.1109/icdsp.1997.627988","title":"The multimodulus blind equalization algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Blind equalization; Quadrature amplitude modulation; QAM; Algorithm; Constellation; Equalization (audio); Computer science; Transmission (telecommunications); Modulation (music); Constellation diagram; Telecommunications; Bit error rate; Acoustics; Decoding methods","retraction":null,"screen_n_in":null,"score":{"opus":0.03386249653146305,"gpt":0.2697406389671704,"spread":0.2358781424357074,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002462686,0.00005787519,0.00004267428,0.00003886441,0.0001879733,0.0002828966,0.0004968776,0.00003558412,0.0000500505],"category_scores_gemma":[0.00003280018,0.00003768348,0.00002537783,0.0002619798,0.00002421572,0.0003483715,0.00009241524,0.00005941578,0.0001873758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001279121,"about_ca_system_score_gemma":0.000006029665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001055823,"about_ca_topic_score_gemma":0.000006757343,"domain_scores_codex":[0.9993373,0.00007124633,0.0001324833,0.0001508032,0.0001842573,0.0001239041],"domain_scores_gemma":[0.9994664,0.00008461877,0.00004202772,0.0003049814,0.00006882955,0.00003310545],"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":[3.654919e-7,0.00002992246,0.000004447442,4.2857e-7,0.000003532142,7.834907e-7,0.0008623407,0.00003707443,0.00009379751,0.3139454,0.01209709,0.6729248],"study_design_scores_gemma":[0.0001363604,0.00002214987,0.00004004134,0.00000110648,5.886326e-7,0.000002907331,0.00001581343,0.9365191,0.003773586,0.004912311,0.05450767,0.0000683129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007123411,0.00009151855,0.972676,0.003060782,0.00008290633,0.0001133951,1.877333e-7,0.0004900057,0.02341393],"genre_scores_gemma":[0.2233505,0.0001567917,0.7565044,0.002837681,0.00008272554,0.00004346877,0.000001557293,0.00001212014,0.01701076],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9364821,"threshold_uncertainty_score":0.2727978,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2140440049","doi":"","title":"Adaptive overrelaxed bound optimization methods","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":84,"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":"","keywords":"Speedup; Computer science; Maximization; Convergence (economics); Simple (philosophy); Mathematical optimization; Algorithm; Upper and lower bounds; Scheme (mathematics); Scaling; Factorization; Mathematics; Parallel computing","retraction":null,"screen_n_in":null,"score":{"opus":0.03391867002147864,"gpt":0.3370380539959439,"spread":0.3031193839744653,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006786789,0.00007466999,0.00007892606,0.00007518955,0.00006817797,0.0001402364,0.000245117,0.0000544917,0.0001087022],"category_scores_gemma":[0.00009017024,0.00006756948,0.00003271027,0.0003274716,0.00001926068,0.0005524809,0.00004543383,0.00007444641,0.00002191789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002828679,"about_ca_system_score_gemma":0.00006160333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008381277,"about_ca_topic_score_gemma":0.00000177008,"domain_scores_codex":[0.9990837,0.0003381153,0.0001307929,0.0002098934,0.0001212384,0.0001162619],"domain_scores_gemma":[0.9994209,0.00009427869,0.00005024708,0.0003154986,0.00007456561,0.00004453786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.197556e-7,0.00001938971,0.00000751378,4.033869e-7,0.000004467344,4.752689e-7,0.0002498641,0.006690817,0.0001155212,0.9869852,0.0009719312,0.004953691],"study_design_scores_gemma":[0.0002113402,0.0001048339,0.0000577095,0.000003949956,0.00000335376,0.00001214686,0.00005344781,0.8673234,0.05041347,0.06262023,0.01896608,0.0002300171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002878364,0.00002888302,0.8561285,0.000201076,0.00008690306,0.00009737629,9.620263e-8,0.0004200308,0.1430084],"genre_scores_gemma":[0.02868447,0.000006843934,0.9688666,0.0009008336,0.000005657121,0.00001235436,5.824407e-7,0.000005308481,0.001517338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.924365,"threshold_uncertainty_score":0.2755404,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147914906","doi":"10.1109/tbme.2004.827265","title":"Fetal MEG Redistribution by Projection Operators","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Welichem Biotech (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke","keywords":"Redistribution (election); Projection (relational algebra); Orthographic projection; Physics; Dipole; SIGNAL (programming language); Interference (communication); Optics; Computer science; Algorithm; Artificial intelligence; Telecommunications; Quantum mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.006631202851678807,"gpt":0.2254631692561853,"spread":0.2188319664045065,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019388,0.0001420996,0.0001113565,0.0002087878,0.0001092545,0.0000855052,0.0002555627,0.0001440616,0.000006612154],"category_scores_gemma":[0.000008995143,0.0001394589,0.00006709091,0.0006880852,0.00003551689,0.0004178133,0.000002176896,0.0003158271,0.00002523298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002015323,"about_ca_system_score_gemma":0.00007402935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002602106,"about_ca_topic_score_gemma":0.000001358063,"domain_scores_codex":[0.9989305,0.0000191682,0.0002145681,0.0002809553,0.0003386441,0.0002161408],"domain_scores_gemma":[0.9995439,0.00002287159,0.0000271123,0.0002241416,0.00003707639,0.0001449199],"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":[0.00004380663,0.002235648,0.000001534329,0.00009233627,0.0002018409,0.00004821509,0.002051868,0.1332088,0.6073973,0.06385674,0.003780844,0.1870811],"study_design_scores_gemma":[0.000899039,0.000453563,0.00001435139,0.00007599404,0.00001148218,0.00006564405,0.00001987488,0.2291663,0.7578943,0.0002977464,0.01067923,0.0004224576],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003954526,0.0000139091,0.9926686,0.001400448,0.0006158076,0.0001607515,0.00001656903,0.001103121,0.00006622292],"genre_scores_gemma":[0.9747893,0.00001476221,0.0249071,0.0001441442,0.00003759833,0.00006149212,0.000009650688,0.0000129316,0.00002300732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9708348,"threshold_uncertainty_score":0.5686969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2546159266","doi":"10.1109/iembs.2004.1403176","title":"Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA","year":2005,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Independent component analysis; Pattern recognition (psychology); Electroencephalography; Higher-order statistics; Wavelet transform; Wavelet; Artificial intelligence; Computer science; Signal processing; Speech recognition; Discrete wavelet transform; SIGNAL (programming language); Digital signal processing","retraction":null,"screen_n_in":null,"score":{"opus":0.01218881064785528,"gpt":0.2401428114492757,"spread":0.2279540008014204,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001581962,0.00007494981,0.0001065283,0.00006716385,0.0000299654,0.00005255176,0.0001348304,0.00005031944,0.00003600084],"category_scores_gemma":[0.000009944407,0.00006197228,0.00001932106,0.00007983529,0.00003520741,0.0002315016,0.00002315418,0.00006408689,0.000005025571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008043787,"about_ca_system_score_gemma":0.00002362712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004312526,"about_ca_topic_score_gemma":0.0000529582,"domain_scores_codex":[0.9993909,0.00002730013,0.0001449887,0.0001976653,0.0001470548,0.00009207935],"domain_scores_gemma":[0.9995884,0.00008474381,0.00003254296,0.0002120683,0.00002343671,0.0000588416],"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":[0.00003279603,0.0001301991,0.0001175726,0.00001003564,0.000009882816,0.00001080027,0.00182811,0.00004831245,0.007349361,0.04446336,0.001045316,0.9449543],"study_design_scores_gemma":[0.0006186487,0.0002543867,0.005048029,0.00002564914,0.000004816704,0.00001347209,0.00002094438,0.7106383,0.2592004,0.006714403,0.01726906,0.0001919221],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1663253,0.00002748359,0.8064498,0.006752966,0.00001327101,0.0001324372,0.000003369398,0.0001691519,0.02012617],"genre_scores_gemma":[0.7181241,0.000008908874,0.2803729,0.001346809,0.000007321544,0.00000173485,0.000001353486,0.000002892688,0.0001339302],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9447623,"threshold_uncertainty_score":0.2527156,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2035349853","doi":"10.3390/s141018370","title":"A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Electroencephalography; Artifact (error); Computer science; Channel (broadcasting); Hilbert–Huang transform; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Psychology; Computer vision; Neuroscience; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.02171559706886661,"gpt":0.2531203450767719,"spread":0.2314047480079053,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004014033,0.0000816055,0.0001364052,0.0001731318,0.00002485335,0.00002484174,0.0002186774,0.00004461748,0.000003098071],"category_scores_gemma":[0.00005799029,0.00008095473,0.00002831933,0.0003010383,0.00001599849,0.0001376039,0.00006634923,0.00008094747,0.000009252612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002764003,"about_ca_system_score_gemma":0.00001430865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001872686,"about_ca_topic_score_gemma":0.0001356324,"domain_scores_codex":[0.9989889,0.0002228607,0.0002357215,0.0002274258,0.0002045747,0.0001205693],"domain_scores_gemma":[0.9993568,0.00006126387,0.0001048071,0.0003935974,0.00005430643,0.00002919239],"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":[0.0002826077,0.00836015,0.03476434,0.0002036474,0.0001202624,0.00008028547,0.3075562,0.4303096,0.03638345,0.02245111,0.001181939,0.1583064],"study_design_scores_gemma":[0.001297552,0.003218291,0.1196791,0.00008099493,0.0000121046,0.000007685931,0.001184069,0.8337328,0.03456933,0.00481417,0.0009577813,0.0004461137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9601267,0.000009208033,0.03779123,0.0001667002,0.00005471513,0.0002798281,1.821981e-7,0.0001174003,0.001454025],"genre_scores_gemma":[0.9965746,9.151186e-7,0.003257836,0.00005546648,0.00001081275,0.000009293274,6.6677e-7,0.00000657322,0.00008379102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4034232,"threshold_uncertainty_score":0.3301238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2166317606","doi":"10.1109/tsp.2008.917354","title":"A Semiblind Channel Estimation Approach for MIMO–OFDM Systems","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Orthogonal frequency-division multiplexing; Weighting; MIMO; MIMO-OFDM; Constraint (computer-aided design); Channel (broadcasting); Computer science; Algorithm; Mean squared error; Closed-form expression; Control theory (sociology); Mathematical optimization; Mathematics; Statistics; Telecommunications; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.05139883940876321,"gpt":0.2812029615370891,"spread":0.2298041221283259,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003745646,0.0001892382,0.000200665,0.0003560797,0.0006520721,0.0002575792,0.0003759899,0.000133177,0.000002076221],"category_scores_gemma":[0.000003610537,0.0001856103,0.00009646179,0.0006333624,0.00005528512,0.001111154,0.000001733632,0.0002247867,0.000008294372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000647937,"about_ca_system_score_gemma":0.0001760095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009535441,"about_ca_topic_score_gemma":6.030035e-7,"domain_scores_codex":[0.9985679,0.00006815042,0.0003282949,0.0004341514,0.000344401,0.0002570913],"domain_scores_gemma":[0.9992145,0.00009156905,0.0001482184,0.0002439182,0.0002131269,0.00008868697],"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":[0.00006422347,0.0005468111,0.00000128209,0.0002881648,0.00003340003,0.000004275859,0.005815576,0.8993767,0.002675282,0.0008672065,0.0003926685,0.0899344],"study_design_scores_gemma":[0.0003390065,0.0001319546,0.00000217759,0.00006185495,0.0000106867,0.00008140878,0.00008058721,0.965382,0.03326436,0.0003083421,0.0001278688,0.0002097576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008206149,0.0001017377,0.9968488,0.0001230339,0.0001104536,0.0006110905,0.000006378722,0.000692977,0.0006849183],"genre_scores_gemma":[0.8151236,0.000006701283,0.1840044,0.0001730139,0.00004126573,0.0003253227,0.00000415625,0.00001990977,0.0003017246],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8143029,"threshold_uncertainty_score":0.756897,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2168380040","doi":"10.1109/tbme.2005.869791","title":"Bayesian Spatio-Temporal Approach for EEG Source Reconstruction: Conciliating ECD and Distributed Models","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"University of Pennsylvania","keywords":"Magnetoencephalography; Computer science; Electroencephalography; Inverse problem; Bayesian probability; Hyperparameter; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01151406951490694,"gpt":0.2134243795968216,"spread":0.2019103100819147,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002606759,0.0001712021,0.0001820734,0.0001996319,0.0001583335,0.0001109758,0.000194608,0.0001513384,0.000003747419],"category_scores_gemma":[0.000007078406,0.000176259,0.00007492836,0.0003656352,0.0000653797,0.0003898649,0.000003213214,0.0002239689,7.006445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006711101,"about_ca_system_score_gemma":0.00004369189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004639993,"about_ca_topic_score_gemma":0.000004416904,"domain_scores_codex":[0.9988225,0.00002450826,0.0003223438,0.0003532492,0.0002332756,0.0002441531],"domain_scores_gemma":[0.9994327,0.0001077385,0.0000608387,0.0002164119,0.00005495487,0.0001273499],"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":[0.00001656768,0.0001755957,0.00001366164,0.00008640178,0.00003601952,0.000001904497,0.0003158382,0.9324158,0.001414958,0.006425387,0.0002274063,0.05887041],"study_design_scores_gemma":[0.0003831255,0.00007033251,0.00001294751,0.00002353819,0.000007880111,0.0000374626,0.00001826394,0.9943302,0.003184655,0.0005418254,0.001200515,0.0001892953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002138543,0.00002054136,0.9962413,0.0002713756,0.0001958005,0.0002787224,0.00003968441,0.0007502367,0.00006373882],"genre_scores_gemma":[0.6778471,0.000003479659,0.3219112,0.00004023907,0.00004150001,0.00008325385,0.00002859408,0.00001297542,0.00003153974],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6757086,"threshold_uncertainty_score":0.7187634,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103677028","doi":"10.1109/tbme.2006.873700","title":"The Adaptive Chirplet Transform and Visual Evoked Potentials","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":73,"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":"","keywords":"Spectrogram; Chirp; Time–frequency analysis; Pattern recognition (psychology); Computer science; Artificial intelligence; Visualization; Speech recognition; Gaussian; Instantaneous phase; Matching pursuit; Transient (computer programming); Computer vision; Algorithm; Optics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.005648010522442367,"gpt":0.2201134803541058,"spread":0.2144654698316634,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002672045,0.0001308072,0.0001118203,0.0001453932,0.0002049815,0.0001215141,0.0002265867,0.00008991148,0.000004563217],"category_scores_gemma":[0.000003322563,0.00009857545,0.0000636759,0.0003253477,0.00008056347,0.0002110407,0.000001990667,0.0002366875,0.000006687816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003049105,"about_ca_system_score_gemma":0.000028164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003679581,"about_ca_topic_score_gemma":0.00001079383,"domain_scores_codex":[0.9990252,0.00002578351,0.0002188491,0.0002117223,0.0002935094,0.0002248978],"domain_scores_gemma":[0.9995631,0.0001478476,0.00002475736,0.0001470581,0.00002438045,0.00009288162],"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":[0.00008012002,0.0007421462,0.000001123066,0.00005255927,0.0001939868,0.00003756127,0.001047665,0.02224673,0.0618622,0.06140256,0.001197088,0.8511363],"study_design_scores_gemma":[0.0004751853,0.0002945648,0.0001466207,0.00003545836,0.00001328281,0.00003599939,0.00002005868,0.9248867,0.0606217,0.001281878,0.01191685,0.000271697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001915765,0.00005665275,0.9950214,0.00194124,0.0002719361,0.000156694,0.000004626131,0.000456918,0.0001747634],"genre_scores_gemma":[0.9865652,0.00006198233,0.01305309,0.00008535126,0.00004732221,0.00004447683,0.000001081139,0.00001179601,0.0001296646],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9846495,"threshold_uncertainty_score":0.401979,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2947659587","doi":"10.1109/jbhi.2019.2920356","title":"Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Non-negative matrix factorization; Pattern recognition (psychology); Computer science; Artificial intelligence; Blind signal separation; Matrix decomposition; Electrocardiography; SIGNAL (programming language); Fetus; Independent component analysis; Channel (broadcasting); Medicine; Cardiology; Pregnancy","retraction":null,"screen_n_in":null,"score":{"opus":0.04086625933964426,"gpt":0.3411678111525153,"spread":0.300301551812871,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009891267,0.0001213068,0.000364644,0.0003395187,0.0000819092,0.00008169593,0.0002243009,0.0001377857,0.000006914651],"category_scores_gemma":[0.0000538122,0.00009780774,0.00005870995,0.0003449558,0.00006384517,0.001526967,0.00004331976,0.0002406554,0.000003371123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297807,"about_ca_system_score_gemma":0.000378271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000430158,"about_ca_topic_score_gemma":0.000001576775,"domain_scores_codex":[0.997694,0.00007379871,0.001375922,0.00007725178,0.0006149188,0.0001641528],"domain_scores_gemma":[0.9972446,0.0001505193,0.001958422,0.000135439,0.0003261631,0.0001848608],"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":[0.001026268,0.002298674,0.006028133,0.003628799,0.0005754639,0.000009806021,0.1694618,0.009042974,0.1592502,0.002659409,0.005483917,0.6405346],"study_design_scores_gemma":[0.001047055,0.0009979935,0.001059031,0.0004145785,0.00001020105,0.00002724023,0.0005046357,0.9703012,0.0221358,0.003189411,0.0001884289,0.0001244825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2596318,0.0000152367,0.7390916,0.0007397773,0.0003019461,0.0001724179,0.000006005039,0.0000144208,0.00002687116],"genre_scores_gemma":[0.7288629,0.00004661179,0.2706094,0.0003672724,0.00009365269,6.62195e-7,0.000007560131,0.00000447408,0.000007403469],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9612582,"threshold_uncertainty_score":0.3988484,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055768354","doi":"10.1016/j.ymssp.2013.06.031","title":"Decentralized modal identification of structures using parallel factor decomposition and sparse blind source separation","year":2013,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Underdetermined system; Blind signal separation; Algorithm; Wavelet packet decomposition; Computer science; Wavelet; Matrix decomposition; Computational complexity theory; Wavelet transform; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03256631955637352,"gpt":0.3092822614856136,"spread":0.2767159419292401,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155503,0.0001374772,0.0002280648,0.00009418363,0.0001781713,0.0006490293,0.0001552507,0.0001229932,0.000005363428],"category_scores_gemma":[0.00001334075,0.0001190241,0.00002867724,0.000153363,0.00003531387,0.001001387,0.00007002078,0.00009883652,9.957487e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002455594,"about_ca_system_score_gemma":0.000037481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000137884,"about_ca_topic_score_gemma":0.000002241537,"domain_scores_codex":[0.9985879,0.0001580119,0.0005084608,0.0003197571,0.0002642355,0.0001616458],"domain_scores_gemma":[0.9992042,0.00004425104,0.0003506838,0.0001324035,0.000173089,0.0000953864],"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":[0.00004603722,0.00007202205,0.0002570448,0.0002920644,0.00002737144,9.775955e-7,0.001833375,0.00498226,0.9026422,0.03651757,0.00001888909,0.0533102],"study_design_scores_gemma":[0.0003602412,0.00005253762,0.0006428689,0.00007148042,0.00001015575,0.00002740765,0.0000772169,0.9625969,0.027525,0.00846669,0.00002328303,0.0001462191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3972742,0.0002796698,0.602039,0.00004919988,0.00002429562,0.0002703406,9.853164e-7,0.00005725795,0.000005036766],"genre_scores_gemma":[0.974178,0.00001296752,0.02570277,0.00003519125,0.00002687234,0.00001982176,0.000003820828,0.000008820207,0.00001174183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9576147,"threshold_uncertainty_score":0.6258605,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100887198","doi":"10.1016/s1053-8119(03)00333-1","title":"Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies","year":2003,"lang":"en","type":"article","venue":"NeuroImage","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; Israel National Road Safety Authority; National Institute of Mental Health; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; National Institutes of Health; Centre d'Imagerie BioMédicale","keywords":"Univariate; Multivariate statistics; Principal component analysis; Independent component analysis; Computer science; Covariance; Partial least squares regression; Pattern recognition (psychology); Noise (video); Artificial intelligence; Statistical hypothesis testing; Mathematics; Machine learning; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1182311547882355,"gpt":0.420952441785027,"spread":0.3027212869967915,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002960824,0.00009606432,0.0001979119,0.0002903379,0.00009626392,0.00009309425,0.0002417767,0.00002488222,0.000012437],"category_scores_gemma":[0.0005900596,0.0000725955,0.00007410325,0.001118511,0.00008076546,0.0002847389,0.00007889596,0.000219474,0.000001109494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008514705,"about_ca_system_score_gemma":0.00002282542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002299206,"about_ca_topic_score_gemma":0.00007599839,"domain_scores_codex":[0.9984474,0.0007054492,0.0002752868,0.0002605777,0.0001870592,0.000124242],"domain_scores_gemma":[0.9984991,0.0009400825,0.00009596798,0.0003660169,0.00007307468,0.00002578128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007433869,0.001643797,0.1552693,0.00003674834,0.0009023672,0.0000686224,0.07630615,0.1105743,0.01866348,0.6295657,0.001759562,0.005135718],"study_design_scores_gemma":[0.0004890149,0.0001175605,0.3945662,0.00001512451,0.0002095725,0.00000974242,0.0003243426,0.5864119,0.002607868,0.01045289,0.004548701,0.0002470499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6837405,0.00004795307,0.3134678,0.002261107,0.00007416688,0.0001443056,0.000007060542,0.00006062338,0.0001964894],"genre_scores_gemma":[0.9803154,0.0000133544,0.0184716,0.001155455,0.00001014971,0.00001334571,0.000001890647,0.000004324901,0.00001450373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6191127,"threshold_uncertainty_score":0.2960359,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104969945","doi":"10.1109/icassp.2004.1326686","title":"Underdetermined blind separation for speech in real environments with sparseness and ICA","year":2004,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":61,"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":"","keywords":"Underdetermined system; Blind signal separation; Separation (statistics); Computer science; Independent component analysis; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Machine learning; Algorithm; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.02865931759704343,"gpt":0.3002514863095846,"spread":0.2715921687125412,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001406055,0.00008643938,0.00009363655,0.00009241279,0.0000399813,0.00008948799,0.0001495345,0.00005216334,0.000001694326],"category_scores_gemma":[0.00000321545,0.00007225169,0.00001140256,0.000125735,0.00002929854,0.0004107178,0.00004578451,0.00004348164,0.00000326974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005057786,"about_ca_system_score_gemma":0.00004240054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006620216,"about_ca_topic_score_gemma":0.0003314946,"domain_scores_codex":[0.9993384,0.00001950001,0.000134832,0.0002590941,0.0001235727,0.0001245549],"domain_scores_gemma":[0.9996716,0.0000300645,0.00004361252,0.0002066949,0.0000103662,0.00003759994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006382677,0.001151906,0.008682289,0.00008564159,0.0000597687,0.00007918609,0.008912065,0.007680592,0.04339489,0.8335073,0.0003038916,0.0955042],"study_design_scores_gemma":[0.02192226,0.003483984,0.08153654,0.000143612,0.00002819504,0.0001948847,0.0003182466,0.0869741,0.6105784,0.1820187,0.01097851,0.001822657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2956191,0.000003532141,0.7020246,0.0005656191,0.0000109033,0.0003313158,4.769909e-7,0.00007158225,0.001372845],"genre_scores_gemma":[0.7811151,0.00001694769,0.218317,0.0002152617,0.000008606058,0.00004150792,0.000003711916,0.000005413274,0.0002765466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6514887,"threshold_uncertainty_score":0.2946338,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2750348520","doi":"10.1103/physrevlett.120.180501","title":"Three-Dimensional Color Code Thresholds via Statistical-Mechanical Mapping","year":2018,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Institute for Quantum Information and Matter, California Institute of Technology; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Code (set theory); Computer science; Statistical physics; Physics; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.03288573477269288,"gpt":0.3179788787918186,"spread":0.2850931440191257,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004867336,0.0002030368,0.0003866111,0.00004518882,0.0001257395,0.00007897683,0.0007348702,0.00002074751,0.00005873482],"category_scores_gemma":[0.00009209778,0.0001696006,0.0001250041,0.0003874854,0.0001749191,0.0003056204,0.0003275162,0.0002502986,0.000641157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004746266,"about_ca_system_score_gemma":0.00004268903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001056541,"about_ca_topic_score_gemma":0.000008626813,"domain_scores_codex":[0.9981557,0.0001339439,0.0003334795,0.0005221604,0.0005145266,0.0003402484],"domain_scores_gemma":[0.9987,0.0002871944,0.0001227376,0.0006298225,0.0001070191,0.0001532262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001650632,0.0004791394,0.0000698748,0.000442532,0.00008505896,0.0000531132,0.0002694348,0.00002764715,0.08504108,0.6217057,0.2437595,0.04805047],"study_design_scores_gemma":[0.0006556396,0.000716439,0.001837302,0.00199797,0.00008365486,0.00007746644,0.000001671784,0.7460454,0.01023147,0.09315833,0.1437944,0.001400212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009463049,0.0002938102,0.9651788,0.02398072,0.0001237721,0.0004018013,0.000006306374,0.0003513702,0.0002003473],"genre_scores_gemma":[0.5365079,0.0001228178,0.2638192,0.1988335,0.0005114231,0.0001428946,0.00001789293,0.00003426347,0.00001004574],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7460178,"threshold_uncertainty_score":0.824099,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1984363480","doi":"10.1109/tcomm.2013.042313.120629","title":"Fourth-Order Statistics for Blind Classification of Spatial Multiplexing and Alamouti Space-Time Block Code Signals","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; Memorial University of Newfoundland","funders":"","keywords":"Space–time block code; Block code; Algorithm; Fading; Spatial multiplexing; Nakagami distribution; Multiplexing; Computer science; Orthogonal frequency-division multiplexing; Antenna (radio); Electronic engineering; Channel (broadcasting); Telecommunications; MIMO; Decoding methods; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05635321420031456,"gpt":0.3122429942863423,"spread":0.2558897800860278,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003310265,0.0001583975,0.0002070126,0.0002407322,0.0003946754,0.0001449867,0.0008555331,0.0001146306,0.00003218352],"category_scores_gemma":[0.00004198285,0.0001697237,0.00006270955,0.0003037378,0.0001894527,0.0004333397,0.00001581973,0.0002360921,0.00003173505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003771987,"about_ca_system_score_gemma":0.0001025712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002309794,"about_ca_topic_score_gemma":0.0002951061,"domain_scores_codex":[0.9986992,0.0002067071,0.0004532264,0.0002708672,0.0001924412,0.0001775905],"domain_scores_gemma":[0.9964488,0.001142044,0.0002422611,0.001490928,0.000588414,0.00008756147],"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":[0.0001851829,0.004807394,0.0001994876,0.0002288174,0.0006742062,6.908135e-7,0.01925273,0.06659792,0.4403114,0.1142401,0.008232567,0.3452695],"study_design_scores_gemma":[0.0006629036,0.0001619474,0.0005262442,0.00003284119,0.00003065504,0.000003726754,0.00007591177,0.9650154,0.02840279,0.00368518,0.001198172,0.0002042389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00251234,0.00003160107,0.9911293,0.00453244,0.00005439144,0.001044861,0.0002002665,0.0002069658,0.0002877942],"genre_scores_gemma":[0.5783812,0.00007511611,0.4209163,0.00007274154,0.000005983357,0.0003080789,0.00001783933,0.00001335159,0.0002093959],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8984175,"threshold_uncertainty_score":0.6921132,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025844008","doi":"10.1002/hbm.21010","title":"Independent component analysis (ICA) of generalized spike wave discharges in fMRI: Comparison with general linear model‐based EEG‐fMRI","year":2010,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":57,"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","funders":"Canadian Institutes of Health Research","keywords":"Independent component analysis; General linear model; Functional magnetic resonance imaging; Electroencephalography; EEG-fMRI; Linear model; Neuroscience; Pattern recognition (psychology); Psychology; Rolandic epilepsy; Artificial intelligence; Mathematics; Statistics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.04488527671287883,"gpt":0.3013307913985112,"spread":0.2564455146856323,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001085381,0.0002775809,0.0006331987,0.0009785375,0.0001730508,0.0001456426,0.0007950034,0.0001483496,0.00003805616],"category_scores_gemma":[0.00001816515,0.0002527933,0.0001830147,0.001024407,0.0001164507,0.0003076365,0.0002261779,0.0005414297,0.000002936926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005263999,"about_ca_system_score_gemma":0.00009095589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000234395,"about_ca_topic_score_gemma":0.001087942,"domain_scores_codex":[0.997408,0.0002489192,0.0007274373,0.0006173456,0.0006270803,0.0003711926],"domain_scores_gemma":[0.9983147,0.00008797496,0.000410075,0.0009268059,0.0001367396,0.000123718],"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":[0.0000349773,0.0006509892,0.04549731,0.0000629358,0.000256932,0.00002201163,0.005830085,0.2459401,0.5501376,0.1503203,0.0006346236,0.0006121154],"study_design_scores_gemma":[0.0008019106,0.00006334949,0.04860533,0.00002927967,0.00002761344,0.000001998478,0.00003934858,0.9410845,0.007360105,0.00141636,0.0002720673,0.0002981112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4847283,0.000009505664,0.5136744,0.0009442017,0.00002198484,0.0002047325,0.00000305537,0.0001286671,0.0002851368],"genre_scores_gemma":[0.7695344,9.021192e-7,0.2297609,0.0004736894,0.00003049882,0.00003821594,0.00004701156,0.00001626605,0.00009811613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6951444,"threshold_uncertainty_score":0.9999924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2142134169","doi":"10.1109/tsp.2002.1011206","title":"A fractionally spaced blind equalizer based on linear programming","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Blind equalization; Baud; Quadrature amplitude modulation; QAM; Linear programming; Intersymbol interference; Convex optimization; Computer science; Piecewise linear function; Equalization (audio); Equalizer; Quadratic programming; Mathematics; Algorithm; Control theory (sociology); Mathematical optimization; Regular polygon; Transmission (telecommunications); Telecommunications; Decoding methods; Bit error rate; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.05052993359516259,"gpt":0.3000487753965221,"spread":0.2495188418013595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003856635,0.0002471072,0.0001894358,0.0004445578,0.0004560841,0.0003586749,0.0004083333,0.0001525406,0.0002098619],"category_scores_gemma":[0.000007406165,0.0002406712,0.0001350723,0.0009466736,0.00005728577,0.0008671063,0.000001975218,0.0005638151,0.0001517256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008716695,"about_ca_system_score_gemma":0.0001050697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005396449,"about_ca_topic_score_gemma":0.000005583711,"domain_scores_codex":[0.9979836,0.0001338737,0.000335304,0.0005315606,0.0006871282,0.0003284932],"domain_scores_gemma":[0.9989816,0.0001919333,0.0001591852,0.0003408037,0.0001917183,0.0001347691],"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":[0.00009868932,0.001088366,0.000002695105,0.00004592727,0.0000218409,0.00001676684,0.001302861,0.1096905,0.002245869,0.000464497,0.0001796357,0.8848423],"study_design_scores_gemma":[0.0007206424,0.0003642754,0.000006510386,0.0001363758,0.00001343392,0.0000108285,0.00002943858,0.9490943,0.04510322,0.0002274837,0.003975576,0.0003179384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006341937,0.0000381094,0.9939263,0.002065247,0.00009995564,0.0003092068,0.000002479727,0.0009432993,0.001981247],"genre_scores_gemma":[0.8583605,0.000003954395,0.1389912,0.001930932,0.00005506668,0.00009806693,0.000001191561,0.00002810844,0.000530993],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8845244,"threshold_uncertainty_score":0.9814288,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170976240","doi":"10.1109/tmi.2004.837791","title":"An information-theoretic criterion for intrasubject alignment of FMRI time series: motion corrected independent component analysis","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia Hospital","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Independent component analysis; Artificial intelligence; Computer science; Entropy (arrow of time); Computer vision; Image registration; Preprocessor; Pattern recognition (psychology); Mathematics; Algorithm; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.005852320285617264,"gpt":0.2613787639813405,"spread":0.2555264436957232,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008521712,0.0001515939,0.000245121,0.0005837164,0.0001393202,0.0001246963,0.0004512977,0.00008345259,0.0002140953],"category_scores_gemma":[0.00002330269,0.0001475066,0.0001537784,0.0006155109,0.00009631425,0.001670119,0.000003923525,0.0002058364,0.00002009223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239135,"about_ca_system_score_gemma":0.00008268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000286005,"about_ca_topic_score_gemma":0.0000168506,"domain_scores_codex":[0.9980823,0.0001756393,0.0005655357,0.0002438972,0.0007280789,0.0002045756],"domain_scores_gemma":[0.9989251,0.0001338682,0.0001625138,0.0004026988,0.0001995653,0.0001761837],"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":[0.0001714559,0.001450555,0.00006978468,0.00008864956,0.0004147664,0.000003994524,0.01194807,0.06930587,0.01366945,0.005385196,0.0005397975,0.8969524],"study_design_scores_gemma":[0.0004410968,0.0001249499,0.000153189,0.00003356891,0.00009630228,0.00001417932,0.0001170268,0.8970049,0.1011291,0.0003069979,0.0004164906,0.0001621809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008870632,0.00000589451,0.9873823,0.002791811,0.0001697197,0.0003330003,0.00001719119,0.0003217084,0.000107767],"genre_scores_gemma":[0.9546592,0.00001835564,0.04422927,0.0009320289,0.00002503133,0.00007762207,0.00003366427,0.000008140234,0.00001672724],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9457885,"threshold_uncertainty_score":0.6015142,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149233205","doi":"10.1109/tvt.2009.2039008","title":"Subspace-Based Blind Channel Estimation for MIMO-OFDM Systems With Reduced Time Averaging","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Orthogonal frequency-division multiplexing; MIMO; Signal subspace; MIMO-OFDM; Subspace topology; Algorithm; Channel (broadcasting); Computer science; Wideband; Orthogonality; Linear subspace; Spatial correlation; Mathematics; Noise (video); Electronic engineering; Telecommunications; Engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01075827012736612,"gpt":0.2419738827755609,"spread":0.2312156126481948,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003824164,0.0002506723,0.0002714622,0.0009485509,0.000315503,0.0001568796,0.0006382109,0.0004458007,0.000006347877],"category_scores_gemma":[0.00001473768,0.0002341289,0.0000945911,0.0009065868,0.0001324717,0.000338217,0.000003113135,0.0006240008,0.00004700187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005906593,"about_ca_system_score_gemma":0.0001531484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001462941,"about_ca_topic_score_gemma":0.00002212579,"domain_scores_codex":[0.9984872,0.0000550281,0.0002833598,0.0005698708,0.0002581091,0.0003464847],"domain_scores_gemma":[0.9984692,0.0001049758,0.000160104,0.0009352556,0.0002551433,0.00007526475],"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":[0.0002718251,0.0009730869,0.000006569819,0.0001353122,0.0002192783,0.00003732639,0.0006724973,0.5472558,0.3834825,0.0297445,0.0004295465,0.03677177],"study_design_scores_gemma":[0.0007777525,0.0003016721,0.00000267016,0.000039161,0.00002065698,0.00005380468,0.00001416386,0.6459497,0.3513214,0.0007618995,0.0005414418,0.0002156906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06383027,0.00001327856,0.9281642,0.004539946,0.0003397031,0.001030019,0.000008627931,0.00201227,0.00006167104],"genre_scores_gemma":[0.8736097,0.000001663701,0.1253425,0.0001535718,0.00001844607,0.0006671487,0.000006534735,0.00003303748,0.0001674262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8097795,"threshold_uncertainty_score":0.9547498,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2607432382","doi":"10.1371/journal.pone.0172500","title":"Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canadian Institutes of Health Research; National Institutes of Health; National Science Foundation","keywords":"Decoding methods; Neuroimaging; Computer science; Multivariate statistics; Artificial intelligence; Neural decoding; Population; Psychology; Cognitive psychology; Machine learning; Neuroscience; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.1181339301922476,"gpt":0.316367186902926,"spread":0.1982332567106784,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005658533,0.0001075467,0.0002080781,0.0001464859,0.0005779965,0.0007494926,0.001365368,0.00005323305,0.00002042522],"category_scores_gemma":[0.0001349495,0.00008160912,0.00009536122,0.0002074132,0.00005034473,0.000554467,0.000451118,0.0001509262,0.00002477136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003114093,"about_ca_system_score_gemma":0.00003037122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006112507,"about_ca_topic_score_gemma":0.0002013063,"domain_scores_codex":[0.9988707,0.00009725207,0.0002008436,0.0002950824,0.0003401479,0.0001960146],"domain_scores_gemma":[0.9980783,0.00008933941,0.0002726114,0.001415341,0.00009212309,0.00005227107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003142745,0.003331228,0.5168309,0.0001068961,0.01011491,0.0001006506,0.04205977,0.002327032,0.2192294,0.01224692,0.0002964411,0.1933244],"study_design_scores_gemma":[0.0001502283,0.00002049499,0.0360796,0.00004958369,0.0002870885,0.000001028703,0.00002088706,0.8769418,0.0856438,0.000555258,0.00005833495,0.0001919029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4426134,0.0000117149,0.5555021,0.001115501,0.00002104071,0.0001182554,0.000001884747,0.00008133122,0.0005347513],"genre_scores_gemma":[0.9066003,0.000005710218,0.09294069,0.0003170955,0.00004405409,0.00001131812,0.000001215201,0.000006943766,0.00007269242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8746148,"threshold_uncertainty_score":0.7227374,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998614909","doi":"10.1088/0031-9155/52/23/019","title":"Characterization of the image-derived carotid artery input function using independent component analysis for the quantitation of [18F] fluorodeoxyglucose positron emission tomography images","year":2007,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":56,"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 Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institute on Aging; National Institutes of Health; Petroleum Technology Alliance Canada","keywords":"Independent component analysis; Positron emission tomography; Internal carotid artery; Fluorodeoxyglucose; Nuclear medicine; Partial volume; Medicine; Radiology; Artificial intelligence; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.08926890729822794,"gpt":0.3501346129230958,"spread":0.2608657056248679,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089014,0.00008033981,0.0002042611,0.0001865866,0.00006673976,0.000007608641,0.0001757745,0.00004567077,5.354814e-7],"category_scores_gemma":[0.00003130842,0.00004639723,0.00006536488,0.0006326829,0.0001508114,0.0001243721,0.00006379843,0.00008503259,2.304504e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001237847,"about_ca_system_score_gemma":0.00001532961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009379428,"about_ca_topic_score_gemma":0.00002602138,"domain_scores_codex":[0.9991856,0.0001186137,0.000330112,0.0001622903,0.0001028686,0.0001005149],"domain_scores_gemma":[0.9990528,0.000244261,0.0003219084,0.0002093556,0.0001556748,0.00001604549],"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":[0.00003101916,0.00003835174,0.01501307,0.00001402566,0.00005390233,5.177284e-8,0.0008670551,0.00003701733,0.9774733,0.003128855,0.000002368157,0.003341044],"study_design_scores_gemma":[0.0003704621,0.0002288749,0.391533,0.00004114121,0.000147288,0.000001229928,0.0002192524,0.01396879,0.5888985,0.004511115,0.00001049671,0.00006983926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4728541,0.00003882839,0.5266497,0.0002192778,0.00006319356,0.0001605498,0.000001962006,0.000007445648,0.000004991386],"genre_scores_gemma":[0.9963056,0.00004163483,0.003426585,0.0001317984,0.00005279161,0.000005290496,0.00003222492,0.000003136154,9.264856e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5234515,"threshold_uncertainty_score":0.1892024,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2508533112","doi":"10.1016/j.neuroimage.2016.08.044","title":"Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; École de Technologie Supérieure; Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Epilepsy Society; Savoy Foundation","keywords":"Electroencephalography; Magnetoencephalography; Neuroscience; High resolution; Computer science; Pattern recognition (psychology); Artificial intelligence; Psychology; Geography; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.09785730437799431,"gpt":0.3616407195003127,"spread":0.2637834151223184,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000652979,0.0001454046,0.0003658161,0.0001856779,0.00005056158,0.00003213771,0.0004781366,0.00006999053,0.00000843001],"category_scores_gemma":[0.0001700413,0.0001193979,0.00002576982,0.000205293,0.0001511669,0.0004398653,0.0005018041,0.0000882075,4.246378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001327074,"about_ca_system_score_gemma":0.00003502155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001234442,"about_ca_topic_score_gemma":0.00004003073,"domain_scores_codex":[0.9980015,0.0006786878,0.000418139,0.0004670848,0.0002996678,0.0001348885],"domain_scores_gemma":[0.9980206,0.0003574523,0.0004854759,0.0009581501,0.0001230926,0.00005518846],"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":[0.00006038663,0.0002495231,0.00491294,0.0001080263,0.00001953053,0.000001285812,0.0008291467,0.0002146857,0.700712,0.005632682,0.0003141457,0.2869456],"study_design_scores_gemma":[0.0007011616,0.00075451,0.1323562,0.0001060177,0.00003049938,0.000004679787,0.00001585077,0.3105655,0.5540729,0.0007096431,0.0004879831,0.0001951511],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3660328,0.00001570467,0.6334885,0.0001797858,0.00003402313,0.0001433941,0.00002947475,0.0000470271,0.00002932481],"genre_scores_gemma":[0.9255279,0.00003031865,0.07428584,0.0001066682,0.00001248358,0.000003097545,0.00001161075,0.00001331407,0.00000881863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5594951,"threshold_uncertainty_score":0.4868904,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134835659","doi":"10.1109/tsp.2005.853220","title":"Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Prior probability; Functional magnetic resonance imaging; Bayesian probability; Inverse problem; Computer science; Neuroimaging; Electroencephalography; Bayes' theorem; Relevance (law); Artificial intelligence; Inference; Bayesian inference; Magnetoencephalography; Machine learning; Bayes factor; Pattern recognition (psychology); Statistical inference; Brain activity and meditation; Psychology; Mathematics; Statistics; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.04606980555749494,"gpt":0.317348896157968,"spread":0.271279090600473,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001191115,0.0001872453,0.0002135838,0.0002118921,0.0004031257,0.0004054249,0.000907158,0.00009545135,0.000002756358],"category_scores_gemma":[0.00000503091,0.0001255229,0.00008889022,0.0009499267,0.0001364887,0.001329661,0.000003343486,0.0006225613,0.000002166325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007785921,"about_ca_system_score_gemma":0.0003592262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000134485,"about_ca_topic_score_gemma":0.00004686903,"domain_scores_codex":[0.9980533,0.0002980695,0.0005008095,0.000361446,0.0005341658,0.0002522052],"domain_scores_gemma":[0.9989443,0.00023222,0.0002594682,0.0004122208,0.0001127551,0.00003901512],"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":[0.00001253396,0.0004196932,0.000009613419,0.00005318241,0.00000422658,4.796999e-7,0.006217458,0.8516067,0.001816476,0.0003364902,0.00004205302,0.1394811],"study_design_scores_gemma":[0.000281659,0.0000477252,0.00003214534,0.0001193833,0.00001244425,0.000005087058,0.0003946272,0.9774908,0.02062946,0.0007673184,0.00007560522,0.0001437514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004431034,0.00005467095,0.9912916,0.002243594,0.00001330839,0.0004534221,0.000001065417,0.0001712659,0.001340055],"genre_scores_gemma":[0.7126767,0.000001849672,0.2863894,0.0008085923,0.000007946071,0.00008505642,4.710564e-7,0.00001127812,0.00001872957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7082457,"threshold_uncertainty_score":0.5118676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2023425418","doi":"10.1109/tifs.2013.2264462","title":"Extracting Spread-Spectrum Hidden Data From Digital Media","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Eion (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; University at Buffalo","keywords":"Computer science; Spread spectrum; Embedding; Host (biology); Autocorrelation; Algorithm; Autocorrelation matrix; Artificial intelligence; Channel (broadcasting); Mathematics; Telecommunications; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02086315380313131,"gpt":0.2386269608509912,"spread":0.2177638070478599,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001650722,0.0001405465,0.0001282558,0.0001351327,0.0001918608,0.001076769,0.0004951072,0.0001001392,0.00005970083],"category_scores_gemma":[0.00001648112,0.0001337772,0.00003655914,0.000182807,0.00005183819,0.0110724,0.00001834344,0.0002865834,0.0001948766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002344225,"about_ca_system_score_gemma":0.00003913236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002844009,"about_ca_topic_score_gemma":0.00007599621,"domain_scores_codex":[0.9989705,0.00002401477,0.0003467923,0.0002067039,0.0002853121,0.0001666523],"domain_scores_gemma":[0.9987986,0.0001948981,0.0001353277,0.0006689627,0.00009320646,0.0001089743],"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":[0.00001926517,0.0001368329,0.00006876719,0.00002127116,0.00007514941,0.000001716654,0.0218352,0.0001521754,0.00005272004,0.04758856,0.008121959,0.9219264],"study_design_scores_gemma":[0.0008628601,0.0001413833,0.001303386,0.00005665371,0.00002384567,0.00003036402,0.0009629471,0.7485324,0.008550001,0.2236502,0.01515514,0.0007308652],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02624823,0.000009477567,0.9694307,0.001183165,0.0002994979,0.0002173973,0.000257413,0.0003336082,0.002020551],"genre_scores_gemma":[0.9855568,0.00003862165,0.01363463,0.0005491301,0.00002856597,0.00001523518,0.0001603645,0.00000557812,0.00001100942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9593086,"threshold_uncertainty_score":0.9999602,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158596240","doi":"10.1109/icassp.2012.6288330","title":"Tuning-free step-size adaptation","year":2012,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","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 Alberta","funders":"","keywords":"Computer science; Adaptation (eye); Algorithm; Reinforcement learning; Set (abstract data type); Meta learning (computer science); Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02946706994576869,"gpt":0.2647637833990947,"spread":0.235296713453326,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003709668,0.00005848549,0.00005405425,0.00004326515,0.00004533337,0.00007958977,0.0005096813,0.00003760828,0.00006301156],"category_scores_gemma":[0.00009296753,0.00005164198,0.00002421881,0.0001733471,0.00001128211,0.001049036,0.0001637129,0.00006077336,0.0001143129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001626106,"about_ca_system_score_gemma":0.00002128179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002941094,"about_ca_topic_score_gemma":0.00001019411,"domain_scores_codex":[0.9994042,0.00005405962,0.0001065142,0.0001066396,0.0001703635,0.00015821],"domain_scores_gemma":[0.9992726,0.00009269387,0.00003982346,0.0004818049,0.00004514093,0.00006788479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001050462,0.0000419015,0.0004367892,0.00000173387,0.000003672277,2.849488e-7,0.002295711,0.000009798336,0.0004584322,0.9509615,0.02172552,0.02406361],"study_design_scores_gemma":[0.001443382,0.0003420394,0.04051175,0.00003207208,0.00001549878,0.00005356437,0.0008695438,0.4256702,0.05182764,0.06709794,0.4108851,0.001251354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001666641,0.00003229726,0.938378,0.001300645,0.0001194784,0.00007378353,2.040844e-7,0.0006674018,0.05776151],"genre_scores_gemma":[0.5573707,0.000002020423,0.4400382,0.0009886374,0.00003610831,0.000007518619,4.136052e-7,0.000003173455,0.001553159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8838636,"threshold_uncertainty_score":0.2105899,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155435112","doi":"10.1109/tbme.2006.876619","title":"Optimal reduction of MCG in fetal MEG recordings","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Welichem Biotech (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke","keywords":"Magnetoencephalography; Magnetocardiography; In utero; SIGNAL (programming language); Signal-to-noise ratio (imaging); Projection (relational algebra); Fetus; Noise reduction; Biomedical engineering; Speech recognition; Medicine; Electroencephalography; Computer science; Neuroscience; Artificial intelligence; Pregnancy; Biology; Internal medicine; Algorithm; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.007006886011178489,"gpt":0.2188628949935221,"spread":0.2118560089823436,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002235388,0.0001021321,0.0001370578,0.0004708815,0.00002569422,0.00002160179,0.0002186624,0.0001053614,0.000009452745],"category_scores_gemma":[0.000005237349,0.0001070742,0.0000645641,0.0007109997,0.00003711719,0.0002623835,0.000001961968,0.0002400722,0.000005024423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000629814,"about_ca_system_score_gemma":0.00002881272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009734203,"about_ca_topic_score_gemma":0.00000357687,"domain_scores_codex":[0.9990724,0.00001882424,0.0002966566,0.000205448,0.0002418364,0.0001648609],"domain_scores_gemma":[0.9996486,0.00003929887,0.00005165393,0.0001822211,0.00002524785,0.00005297997],"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":[0.00003017631,0.0008614976,0.000006949634,0.00006334911,0.00003269042,0.00001918588,0.0007087716,0.6284285,0.244364,0.010534,0.0003954798,0.1145553],"study_design_scores_gemma":[0.0004707785,0.0002057535,0.000234293,0.00009100925,0.000005594119,0.00004564704,0.00001923029,0.6606798,0.3362827,0.0001767822,0.001550771,0.000237639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05475463,0.00001009627,0.9439367,0.0004213207,0.0003810506,0.00008506375,0.000002396383,0.0002937853,0.0001149426],"genre_scores_gemma":[0.9014395,0.000008259653,0.0984375,0.00001284032,0.00003084159,0.00001983942,0.000001130308,0.000008887429,0.00004121745],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8466849,"threshold_uncertainty_score":0.4366358,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2704243436","doi":"10.1016/j.compbiomed.2017.06.013","title":"Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics","year":2017,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; Swedish Orphan Biovitrum; National Natural Science Foundation of China","keywords":"Electroencephalography; Artifact (error); Computer science; Autocorrelation; Pattern recognition (psychology); Artificial intelligence; Higher-order statistics; Independent component analysis; Blind signal separation; Independence (probability theory); Noise (video); Speech recognition; Signal processing; Statistics; Mathematics; Channel (broadcasting); Neuroscience; Psychology; Image (mathematics); Digital signal processing","retraction":null,"screen_n_in":null,"score":{"opus":0.0479768094229454,"gpt":0.352243723385617,"spread":0.3042669139626716,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000414156,0.0001051428,0.0002057012,0.00005172924,0.000201651,0.00005407757,0.0007649437,0.00008403303,0.000004245137],"category_scores_gemma":[0.0003873862,0.0000879809,0.000005166226,0.00002930624,0.0003852565,0.0002283854,0.0009225075,0.0001500103,0.000001423413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008809662,"about_ca_system_score_gemma":0.00001373349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002660675,"about_ca_topic_score_gemma":0.00004046221,"domain_scores_codex":[0.9990856,0.00009576026,0.0001803783,0.0004240795,0.0000648123,0.0001493458],"domain_scores_gemma":[0.9984937,0.0005214568,0.0001233156,0.0007621606,0.0000248619,0.00007449678],"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":[0.00002040713,0.00005069267,0.008559487,0.00001622874,0.00003705278,0.0003943483,0.002793061,0.000009201024,0.005095673,0.02399679,0.01255497,0.9464721],"study_design_scores_gemma":[0.003215576,0.0006622951,0.02975399,0.0003210266,0.00003560117,0.0001018729,0.0003435428,0.8145053,0.0004658138,0.07312489,0.07685948,0.0006105886],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1350711,0.0005523764,0.8617318,0.002044319,0.000280052,0.00009517775,0.00004362866,0.00006547124,0.0001160545],"genre_scores_gemma":[0.8039133,0.000460818,0.1940681,0.00134115,0.00006890429,0.000001438482,0.0001286242,0.000004218295,0.0000134621],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9458615,"threshold_uncertainty_score":0.3587757,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152161325","doi":"10.1109/wcre.2008.49","title":"Automated Concept Location Using Independent Component Analysis","year":2008,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Independent component analysis; Computer science; Singular value decomposition; Source code; Code (set theory); Component (thermodynamics); Blind signal separation; Data mining; Search engine indexing; Source separation; Decomposition; Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.03678792353322075,"gpt":0.2971827998093445,"spread":0.2603948762761238,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001905021,0.00008224942,0.0001369049,0.0002778088,0.0001255422,0.00006716433,0.0003546223,0.00005179088,0.00003303468],"category_scores_gemma":[0.000008682905,0.00007644962,0.00006020142,0.001367641,0.00003710276,0.0003897415,0.0001027121,0.00006543397,0.00002331089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006553539,"about_ca_system_score_gemma":0.00007488373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002562354,"about_ca_topic_score_gemma":0.0000169402,"domain_scores_codex":[0.9990359,0.0001024687,0.0002126393,0.0002359656,0.0002902236,0.0001228238],"domain_scores_gemma":[0.9992928,0.00002492733,0.00008826044,0.0003835979,0.0001559241,0.00005451755],"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":[0.00001453054,0.001012623,0.02902383,0.00001813058,0.001396447,0.0001136507,0.02696157,0.5654505,0.0211671,0.3385641,0.0107736,0.005503919],"study_design_scores_gemma":[0.00009300624,0.00001455648,0.01873567,0.000001677409,0.00001844207,0.00001651439,0.0000163992,0.9687296,0.01202091,0.0001169585,0.0001330663,0.000103256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1121387,0.00001979044,0.884851,0.0001962231,0.00003440802,0.00009180502,3.239592e-7,0.001744293,0.00092347],"genre_scores_gemma":[0.8764451,0.000002606187,0.1229877,0.0004237485,0.000007937411,0.000003458898,0.000006747525,0.000003233144,0.0001194843],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7643064,"threshold_uncertainty_score":0.3117525,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3142170516","doi":"10.1007/978-1-4471-5571-3_12","title":"Principal Component Analysis","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Principal component analysis; Principal (computer security); Computer science; Mathematics; Statistics; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.02453148048238817,"gpt":0.2514881063145006,"spread":0.2269566258321124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001846009,0.0002327948,0.0003644451,0.0005025303,0.00004582257,0.0002376247,0.0009709467,0.0002260172,0.00206702],"category_scores_gemma":[0.000002936741,0.0002010216,0.000299642,0.0001029615,0.00003489671,0.000219292,0.0004147072,0.0002470592,0.001605859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004935144,"about_ca_system_score_gemma":0.00004868487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003742155,"about_ca_topic_score_gemma":0.00002761567,"domain_scores_codex":[0.9986674,0.00001934689,0.0003170357,0.000461044,0.0003872167,0.0001480137],"domain_scores_gemma":[0.9985145,0.00004136985,0.0001828461,0.001032496,0.0001309725,0.00009778207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[2.088744e-7,0.000006596186,0.000002439059,0.000002166873,0.000209609,0.00000270143,0.00005874182,0.00001754297,0.000005069737,0.9909093,0.005720669,0.003064966],"study_design_scores_gemma":[0.00006973311,0.00004831313,0.0001970642,0.00001306141,0.000170846,0.000004082359,0.000001002225,0.02783434,0.0002172186,0.0774354,0.8934601,0.0005488704],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000003706706,0.00002383794,0.4512658,0.000347937,0.00003445563,0.0001241685,0.000001169255,0.0004496708,0.5477493],"genre_scores_gemma":[0.003200684,0.00003062763,0.1093158,0.001041714,0.00003735513,0.00001486505,0.00002497627,0.00001613491,0.8863178],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9134739,"threshold_uncertainty_score":0.9991715,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2009988422","doi":"10.1190/1.2890407","title":"Adaptive separation of free-surface multiples through independent component analysis","year":2008,"lang":"en","type":"article","venue":"Geophysics","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":47,"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":"Multiple; Subtraction; TRACE (psycholinguistics); Computer science; Algorithm; Blind signal separation; Construct (python library); Filter (signal processing); Surface (topology); Wavelet; Independent component analysis; Component (thermodynamics); Artificial intelligence; Mathematics; Arithmetic; Physics; Computer vision; Channel (broadcasting)","retraction":null,"screen_n_in":null,"score":{"opus":0.03458149444815641,"gpt":0.2754728949572221,"spread":0.2408914005090657,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001394183,0.0001381401,0.0002799372,0.00007459758,0.0001123082,0.00002770991,0.0006557499,0.00006633769,0.000005018223],"category_scores_gemma":[0.00001200382,0.000137331,0.0001710583,0.0009457998,0.00007517065,0.0006122277,0.000248963,0.0001254295,0.00002277792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003490045,"about_ca_system_score_gemma":0.00006570333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003876079,"about_ca_topic_score_gemma":0.00003667602,"domain_scores_codex":[0.9986598,0.0001118894,0.000294075,0.0003076631,0.0004683067,0.0001582385],"domain_scores_gemma":[0.9986502,0.00008428069,0.0002422373,0.000779569,0.0002050067,0.00003871681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001078437,0.002251042,0.01202197,0.00004252584,0.002093296,0.0000454417,0.07174838,0.3278354,0.04499305,0.52827,0.006427725,0.004163333],"study_design_scores_gemma":[0.001051809,0.0004018232,0.08514981,0.00002017133,0.0001929563,0.000009451232,0.000266257,0.6362791,0.2269206,0.04779616,0.001177989,0.0007339244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.294516,0.00003371006,0.7041009,0.000135514,0.00003896487,0.0001331032,0.000009771106,0.0001476472,0.0008843649],"genre_scores_gemma":[0.8723792,0.00003756375,0.1273276,0.0001236865,0.00002119383,0.000007077645,0.00001506239,0.000005915999,0.00008272626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5778632,"threshold_uncertainty_score":0.5600196,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2048588522","doi":"10.1016/s1388-2457(02)00046-9","title":"Systematic source estimation of spikes by a combination of independent component analysis and RAP-MUSIC","year":2002,"lang":"en","type":"article","venue":"Clinical Neurophysiology","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Independent component analysis; Electroencephalography; Waveform; Pattern recognition (psychology); Covariance matrix; Matrix (chemical analysis); Computer science; Artificial intelligence; Speech recognition; Mathematics; Algorithm; Psychology; Chemistry; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.03091658915278169,"gpt":0.2976352498980019,"spread":0.2667186607452203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004342181,0.0001024181,0.0006339008,0.0002030049,0.00003108308,0.00001684045,0.0003550162,0.00009922023,0.000009447201],"category_scores_gemma":[0.0003394147,0.00008953678,0.0001508356,0.0004916971,0.0001803307,0.0001254938,0.0001614474,0.0001406541,0.000005830093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005784408,"about_ca_system_score_gemma":0.000006511311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001246212,"about_ca_topic_score_gemma":8.535433e-7,"domain_scores_codex":[0.9976039,0.0008422128,0.0009536794,0.0003142153,0.0001874227,0.00009856989],"domain_scores_gemma":[0.9978024,0.0009355045,0.0006500607,0.0004555495,0.0001055342,0.00005088642],"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":[0.000339803,0.02009218,0.02095893,0.02011445,0.004179352,0.00003842211,0.01208973,0.04260061,0.5950945,0.1766583,0.005725201,0.1021085],"study_design_scores_gemma":[0.0005211412,0.001012931,0.03614364,0.0001079651,0.0001776058,0.000003445945,0.00001281732,0.9563883,0.003456216,0.002030667,0.00001042796,0.0001348258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6175668,0.00004960823,0.38191,0.000158291,0.00004907292,0.0001899305,0.000001466211,0.0000491463,0.00002568031],"genre_scores_gemma":[0.9956784,0.00005813121,0.003935575,0.0002745061,0.000005502623,0.00001073846,0.000005465662,0.000005129585,0.00002660378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9137877,"threshold_uncertainty_score":0.3651204,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2168599920","doi":"10.1109/tnn.2002.806949","title":"A neural-network appearance-based 3-D object recognition using independent component analysis","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; McGill University","funders":"","keywords":"Independent component analysis; Computer science; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Artificial neural network; Facial recognition system; Object (grammar); Feature extraction; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.0341544514731379,"gpt":0.2628602818086428,"spread":0.228705830335505,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006108773,0.0003377512,0.0003943791,0.0004632206,0.00047684,0.0002947543,0.0004480939,0.0002053966,0.00004845914],"category_scores_gemma":[0.000004086984,0.0003447162,0.0004432057,0.002664897,0.00006094131,0.0005010961,0.000003322377,0.000719679,0.00001222133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228705,"about_ca_system_score_gemma":0.00005467794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005225609,"about_ca_topic_score_gemma":0.0001089264,"domain_scores_codex":[0.9970307,0.0008020905,0.0005104165,0.0006863602,0.0004205241,0.0005498959],"domain_scores_gemma":[0.9986056,0.0001653062,0.0002166065,0.0007086513,0.0001281389,0.0001756715],"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":[0.00003111832,0.0001431016,0.00005423426,0.000004068746,0.0001088456,0.000009123501,0.00005680003,0.9854166,0.00009048891,0.00007328799,0.00003502803,0.01397732],"study_design_scores_gemma":[0.0004429656,0.000138081,0.000229031,0.0000303842,0.0001748451,0.00002068541,0.000006771541,0.9957772,0.002518062,0.0002282318,0.00006784847,0.0003658969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0470867,0.0001016212,0.9506502,0.0001983026,0.0007783343,0.0004253611,0.000004716936,0.0006171283,0.0001376201],"genre_scores_gemma":[0.9754465,0.00002312042,0.02274302,0.001602625,0.00006591708,0.00006369775,0.000007122555,0.00002805928,0.0000198952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9283599,"threshold_uncertainty_score":0.9999005,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1991461753","doi":"10.1016/j.neunet.2004.08.002","title":"Complex-valued neural networks for nonlinear complex principal component analysis","year":2004,"lang":"en","type":"article","venue":"Neural Networks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Nonlinear system; Artificial neural network; Pattern recognition (psychology); Independent component analysis; Data set; Artificial intelligence; Dimensionality reduction; Feature extraction; Mathematics; Dimension (graph theory); Algorithm; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.04677720759505942,"gpt":0.3059106745926465,"spread":0.259133466997587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005538044,0.0004515926,0.000703224,0.0002945223,0.0004439256,0.0004704896,0.001544608,0.0002348744,0.00002803275],"category_scores_gemma":[0.00002474037,0.0004320691,0.0005789666,0.001630315,0.0001459722,0.0004929389,0.0004709539,0.0005488626,0.000005675279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001189937,"about_ca_system_score_gemma":0.00003560278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007967185,"about_ca_topic_score_gemma":0.000176421,"domain_scores_codex":[0.9967268,0.0002354919,0.0007756075,0.0008954645,0.0004867749,0.0008799328],"domain_scores_gemma":[0.9978133,0.0002158694,0.0003524956,0.001072376,0.0002460665,0.0002998793],"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":[0.00004440137,0.0001312818,0.0004138981,0.000005712739,0.0001408688,0.00001119441,0.0001391031,0.9805681,0.00005130091,0.0129979,0.0009629381,0.00453328],"study_design_scores_gemma":[0.001027985,0.0002518984,0.008002871,0.000007058449,0.0001184382,0.00001876163,0.000007859707,0.9869736,0.00004501005,0.0005368071,0.002556491,0.0004531974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0310969,0.00009385376,0.9639016,0.002406138,0.0004033653,0.0008911956,0.00001531416,0.001014644,0.0001770379],"genre_scores_gemma":[0.8586335,0.00001029493,0.135796,0.004570765,0.0004770726,0.00008723722,0.0003529997,0.00003716961,0.00003498545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8281056,"threshold_uncertainty_score":0.9998131,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087364536","doi":"10.1109/lsp.2014.2362932","title":"Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering","year":2014,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Kalman filter; Constant (computer programming); Algorithm; Beamforming; Control theory (sociology); Noise (video); Extended Kalman filter; Adaptive filter; Covariance matrix; Covariance; Mathematics; Computer science; Artificial intelligence; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02240396972066053,"gpt":0.249937865640121,"spread":0.2275338959194605,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005953068,0.0002525371,0.0002186159,0.000297923,0.0002833178,0.0003689314,0.0006570705,0.00007705409,0.000006304204],"category_scores_gemma":[0.00002163575,0.0002432068,0.00007674408,0.0004128205,0.0001088502,0.0006671151,0.00005313879,0.000325126,0.0000197932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009283016,"about_ca_system_score_gemma":0.00009550001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001284281,"about_ca_topic_score_gemma":0.00000236461,"domain_scores_codex":[0.9981039,0.0001443571,0.0003209061,0.0005345598,0.000497863,0.0003983992],"domain_scores_gemma":[0.9990919,0.000114593,0.0002208563,0.0003603527,0.00009714245,0.0001152051],"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":[0.0003195196,0.0003347787,0.0001439643,0.0001664172,0.00004453797,0.00006879195,0.003444772,0.1657649,0.5385188,0.007154695,0.003866998,0.2801718],"study_design_scores_gemma":[0.0005464898,0.0001797425,0.0000307101,0.0002540233,0.000005816317,0.000006720419,0.00001267882,0.8996539,0.09777486,0.0004398962,0.0007842994,0.0003109168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01882348,0.000008638276,0.9758937,0.002791276,0.0001428248,0.0002010443,0.000001548327,0.0006251645,0.001512346],"genre_scores_gemma":[0.8945866,2.862896e-7,0.09134289,0.01390731,0.00009449536,0.00002324369,0.000002827467,0.00002345438,0.0000188544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8845508,"threshold_uncertainty_score":0.9917687,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1992647454","doi":"10.1088/0964-1726/19/2/025005","title":"Wavelet-based blind identification of the UCLA Factor building using ambient and earthquake responses","year":2009,"lang":"en","type":"article","venue":"Smart Materials and Structures","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Identification (biology); Covariance; Wavelet; SIGNAL (programming language); Wavelet transform; Algorithm; Modal; Computer science; Mathematics; Engineering; Artificial intelligence; Statistics; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.02403914162013778,"gpt":0.2851252795623017,"spread":0.261086137942164,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002770134,0.00009285326,0.000130942,0.00007529849,0.0001388545,0.0002834047,0.0001835312,0.00005550099,0.000005790792],"category_scores_gemma":[0.00004738421,0.00006421189,0.00002067261,0.00009418979,0.00007138905,0.0001547791,0.00005896571,0.00003858251,1.074861e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007069294,"about_ca_system_score_gemma":0.00003202538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002238919,"about_ca_topic_score_gemma":0.000002749817,"domain_scores_codex":[0.9991496,0.0001589843,0.0002520227,0.0001937449,0.0001473057,0.00009836465],"domain_scores_gemma":[0.9994544,0.00003402095,0.0001717309,0.000264965,0.00004761012,0.0000273039],"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":[0.00002358117,0.000004692189,0.0001818051,0.00001004768,0.000002563404,2.899199e-7,0.0002795022,0.000008733931,0.9794061,0.01773824,0.000008311898,0.00233615],"study_design_scores_gemma":[0.00012139,0.0000303281,0.2471331,0.00001311932,0.000003314338,0.000005120801,0.000005222126,0.0006127444,0.7431262,0.008794657,0.00009256485,0.00006224465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805329,0.00003894427,0.01854152,0.0005042079,0.0001560746,0.0001640627,0.00001716957,0.0000409086,0.00000415245],"genre_scores_gemma":[0.9858673,0.000005286685,0.01386579,0.0002260185,0.00002077554,0.000001468914,9.969389e-7,0.000003608663,0.000008723485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2469513,"threshold_uncertainty_score":0.2732878,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}