{"meta":{"query_hash":"002a72ef61e3","filters":{"venue":"IEEE Journal of Selected Topics in Signal Processing"},"cohort_total":82,"direct_labels_cover":0,"predictions_cover":82,"exported":82,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/002a72ef61e3","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Journal+of+Selected+Topics+in+Signal+Processing"},"results":[{"id":"W1965305830","doi":"10.1109/jstsp.2013.2256772","title":"A Multiple-Detection Joint Probabilistic Data Association Filter","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Clutter; Computer science; Probabilistic logic; Data association; Filter (signal processing); Radar tracker; Artificial intelligence; Tracking (education); Multipath propagation; Algorithm; Radar; Pattern recognition (psychology); Computer vision; Telecommunications","score_opus":0.03675506028158698,"score_gpt":0.252280432335527,"score_spread":0.21552537205394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965305830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16133453,0.00026437678,0.83637744,0.000815103,0.00081871386,0.00019179533,0.0000028837737,0.000075431264,0.00011970423],"genre_scores_gemma":[0.96729445,0.000018752806,0.031770453,0.00010984656,0.00072133803,0.0000034225918,0.000003861489,0.000010280178,0.000067577894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980566,0.000151863,0.0007131153,0.00027252475,0.0005050365,0.00030089912],"domain_scores_gemma":[0.9978195,0.00021819519,0.000709025,0.00030153996,0.00086945813,0.00008227055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080260995,0.000133102,0.00024179199,0.00021639337,0.00012016284,0.00043230513,0.00087124534,0.00013764243,0.000021890475],"category_scores_gemma":[0.00060544495,0.00011421223,0.00003580713,0.0007513983,0.000018366709,0.0018735465,0.00011223225,0.00067271694,0.000009800288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045506735,0.0004610545,0.0097248815,0.00018171227,0.00006599842,0.00006728657,0.0014070218,0.013631015,0.083615616,0.000052404732,0.007851079,0.8828964],"study_design_scores_gemma":[0.0008965529,0.00018214087,0.014934405,0.00038120284,0.000020637011,0.0001569349,0.000034568875,0.96942616,0.008737009,0.0035816524,0.0013685419,0.00028017472],"about_ca_topic_score_codex":0.00003256196,"about_ca_topic_score_gemma":0.000026245958,"teacher_disagreement_score":0.95579517,"about_ca_system_score_codex":0.00020774374,"about_ca_system_score_gemma":0.00017262583,"threshold_uncertainty_score":0.46574396},"labels":[],"label_agreement":null},{"id":"W1968476015","doi":"10.1109/jstsp.2012.2233459","title":"Detection of Anomalous Trajectory Patterns in Target Tracking via Stochastic Context-Free Grammars and Reciprocal Process Models","year":2012,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Adelaide","keywords":"Computer science; Trajectory; Reciprocal; Markov chain; Context (archaeology); Markov process; Hidden Markov model; Random walk; Bayesian probability; Stochastic process; Markov model; Point process; Artificial intelligence; Algorithm; Machine learning; Mathematics; Statistics","score_opus":0.021676001603282324,"score_gpt":0.24858324742120663,"score_spread":0.22690724581792432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968476015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44086003,0.0011468141,0.5576734,0.000023814693,0.00020020397,0.00007182966,0.0000012241384,0.000015754624,0.000006938474],"genre_scores_gemma":[0.9887937,0.000024293939,0.010788282,0.00003787394,0.00033201627,0.0000035506932,7.7586316e-7,0.000017280056,0.0000022781421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978243,0.00014428202,0.0008934069,0.00023877452,0.0004548598,0.00044439087],"domain_scores_gemma":[0.9985105,0.00015835944,0.00060517166,0.00016411746,0.0004292882,0.0001326096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008011727,0.00019999659,0.00041568623,0.00046006366,0.00008618087,0.00008996655,0.0005660047,0.0001738613,0.000002412611],"category_scores_gemma":[0.00010174772,0.00018752174,0.00004713317,0.0008074491,0.00006100415,0.0017577248,0.00005021898,0.00077855116,1.2571748e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041355914,0.0008463136,0.04006853,0.00066019397,0.000048758044,0.000100832294,0.01509194,0.11173666,0.03338238,0.00016277807,0.000014597714,0.7974735],"study_design_scores_gemma":[0.0031097888,0.000674691,0.024229158,0.0020395692,0.00004460088,0.0011132708,0.00066419627,0.91183114,0.03738054,0.018183444,0.000014083769,0.000715508],"about_ca_topic_score_codex":0.000029306957,"about_ca_topic_score_gemma":0.00005457181,"teacher_disagreement_score":0.8000945,"about_ca_system_score_codex":0.00009500369,"about_ca_system_score_gemma":0.00015027438,"threshold_uncertainty_score":0.7646915},"labels":[],"label_agreement":null},{"id":"W1968974808","doi":"10.1109/jstsp.2014.2388191","title":"Streaming Codes With Partial Recovery Over Channels With Burst and Isolated Erasures","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Hewlett-Packard Development Company","keywords":"Erasure; Computer science; Erasure code; Online codes; Decoding methods; Fountain code; Encoder; Tornado code; Network packet; Algorithm; Luby transform code; Forward error correction; Binary erasure channel; Error detection and correction; Coding (social sciences); Concatenated error correction code; Channel (broadcasting); Burst error; Block code; Computer network; Channel capacity; Mathematics","score_opus":0.022684937308261547,"score_gpt":0.2639393366796964,"score_spread":0.24125439937143484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968974808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58878535,0.00036599181,0.41041493,0.00012805914,0.00009707705,0.00006656891,2.1997212e-7,0.00006678638,0.0000750346],"genre_scores_gemma":[0.9559646,0.00001428211,0.04367745,0.000060516617,0.0002410594,0.0000022374597,2.3485975e-7,0.000016407836,0.000023160239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985172,0.00009836433,0.00039194818,0.00023717915,0.00046493087,0.00029038798],"domain_scores_gemma":[0.9984407,0.00008709524,0.00046670673,0.00011529748,0.0007602114,0.00012998938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005476345,0.00018517775,0.0003089232,0.00028730967,0.00008935126,0.00032295354,0.00035332103,0.00009179845,9.606545e-7],"category_scores_gemma":[0.00006827898,0.0001367218,0.000018899005,0.00084435585,0.0000640453,0.0011725972,0.000036967936,0.00053060433,1.464002e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046750316,0.0011770272,0.23256612,0.00060646597,0.00050845096,0.0031555735,0.032396425,0.04831908,0.058376033,0.00039948671,0.0010723191,0.616748],"study_design_scores_gemma":[0.0098398095,0.02048553,0.014354495,0.011098177,0.00025806195,0.0103069795,0.0014708019,0.65935504,0.2562932,0.012817305,0.000833826,0.0028867577],"about_ca_topic_score_codex":0.0000374683,"about_ca_topic_score_gemma":0.000060309423,"teacher_disagreement_score":0.61386126,"about_ca_system_score_codex":0.00011366079,"about_ca_system_score_gemma":0.00054982764,"threshold_uncertainty_score":0.55753535},"labels":[],"label_agreement":null},{"id":"W1995035072","doi":"10.1109/jstsp.2009.2032314","title":"Introduction to the Issue on Advanced Signal Processing for GNSS and Robust Navigation","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; École de Technologie Supérieure","funders":"","keywords":"GNSS applications; Satellite navigation; Computer science; Satellite system; GNSS augmentation; Global Positioning System; Satellite; Precise Point Positioning; Real-time computing; Remote sensing; Telecommunications; Geography; Engineering; Aerospace engineering","score_opus":0.011481344220144437,"score_gpt":0.2510612943914009,"score_spread":0.23957995017125647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995035072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71960586,0.00091534253,0.27465588,0.0038463422,0.00033841463,0.00027270906,0.0000016458738,0.00006711313,0.0002966558],"genre_scores_gemma":[0.990733,0.000015343423,0.0066869445,0.00014200453,0.0023541562,0.000005475759,0.0000020358823,0.000013868509,0.00004716727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911034,0.000020755979,0.00036477722,0.00013151014,0.00017927341,0.00019333568],"domain_scores_gemma":[0.9993618,0.00003283728,0.00012694526,0.00004692138,0.0003791123,0.000052343137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024665045,0.00012837656,0.00016717539,0.00014678352,0.00014151873,0.00013819146,0.00011808373,0.00006594582,0.000004031197],"category_scores_gemma":[0.000033513214,0.00010227669,0.00002350775,0.00038556347,0.00001733537,0.00036595634,0.0000030944943,0.0003890832,9.1075185e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012665502,0.00004023452,0.00001857264,0.00010473877,0.0000075632115,0.0000021653173,0.0011707642,0.3912862,0.07747569,0.000019415837,0.000941356,0.5288066],"study_design_scores_gemma":[0.0019991829,0.0033709554,0.0036689895,0.004170431,0.0000941606,0.00042868347,0.0007192166,0.5740109,0.3988634,0.0029738087,0.008936402,0.0007638524],"about_ca_topic_score_codex":6.455371e-7,"about_ca_topic_score_gemma":0.0000014911993,"teacher_disagreement_score":0.5280428,"about_ca_system_score_codex":0.000106744024,"about_ca_system_score_gemma":0.000050738898,"threshold_uncertainty_score":0.41707224},"labels":[],"label_agreement":null},{"id":"W2002365632","doi":"10.1109/jstsp.2013.2245630","title":"Mis-Information Removal in Social Networks: Constrained Estimation on Dynamic Directed Acyclic Graphs","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Key (lock); Information flow; Information exchange; Theoretical computer science; Directed acyclic graph; Graph; Directed graph; Network topology; State (computer science); Social network (sociolinguistics); Algorithm; Mathematical optimization; Mathematics; Social media; Computer network; Computer security","score_opus":0.008017647367276452,"score_gpt":0.2658263316152951,"score_spread":0.25780868424801867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002365632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96750295,0.000038199538,0.030082304,0.0003076622,0.00018706317,0.00019656417,0.0000030913652,0.00002031268,0.0016618571],"genre_scores_gemma":[0.9980502,0.0000036575968,0.0015985769,0.000067286586,0.00023484488,0.0000059947974,0.000014148995,0.000008946169,0.00001635281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986401,0.00007462804,0.0007071236,0.000094726296,0.00023315696,0.00025026657],"domain_scores_gemma":[0.9989101,0.000058034533,0.0005436718,0.00004077834,0.00039611247,0.0000512631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021784914,0.00014264116,0.00026037713,0.0002831126,0.0001357684,0.00016740164,0.00014472808,0.000096833704,0.000038646383],"category_scores_gemma":[0.000020347923,0.00013849439,0.000064476895,0.000743695,0.00005379834,0.00086897996,0.0000051604848,0.00054241176,0.0000028663549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013697348,0.00039143916,0.017386321,0.00008700707,0.000072181945,0.000014302306,0.004148642,0.09610323,0.0025134718,0.0040530367,0.00023150792,0.8748619],"study_design_scores_gemma":[0.0014297504,0.000110876834,0.04238577,0.0003790902,0.000014433365,0.000010071307,0.00059220486,0.93379426,0.00013812036,0.020824745,0.000050082483,0.00027057857],"about_ca_topic_score_codex":0.000066391956,"about_ca_topic_score_gemma":0.000007263992,"teacher_disagreement_score":0.8745913,"about_ca_system_score_codex":0.00012133226,"about_ca_system_score_gemma":0.00019304198,"threshold_uncertainty_score":0.5647637},"labels":[],"label_agreement":null},{"id":"W2005328778","doi":"10.1109/jstsp.2013.2291302","title":"Introduction to the Special Issue on Non-Cooperative Localization Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.005920381985443222,"score_gpt":0.21654183999035215,"score_spread":0.21062145800490892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005328778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040266644,0.00018174497,0.953611,0.0027461248,0.0015717897,0.0003151365,4.1288743e-7,0.000091724185,0.0012154151],"genre_scores_gemma":[0.9793633,0.00005141292,0.00052886497,0.00022115663,0.019693965,0.000007664758,0.0000014046503,0.000019632944,0.00011257664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991098,0.000030121013,0.00038448113,0.00009519963,0.00019261923,0.00018775319],"domain_scores_gemma":[0.9992979,0.000023979484,0.00009033714,0.00007162639,0.0004817687,0.000034398985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015513512,0.000120950266,0.00016379416,0.00020901272,0.00010244474,0.00011844437,0.0001796629,0.00011068057,0.00013200832],"category_scores_gemma":[0.0000705518,0.000087201865,0.000022857977,0.00094388763,0.000027427375,0.00027930972,0.000008251876,0.0004488931,0.000020563717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013873491,0.00001591445,0.000088560875,0.00001412534,0.00000911279,0.0000024552085,0.00055622496,0.86182684,0.0008559687,0.00002361502,0.034100708,0.102492586],"study_design_scores_gemma":[0.00045936648,0.00033576365,0.0009505469,0.00021796947,0.000017458606,0.00003704381,0.000553052,0.8861398,0.07398143,0.00035622236,0.036679428,0.0002718903],"about_ca_topic_score_codex":0.0000033576173,"about_ca_topic_score_gemma":0.00001151261,"teacher_disagreement_score":0.95308214,"about_ca_system_score_codex":0.00014007349,"about_ca_system_score_gemma":0.000038723487,"threshold_uncertainty_score":0.35559887},"labels":[],"label_agreement":null},{"id":"W2017807702","doi":"10.1109/jstsp.2013.2245629","title":"The Effect of Exogenous Inputs and Defiant Agents on Opinion Dynamics With Local and Global Interactions","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Dynamics (music); Psychology","score_opus":0.00670403052066358,"score_gpt":0.26763663284623285,"score_spread":0.26093260232556925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017807702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98591083,0.0001270652,0.013322703,0.00018821884,0.00009465964,0.00010354957,0.000003202666,0.0000022328484,0.00024753285],"genre_scores_gemma":[0.999683,0.000022196562,0.00016053113,0.000015008432,0.00010205952,0.000002740935,0.0000010957505,0.0000053535045,0.000008015731],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993283,0.00004977421,0.00026849442,0.00008053846,0.00014146454,0.00013139023],"domain_scores_gemma":[0.99936,0.000086120075,0.00026800498,0.000038562797,0.00019632281,0.000050943087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012735074,0.000096708034,0.00017165995,0.000038014663,0.00014108936,0.000091548274,0.00007966871,0.00002995908,0.0000031047643],"category_scores_gemma":[0.000005847782,0.000061416344,0.000022265787,0.00018533856,0.00010367807,0.00017044491,0.000013691807,0.00025080054,2.145234e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029030335,0.00014385203,0.38049603,0.00012784531,0.0001412393,0.000005876078,0.0008088233,0.0036862728,0.00057348673,0.0020997154,0.00004185636,0.6115847],"study_design_scores_gemma":[0.008643161,0.0097406795,0.44859698,0.0050072577,0.00021914102,0.00033896268,0.002998481,0.4860065,0.0068002953,0.029791314,0.0006140428,0.0012431794],"about_ca_topic_score_codex":0.000095838244,"about_ca_topic_score_gemma":0.00002833901,"teacher_disagreement_score":0.6103415,"about_ca_system_score_codex":0.00006280049,"about_ca_system_score_gemma":0.00007955445,"threshold_uncertainty_score":0.25044858},"labels":[],"label_agreement":null},{"id":"W2019286445","doi":"10.1109/jstsp.2010.2051258","title":"Introduction to the Special Issue on Signal and Information Processing for Social Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Signal processing; SIGNAL (programming language); Data science; Information processing; Telecommunications; Radar; Psychology","score_opus":0.010818985491800359,"score_gpt":0.2596171325853093,"score_spread":0.24879814709350892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019286445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048259724,0.000047129277,0.921418,0.028876364,0.00081913633,0.00034201163,6.9976903e-7,0.000025008994,0.00021192759],"genre_scores_gemma":[0.93397826,0.000007212509,0.011447098,0.00084670243,0.05365963,0.000014852483,0.0000012386462,0.0000076174847,0.0000373802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990172,0.000026673397,0.000397745,0.00013139333,0.00022425588,0.00020278001],"domain_scores_gemma":[0.99902654,0.00005590035,0.0003204377,0.00006432258,0.00047727535,0.000055521592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043460948,0.000103094535,0.00014341655,0.00013635487,0.00038738022,0.00042927745,0.00034102416,0.00008089971,0.0000070993115],"category_scores_gemma":[0.000031908887,0.000075958094,0.000028649549,0.00070932397,0.00003348416,0.001172978,0.000024641915,0.0005756338,0.0000011649785],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005653092,0.00003498786,0.00003136419,0.000020812027,0.0000041229655,7.5015475e-7,0.0009519133,0.009773699,0.0017581979,0.0012507216,0.009927756,0.97618914],"study_design_scores_gemma":[0.0010477018,0.00058956933,0.0038261407,0.00014322753,0.000030733285,0.000158942,0.00013211434,0.6250782,0.006499153,0.00507582,0.35700676,0.00041165776],"about_ca_topic_score_codex":9.897917e-7,"about_ca_topic_score_gemma":0.000010225532,"teacher_disagreement_score":0.9757775,"about_ca_system_score_codex":0.000031816504,"about_ca_system_score_gemma":0.00011964633,"threshold_uncertainty_score":0.41395324},"labels":[],"label_agreement":null},{"id":"W2022111925","doi":"10.1109/jstsp.2008.2009263","title":"Introduction to the Issue on fMRI Analysis for Human Brain Mapping","year":2008,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Computer science; Preprocessor; Resting state fMRI; Field (mathematics); Data science; Functional connectivity; Signal processing; Artificial intelligence; Key (lock); Brain activity and meditation; Machine learning; Pattern recognition (psychology); Neuroscience; Psychology; Electroencephalography; Digital signal processing","score_opus":0.05498052999500555,"score_gpt":0.30176549160168353,"score_spread":0.24678496160667798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022111925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79485804,0.00008673095,0.088382214,0.11549808,0.0005698816,0.00032634972,0.000002975182,0.000031797856,0.00024391999],"genre_scores_gemma":[0.98962,0.000006079444,0.00069281424,0.003785939,0.0052283816,0.000011575984,4.9287206e-7,0.0000134645725,0.00064125506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983887,0.00015346991,0.00046336636,0.00030114895,0.0004434812,0.00024982088],"domain_scores_gemma":[0.9977093,0.0013722605,0.00032164602,0.00011293311,0.0004331803,0.00005068442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006544521,0.00014015628,0.00029781464,0.00064885843,0.0007417943,0.00006229485,0.00025844315,0.00005254475,0.000019862799],"category_scores_gemma":[0.003956821,0.00010614151,0.00011003052,0.0025445486,0.00007053745,0.00023546167,0.00001954489,0.00041464376,0.0000037645193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041636275,0.00035510724,0.003771812,0.00008068658,0.00019688929,0.000057287994,0.0055936775,0.09147055,0.8220376,0.00037120047,0.056203272,0.01944556],"study_design_scores_gemma":[0.0029923161,0.003460507,0.0725002,0.00035356934,0.00035715374,0.0007817617,0.0012016528,0.0146817835,0.6323956,0.006821496,0.2633163,0.001137621],"about_ca_topic_score_codex":0.00000625779,"about_ca_topic_score_gemma":0.0000337714,"teacher_disagreement_score":0.20711304,"about_ca_system_score_codex":0.00017148964,"about_ca_system_score_gemma":0.00011117855,"threshold_uncertainty_score":0.5705357},"labels":[],"label_agreement":null},{"id":"W2029002679","doi":"10.1109/jstsp.2014.2333499","title":"Real-Time Power Balancing in Electric Grids With Distributed Storage","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of Toronto","funders":"","keywords":"Computer science; Distributed generation; Power (physics); Distributed computing; Physics","score_opus":0.0028833069919044557,"score_gpt":0.1898415908911097,"score_spread":0.18695828389920524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029002679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9714414,0.00032606095,0.027290314,0.000042854637,0.00007233937,0.00006920554,0.0000010983733,0.000053389685,0.0007033349],"genre_scores_gemma":[0.99787056,0.00004115809,0.0017047438,0.000021233467,0.00030758418,0.0000013691545,0.000002332264,0.000035118348,0.000015879836],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850416,0.000059459064,0.0005612823,0.00013717575,0.00030771905,0.00043018238],"domain_scores_gemma":[0.999341,0.000061422434,0.00018986852,0.00007880082,0.00024230593,0.00008657736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034094232,0.00020367421,0.00038809748,0.00038887069,0.00004679303,0.00007071727,0.0002043721,0.00014785338,0.000023303226],"category_scores_gemma":[0.00003724685,0.00016993222,0.00003485115,0.0016823005,0.000016872482,0.0003378968,0.0000062958466,0.0009059902,0.0000011177523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002311657,0.00011995976,0.024437236,0.00029026376,0.0000768339,0.00026241678,0.0009203831,0.26658866,0.65302247,0.000046774112,0.00081325445,0.053190585],"study_design_scores_gemma":[0.0039505213,0.0015995239,0.12982227,0.0015201326,0.0000712493,0.0009428326,0.00008812208,0.773043,0.085438475,0.0016556563,0.0007792119,0.0010890496],"about_ca_topic_score_codex":0.000008005205,"about_ca_topic_score_gemma":0.00001014638,"teacher_disagreement_score":0.567584,"about_ca_system_score_codex":0.00032010293,"about_ca_system_score_gemma":0.00016388916,"threshold_uncertainty_score":0.6929634},"labels":[],"label_agreement":null},{"id":"W2043241420","doi":"10.1109/jstsp.2009.2015485","title":"Introduction to the Issue on Visual Media Quality Assessment","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Quality assessment; Quality (philosophy); Cover (algebra); Data science; Artificial intelligence; Engineering; Evaluation methods; Reliability engineering","score_opus":0.03061723446250214,"score_gpt":0.35562448890665854,"score_spread":0.3250072544441564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043241420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57166183,0.00015433643,0.3436485,0.080234155,0.0023467334,0.00023820897,5.8552905e-7,0.0000653876,0.0016502931],"genre_scores_gemma":[0.98796874,0.0000058137575,0.0053360597,0.001503509,0.0051090834,0.0000013379193,3.9141239e-7,0.000004491972,0.00007057266],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982636,0.00012868932,0.0005313359,0.00019598994,0.00064377504,0.00023658661],"domain_scores_gemma":[0.9989664,0.00012011902,0.00026176896,0.00012297079,0.00041808846,0.000110675064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086570287,0.00012103998,0.00021413376,0.0002009067,0.000093736475,0.0002852213,0.0005446005,0.000049483693,0.000008068052],"category_scores_gemma":[0.00020105804,0.000081737024,0.00004095995,0.0009830134,0.00001964808,0.00068678166,0.000022956605,0.00046315897,0.00000951868],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056095305,0.00038438122,0.00023023074,0.000013534585,0.000008316906,0.000023436327,0.0017782627,0.0010993975,0.008589317,0.001570558,0.0033299273,0.98291653],"study_design_scores_gemma":[0.0055220146,0.020223709,0.34989756,0.0018671796,0.000091913906,0.00083624717,0.0014116772,0.08553404,0.2720022,0.07373894,0.1862613,0.002613234],"about_ca_topic_score_codex":0.0000017364669,"about_ca_topic_score_gemma":0.000004293695,"teacher_disagreement_score":0.9803033,"about_ca_system_score_codex":0.0001271395,"about_ca_system_score_gemma":0.0002454988,"threshold_uncertainty_score":0.3333139},"labels":[],"label_agreement":null},{"id":"W2056583019","doi":"10.1109/jstsp.2013.2260320","title":"Riemannian Distances for Signal Classification by Power Spectral Density","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Euclidean distance; Spectral density; SIGNAL (programming language); Manifold (fluid mechanics); Weighting; Pattern recognition (psychology); Metric (unit); Distance measures; Artificial intelligence; Mathematics; Feature (linguistics); Measure (data warehouse); Euclidean space; Statistical manifold; Distance matrix; Similarity (geometry); Nonlinear dimensionality reduction; Computer science; Information geometry; Algorithm; Data mining; Dimensionality reduction; Mathematical analysis; Statistics; Physics","score_opus":0.025259284821897102,"score_gpt":0.2642646342709925,"score_spread":0.2390053494490954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056583019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95195216,0.0000891539,0.04598802,0.0011694356,0.0002758933,0.00023113065,0.000004039641,0.000019356497,0.0002708112],"genre_scores_gemma":[0.9983448,0.0000148029185,0.0007658872,0.00029370017,0.00030936295,0.000007241746,0.0000015798416,0.000015335265,0.0002473333],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985576,0.000075394746,0.0005144343,0.00023254463,0.00032947492,0.0002904988],"domain_scores_gemma":[0.998835,0.00014935073,0.00047242932,0.000061028964,0.00039650616,0.00008566421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002167075,0.00014297287,0.00021739288,0.00013325483,0.00019333641,0.0002027535,0.00021506943,0.00008756536,0.00004095571],"category_scores_gemma":[0.00013581374,0.00012001374,0.00006565079,0.00045523525,0.00007682126,0.0007169346,0.000008062158,0.00037870405,0.000002678892],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009434328,0.00011549582,0.001137486,0.000035128407,0.0000038492735,0.0000073080696,0.00013951602,0.00009365408,0.980874,0.00020283551,0.0011000093,0.016196363],"study_design_scores_gemma":[0.0017122043,0.0011658996,0.021252677,0.00027977754,0.00003784645,0.00022174371,0.00021342954,0.08489751,0.8608368,0.026636645,0.0021746191,0.0005708435],"about_ca_topic_score_codex":0.000005771435,"about_ca_topic_score_gemma":0.000008000853,"teacher_disagreement_score":0.12003721,"about_ca_system_score_codex":0.00012124065,"about_ca_system_score_gemma":0.0001234618,"threshold_uncertainty_score":0.48940185},"labels":[],"label_agreement":null},{"id":"W2057111506","doi":"10.1109/jstsp.2012.2232280","title":"Frenet-Serret and the Estimation of Curvature and Torsion","year":2012,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Guelph","funders":"","keywords":"Torsion of a curve; Frenet–Serret formulas; Curvature; Torsion (gastropod); Tangent; Mathematical analysis; Mathematics; Differential geometry; Differential geometry of curves; Tangent vector; Plane curve; Center of curvature; Geometry; Differential equation; Mean curvature; Ordinary differential equation","score_opus":0.0066331859722816265,"score_gpt":0.2445145073914979,"score_spread":0.23788132141921628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057111506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79548997,0.009926379,0.19431527,0.00008167238,0.00004329207,0.000050012433,3.16097e-7,0.000020663334,0.00007242213],"genre_scores_gemma":[0.9811273,0.00026679668,0.01845746,0.000013559988,0.00012303691,8.043389e-7,2.921852e-7,0.000007218139,0.000003534767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941224,0.000029877008,0.00028933,0.000038715054,0.00012743994,0.00010240779],"domain_scores_gemma":[0.99963295,0.00005751699,0.00014664402,0.00003178571,0.00009659273,0.000034518744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024109684,0.00007245511,0.0001939599,0.000089834124,0.00002826232,0.000015029505,0.000058057583,0.000057874506,0.0000021002315],"category_scores_gemma":[0.000049033817,0.000047903828,0.000018223496,0.00029977201,0.000057592304,0.0003282021,0.00000817852,0.00027481772,3.6067004e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023146169,0.00011997166,0.022234961,0.0007459866,0.00013681888,0.000006339867,0.0046492247,0.070835695,0.102651335,0.00044123776,0.00025058503,0.7976964],"study_design_scores_gemma":[0.0018688521,0.00018478134,0.020848334,0.0009974327,0.00022317986,0.00020967283,0.00018212204,0.7356469,0.22322993,0.01563388,0.0005850735,0.00038982276],"about_ca_topic_score_codex":0.0000022416264,"about_ca_topic_score_gemma":0.0000010770881,"teacher_disagreement_score":0.79730654,"about_ca_system_score_codex":0.00002540434,"about_ca_system_score_gemma":0.000010761461,"threshold_uncertainty_score":0.19534613},"labels":[],"label_agreement":null},{"id":"W2066073395","doi":"10.1109/jstsp.2014.2318078","title":"Introduction to the Issue on Perception Inspired Video Processing","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Perception; Artificial intelligence; Computer vision; Psychology","score_opus":0.0136193724607914,"score_gpt":0.26232331756431054,"score_spread":0.24870394510351915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066073395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1408387,0.000101101556,0.816791,0.041421965,0.0003804161,0.00016588594,1.3263828e-7,0.00004471552,0.00025606496],"genre_scores_gemma":[0.9803755,0.000009979472,0.011457242,0.0012445431,0.006788043,0.000006652495,3.3288475e-7,0.000009128056,0.00010855107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871993,0.00009583281,0.00041768016,0.00022100015,0.00033580244,0.00020977903],"domain_scores_gemma":[0.999017,0.000045838937,0.0002909388,0.00015715261,0.00041973573,0.00006933024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000544502,0.000115411756,0.00015913665,0.00017203865,0.00024453254,0.00028070583,0.00056020234,0.000055582776,0.000007201869],"category_scores_gemma":[0.00005991848,0.000080742066,0.000033000004,0.0012313041,0.000023842395,0.00052178383,0.00002732298,0.00044456086,0.000010620627],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020953139,0.00006758927,0.00007069367,0.00002019783,0.0000028643462,0.0000017504583,0.00091672345,0.02150963,0.018936932,0.00023144303,0.0035736647,0.95464754],"study_design_scores_gemma":[0.0010606562,0.0014085146,0.013032112,0.00079029467,0.000036840313,0.00033075747,0.00021562578,0.7338725,0.025705058,0.007294373,0.21558905,0.00066424796],"about_ca_topic_score_codex":0.000003027434,"about_ca_topic_score_gemma":0.0000066237603,"teacher_disagreement_score":0.9539833,"about_ca_system_score_codex":0.000087208144,"about_ca_system_score_gemma":0.000097125354,"threshold_uncertainty_score":0.3292566},"labels":[],"label_agreement":null},{"id":"W2092614423","doi":"10.1109/jstsp.2009.2013533","title":"Introduction to the Issue on Digital Image Processing Techniques for Oncology","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Digital image processing; Computer science; Image processing; Focus (optics); Image analysis; Artificial intelligence; Digital image; Medical physics; Computer vision; Multimedia; Medicine; Image (mathematics)","score_opus":0.027081625025414585,"score_gpt":0.31756157627123893,"score_spread":0.2904799512458244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092614423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26258406,0.00020105789,0.48742068,0.23922311,0.0014818843,0.0018329117,0.0000074966565,0.00032340703,0.0069253966],"genre_scores_gemma":[0.9908901,0.00000755746,0.002607815,0.0019932226,0.0042026127,0.0000111799145,4.4080946e-7,0.000012426629,0.0002746855],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998766,0.000074792515,0.00045830916,0.00022433874,0.00026542568,0.00021113972],"domain_scores_gemma":[0.99896675,0.0000977984,0.00039301845,0.00008095633,0.00040455023,0.000056911504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041158783,0.00011736618,0.00017529087,0.00025671028,0.00021632925,0.0002562446,0.00024669178,0.000078734694,0.000008744982],"category_scores_gemma":[0.00069220766,0.00008566383,0.000042372492,0.00084538304,0.000049214017,0.00055287033,0.0000058560922,0.00045157888,0.0000040628847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017512571,0.00010792752,0.0000027647754,0.000012980536,7.271428e-7,0.0000044650474,0.00032931622,0.00008691659,0.38902694,0.000032892396,0.0016642592,0.6085557],"study_design_scores_gemma":[0.00031812687,0.0015474041,0.00023530393,0.00010409152,0.000009089764,0.0002766146,0.00015822779,0.0026858728,0.8925524,0.0017819334,0.10019475,0.00013620051],"about_ca_topic_score_codex":2.8474054e-7,"about_ca_topic_score_gemma":0.000001180367,"teacher_disagreement_score":0.728306,"about_ca_system_score_codex":0.00021600915,"about_ca_system_score_gemma":0.00020805992,"threshold_uncertainty_score":0.34932694},"labels":[],"label_agreement":null},{"id":"W2095924090","doi":"10.1109/jstsp.2009.2035860","title":"Interference Cancellation Based Detection for V-BLAST With Diversity Maximizing Channel Partition","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Single antenna interference cancellation; MIMO; Computer science; Algorithm; Multiplexing; Partition (number theory); Diversity gain; Computational complexity theory; Constellation; Interference (communication); Bandwidth (computing); Decoding methods; Fading; Channel (broadcasting); Mathematics; Telecommunications","score_opus":0.02138297662739158,"score_gpt":0.24470529570889055,"score_spread":0.22332231908149897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095924090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14157306,0.00013208968,0.8579673,0.000070356284,0.00003529623,0.000096766846,6.327148e-7,0.0000718968,0.000052620377],"genre_scores_gemma":[0.9795288,0.00003409434,0.020299189,0.000025571608,0.00009459538,0.000004122058,0.0000013519576,0.0000098774035,0.0000024051606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938494,0.000020804886,0.00026658963,0.000071960145,0.00012332934,0.00013234679],"domain_scores_gemma":[0.99931866,0.00003404216,0.0001857803,0.00005895633,0.00037215822,0.00003041373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013391497,0.00009424366,0.00014185051,0.00019002336,0.00011578024,0.000029969588,0.00013174588,0.00006079494,0.0000014366099],"category_scores_gemma":[0.000015411175,0.00009241682,0.000021930686,0.00033844143,0.000016468415,0.00040248482,0.000006024841,0.0002689592,7.48309e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031442379,0.00006672074,0.00052008976,0.00015950057,0.000013512347,0.000004543816,0.00064646325,0.5496347,0.13362257,0.000015544772,0.000018168412,0.3149838],"study_design_scores_gemma":[0.0006318636,0.00041145677,0.0016038634,0.00062875816,0.000016666092,0.000011799084,0.00006617571,0.5010864,0.49263093,0.0027131215,0.000034437053,0.00016452315],"about_ca_topic_score_codex":0.0000020437885,"about_ca_topic_score_gemma":0.000050502826,"teacher_disagreement_score":0.8379557,"about_ca_system_score_codex":0.00022165242,"about_ca_system_score_gemma":0.000051857933,"threshold_uncertainty_score":0.37686485},"labels":[],"label_agreement":null},{"id":"W2106993652","doi":"10.1109/jstsp.2007.910623","title":"Convex Conic Formulations of Robust Downlink Precoder Designs With Quality of Service Constraints","year":2007,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":203,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematical optimization; Computer science; Semidefinite programming; Transmitter; Robustness (evolution); Quality of service; Telecommunications link; Channel state information; Convex optimization; Transmitter power output; Channel (broadcasting); Transmission (telecommunications); Mathematics; Wireless; Regular polygon; Telecommunications","score_opus":0.04314146787091745,"score_gpt":0.2900214465327398,"score_spread":0.24687997866182237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106993652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25004697,0.00022259506,0.74911684,0.000012991865,0.0000534897,0.00012260315,0.0000030117383,0.00001785928,0.00040364795],"genre_scores_gemma":[0.93078923,0.000010662842,0.06907722,0.000011413194,0.00008111523,0.0000010061337,0.0000022162233,0.000019801517,0.0000073532074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982657,0.000043818287,0.0011508933,0.00008848377,0.00024500015,0.00020610094],"domain_scores_gemma":[0.99759156,0.0001717611,0.00070106913,0.000077658304,0.0014002604,0.000057698093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006335379,0.00013492184,0.0004045711,0.00024138986,0.000032238113,0.000012764784,0.00013127118,0.00012036841,0.00001631692],"category_scores_gemma":[0.000055088272,0.00012341153,0.00003266312,0.0007979698,0.000067734574,0.00040707557,0.0000050491444,0.0003206409,1.8498055e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107430096,0.00005967706,0.004716402,0.000731121,0.000059145972,0.000005185613,0.0011088968,0.9075282,0.07968449,0.00008942716,0.0000036999886,0.0059062764],"study_design_scores_gemma":[0.0071321507,0.00066446175,0.021886956,0.0048220414,0.00019965685,0.0003923883,0.0017474059,0.44430253,0.5158656,0.0020164787,0.00003068298,0.00093964976],"about_ca_topic_score_codex":0.000008864254,"about_ca_topic_score_gemma":0.00011593295,"teacher_disagreement_score":0.68074226,"about_ca_system_score_codex":0.00012400177,"about_ca_system_score_gemma":0.0002383744,"threshold_uncertainty_score":0.50325763},"labels":[],"label_agreement":null},{"id":"W2111438680","doi":"10.1109/jstsp.2010.2052236","title":"Frame Rate Converter With Pixel-Based Motion Vectors Selection and Halo Reduction Using Preliminary Interpolation","year":2010,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Interpolation (computer graphics); Pixel; Motion vector; Artificial intelligence; Computer science; Computer vision; Halo; Motion estimation; Frame rate; Blocking (statistics); Stairstep interpolation; Block (permutation group theory); Frame (networking); Reduction (mathematics); Mathematics; Multivariate interpolation; Bilinear interpolation; Image (mathematics); Physics","score_opus":0.012832788433345262,"score_gpt":0.26570156128284833,"score_spread":0.25286877284950304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111438680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38647917,0.00005528249,0.61307573,0.00012924559,0.00012760604,0.00007083289,7.5786176e-8,0.000055442266,0.0000066053735],"genre_scores_gemma":[0.65114397,0.0000026699206,0.34864601,0.000025149351,0.00016465619,0.0000018360371,3.6118053e-7,0.000011888116,0.0000034621232],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987373,0.00009707857,0.00044386362,0.0002579591,0.0002510605,0.00021278561],"domain_scores_gemma":[0.9982906,0.000045303987,0.0006251763,0.00009435555,0.00087895704,0.000065613895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044658844,0.00017185321,0.00021152255,0.0004672643,0.00016297106,0.0002601316,0.0002313656,0.00013864071,0.0000027016085],"category_scores_gemma":[0.00006850947,0.00015224497,0.000023486027,0.00088853034,0.000082796665,0.002291279,0.000021083679,0.0008629668,1.5553734e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015971148,0.00008097685,0.0024058193,0.00008246756,0.000008058131,0.000009377877,0.00052910676,0.0009907471,0.92428035,0.0000325084,0.0000048743686,0.071416005],"study_design_scores_gemma":[0.00045410646,0.0004732845,0.0021325352,0.00042871202,0.000019643481,0.00058172585,0.000024738867,0.8112854,0.18155977,0.002845753,0.000012191277,0.00018218879],"about_ca_topic_score_codex":0.0000076232627,"about_ca_topic_score_gemma":0.0000067898045,"teacher_disagreement_score":0.8102946,"about_ca_system_score_codex":0.00011592726,"about_ca_system_score_gemma":0.00037147847,"threshold_uncertainty_score":0.620837},"labels":[],"label_agreement":null},{"id":"W2115599677","doi":"10.1109/jstsp.2010.2081790","title":"Diarization of Telephone Conversations Using Factor Analysis","year":2010,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"Johns Hopkins University","keywords":"Speaker diarisation; Computer science; Cluster analysis; Speech recognition; Word error rate; Speaker recognition; NIST; Channel (broadcasting); Hierarchical clustering; Bayes' theorem; Exploit; Artificial intelligence; Pattern recognition (psychology); Telecommunications; Bayesian probability","score_opus":0.025827002893712158,"score_gpt":0.2748875302374324,"score_spread":0.24906052734372025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115599677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49863514,0.000021113297,0.50107664,0.00008187309,0.00009780111,0.000023853327,8.003159e-7,0.000007547234,0.000055233697],"genre_scores_gemma":[0.90237796,0.0000062449312,0.09744916,0.000032707612,0.000118890384,3.3020856e-7,4.9529507e-7,0.000004152143,0.000010038923],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885213,0.000060004026,0.0005295904,0.00011353098,0.0003150819,0.00012966809],"domain_scores_gemma":[0.99831706,0.0001045184,0.00055895245,0.00009086099,0.0008702298,0.000058346548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002670704,0.00008259673,0.00025246022,0.0007486649,0.000063340914,0.000087684406,0.00030207538,0.00008730608,0.00006096669],"category_scores_gemma":[0.0001447933,0.000075740354,0.000082387254,0.0023881788,0.000038514063,0.00060099503,0.000013820204,0.00031689624,5.574376e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016782926,0.0002073649,0.029188635,0.00004174437,0.0001743309,0.00002110495,0.0016587963,0.0016084318,0.78911334,0.00016784991,0.0000083391715,0.17779328],"study_design_scores_gemma":[0.0005358754,0.000068190006,0.032196123,0.000093948656,0.00016731072,0.000075148746,0.000080255195,0.4514744,0.513439,0.0016181234,0.000044800116,0.00020686198],"about_ca_topic_score_codex":0.000012949213,"about_ca_topic_score_gemma":0.000031855147,"teacher_disagreement_score":0.44986597,"about_ca_system_score_codex":0.00003949958,"about_ca_system_score_gemma":0.0003165596,"threshold_uncertainty_score":0.30886018},"labels":[],"label_agreement":null},{"id":"W2132242363","doi":"10.1109/jstsp.2007.914897","title":"Interference Aggregation in Spectrum-Sensing Cognitive Wireless Networks","year":2008,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":276,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cognitive radio; White spaces; Computer science; Interference (communication); Spectrum management; Radio spectrum; Wireless; Telecommunications; Computer network; Fading; Aggregate (composite); Channel (broadcasting)","score_opus":0.019855192959678376,"score_gpt":0.24618184235078666,"score_spread":0.22632664939110828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132242363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47964385,0.0006573576,0.51906633,0.0001499424,0.00018844583,0.000064635155,1.0235506e-7,0.00001785631,0.00021148133],"genre_scores_gemma":[0.9943656,0.0002890122,0.0044951374,0.00012370295,0.0006959308,4.4749487e-7,5.889067e-7,0.000015016435,0.000014612836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784964,0.00019411396,0.0008208349,0.00030211944,0.00036696385,0.0004663414],"domain_scores_gemma":[0.9984615,0.00025008706,0.00055468705,0.000090969144,0.0005493059,0.00009343866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047985604,0.00021456332,0.00042495396,0.00054865563,0.00015942458,0.00016001056,0.00034987283,0.00013182682,0.0000034601687],"category_scores_gemma":[0.000076910525,0.00021078716,0.00006597811,0.0019406283,0.000101047925,0.0009475833,0.000043931406,0.0010583396,8.30823e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018299786,0.00019124482,0.012876878,0.000037105416,0.000032614644,0.0024478522,0.004256088,0.010331767,0.002125908,0.00020180918,0.000027719238,0.967288],"study_design_scores_gemma":[0.0012868405,0.0002796644,0.01690198,0.0025334812,0.000012544815,0.0023609668,0.00012461182,0.9638456,0.009697607,0.002587335,0.000011183519,0.00035820217],"about_ca_topic_score_codex":0.000026461423,"about_ca_topic_score_gemma":0.00014783606,"teacher_disagreement_score":0.9669298,"about_ca_system_score_codex":0.00024751993,"about_ca_system_score_gemma":0.0003975501,"threshold_uncertainty_score":0.8595651},"labels":[],"label_agreement":null},{"id":"W2136320194","doi":"10.1109/jstsp.2008.2006386","title":"A New Wave-Front Reconstruction Method for Adaptive Optics Systems Using Wavelets","year":2008,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wavelet; Haar wavelet; Computer science; Iterative reconstruction; Wavefront; Algorithm; Sampling (signal processing); Noise reduction; Wavelet transform; Haar; Data set; Artificial intelligence; Computer vision; Discrete wavelet transform; Optics; Physics; Filter (signal processing)","score_opus":0.049195017765827874,"score_gpt":0.2864250127388613,"score_spread":0.2372299949730334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136320194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13207361,0.00028595343,0.86657906,0.000032119508,0.0003960412,0.00019154802,0.0000040445634,0.0000075952903,0.00043005054],"genre_scores_gemma":[0.5924328,0.0000055397336,0.4055196,0.0000081381295,0.0018211971,0.0000010117099,0.0000012139388,0.000023973957,0.00018650062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982853,0.00008540026,0.0007897887,0.00021065232,0.00027443186,0.00035444336],"domain_scores_gemma":[0.99787724,0.00011306906,0.00082299445,0.00007594137,0.0009780488,0.0001327037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003426877,0.00022233305,0.000494997,0.0002323305,0.00024143746,0.00007587455,0.000112662165,0.00010023088,0.0000110023875],"category_scores_gemma":[0.000018608587,0.00020875104,0.000121885714,0.00028806448,0.000042619817,0.00040545996,0.000014066988,0.0004609836,4.2133883e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010053808,0.00044040146,0.0051953266,0.00022711442,0.00072601455,0.00020156105,0.005514205,0.16631126,0.10828333,0.00450462,0.0005498932,0.7070409],"study_design_scores_gemma":[0.0017823712,0.00034636856,0.00028373755,0.0007677516,0.00013233173,0.0009220977,0.0010146546,0.97531533,0.013214179,0.0055898437,0.00022293862,0.0004083708],"about_ca_topic_score_codex":0.00010012854,"about_ca_topic_score_gemma":0.0000031321813,"teacher_disagreement_score":0.80900407,"about_ca_system_score_codex":0.000185134,"about_ca_system_score_gemma":0.0008391719,"threshold_uncertainty_score":0.85126203},"labels":[],"label_agreement":null},{"id":"W2140247984","doi":"10.1109/jstsp.2008.2007816","title":"Probabilistic Boolean Network Analysis of Brain Connectivity in Parkinson's Disease","year":2008,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Probabilistic logic; Computer science; Functional magnetic resonance imaging; Robustness (evolution); Artificial intelligence; Functional connectivity; Parkinson's disease; Neuroimaging; Machine learning; Computational model; Neuroscience; Disease; Psychology; Medicine; Pathology; Biology","score_opus":0.012673672819597796,"score_gpt":0.24698586591490052,"score_spread":0.2343121930953027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140247984","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992231,0.0024582008,0.005064453,0.00008362027,0.00004019658,0.000084518026,0.0000021210078,0.0000031921932,0.00003271286],"genre_scores_gemma":[0.9987995,0.000114688286,0.0006081012,0.000054158794,0.0003650082,0.000002835767,0.000009584027,0.000014930119,0.0000312068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981504,0.00029407121,0.00071266,0.00025809414,0.00028933093,0.00029543263],"domain_scores_gemma":[0.9986776,0.000069370115,0.00054594607,0.00016709429,0.0004205942,0.00011937384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073273043,0.00016708417,0.0005376516,0.0004038426,0.000061852304,0.000013920319,0.0002259303,0.00012613465,0.000008949815],"category_scores_gemma":[0.00022476962,0.00016274942,0.00020621224,0.0022273124,0.00011484827,0.000015841284,0.000027804093,0.0002576649,1.18536704e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003844483,0.00022226218,0.47247487,0.00007365178,0.00038877895,0.00008577308,0.000109284534,0.49831828,0.025228329,0.000006365311,0.00017348987,0.0025344416],"study_design_scores_gemma":[0.0012660469,0.00033050365,0.9271621,0.0002770842,0.00076615415,0.00005224594,0.00004055251,0.056820173,0.0113021415,0.00069942384,0.0008744301,0.00040911007],"about_ca_topic_score_codex":0.000010423272,"about_ca_topic_score_gemma":0.00020396138,"teacher_disagreement_score":0.45468727,"about_ca_system_score_codex":0.000071181435,"about_ca_system_score_gemma":0.00052927795,"threshold_uncertainty_score":0.66367286},"labels":[],"label_agreement":null},{"id":"W2141685823","doi":"10.1109/jstsp.2009.2023349","title":"Collaborative Code Tracking of Composite GNSS Signals","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Ranging; Channel (broadcasting); Jitter; Exploit; Satellite navigation; Real-time computing; GNSS augmentation; Code (set theory); Satellite system; Global Positioning System; Telecommunications","score_opus":0.013724057372024543,"score_gpt":0.2636884673310222,"score_spread":0.24996440995899766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141685823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95355475,0.0018266565,0.041935727,0.000094094554,0.00014045888,0.00006565386,0.000003856623,0.00003887224,0.002339908],"genre_scores_gemma":[0.9966232,0.0000526636,0.0030708453,0.00003685002,0.00018843553,5.677747e-7,9.2838724e-7,0.000011932212,0.0000145692065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987747,0.00004842868,0.0006506426,0.00008563291,0.00024276662,0.00019781485],"domain_scores_gemma":[0.99878526,0.0000541178,0.00026474387,0.000049402202,0.00079106377,0.0000554305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020590056,0.00013606745,0.00033110537,0.00024020189,0.000047144524,0.0000659857,0.00018527944,0.00008928392,0.000009352212],"category_scores_gemma":[0.000026842106,0.00013082413,0.000042960037,0.00070908986,0.00003413822,0.00037256908,0.0000033819106,0.0004574178,6.1710233e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007717489,0.00012484464,0.0007869973,0.00011769188,0.000040239578,0.000030405792,0.0025093872,0.12136132,0.83742195,0.000046863406,0.00020052693,0.03728257],"study_design_scores_gemma":[0.0007729523,0.00055783516,0.00835692,0.0021717118,0.00004122372,0.0001248263,0.00021210441,0.0460246,0.93965185,0.0017296524,0.00009185397,0.0002644983],"about_ca_topic_score_codex":0.0000012739057,"about_ca_topic_score_gemma":0.0000025351421,"teacher_disagreement_score":0.10222985,"about_ca_system_score_codex":0.00008784246,"about_ca_system_score_gemma":0.00011625732,"threshold_uncertainty_score":0.53348535},"labels":[],"label_agreement":null},{"id":"W2155628618","doi":"10.1109/jstsp.2008.2006664","title":"SPHARM-Based Spatial fMRI Characterization With Intersubject Anatomical Variability Reduction","year":2008,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Spatial normalization; Normalization (sociology); Pattern recognition (psychology); Artificial intelligence; Functional magnetic resonance imaging; Computer science; Region of interest; Computer vision; Voxel; Neuroscience; Psychology","score_opus":0.0298305915377081,"score_gpt":0.26002361992105316,"score_spread":0.23019302838334507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155628618","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9146837,0.000012622699,0.083510846,0.001231021,0.0003262908,0.00012561437,0.0000020614307,0.000030248728,0.00007756706],"genre_scores_gemma":[0.9980471,0.000007704324,0.00084268634,0.00029851613,0.0007613867,0.00000432641,0.0000014075147,0.000016487274,0.000020355948],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982036,0.00028418395,0.0004877327,0.000306745,0.0004904957,0.00022729304],"domain_scores_gemma":[0.9983561,0.000547283,0.00045349132,0.000081267324,0.0004981006,0.00006377871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041842862,0.00016396532,0.00028311802,0.00027695837,0.00022410302,0.00004504569,0.00016110182,0.000079888,0.00002182696],"category_scores_gemma":[0.0014347727,0.00013866853,0.00004739647,0.0009066139,0.00019554005,0.0005603457,0.000013560972,0.0005793011,0.0000011120463],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015525193,0.000406857,0.014384343,0.000076239055,0.000015306487,0.00013337398,0.0006188256,0.002631373,0.9649151,0.000013000614,0.000060426813,0.015192641],"study_design_scores_gemma":[0.0019394383,0.00081654446,0.044803634,0.00026665497,0.00003125743,0.00096822216,0.000036178826,0.027146678,0.92317784,0.0002665577,0.0002470558,0.00029992833],"about_ca_topic_score_codex":0.000008357611,"about_ca_topic_score_gemma":0.0000064532674,"teacher_disagreement_score":0.08336341,"about_ca_system_score_codex":0.00027740147,"about_ca_system_score_gemma":0.00060193037,"threshold_uncertainty_score":0.56547385},"labels":[],"label_agreement":null},{"id":"W2156292614","doi":"10.1109/jstsp.2012.2193555","title":"Rendering 3-D High Dynamic Range Images: Subjective Evaluation of Tone-Mapping Methods and Preferred 3-D Image Attributes","year":2012,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tone mapping; High dynamic range; Stereoscopy; Rendering (computer graphics); Computer science; Artificial intelligence; Computer vision; Brightness; Tone (literature); High-dynamic-range imaging; Image quality; Dynamic range; Computer graphics (images); Image (mathematics); Physics; Optics","score_opus":0.04946989989573047,"score_gpt":0.37611264856638377,"score_spread":0.3266427486706533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156292614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26368624,0.0023377936,0.73346555,0.00008649417,0.00012811588,0.00015990777,6.1057904e-7,0.00003288055,0.000102400525],"genre_scores_gemma":[0.6380293,0.000042026284,0.36180508,0.00001124841,0.00009114132,0.000005875052,4.6416534e-7,0.000008456241,0.000006428605],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99760365,0.00051169976,0.0007196373,0.00020276078,0.0006223291,0.00033989214],"domain_scores_gemma":[0.99736774,0.00017957864,0.00077655946,0.00014298878,0.0014649578,0.00006819072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042180493,0.00017387861,0.00037122803,0.0004642205,0.00008705882,0.0001224364,0.0003891584,0.00009087662,0.0000056643557],"category_scores_gemma":[0.00035280944,0.00016400195,0.00004433869,0.0009321123,0.00006978377,0.0025994622,0.00008292693,0.00041546073,1.7942635e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002374205,0.00010877975,0.0017366314,0.00014494578,0.00003680373,0.00000452068,0.0028963315,0.0000936935,0.7020089,0.00003234179,0.000018288285,0.29289505],"study_design_scores_gemma":[0.0010101796,0.00017211032,0.03500284,0.00067025924,0.00006827189,0.00011227401,0.00017388805,0.06112227,0.896799,0.0045953295,0.00001439382,0.00025916452],"about_ca_topic_score_codex":0.000010534344,"about_ca_topic_score_gemma":0.0000022084778,"teacher_disagreement_score":0.37434304,"about_ca_system_score_codex":0.00030821736,"about_ca_system_score_gemma":0.0002655626,"threshold_uncertainty_score":0.66878057},"labels":[],"label_agreement":null},{"id":"W2156416207","doi":"10.1109/jstsp.2013.2272241","title":"Pixel-Wise Unified Rate-Quantization Model for Multi-Level Rate Control","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Quantization (signal processing); Pixel; Algorithm; Bit rate; Coding (social sciences); Computer science; Constant bitrate; Mathematics; Block size; Harmonic Vector Excitation Coding; Artificial intelligence; Real-time computing; Variable bitrate; Statistics","score_opus":0.08120076401810299,"score_gpt":0.2994491346110902,"score_spread":0.21824837059298718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156416207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072605364,0.00025742225,0.9257113,0.00095313886,0.00015461053,0.00021123566,9.929066e-7,0.00009104132,0.000014930902],"genre_scores_gemma":[0.8794711,0.000027281105,0.11999717,0.00020040348,0.00008790177,0.000017392491,4.310223e-7,0.000011238593,0.00018702273],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985204,0.00010042485,0.00063922076,0.00022134799,0.00020835544,0.0003102797],"domain_scores_gemma":[0.997799,0.00013923715,0.0005574771,0.00015143721,0.0012851028,0.00006778539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056415837,0.00016598467,0.00030888323,0.0003388391,0.00016619441,0.0003172611,0.00079913595,0.00014533238,0.0000023952302],"category_scores_gemma":[0.0002492288,0.00013604216,0.00006285813,0.00064060936,0.000040528976,0.0010454451,0.000039068633,0.00039160263,0.0000017442167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013053251,0.0003510682,0.000639781,0.00017279453,0.000051647887,0.00001635188,0.0010811978,0.1394039,0.48236632,0.002375188,0.0009540758,0.37245715],"study_design_scores_gemma":[0.001471887,0.0001111304,0.00064302655,0.00021801368,0.0000101203195,0.000014363353,0.000033907527,0.94935715,0.03494703,0.012999751,0.000032339063,0.00016127176],"about_ca_topic_score_codex":0.0000038553976,"about_ca_topic_score_gemma":0.0000033868673,"teacher_disagreement_score":0.8099533,"about_ca_system_score_codex":0.00007528791,"about_ca_system_score_gemma":0.0003469496,"threshold_uncertainty_score":0.55476385},"labels":[],"label_agreement":null},{"id":"W2272804037","doi":"10.1109/jstsp.2016.2520912","title":"Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":1452,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Beamforming; Electronic engineering; Computer science; Radio frequency; Antenna (radio); WSDMA; MIMO; Antenna array; Transmission (telecommunications); Engineering; Telecommunications; Precoding","score_opus":0.020871781731262407,"score_gpt":0.2336632328719227,"score_spread":0.21279145114066028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272804037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23717175,0.00041626705,0.76213086,0.00004826719,0.00008384548,0.000082391,0.000005647052,0.000025637184,0.000035321424],"genre_scores_gemma":[0.97353,0.0001003757,0.025984764,0.000026368072,0.0002978563,0.0000032572143,8.412329e-7,0.000025785495,0.00003073147],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998961,0.000015129472,0.00047695506,0.00011416014,0.00015273617,0.0002800236],"domain_scores_gemma":[0.99936557,0.00008117419,0.00013581291,0.000044759763,0.0002887787,0.000083911385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003356398,0.00013569696,0.0002290296,0.00021865816,0.00007207036,0.00009616138,0.000098951605,0.00005687822,0.0000051264396],"category_scores_gemma":[0.000060563583,0.00010157077,0.0000438697,0.00014992763,0.000018865145,0.0006233639,0.000007985891,0.00017618052,4.6097446e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012538463,0.000055804267,0.00059530075,0.00022700409,0.00005016557,0.000019143832,0.0007252864,0.008438505,0.75101864,0.0000069207554,0.00012306444,0.2386148],"study_design_scores_gemma":[0.0017220979,0.0002971453,0.00007036264,0.00094817934,0.000037513895,0.00024098197,0.00011637316,0.6002011,0.39109832,0.00440912,0.0004941211,0.00036470845],"about_ca_topic_score_codex":1.9444204e-7,"about_ca_topic_score_gemma":9.5028247e-7,"teacher_disagreement_score":0.7363583,"about_ca_system_score_codex":0.00007208667,"about_ca_system_score_gemma":0.000068333786,"threshold_uncertainty_score":0.41419357},"labels":[],"label_agreement":null},{"id":"W2322447463","doi":"10.1109/jstsp.2016.2532847","title":"A Gaussian Mixture Framework for Co-Operative Rehabilitation Therapy in Assistive Impedance-Based Tasks","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Task (project management); Rehabilitation; Robot; Computer science; Physical medicine and rehabilitation; Artificial intelligence; Human–computer interaction; Simulation; Physical therapy; Medicine; Engineering","score_opus":0.020376151468483924,"score_gpt":0.30554729361665034,"score_spread":0.2851711421481664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322447463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27051634,0.0004257814,0.72786146,0.0007665962,0.00012863189,0.00019260039,9.5069475e-7,0.00002891834,0.0000787189],"genre_scores_gemma":[0.97313225,0.000024269582,0.026395569,0.00007368499,0.0003190645,0.000013790152,0.0000012156016,0.000024918198,0.000015211234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889255,0.00009738181,0.0004941085,0.00012134983,0.0001847261,0.00020991339],"domain_scores_gemma":[0.99890834,0.0005042936,0.00019132321,0.000051822608,0.00029657688,0.000047627625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000356102,0.00014087251,0.0002505987,0.00029266014,0.00005592054,0.00005195942,0.00010258454,0.00020320067,0.0000268136],"category_scores_gemma":[0.0002846237,0.000103175,0.000053309286,0.0004327239,0.00003202328,0.000311488,0.000001617607,0.0005815418,7.111708e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073991367,0.0001708975,0.034729812,0.0002933485,0.000059742168,0.00002514225,0.0062746946,0.41201535,0.09813558,0.0003320683,0.00027301806,0.44695044],"study_design_scores_gemma":[0.013329749,0.0030154595,0.24925622,0.010584739,0.00004566196,0.00004551923,0.0012089069,0.5894272,0.11137541,0.017481832,0.0027946767,0.0014346463],"about_ca_topic_score_codex":0.0000013289982,"about_ca_topic_score_gemma":0.000009648784,"teacher_disagreement_score":0.702616,"about_ca_system_score_codex":0.00024610912,"about_ca_system_score_gemma":0.0001697537,"threshold_uncertainty_score":0.42073542},"labels":[],"label_agreement":null},{"id":"W2332282366","doi":"10.1109/jstsp.2016.2549499","title":"Filtering of a Discrete-Time HMM-Driven Multivariate Ornstein-Uhlenbeck Model With Application to Forecasting Market Liquidity Regimes","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TD Bank Group; Western University","funders":"","keywords":"Market liquidity; Econometrics; Computer science; Financial market; Multivariate statistics; Liquidity risk; Metric (unit); Model selection; Economics; Finance; Artificial intelligence; Machine learning","score_opus":0.032996941399242447,"score_gpt":0.24211913773430915,"score_spread":0.2091221963350667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332282366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49491185,0.00010645459,0.50439084,0.00014737269,0.00002265646,0.00010119923,0.000010439438,0.0000074265586,0.00030177418],"genre_scores_gemma":[0.9612838,0.000023013452,0.03826896,0.000015837772,0.00017671454,0.0000065490676,6.220876e-7,0.000023525748,0.00020096266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983002,0.000024010547,0.0010308695,0.00026349555,0.000104100065,0.00027733383],"domain_scores_gemma":[0.99835825,0.00008217569,0.0009988807,0.00013146806,0.00034729677,0.00008193245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006471163,0.00016245975,0.00050161936,0.00032469322,0.000076641605,0.000038664573,0.00024586494,0.00010475676,0.000016474036],"category_scores_gemma":[0.00019650036,0.00013152306,0.00006376998,0.00045341547,0.000039202776,0.0005214328,0.00003393587,0.00018230603,0.0000022975955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004109627,0.00067489996,0.09756428,0.0010125366,0.00021386388,0.000041175965,0.0075143888,0.46474153,0.22506346,0.0019064602,0.00030105523,0.19685672],"study_design_scores_gemma":[0.00090984564,0.00031433388,0.0034345149,0.0010735466,0.000013532682,0.000016947035,0.000022807713,0.97883326,0.0080046095,0.0070036165,0.00011512548,0.0002578687],"about_ca_topic_score_codex":0.000041018426,"about_ca_topic_score_gemma":0.000015844234,"teacher_disagreement_score":0.51409173,"about_ca_system_score_codex":0.00015876835,"about_ca_system_score_gemma":0.00014388928,"threshold_uncertainty_score":0.5363355},"labels":[],"label_agreement":null},{"id":"W2336147758","doi":"10.1109/jstsp.2016.2548995","title":"Sequential Detection of Market Shocks With Risk-Averse CVaR Social Sensors","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"CVAR; Computer science; Econometrics; Stock market; Bayesian probability; Expected shortfall; Risk management; Economics; Artificial intelligence; Context (archaeology)","score_opus":0.01905369556241225,"score_gpt":0.21435925759075586,"score_spread":0.1953055620283436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336147758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94533825,0.00033655303,0.052605513,0.00011091954,0.00012449855,0.0000610467,0.000016879352,0.000008044448,0.0013982733],"genre_scores_gemma":[0.99854445,0.00005254042,0.000506066,0.0000068380477,0.0004736816,0.0000010425899,2.5703812e-7,0.000015821726,0.00039927405],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998542,0.00005440416,0.00092403876,0.00016918125,0.00010538401,0.0002049541],"domain_scores_gemma":[0.9979469,0.000042379972,0.0015763657,0.000071000584,0.0003167392,0.00004659423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005901905,0.0001266102,0.00048735196,0.0004084754,0.000107636864,0.000041771782,0.00013724559,0.00009892249,0.00028644418],"category_scores_gemma":[0.000053371143,0.00009887389,0.00011741418,0.0006232639,0.00006484428,0.00032227268,0.00001204239,0.00022534964,0.0000032941057],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050238953,0.0010961641,0.48657134,0.0011084757,0.0025562237,0.00029916078,0.008225564,0.0050726393,0.13887827,0.0023363451,0.0009457975,0.34788612],"study_design_scores_gemma":[0.035770386,0.0075038034,0.51505804,0.0044781687,0.0013471638,0.001650458,0.003922919,0.09974263,0.21382771,0.065483026,0.045729354,0.0054863254],"about_ca_topic_score_codex":0.00013966468,"about_ca_topic_score_gemma":0.000102080216,"teacher_disagreement_score":0.3423998,"about_ca_system_score_codex":0.00016034694,"about_ca_system_score_gemma":0.000077898665,"threshold_uncertainty_score":0.403196},"labels":[],"label_agreement":null},{"id":"W2338019868","doi":"10.1109/jstsp.2016.2555482","title":"Off-the-Grid Low-Rank Matrix Recovery and Seismic Data Reconstruction","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Low-rank approximation; Matrix completion; Matrix (chemical analysis); Computer science; Algorithm; Grid; Sparse matrix; Rank (graph theory); Matrix norm; Interpolation (computer graphics); Data recovery; TRACE (psycholinguistics); Regularization (linguistics); Compressed sensing; Mathematical optimization; Data mining; Mathematics; Artificial intelligence; Geometry","score_opus":0.01795903313465918,"score_gpt":0.24721910485984397,"score_spread":0.22926007172518478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338019868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90303767,0.0038317603,0.09182841,0.0003868819,0.00056438206,0.0000836425,0.0000051997235,0.00009559118,0.00016644178],"genre_scores_gemma":[0.99430615,0.0011934837,0.0035203227,0.000034054705,0.00089319487,7.007104e-7,7.1216766e-7,0.000019929628,0.00003146882],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910325,0.000043215932,0.00040181982,0.00012399042,0.00015301576,0.00017468895],"domain_scores_gemma":[0.99937236,0.00009017478,0.00016837096,0.00015319325,0.000177166,0.000038756927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027009015,0.000119517186,0.00019394643,0.00015951072,0.00006007323,0.00007154218,0.00026533537,0.00008809378,0.0000068062936],"category_scores_gemma":[0.00004736057,0.00007833784,0.000022709963,0.0002432886,0.000055661367,0.00058509066,0.000028172677,0.0003103177,7.7469895e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004702401,0.00001258325,0.00051045354,0.000045254725,0.00003420997,0.00002802927,0.000073380885,0.0016199502,0.09972498,0.0000033149056,0.0017568643,0.896144],"study_design_scores_gemma":[0.0031677687,0.00051978853,0.0038049547,0.010105989,0.00024137902,0.006397985,0.00020779282,0.43666384,0.5049372,0.018926786,0.013761465,0.0012650846],"about_ca_topic_score_codex":0.000003302726,"about_ca_topic_score_gemma":0.0000061347932,"teacher_disagreement_score":0.89487886,"about_ca_system_score_codex":0.00006725721,"about_ca_system_score_gemma":0.000078895784,"threshold_uncertainty_score":0.31945243},"labels":[],"label_agreement":null},{"id":"W2344177016","doi":"10.1109/jstsp.2016.2530632","title":"Characterization of Upper-Limb Pathological Tremors: Application to Design of an Augmented Haptic Rehabilitation System","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; London Health Sciences Centre; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Haptic technology; Computer science; Rehabilitation; Filter (signal processing); Adaptive filter; Simulation; Artificial intelligence; Physical medicine and rehabilitation; Computer vision; Medicine; Physical therapy; Algorithm","score_opus":0.019228299878238275,"score_gpt":0.2752100119564936,"score_spread":0.2559817120782553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344177016","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8133122,0.00002848381,0.18594635,0.00036646618,0.000022618397,0.00029992568,0.0000019749032,0.000010249998,0.000011776543],"genre_scores_gemma":[0.99367875,0.000009519726,0.0061648055,0.000050840674,0.00006492833,0.000009725663,0.0000029090966,0.000009050436,0.000009468416],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986595,0.00014698064,0.0006638133,0.00014834134,0.00025879432,0.00012258586],"domain_scores_gemma":[0.9986802,0.00010329496,0.00049265084,0.00008015519,0.00057039753,0.00007335348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002818064,0.000099904304,0.0003430318,0.00022305227,0.000024090097,0.0000067192127,0.000075837284,0.00009173398,0.0000059803324],"category_scores_gemma":[0.00015302552,0.000061517465,0.000045218367,0.0004113688,0.00004270445,0.00016295385,0.0000059916415,0.000099072844,6.7904233e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00105482,0.0005644844,0.012568474,0.0001647329,0.000013891217,0.00001790191,0.00019103839,0.000089219546,0.9187363,0.000018621971,0.0000011171819,0.0665794],"study_design_scores_gemma":[0.006707295,0.022192698,0.5896398,0.0031751064,0.00026553907,0.00020104102,0.00028111623,0.012275534,0.3631571,0.0018161192,0.000015213247,0.00027343174],"about_ca_topic_score_codex":0.0000016984785,"about_ca_topic_score_gemma":3.8593717e-7,"teacher_disagreement_score":0.5770713,"about_ca_system_score_codex":0.00009095702,"about_ca_system_score_gemma":0.00009095938,"threshold_uncertainty_score":0.25086093},"labels":[],"label_agreement":null},{"id":"W2510122520","doi":"10.1109/jstsp.2016.2600400","title":"Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; Agence Nationale de la Recherche; Alzheimer's Disease Neuroimaging Initiative","keywords":"Computer science; Artificial intelligence; Alzheimer's disease; Disease; Machine learning; Pattern recognition (psychology); Medicine","score_opus":0.04260969853340917,"score_gpt":0.2664667441513749,"score_spread":0.2238570456179657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510122520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3107535,0.0009866065,0.68396515,0.0031773858,0.000751242,0.00024078945,0.0000127662315,0.000036740956,0.00007579204],"genre_scores_gemma":[0.99848694,0.00005259503,0.00029080102,0.00012717611,0.00095366145,0.000010024788,5.5748626e-7,0.000017119235,0.00006111365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984762,0.00012439262,0.0005359382,0.00022428774,0.00040691637,0.00023225333],"domain_scores_gemma":[0.9969624,0.0019366812,0.00042933092,0.0000489194,0.00054352125,0.00007912399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040518126,0.00013277665,0.00024383223,0.00021739598,0.0001697049,0.000023627163,0.00012795636,0.00007032907,0.000016132952],"category_scores_gemma":[0.0018565455,0.000098648736,0.00010157196,0.00044350274,0.00011857745,0.0004975105,0.000010863536,0.00029895842,3.060236e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043520587,0.0005242048,0.03631732,0.00018482287,0.00012720378,0.000036217214,0.00042965054,0.15499072,0.6136124,0.0007235873,0.0009665898,0.18773524],"study_design_scores_gemma":[0.013539357,0.0043265163,0.17116241,0.0034055782,0.00071954186,0.00038336363,0.0002461706,0.29724017,0.47686243,0.025771942,0.0051430897,0.001199412],"about_ca_topic_score_codex":8.526494e-7,"about_ca_topic_score_gemma":0.0000017788564,"teacher_disagreement_score":0.6877335,"about_ca_system_score_codex":0.00008265333,"about_ca_system_score_gemma":0.00028510764,"threshold_uncertainty_score":0.40227786},"labels":[],"label_agreement":null},{"id":"W2520315817","doi":"10.1109/jstsp.2016.2608329","title":"A Quality-of-Experience Index for Streaming Video","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":183,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Quality of experience; Video quality; ENCODE; Quality (philosophy); Multimedia; Index (typography); Data compression; Presentation (obstetrics); Real-time computing; Computer network; Quality of service; Artificial intelligence; World Wide Web","score_opus":0.05018974229768852,"score_gpt":0.36188694667103755,"score_spread":0.311697204373349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520315817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2237644,0.00017152505,0.77520895,0.00058960967,0.0001237113,0.0000733484,7.553982e-7,0.000011276477,0.000056453624],"genre_scores_gemma":[0.95928955,0.000013821796,0.0402937,0.000104457264,0.00022964436,0.000004897922,9.416851e-8,0.000006992923,0.000056813045],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797255,0.00012793884,0.00096877833,0.000192792,0.00045946846,0.00027850067],"domain_scores_gemma":[0.9976711,0.00040011224,0.0008195317,0.00014575107,0.0008952171,0.000068246314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096339075,0.00012379444,0.00033654616,0.00024084399,0.0000750286,0.00009460825,0.0006601735,0.000075190386,0.000004451442],"category_scores_gemma":[0.00027009353,0.00008809502,0.00008039318,0.00058278034,0.000059898917,0.0012444261,0.00004267421,0.00018268319,3.072272e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013238972,0.0002780339,0.0144859245,0.00026589935,0.00003271573,0.0000238113,0.005017789,0.00013220214,0.27252245,0.002178287,0.00007315812,0.70485735],"study_design_scores_gemma":[0.0077141253,0.0017229806,0.047058675,0.004053619,0.000034126693,0.00020835298,0.0012433018,0.027875297,0.850406,0.05676865,0.0018046238,0.001110231],"about_ca_topic_score_codex":0.0000120334835,"about_ca_topic_score_gemma":0.000009525423,"teacher_disagreement_score":0.7355252,"about_ca_system_score_codex":0.000113700975,"about_ca_system_score_gemma":0.00051756075,"threshold_uncertainty_score":0.35924104},"labels":[],"label_agreement":null},{"id":"W2526637692","doi":"10.1109/jstsp.2016.2602945","title":"Introduction to the Issue on Advanced Signal Processing for Brain Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Université de Genève; Rotman Research Institute, Baycrest; University of Toronto; Institut National de la Santé et de la Recherche Médicale; Aix-Marseille Université","keywords":"Computer science; Artificial intelligence; Connectome; Graph theory; Key (lock); Data science; Field (mathematics); Human Connectome Project; Machine learning; Human–computer interaction; Functional connectivity; Neuroscience; Psychology","score_opus":0.0242488815372083,"score_gpt":0.28592951658017246,"score_spread":0.2616806350429642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526637692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16222498,0.0005254791,0.56445754,0.26986343,0.0016777,0.00092538435,0.0000063968987,0.00008599357,0.00023306714],"genre_scores_gemma":[0.9832125,0.000016541007,0.0013673736,0.004946744,0.009724928,0.000032779386,2.3535388e-7,0.000033488235,0.00066544703],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787116,0.0001877068,0.00057018246,0.00042932478,0.00051234587,0.000429263],"domain_scores_gemma":[0.99590796,0.0027621845,0.00047006263,0.00011439116,0.0006623897,0.00008303607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091343466,0.00021887319,0.00030813168,0.00027108955,0.00043869432,0.000121235724,0.0003513771,0.000089220346,0.000024051373],"category_scores_gemma":[0.0044788704,0.00013262752,0.00007366965,0.0010593276,0.00009971415,0.0005910367,0.000029933992,0.0004529558,0.000005162826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009521878,0.00012017141,0.00008838756,0.00005001969,0.0000106193675,0.00001018413,0.00044526122,0.028834287,0.31393152,0.0000772138,0.015667383,0.63981277],"study_design_scores_gemma":[0.0037979076,0.0037530805,0.0012773988,0.0016724817,0.00005971791,0.00037168688,0.00033683653,0.026327332,0.7011341,0.004147342,0.25627428,0.00084782124],"about_ca_topic_score_codex":7.640773e-7,"about_ca_topic_score_gemma":0.000011745157,"teacher_disagreement_score":0.82098746,"about_ca_system_score_codex":0.00026501203,"about_ca_system_score_gemma":0.00024262938,"threshold_uncertainty_score":0.5408393},"labels":[],"label_agreement":null},{"id":"W2567462284","doi":"10.1109/jstsp.2016.2638538","title":"Multimodal Physiological Quality-of-Experience Assessment of Text-to-Speech Systems","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Neuroimaging; Electroencephalography; Perception; Active listening; Quality (philosophy); Quality of experience; Functional near-infrared spectroscopy; Brain activity and meditation; Speech recognition; Human–computer interaction; Artificial intelligence; Cognition; Psychology","score_opus":0.07240753945071553,"score_gpt":0.37372579202516754,"score_spread":0.301318252574452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567462284","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9600779,0.00006918973,0.03916813,0.00015341179,0.00026530965,0.00011223352,0.0000032853147,0.0000117027375,0.00013883534],"genre_scores_gemma":[0.9955641,0.000013138669,0.004055111,0.00006731925,0.00023628604,0.0000028617496,6.1455964e-8,0.000008772211,0.000052373936],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99748796,0.00031609478,0.0010941476,0.00024608555,0.0005924995,0.00026322607],"domain_scores_gemma":[0.99797237,0.0004604679,0.0008715588,0.000109181696,0.00049876753,0.00008763554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054417417,0.00015012124,0.0004933043,0.00021621893,0.00005329077,0.000042662887,0.0005319361,0.00009264776,0.000013665104],"category_scores_gemma":[0.00040607955,0.00009233687,0.00007074653,0.000578454,0.00014712207,0.00032157404,0.00004919833,0.0002949563,7.677899e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006832077,0.00020713561,0.0038393727,0.00010628927,0.0000054069824,0.000016553699,0.0006145264,0.0010709794,0.97916585,0.00010654144,0.000020063802,0.014778929],"study_design_scores_gemma":[0.0007069238,0.00066249346,0.016743988,0.0010884682,0.000005363523,0.00007277363,0.00014360264,0.0046812524,0.9754146,0.00025360167,0.000060243598,0.00016668851],"about_ca_topic_score_codex":0.000011857916,"about_ca_topic_score_gemma":0.0000014236679,"teacher_disagreement_score":0.035486173,"about_ca_system_score_codex":0.00008644307,"about_ca_system_score_gemma":0.00021788741,"threshold_uncertainty_score":0.37653884},"labels":[],"label_agreement":null},{"id":"W2804785295","doi":"10.1109/jstsp.2018.2840517","title":"Noncircular Attacks on Phasor Measurement Units for State Estimation in Smart Grid","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phasor; Smart grid; Units of measurement; Computer science; State (computer science); Grid; Bhattacharyya distance; Data mining; Algorithm; Engineering; Artificial intelligence; Electric power system; Electrical engineering; Mathematics; Power (physics)","score_opus":0.032723440380816246,"score_gpt":0.26742591838358915,"score_spread":0.23470247800277289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804785295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86057526,0.00049294566,0.13786165,0.00007743087,0.0006545633,0.00017481283,0.0000020089253,0.00002612751,0.00013518753],"genre_scores_gemma":[0.99775803,0.000027137723,0.0016498842,0.00004274277,0.0004938432,0.0000049210166,0.0000010463291,0.000016944176,0.000005420985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987229,0.000036524016,0.00048461024,0.000108312284,0.0003907496,0.0002568924],"domain_scores_gemma":[0.99895114,0.000046195997,0.0001287475,0.00005537481,0.00076174457,0.00005681375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061190926,0.0001289138,0.00020725545,0.0002840714,0.00006784531,0.00005088393,0.00015181914,0.00007775878,0.0000046023856],"category_scores_gemma":[0.0001387313,0.000119670636,0.000027120248,0.00069579907,0.00003869929,0.0002716652,0.000004235097,0.00036811852,0.0000015741155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047309164,0.0003384177,0.003368056,0.0009008087,0.00008631844,0.00008949253,0.0065550064,0.7072823,0.14151996,0.000024753468,0.0011156771,0.1382461],"study_design_scores_gemma":[0.0025721607,0.00090409856,0.0074315,0.0022242537,0.000031979987,0.000064640844,0.00011499352,0.7275295,0.2554835,0.0012346345,0.0019882196,0.00042051356],"about_ca_topic_score_codex":0.0000036714928,"about_ca_topic_score_gemma":0.00007091994,"teacher_disagreement_score":0.1378256,"about_ca_system_score_codex":0.0002704405,"about_ca_system_score_gemma":0.00021807068,"threshold_uncertainty_score":0.4880027},"labels":[],"label_agreement":null},{"id":"W2896601578","doi":"10.1109/jstsp.2018.2867249","title":"Introduction to the Issue on Information-Theoretic Methods in Data Acquisition, Analysis, and Processing","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data science; Special section; Cover (algebra); Range (aeronautics); Data analysis; Data mining; Information retrieval; Engineering","score_opus":0.038588200586111635,"score_gpt":0.34523468090922926,"score_spread":0.30664648032311764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896601578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14741004,0.0006013255,0.82565695,0.024128504,0.0008229637,0.0003482428,0.000004836727,0.000038795373,0.000988327],"genre_scores_gemma":[0.97842604,0.000026490054,0.01018859,0.0020028192,0.009289588,0.0000029454502,0.000025330628,0.000010592974,0.000027604456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861264,0.000045453256,0.0006377048,0.00019862132,0.00030303735,0.00020251898],"domain_scores_gemma":[0.9982679,0.000048033322,0.0004932627,0.00020790125,0.00096771395,0.0000152169505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017475741,0.00013561035,0.00023892889,0.0010529362,0.0001851901,0.0006104835,0.0005342473,0.00006771523,0.0001099329],"category_scores_gemma":[0.00039146736,0.000096403106,0.000018845622,0.0041656247,0.00009505255,0.0035343287,0.00009924332,0.00032602772,0.0000141598675],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002898994,0.000113152666,0.006596549,0.00034434503,0.00005397138,0.0000055665664,0.0010701385,0.0021781125,0.001474619,0.00042627004,0.005548976,0.9818984],"study_design_scores_gemma":[0.0015514113,0.00026361475,0.08646105,0.0018749834,0.0010446259,0.0001378765,0.002197493,0.43224525,0.013911586,0.013107904,0.44596967,0.0012345419],"about_ca_topic_score_codex":0.00004069497,"about_ca_topic_score_gemma":0.0001090965,"teacher_disagreement_score":0.98066384,"about_ca_system_score_codex":0.000043524124,"about_ca_system_score_gemma":0.00008317411,"threshold_uncertainty_score":0.58869064},"labels":[],"label_agreement":null},{"id":"W2897433210","doi":"10.1109/jstsp.2018.2877041","title":"Graph and Sparse-Based Robust Nonnegative Block Value Decomposition for Clustering","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Robustness (evolution); Outlier; Mathematics; Graph; Sparse approximation; Sparse matrix; Matrix norm; Computer science; Algorithm; Discrete mathematics; Artificial intelligence; Eigenvalues and eigenvectors","score_opus":0.02693701711978837,"score_gpt":0.28109854936478756,"score_spread":0.2541615322449992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897433210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26923704,0.00009415737,0.7300225,0.0003349294,0.00015701736,0.000087028406,7.1886063e-7,0.00001501804,0.000051599272],"genre_scores_gemma":[0.8430329,0.0000072693265,0.15640089,0.00018937046,0.00035303965,0.0000031219643,6.6753097e-7,0.000006771424,0.000005980449],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990054,0.000058992413,0.0003730645,0.00017107461,0.00019551034,0.00019596482],"domain_scores_gemma":[0.998779,0.0001022375,0.00031279697,0.0000577733,0.00068086677,0.000067329376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003205505,0.000108875945,0.00018068434,0.00026974524,0.0001781005,0.00016839468,0.00021148639,0.00007716299,0.000002213997],"category_scores_gemma":[0.00003566558,0.000096946,0.000037511105,0.0004505632,0.000051967218,0.0006115591,0.000020490446,0.00019918632,4.9630785e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010347834,0.00070750504,0.0025118005,0.00069691637,0.00010531546,0.00009711873,0.006927395,0.08361526,0.48077267,0.00029915746,0.0011592143,0.4220729],"study_design_scores_gemma":[0.0015764692,0.00077061926,0.0009743568,0.0011124868,0.000020887055,0.00011258938,0.000056769863,0.77025414,0.21907817,0.0057455483,0.000085881846,0.00021207868],"about_ca_topic_score_codex":0.0000039829833,"about_ca_topic_score_gemma":0.0000126517725,"teacher_disagreement_score":0.6866389,"about_ca_system_score_codex":0.000045105557,"about_ca_system_score_gemma":0.00014958004,"threshold_uncertainty_score":0.39533433},"labels":[],"label_agreement":null},{"id":"W2909432978","doi":"10.1109/jstsp.2019.2893057","title":"Delay-Minimization Nonorthogonal Multiple Access Enabled Multi-User Mobile Edge Computation Offloading","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Mobile Communications Research Laboratory, Southeast University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Minification; Computation offloading; Computation; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Edge computing; Computer network; Algorithm; Server; Artificial intelligence","score_opus":0.020658797388858934,"score_gpt":0.27916020351011683,"score_spread":0.2585014061212579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909432978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54757917,0.0006774233,0.45121893,0.000015354948,0.00015673992,0.00016211535,0.0000010886575,0.00012797593,0.00006119119],"genre_scores_gemma":[0.9655884,0.00017904828,0.03402304,0.000016326041,0.000096741554,0.000012606506,0.000007088606,0.00003874266,0.000038003705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859715,0.000047007263,0.0007146376,0.0001445564,0.00023984807,0.00025679573],"domain_scores_gemma":[0.998752,0.00014789251,0.00036890045,0.00012969306,0.0005553788,0.000046114088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017419767,0.0001830293,0.00031698254,0.0004549363,0.000077324694,0.0001122204,0.00047833443,0.00016676034,0.000017308057],"category_scores_gemma":[0.000067094945,0.0001880967,0.000045953515,0.000914524,0.000035579465,0.0011843436,0.00004723258,0.00066397025,0.000004165671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027426997,0.000061206905,0.01612851,0.00013214108,0.000021071473,0.000006278536,0.00028346,0.8956437,0.029467516,0.000009226435,0.000026859952,0.058192614],"study_design_scores_gemma":[0.0014870054,0.00008122408,0.004645099,0.00039564492,0.000012477866,0.000044203596,0.00020218677,0.9298414,0.06231182,0.0002090328,0.00048839615,0.00028148826],"about_ca_topic_score_codex":0.0000024135013,"about_ca_topic_score_gemma":0.000014636251,"teacher_disagreement_score":0.41800922,"about_ca_system_score_codex":0.00023384858,"about_ca_system_score_gemma":0.000112042195,"threshold_uncertainty_score":0.7670361},"labels":[],"label_agreement":null},{"id":"W2914107326","doi":"10.1109/jstsp.2019.2898643","title":"Delay Minimization for Massive Internet of Things With Non-Orthogonal Multiple Access","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Victoria","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Telecommunications link; Scheduling (production processes); Mathematical optimization; Iterative method; Integer programming; Minification; Noma; Job shop scheduling; Linear programming; Optimization problem; Algorithm; Computer network; Mathematics","score_opus":0.013658941578491127,"score_gpt":0.2545324578519035,"score_spread":0.24087351627341236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914107326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48683888,0.00020339528,0.51265407,0.000031377745,0.000047389425,0.000121156496,8.3898004e-7,0.000030861404,0.00007205132],"genre_scores_gemma":[0.94662696,0.000051120867,0.05321758,0.000013567629,0.00003599824,0.000007692143,0.0000029522819,0.000024464025,0.000019643101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907607,0.000013692686,0.0004914496,0.00008990488,0.00017495266,0.0001539587],"domain_scores_gemma":[0.99872166,0.00013808903,0.00041319046,0.00010356194,0.00060010306,0.00002339294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010809958,0.000121826255,0.00027004562,0.0002703536,0.000021287387,0.000037121004,0.0004393361,0.000104959654,0.0000072893135],"category_scores_gemma":[0.000058638445,0.00010567085,0.00003266298,0.00040764507,0.0000411637,0.000803613,0.000026033487,0.00036348597,2.5222258e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027031748,0.00008003538,0.024094438,0.0006413252,0.00008240231,0.000006396729,0.0013166403,0.8321548,0.05800609,0.000078536716,0.00006286686,0.08320611],"study_design_scores_gemma":[0.0014055828,0.00025482127,0.0019857427,0.00089544785,0.000017578264,0.000029288023,0.00019914524,0.7133945,0.2806326,0.00085919217,0.00012881542,0.00019729848],"about_ca_topic_score_codex":0.000001679814,"about_ca_topic_score_gemma":0.0000047528238,"teacher_disagreement_score":0.45978808,"about_ca_system_score_codex":0.00008719977,"about_ca_system_score_gemma":0.00009350481,"threshold_uncertainty_score":0.4309132},"labels":[],"label_agreement":null},{"id":"W2917520730","doi":"10.1109/jstsp.2019.2901170","title":"Securing Downlink Massive MIMO-NOMA Networks With Artificial Noise","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Artificial noise; Telecommunications link; Computer science; Base station; MIMO; Channel state information; Transmission (telecommunications); Computer network; Secrecy; Transmitter power output; Secure transmission; Wireless; Channel (broadcasting); Telecommunications; Transmitter; Computer security","score_opus":0.009357348530791496,"score_gpt":0.22338681074065947,"score_spread":0.21402946220986796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917520730","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7676461,0.0015907083,0.22987409,0.000118406926,0.00015334357,0.00011997304,5.472112e-7,0.000156419,0.0003404128],"genre_scores_gemma":[0.9880306,0.00015368115,0.011516354,0.000017345179,0.00022569223,0.0000040266623,0.0000010928396,0.000035531026,0.000015689247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877995,0.00002845939,0.00054438564,0.000123586,0.00022690596,0.00029671838],"domain_scores_gemma":[0.9990891,0.00007700524,0.00028688487,0.0001758736,0.0003239651,0.00004714383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001416212,0.000180944,0.000314515,0.0002873308,0.000058287795,0.00006845034,0.00040399857,0.00015974075,0.000015167508],"category_scores_gemma":[0.000022965483,0.0001600109,0.000036097448,0.0007727961,0.000054410924,0.00046112528,0.000026377096,0.0010606063,0.0000028360255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055575136,0.000030695002,0.0024456764,0.00008177827,0.00002794329,0.000031769927,0.0002683865,0.90802485,0.02202539,0.000056476183,0.00001615827,0.066935286],"study_design_scores_gemma":[0.001422132,0.00041640364,0.0031635626,0.0017663944,0.000036697045,0.00020930434,0.0010142507,0.83787227,0.15020715,0.0026268817,0.00052010035,0.00074485684],"about_ca_topic_score_codex":0.0000010498919,"about_ca_topic_score_gemma":0.000008245819,"teacher_disagreement_score":0.2203845,"about_ca_system_score_codex":0.00019501358,"about_ca_system_score_gemma":0.00009627346,"threshold_uncertainty_score":0.65250546},"labels":[],"label_agreement":null},{"id":"W2918594466","doi":"10.1109/jstsp.2019.2901993","title":"Non-Orthogonal Multiple Access With Improper Gaussian Signaling","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Beamforming; Throughput; Relaxation (psychology); Transmitter; Mathematical optimization; Gaussian; Noma; Optimization problem; Algorithm; MIMO; Computational complexity theory; Transmitter power output; Wireless; Mathematics; Telecommunications; Telecommunications link","score_opus":0.010193760178539686,"score_gpt":0.23771094776684415,"score_spread":0.22751718758830447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918594466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6846418,0.0003017673,0.31407285,0.000016790324,0.00021606835,0.00018665977,9.003351e-7,0.00004633488,0.00051687483],"genre_scores_gemma":[0.9882821,0.000015624699,0.011113589,0.00002085834,0.00043680408,0.000004531299,0.0000020678578,0.000059410933,0.00006503055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985284,0.000025003454,0.0006432115,0.00016613911,0.0003093952,0.0003278195],"domain_scores_gemma":[0.99899346,0.000042709846,0.00029780107,0.000095421834,0.0004818066,0.00008880217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021815725,0.00021643948,0.00036936335,0.00031340265,0.00005278159,0.00013359958,0.00028084032,0.000121875666,0.000031747706],"category_scores_gemma":[0.00001622736,0.00017707377,0.000041008003,0.0006882852,0.000023148013,0.0011957648,0.000013776495,0.0006042714,0.0000036659967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008256568,0.000031138876,0.026433876,0.00028761776,0.00003339867,0.00003851864,0.00036931658,0.8603689,0.10608173,0.0000026144808,0.000014916748,0.0062554097],"study_design_scores_gemma":[0.0036715446,0.0004245346,0.005850079,0.002723415,0.000057245798,0.00044251574,0.00024372472,0.81805897,0.16735257,0.00017280938,0.00019879169,0.0008037909],"about_ca_topic_score_codex":0.000004581677,"about_ca_topic_score_gemma":0.000015232243,"teacher_disagreement_score":0.30364034,"about_ca_system_score_codex":0.0001740953,"about_ca_system_score_gemma":0.00019282903,"threshold_uncertainty_score":0.7220859},"labels":[],"label_agreement":null},{"id":"W2929750792","doi":"10.1109/jstsp.2019.2909193","title":"Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Non-negative matrix factorization; Computer science; Speech enhancement; Speech recognition; Intelligibility (philosophy); Spectrogram; Short-time Fourier transform; Source separation; PESQ; Artificial intelligence; Pattern recognition (psychology); Matrix decomposition; Fourier transform; Noise reduction; Mathematics","score_opus":0.009430378157480598,"score_gpt":0.23021413303643473,"score_spread":0.22078375487895413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929750792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80648434,0.00075526466,0.19072965,0.00047805472,0.00025174706,0.00013602828,1.9577085e-7,0.000033903667,0.0011308328],"genre_scores_gemma":[0.92775,0.00004103501,0.071241535,0.0002630304,0.00032152102,0.0000018268956,4.6572112e-7,0.000017800074,0.00036282523],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976987,0.00006500136,0.0006901727,0.00033305053,0.0007197035,0.0004933448],"domain_scores_gemma":[0.99822676,0.000052056588,0.00055803964,0.00019945246,0.0008318267,0.000131849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004756546,0.00023899581,0.0004098407,0.00034853816,0.00010792301,0.00034666763,0.0008736254,0.00010862345,0.00005643585],"category_scores_gemma":[0.000025404142,0.00018613396,0.00005793116,0.0014575024,0.000039225168,0.0014374641,0.000048137474,0.0006509989,0.000016338065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002345764,0.0005860877,0.022403067,0.00057171186,0.0000787203,0.0005619608,0.0023294238,0.0018907061,0.3719114,0.000066330125,0.00015310958,0.5992129],"study_design_scores_gemma":[0.0030338087,0.0009965255,0.0024482198,0.0020269044,0.000022753933,0.0005815938,0.00010557106,0.012412291,0.9754927,0.002017973,0.0003470306,0.0005145918],"about_ca_topic_score_codex":0.0000042703296,"about_ca_topic_score_gemma":0.000005883234,"teacher_disagreement_score":0.6035813,"about_ca_system_score_codex":0.0001553345,"about_ca_system_score_gemma":0.000873281,"threshold_uncertainty_score":0.75903225},"labels":[],"label_agreement":null},{"id":"W2930834451","doi":"10.1109/jstsp.2019.2907864","title":"Cache-Aided Non-Orthogonal Multiple Access: The Two-User Case","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Computer network","score_opus":0.018523382990752003,"score_gpt":0.2782577919448549,"score_spread":0.2597344089541029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2930834451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9285397,0.0012953995,0.06920179,0.00018976844,0.00018227755,0.0001606154,0.0000013130513,0.0001138014,0.00031534347],"genre_scores_gemma":[0.9943644,0.00013301462,0.005215585,0.000047270358,0.00015793012,0.0000075757075,8.801438e-7,0.000033695585,0.0000396653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987062,0.000047712238,0.0006009014,0.00011795094,0.0002497216,0.00027751917],"domain_scores_gemma":[0.9988404,0.0002285017,0.00027807994,0.00025556263,0.00035440573,0.00004304765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028260818,0.00017902374,0.0002832669,0.0002474237,0.00010229724,0.00011645226,0.0007796665,0.00011784911,0.000023142575],"category_scores_gemma":[0.00009225036,0.00013803176,0.00005576486,0.0007338295,0.00006669436,0.00077268254,0.000069585454,0.0011263745,0.000004432827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052624448,0.00008123325,0.022460774,0.00020152997,0.0000751601,0.00037504514,0.00087896903,0.7754415,0.07378325,0.00007522608,0.00020222657,0.12637244],"study_design_scores_gemma":[0.00427008,0.00021535334,0.008628135,0.0011107426,0.00006055704,0.004952902,0.0016012512,0.6879235,0.28288314,0.0044967476,0.0028750151,0.0009825683],"about_ca_topic_score_codex":0.000008685191,"about_ca_topic_score_gemma":0.00006117398,"teacher_disagreement_score":0.20909989,"about_ca_system_score_codex":0.00014681098,"about_ca_system_score_gemma":0.0001199836,"threshold_uncertainty_score":0.5628772},"labels":[],"label_agreement":null},{"id":"W2963023977","doi":"10.1109/jstsp.2014.2327594","title":"Large-Scale MIMO Versus Network MIMO for Multicell Interference Mitigation","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"MIMO; Telecommunications link; Beamforming; Multi-user MIMO; Computer science; Duplex (building); Channel state information; Computer network; 3G MIMO; Base station; Fading; Electronic engineering; Channel (broadcasting); Engineering; Wireless; Telecommunications","score_opus":0.013127537160145074,"score_gpt":0.2513719039928044,"score_spread":0.23824436683265932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963023977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064724,0.00049729267,0.9334976,0.000024421699,0.0007883948,0.00014709523,0.0000013590198,0.00004915545,0.00027069834],"genre_scores_gemma":[0.9325761,0.000023416505,0.066099666,0.000016709622,0.0012037907,0.000007458734,0.0000035933606,0.000033347686,0.000035897334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987883,0.000046392295,0.0005967533,0.000123379,0.00013775511,0.0003073993],"domain_scores_gemma":[0.99897325,0.00016192863,0.00026985048,0.00007067273,0.0004589597,0.00006532801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036102434,0.0001506693,0.00026669976,0.00013231595,0.00007359621,0.000057163932,0.00016138409,0.00012259601,0.0000053326994],"category_scores_gemma":[0.00009923224,0.00015423798,0.000046637273,0.00037122256,0.000017754664,0.00040958935,0.000007409858,0.00032335235,0.0000011503631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001349497,0.000026767517,0.00033563888,0.0002460572,0.000019609799,0.00000176622,0.0006699582,0.97399604,0.011212414,0.000027293323,0.00015994009,0.01316959],"study_design_scores_gemma":[0.0019580899,0.00018894344,0.000105679195,0.00063075684,0.00003158816,0.0000097188595,0.00011367028,0.9806357,0.014583592,0.00055993843,0.0009828708,0.00019944175],"about_ca_topic_score_codex":6.8708744e-7,"about_ca_topic_score_gemma":0.00003161689,"teacher_disagreement_score":0.86785215,"about_ca_system_score_codex":0.00014825282,"about_ca_system_score_gemma":0.000052860007,"threshold_uncertainty_score":0.62896425},"labels":[],"label_agreement":null},{"id":"W2964597363","doi":"10.1109/jstsp.2019.2930889","title":"Domain Selective Precoding in 3-D Massive MIMO Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Precoding; Zero-forcing precoding; MIMO; Computer science; Telecommunications link; Algorithm; Computational complexity theory; Channel state information; Multi-user MIMO; Base station; Spectral efficiency; Interference (communication); Azimuth; Electronic engineering; Channel (broadcasting); Mathematics; Telecommunications; Wireless; Engineering","score_opus":0.008517682507242613,"score_gpt":0.22948723850738786,"score_spread":0.22096955600014526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964597363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72830206,0.0023139953,0.26587376,0.000022101429,0.0007732619,0.00043706593,0.000001434605,0.00006696242,0.0022093253],"genre_scores_gemma":[0.9931606,0.000042897547,0.00633248,0.0000064815827,0.00033642215,0.0000073694164,9.921532e-7,0.000041559066,0.00007117486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835837,0.00008814739,0.0008285548,0.00015693206,0.00023625846,0.00033176382],"domain_scores_gemma":[0.9990508,0.000100982514,0.00033501346,0.000074047035,0.00038132907,0.00005780849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037167678,0.00018888335,0.00043230478,0.0005335396,0.00003061654,0.00007288749,0.0001776755,0.0001598805,0.000007806759],"category_scores_gemma":[0.00003664329,0.00019056858,0.000037526934,0.0010972144,0.000014528647,0.0007026236,0.000008534284,0.0006696115,0.0000037230438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003738335,0.000027921758,0.008436489,0.00036419716,0.000023359375,0.000039944305,0.0013703915,0.9448844,0.042420443,0.000045866374,0.000032367217,0.0023171874],"study_design_scores_gemma":[0.005113336,0.00047718163,0.0043989257,0.00730432,0.00004319595,0.0005633055,0.002769154,0.9274135,0.048022192,0.0020776228,0.0006408746,0.0011763772],"about_ca_topic_score_codex":0.0000059637823,"about_ca_topic_score_gemma":0.000013897074,"teacher_disagreement_score":0.26485854,"about_ca_system_score_codex":0.00065527303,"about_ca_system_score_gemma":0.00015183889,"threshold_uncertainty_score":0.7771161},"labels":[],"label_agreement":null},{"id":"W2969205886","doi":"10.1109/jstsp.2019.2933056","title":"Joint Code-Frequency Index Modulation for IoT and Multi-User Communications","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hydro-Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimler Akademisi; Hydro-Québec","keywords":"Computer science; Orthogonal frequency-division multiplexing; Bit error rate; Modulation (music); Electronic engineering; Rayleigh fading; Joint (building); Code (set theory); Channel (broadcasting); Real-time computing; Fading; Computer network; Engineering","score_opus":0.0362434426239615,"score_gpt":0.28546458936433483,"score_spread":0.24922114674037332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969205886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32136106,0.0030472542,0.67473793,0.00037444453,0.00007167444,0.00022353807,0.0000027634899,0.0001060179,0.00007530737],"genre_scores_gemma":[0.8744951,0.00030925358,0.12508564,0.000012640316,0.000037579084,0.000009023428,0.0000017175703,0.000020588082,0.000028481474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991515,0.000025788246,0.00048451556,0.00008032304,0.00011093332,0.0001469242],"domain_scores_gemma":[0.9990473,0.00008964536,0.00022036502,0.00024592638,0.0003691068,0.000027685188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018342205,0.000108041255,0.0002153416,0.00024780474,0.00006725974,0.00003853295,0.00035825337,0.00012006734,0.0000027626393],"category_scores_gemma":[0.000068804984,0.00010856804,0.000026339563,0.00034287552,0.00005528566,0.00030031518,0.00003466526,0.0004817344,5.4665026e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023949737,0.00009654188,0.02420227,0.00041428997,0.000053621523,0.000001717904,0.0009230091,0.3860329,0.26777142,0.0013372042,0.00003156967,0.3191115],"study_design_scores_gemma":[0.0012370088,0.00008730899,0.013089376,0.00041970462,0.000009849642,0.000029569788,0.0002457567,0.95556813,0.020731349,0.007765962,0.00058507547,0.00023092904],"about_ca_topic_score_codex":0.0000018076605,"about_ca_topic_score_gemma":0.00002196748,"teacher_disagreement_score":0.5695352,"about_ca_system_score_codex":0.00012129559,"about_ca_system_score_gemma":0.00006593313,"threshold_uncertainty_score":0.44272763},"labels":[],"label_agreement":null},{"id":"W2991435809","doi":"10.1109/jstsp.2019.2955022","title":"An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer's Dementia in Spontaneous Speech","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":187,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; Medical Research Council Canada; European Commission","keywords":"Paralanguage; Computer science; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Set (abstract data type)","score_opus":0.017521107657468876,"score_gpt":0.33905593284494645,"score_spread":0.32153482518747756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991435809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911886,0.0014347982,0.0067931404,0.000038609174,0.00014300532,0.00029391752,0.0000014091191,0.000005067812,0.00010146398],"genre_scores_gemma":[0.9928956,0.000034229994,0.0068347277,0.000032149037,0.00017347977,0.0000021049034,0.0000025167387,0.000015335394,0.000009849585],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860835,0.000047362537,0.00069994177,0.00012646113,0.00032940137,0.00018850304],"domain_scores_gemma":[0.9985916,0.000070964634,0.0005325808,0.00008118183,0.00067334104,0.0000503121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042241995,0.00011562159,0.0004488859,0.00036794096,0.000020503543,0.000013147829,0.00009911773,0.00011550271,0.000014381673],"category_scores_gemma":[0.00010756187,0.00010323475,0.0000667748,0.00038508108,0.000027364935,0.000113188915,0.0000045837955,0.00036332666,1.11572916e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007326666,0.0005715279,0.032369234,0.0008019649,0.00023295458,0.00020353701,0.00045872867,0.008250567,0.9177983,0.000006306299,0.000007144592,0.038567096],"study_design_scores_gemma":[0.009749677,0.008280976,0.23497373,0.003594373,0.0020232513,0.0023709019,0.0015625454,0.06456679,0.67059916,0.0017385168,0.000055970057,0.00048411102],"about_ca_topic_score_codex":0.000033289234,"about_ca_topic_score_gemma":0.00017441432,"teacher_disagreement_score":0.24719912,"about_ca_system_score_codex":0.000067776076,"about_ca_system_score_gemma":0.00044178724,"threshold_uncertainty_score":0.42097908},"labels":[],"label_agreement":null},{"id":"W3014927864","doi":"10.1109/jstsp.2020.2983607","title":"Impact of Synaptic Strength on Propagation of Asynchronous Spikes in Biologically Realistic Feed-Forward Neural Network","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; Krembil Foundation","funders":"","keywords":"Computer science; Asynchronous communication; Artificial neural network; Backpropagation; Artificial intelligence; Computer network","score_opus":0.022360573999809768,"score_gpt":0.26855153659123077,"score_spread":0.246190962591421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014927864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98542863,0.0003557214,0.013964146,0.000028061031,0.00006199525,0.00008721025,0.0000012964458,0.000021016906,0.00005191613],"genre_scores_gemma":[0.9984801,0.000027674447,0.0011473943,0.000014829926,0.00031445283,6.00701e-7,0.0000012582833,0.000013266362,4.276557e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987233,0.00006425427,0.0007241686,0.000105936255,0.0001606295,0.00022168622],"domain_scores_gemma":[0.99921006,0.0001589513,0.00036487618,0.00004188302,0.00016374062,0.000060489143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001649203,0.00014444102,0.00039497792,0.0001224361,0.000023872366,0.000011883995,0.00014591003,0.00008085926,0.0000044792805],"category_scores_gemma":[0.00019256379,0.000115284995,0.00006521753,0.00069046376,0.00003378157,0.00015539982,0.00000970634,0.0005361154,1.3348856e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018555018,0.000038469585,0.005273268,0.00019353667,0.000014703448,0.000036053163,0.00020757047,0.8668799,0.10241022,0.000007468376,0.000003967817,0.024749339],"study_design_scores_gemma":[0.0013995875,0.0042365035,0.04918108,0.0018323928,0.00004691092,0.00009224662,0.00006936415,0.85502684,0.086895466,0.00085848855,0.0000022224572,0.0003588862],"about_ca_topic_score_codex":0.0000024294704,"about_ca_topic_score_gemma":0.0000029625937,"teacher_disagreement_score":0.04390781,"about_ca_system_score_codex":0.00010361221,"about_ca_system_score_gemma":0.000088964065,"threshold_uncertainty_score":0.47011858},"labels":[],"label_agreement":null},{"id":"W3034964388","doi":"10.1109/jstsp.2020.3001525","title":"Enhanced Deep-Learning-Based Magnetic Resonance Image Reconstruction by Leveraging Prior Subject-Specific Brain Imaging: Proof-of-Concept Using a Cohort of Presumed Normal Subjects","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Electric (Canada); Foothills Medical Centre; University of Calgary; Alberta Health Services","funders":"Science and Engineering Research Council","keywords":"Artificial intelligence; Computer science; Iterative reconstruction; Wilcoxon signed-rank test; Magnetic resonance imaging; Neuroimaging; Image quality; Pattern recognition (psychology); Deep learning; Computer vision; Similarity (geometry); Mean squared error; Nuclear medicine; Mathematics; Image (mathematics); Medicine; Statistics; Radiology","score_opus":0.015375999286061861,"score_gpt":0.2737049503597645,"score_spread":0.25832895107370263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034964388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45848292,0.0032320586,0.5373999,0.00037555097,0.00002784643,0.00038634395,0.0000026548123,0.000028170429,0.00006455104],"genre_scores_gemma":[0.91386336,0.00004669336,0.08573624,0.00009559848,0.00019216134,0.000009416282,0.0000050464246,0.00003339441,0.000018106264],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978892,0.00009598128,0.0010309676,0.00028441907,0.00040614457,0.00029325325],"domain_scores_gemma":[0.9976783,0.00010569467,0.001024359,0.000108681736,0.00095759117,0.00012540014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029627883,0.00020474917,0.00055469054,0.00021036575,0.00009459293,0.00002753499,0.00017009379,0.00010001506,0.000034742545],"category_scores_gemma":[0.00019768711,0.00020501095,0.00009231718,0.0010111404,0.00019318964,0.00032670257,0.000016804328,0.0007748455,1.069751e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004713827,0.00012920554,0.0046540266,0.00023799585,0.000008095407,0.0000203487,0.0007474646,0.0030700448,0.7091455,0.0000021942726,0.00004251345,0.28147122],"study_design_scores_gemma":[0.0018043302,0.0005238328,0.0015648998,0.0011347157,0.000064556705,0.00016116902,0.0001762315,0.06814162,0.92577213,0.00009011336,0.00037216768,0.00019425685],"about_ca_topic_score_codex":0.00000851388,"about_ca_topic_score_gemma":0.0000014498181,"teacher_disagreement_score":0.4553804,"about_ca_system_score_codex":0.0001527261,"about_ca_system_score_gemma":0.00048825538,"threshold_uncertainty_score":0.8360104},"labels":[],"label_agreement":null},{"id":"W3188962160","doi":"10.1109/jstsp.2021.3134162","title":"Reconfigurable Intelligent Surface-Assisted Multi-UAV Networks: Efficient Resource Allocation With Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast; Royal Academy of Engineering","keywords":"Reinforcement learning; Computer science; Resource allocation; Artificial intelligence; Resource management (computing); Distributed computing; Computer network","score_opus":0.013478447613981238,"score_gpt":0.22537228035829962,"score_spread":0.2118938327443184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188962160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108090185,0.001424808,0.88971055,0.00005680103,0.00005945178,0.000100330835,8.483053e-8,0.000044101555,0.00051369675],"genre_scores_gemma":[0.9828871,0.00019448288,0.016569296,0.0000207878,0.00011997333,0.000004305435,0.0000110356295,0.000027961463,0.00016506833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987771,0.00005311711,0.000574544,0.00013515152,0.00023093123,0.00022915333],"domain_scores_gemma":[0.99890834,0.000043704527,0.0002387265,0.00008226715,0.00065875606,0.00006821315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002696619,0.00014130016,0.00020706249,0.00011760785,0.00011512722,0.000109363114,0.00010898749,0.00009303454,0.000030695144],"category_scores_gemma":[0.000025673922,0.00013225677,0.00003091462,0.0009561308,0.000019503279,0.00013499048,0.0000061372452,0.00050694164,0.0000011111064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025721538,0.000057859215,0.00034261512,0.00006625007,0.00002887232,0.00001316462,0.0004458013,0.9684616,0.00681292,0.0000069019657,0.000018338415,0.023719981],"study_design_scores_gemma":[0.00046015086,0.00006449659,0.00045414973,0.0003341266,0.000028085346,0.000074890515,0.00030097607,0.97040415,0.026998492,0.0000039400093,0.0007247987,0.00015176724],"about_ca_topic_score_codex":0.0000039695906,"about_ca_topic_score_gemma":0.000020918493,"teacher_disagreement_score":0.8747969,"about_ca_system_score_codex":0.00025357335,"about_ca_system_score_gemma":0.00013527548,"threshold_uncertainty_score":0.53932744},"labels":[],"label_agreement":null},{"id":"W3189642105","doi":"10.1109/jstsp.2022.3172587","title":"RIS-Assisted UAV Communications for IoT With Wireless Power Transfer Using Deep Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast; Royal Academy of Engineering","keywords":"Reinforcement learning; Computer science; Wireless; Transfer of learning; Internet of Things; Wireless power transfer; Artificial intelligence; Telecommunications; Embedded system","score_opus":0.023189293616411245,"score_gpt":0.24895015752734126,"score_spread":0.22576086391093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189642105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48201466,0.00088550657,0.5164328,0.000055577,0.00014784561,0.0001452079,7.5205395e-7,0.000071902876,0.00024572856],"genre_scores_gemma":[0.98248595,0.000033554188,0.01713955,0.000034292163,0.00016512249,0.000025460013,0.0000059536715,0.00007029874,0.000039837683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824965,0.00012858292,0.00071833906,0.0001367785,0.00039415018,0.00037250982],"domain_scores_gemma":[0.9989701,0.00017916552,0.00021587372,0.00016464053,0.00039271443,0.0000774865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047963258,0.00020541548,0.0003442601,0.00028368743,0.0004747545,0.00008189721,0.0004894277,0.000086229804,0.000021401238],"category_scores_gemma":[0.00002061484,0.00020779776,0.00006539294,0.0008960391,0.000060611354,0.00020420615,0.000033436863,0.0013434497,9.707636e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089642715,0.00005639929,0.00070535246,0.00010024944,0.00007151574,0.000017056598,0.0010726298,0.9702962,0.01564504,0.00006032943,0.000012258891,0.011873334],"study_design_scores_gemma":[0.0011882044,0.0003415695,0.00031668958,0.0004348123,0.0000688003,0.0002281249,0.00042343227,0.9925349,0.0033119232,0.000059366714,0.0007989541,0.00029326617],"about_ca_topic_score_codex":0.000007805442,"about_ca_topic_score_gemma":0.00003635,"teacher_disagreement_score":0.5004713,"about_ca_system_score_codex":0.00046722332,"about_ca_system_score_gemma":0.00026131532,"threshold_uncertainty_score":0.8473747},"labels":[],"label_agreement":null},{"id":"W4205224043","doi":"10.1109/jstsp.2021.3137669","title":"Private 5G Networks: Concepts, Architectures, and Research Landscape","year":2021,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":192,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Private network; Key (lock); Computer science; Private sector; Architecture; Telecommunications; Private life; Network architecture; Computer security","score_opus":0.02589689025487741,"score_gpt":0.3121589554575394,"score_spread":0.286262065202662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205224043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8269786,0.043736592,0.12801987,0.00038851294,0.000111943955,0.00008526072,8.558425e-7,0.00013994658,0.00053840294],"genre_scores_gemma":[0.9877753,0.0020268506,0.009965187,0.000017016646,0.00017413744,0.0000028218399,0.0000010449362,0.000019909789,0.00001772423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988903,0.000099755285,0.0003894439,0.00010559212,0.00024421053,0.0002707277],"domain_scores_gemma":[0.9990705,0.00019650951,0.00009618803,0.00013865157,0.00044983436,0.00004831205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034975717,0.000106356085,0.00022270164,0.00025959915,0.00009129451,0.000078265635,0.00027946226,0.00012393207,0.000008690814],"category_scores_gemma":[0.00016115647,0.000101183854,0.000020428684,0.0010182686,0.00011018153,0.00013543446,0.00004365018,0.0013588199,3.0249427e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003243158,0.000047105084,0.004532196,0.00019250711,0.000041090083,0.00015401274,0.00074421166,0.5267862,0.04250545,0.00024508333,0.00022147581,0.42449823],"study_design_scores_gemma":[0.0028306497,0.00034417093,0.012767418,0.0028796538,0.000031449446,0.0013839549,0.0018071652,0.50376445,0.42899024,0.03238092,0.011880767,0.0009391489],"about_ca_topic_score_codex":6.138568e-7,"about_ca_topic_score_gemma":0.000009589866,"teacher_disagreement_score":0.4235591,"about_ca_system_score_codex":0.000068024994,"about_ca_system_score_gemma":0.00009991814,"threshold_uncertainty_score":0.59034723},"labels":[],"label_agreement":null},{"id":"W4210890614","doi":"10.1109/jstsp.2021.3128751","title":"Guest Editorial Advanced Signal Processing for Local and Private 5G Networks","year":2022,"lang":"en","type":"editorial","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Southern Taiwan Science Park; University of Bedfordshire; National Science Foundation","keywords":"Computer science; Reliability (semiconductor); Throughput; Special section; Latency (audio); Telecommunications; Cellular network; Low latency (capital markets); Computer network; Key (lock); Signal processing; Focus (optics); Wireless; Computer security; Engineering","score_opus":0.008826320908814382,"score_gpt":0.2571430692257467,"score_spread":0.24831674831693232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210890614","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047626114,0.0127006695,0.45526248,0.0000380824,0.5307928,0.00038438337,0.000022400374,0.00029159884,0.00003135467],"genre_scores_gemma":[0.11542438,0.00221169,0.010432659,0.000008434745,0.8715343,0.00009950844,0.000063402,0.0001989669,0.000026654949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964582,0.00009055363,0.0014488591,0.00039175042,0.0009640588,0.00064658123],"domain_scores_gemma":[0.9965964,0.00074621546,0.0010851821,0.00024391207,0.0012142874,0.000113996626],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006923145,0.0005646006,0.0010064599,0.0005867878,0.00033307105,0.00021556184,0.0010679237,0.0011154242,0.0000074185205],"category_scores_gemma":[0.00035350388,0.00059870316,0.000112099486,0.00096299,0.00021411951,0.00078757,0.00011931046,0.0046821143,1.8474904e-7],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037286105,0.00008369626,0.000017271805,0.0011844776,0.00008310626,0.000027605713,0.00024701032,0.53619057,0.0016815853,0.0000071356567,0.17489225,0.28521246],"study_design_scores_gemma":[0.0026247317,0.00059951394,0.000006030861,0.0019758574,0.00010754552,0.00003857111,0.00027370863,0.15601972,0.0032241992,0.001328554,0.83279413,0.0010074624],"about_ca_topic_score_codex":0.0000017068794,"about_ca_topic_score_gemma":0.000008982326,"teacher_disagreement_score":0.6579019,"about_ca_system_score_codex":0.0007740608,"about_ca_system_score_gemma":0.00065758365,"threshold_uncertainty_score":0.9996464},"labels":[],"label_agreement":null},{"id":"W4221140961","doi":"10.1109/jstsp.2022.3200909","title":"Are Discrete Units Necessary for Spoken Language Modeling?","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Grand Équipement National De Calcul Intensif; Agence Nationale de la Recherche; École des Hautes Etudes en Sciences Sociales; Canadian Institute for Advanced Research","keywords":"Computer science; Spoken language; Natural language processing; Linguistics","score_opus":0.04163231579509832,"score_gpt":0.2834469320030519,"score_spread":0.24181461620795355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221140961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33952415,0.0007416249,0.6578439,0.0011735717,0.00027703546,0.00012471735,0.00000688571,0.00004024116,0.00026790658],"genre_scores_gemma":[0.9736285,0.000008380566,0.025428833,0.00049079495,0.0003039247,0.000010214475,0.0000014136059,0.000012853874,0.00011505809],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864805,0.000111233596,0.0004415649,0.00016694819,0.0003992718,0.0002329108],"domain_scores_gemma":[0.99876004,0.00009737162,0.00047052113,0.00009339083,0.00050949043,0.00006919086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054336793,0.00011096732,0.00023238701,0.0003178404,0.00022773705,0.00013190476,0.00063818356,0.000042722615,0.000028037795],"category_scores_gemma":[0.00013089755,0.00010410938,0.00005736223,0.0010704052,0.000013735715,0.00048231918,0.00005841618,0.00043396745,4.916855e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003968725,0.0005004467,0.0022516276,0.00034721728,0.00011587838,0.0009953964,0.009654868,0.05828054,0.020545416,0.00049015856,0.0017845454,0.90463704],"study_design_scores_gemma":[0.0010207498,0.00021415288,0.00023982872,0.00020408415,0.00002311026,0.00037136517,0.0018064993,0.9791167,0.010494369,0.0053791916,0.0008444281,0.00028552502],"about_ca_topic_score_codex":0.0000047977956,"about_ca_topic_score_gemma":0.0000070528677,"teacher_disagreement_score":0.92083615,"about_ca_system_score_codex":0.00010971396,"about_ca_system_score_gemma":0.00031045752,"threshold_uncertainty_score":0.42454574},"labels":[],"label_agreement":null},{"id":"W4221153184","doi":"10.1109/jstsp.2022.3158820","title":"Learning Progressive Distributed Compression Strategies From Local Channel State Information","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Fusion center; Computer science; Channel state information; Quantization (signal processing); Overhead (engineering); Scalability; Data compression; Algorithm; Bandwidth (computing); Decoding methods; Compressed sensing; Computer engineering; Distributed computing; Real-time computing; Telecommunications; Cognitive radio; Wireless","score_opus":0.008996017593758858,"score_gpt":0.23147570413350618,"score_spread":0.2224796865397473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221153184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0986485,0.0003763799,0.9002279,0.00016085086,0.00036835222,0.00008657378,0.000016365186,0.00006305479,0.00005200198],"genre_scores_gemma":[0.9970376,0.000013729348,0.0026577523,0.00006023231,0.00017265868,0.000006351139,0.00003657052,0.0000068486265,0.000008274103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980014,0.0002199125,0.0006834498,0.00015532172,0.00064805505,0.0002918488],"domain_scores_gemma":[0.9983055,0.00006987109,0.00084993005,0.00008518282,0.00059851015,0.00009102872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033491972,0.00015142764,0.00025124356,0.0002465748,0.0004059374,0.00050279713,0.0005093606,0.000057916728,0.000020921216],"category_scores_gemma":[0.000024929788,0.00014188146,0.000051940533,0.0012410099,0.000043204527,0.0020663308,0.0001103945,0.0011399866,0.0000011028128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089573696,0.00008030466,0.000079836376,0.0000159861,0.000018464556,0.00009013233,0.0022899816,0.7199255,0.00031530307,0.000026896809,0.00013929233,0.27692872],"study_design_scores_gemma":[0.00092108356,0.0005074992,0.0012749434,0.00013728988,0.00000902848,0.00016020438,0.0019608142,0.9869337,0.001970313,0.0035354572,0.0023762058,0.00021343076],"about_ca_topic_score_codex":0.000022425762,"about_ca_topic_score_gemma":0.0000025034406,"teacher_disagreement_score":0.8983891,"about_ca_system_score_codex":0.00021980666,"about_ca_system_score_gemma":0.00038016058,"threshold_uncertainty_score":0.57857585},"labels":[],"label_agreement":null},{"id":"W4225759418","doi":"10.1109/jstsp.2022.3156756","title":"Distributed Learning for Wireless Communications: Methods, Applications and Challenges","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless network; Wireless; Distributed computing; Computer network; Distributed learning; Telecommunications","score_opus":0.09406988135292749,"score_gpt":0.35701659342915265,"score_spread":0.26294671207622516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225759418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013614949,0.029891508,0.96435666,0.004084034,0.00003674221,0.00016937536,8.304725e-7,0.00002595335,0.00007341954],"genre_scores_gemma":[0.82488215,0.008490477,0.16632487,0.000085040105,0.00008472875,0.00009889814,0.0000041054273,0.000008724379,0.00002102279],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986546,0.00051847316,0.00040195393,0.00013495568,0.0001485064,0.00014149964],"domain_scores_gemma":[0.99840945,0.0004626051,0.00037021592,0.00022389491,0.00048889185,0.00004495577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012472261,0.000082570186,0.00018562893,0.00016818826,0.00066339073,0.00010008366,0.0010279322,0.000029237863,0.0000023042344],"category_scores_gemma":[0.00006247845,0.0000854211,0.000029787174,0.0007390545,0.00004617813,0.00028924333,0.0002728779,0.00059593807,6.97602e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010358996,0.00008849703,0.00015198789,0.000039228707,0.000013325376,8.209803e-7,0.0013677785,0.0014496814,0.0028563265,0.007568572,0.000023838535,0.9864296],"study_design_scores_gemma":[0.0011605562,0.0003676994,0.0010826851,0.00016194048,0.00002597835,0.00017826387,0.0011522917,0.81832474,0.0017659471,0.0106101995,0.16483782,0.00033187697],"about_ca_topic_score_codex":5.238495e-7,"about_ca_topic_score_gemma":0.00000431117,"teacher_disagreement_score":0.9860977,"about_ca_system_score_codex":0.000098570104,"about_ca_system_score_gemma":0.00017917139,"threshold_uncertainty_score":0.5102332},"labels":[],"label_agreement":null},{"id":"W4229495760","doi":"10.1109/jstsp.2013.2251805","title":"IEEE Journal of Selected Topics in Signal Processing publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Institute of Electrical and Electronics Engineers","keywords":"Computer science; Signal processing; Information retrieval; Telecommunications","score_opus":0.022726839640307323,"score_gpt":0.24933027217540182,"score_spread":0.2266034325350945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229495760","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5966739,0.00088533247,0.39877892,0.002395881,0.0005095681,0.0003434047,4.1078786e-7,0.00006783256,0.00034476462],"genre_scores_gemma":[0.98798656,0.000033107466,0.011157361,0.00018986927,0.0005707655,0.0000067189903,0.0000011172906,0.00001128813,0.000043183554],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99635595,0.00020769189,0.0019268469,0.00018226774,0.00087849796,0.00044873724],"domain_scores_gemma":[0.9923866,0.00006584055,0.0022198602,0.00015441782,0.0050565633,0.00011676067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001359443,0.00023011546,0.00048120276,0.0014155463,0.00014256696,0.00049453514,0.001009501,0.00032370124,0.000013882052],"category_scores_gemma":[0.0003153579,0.00020377254,0.00006999332,0.0028219004,0.00006486236,0.0055753286,0.00001818471,0.0012280749,0.0000048454676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010801121,0.00050826016,0.024988461,0.00051240885,0.000073888106,0.000054250166,0.006062811,0.0027529162,0.16898498,0.00022536876,0.0016564414,0.7940722],"study_design_scores_gemma":[0.009946516,0.0024997722,0.09295992,0.0059821378,0.00014259682,0.0036094284,0.0015309437,0.28568822,0.578327,0.011403876,0.0060484717,0.0018610912],"about_ca_topic_score_codex":0.00001805153,"about_ca_topic_score_gemma":0.000013713912,"teacher_disagreement_score":0.7922111,"about_ca_system_score_codex":0.00036435094,"about_ca_system_score_gemma":0.0011738157,"threshold_uncertainty_score":0.8309603},"labels":[],"label_agreement":null},{"id":"W4280571452","doi":"10.1109/jstsp.2022.3178213","title":"Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Huawei Technologies","keywords":"Telecommunications link; Computer science; Scheduling (production processes); Distributed computing; Computer network; Mathematical optimization; Mathematics","score_opus":0.023405183008352766,"score_gpt":0.26118851114034763,"score_spread":0.23778332813199488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280571452","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88957494,0.002301664,0.10731718,0.00021024274,0.00015545542,0.00011973602,0.0000011078085,0.00017784804,0.00014180606],"genre_scores_gemma":[0.97318393,0.00016028536,0.026498025,0.00002601169,0.000037666192,0.000009196242,0.0000032480398,0.00003788823,0.000043760396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982696,0.00015923276,0.0008081439,0.00014425044,0.00030686337,0.00031187368],"domain_scores_gemma":[0.99906266,0.00018931637,0.00031816572,0.00014490983,0.00024336968,0.000041562467],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006123862,0.00017310062,0.00033040278,0.0005011581,0.00015653283,0.000054959655,0.00051125424,0.000107992884,0.00007660997],"category_scores_gemma":[0.00015980973,0.0001912825,0.00004842786,0.0014566443,0.000036501868,0.00033340923,0.000036975704,0.002388218,0.0000010685992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003172091,0.00006446983,0.0041464376,0.000060993203,0.0000111672325,0.000027931801,0.00034365247,0.8959099,0.047105864,0.0000044963176,0.00000827063,0.052285094],"study_design_scores_gemma":[0.00084092194,0.00012742258,0.0011996136,0.00038411745,0.000007640714,0.000046290454,0.0013698065,0.87323004,0.12044244,0.00021806273,0.0018398819,0.000293748],"about_ca_topic_score_codex":0.000008584526,"about_ca_topic_score_gemma":0.000022795715,"teacher_disagreement_score":0.083608955,"about_ca_system_score_codex":0.000613561,"about_ca_system_score_gemma":0.00017642915,"threshold_uncertainty_score":0.99991333},"labels":[],"label_agreement":null},{"id":"W4285121428","doi":"10.1109/jstsp.2022.3174655","title":"FLD-SRC: Fingerprint Liveness Detection for AFIS Based on Spatial Ridges Continuity","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Ministry of Public Security of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Fingerprint (computing); Liveness; Pattern recognition (psychology); Spoofing attack; Fingerprint recognition; Computer vision; Feature extraction; Minutiae; Pruning","score_opus":0.021552847069399722,"score_gpt":0.2652068262916054,"score_spread":0.2436539792222057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285121428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14102986,0.00010017165,0.85764265,0.0004084773,0.00061778363,0.0001336189,0.0000029560713,0.000024634675,0.000039844497],"genre_scores_gemma":[0.99409366,0.000002677402,0.0054247705,0.00016928166,0.0002529516,0.000014745795,0.0000011652643,0.000007106658,0.00003362079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984294,0.00017612679,0.0004722879,0.0002136226,0.00050803804,0.00020051573],"domain_scores_gemma":[0.9985831,0.0001756565,0.0004907128,0.00011737185,0.00057583826,0.000057334066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010959876,0.000104591214,0.00020139068,0.00067741936,0.00030966336,0.0001896125,0.00054244726,0.000057128105,0.00001375615],"category_scores_gemma":[0.00015663927,0.00010516234,0.00007690676,0.0015557155,0.00002290717,0.0002456033,0.000041036034,0.0004795651,4.3976289e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030841905,0.00059895107,0.0011364025,0.0001310251,0.000018465918,0.00003839062,0.0012339937,0.012136457,0.024123205,0.00018357443,0.00010407628,0.95998704],"study_design_scores_gemma":[0.0018037113,0.00092535367,0.018337978,0.00010523056,0.000019674206,0.00008797088,0.00007794599,0.83739936,0.13421576,0.0016830178,0.005037391,0.00030662958],"about_ca_topic_score_codex":0.000023001001,"about_ca_topic_score_gemma":0.000024286519,"teacher_disagreement_score":0.95968044,"about_ca_system_score_codex":0.0002802178,"about_ca_system_score_gemma":0.0003806802,"threshold_uncertainty_score":0.42883956},"labels":[],"label_agreement":null},{"id":"W4285151347","doi":"10.1109/jstsp.2022.3172592","title":"Deep-Learning Supervised Snapshot Compressive Imaging Enabled by an End-to-End Adaptive Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Sistema General de Regalías de Colombia","keywords":"Computer science; Artificial intelligence; Snapshot (computer storage); Deep learning; Compressed sensing; Convolutional neural network; Iterative reconstruction; Computer vision; End-to-end principle; Hyperspectral imaging; Artificial neural network; Visualization; Pattern recognition (psychology)","score_opus":0.014391634883502221,"score_gpt":0.2349475359821612,"score_spread":0.22055590109865897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285151347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89235026,0.0045574554,0.1010028,0.00014402566,0.0005420846,0.00027418716,0.000003987258,0.00030215812,0.00082306296],"genre_scores_gemma":[0.9927396,0.000026059319,0.0062817032,0.00020596522,0.0006415775,0.000011605935,0.000013265592,0.00006501461,0.0000152063885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976949,0.0002972803,0.00065873825,0.00024966715,0.00052528275,0.000574134],"domain_scores_gemma":[0.9989493,0.000108764056,0.00027076792,0.00012122738,0.00037951831,0.00017041802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038998885,0.0002860738,0.0004575705,0.00032977297,0.00037996809,0.00015876844,0.00046859242,0.00006666548,0.00015078155],"category_scores_gemma":[0.000025957988,0.00030918335,0.00007500351,0.0009813836,0.000040794217,0.000515085,0.000074849966,0.0016265601,4.1982227e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013548987,0.00005960275,0.0013612533,0.000016931655,0.000039712042,0.00021761813,0.001271458,0.8578408,0.086664684,0.0000053480735,0.0013421758,0.051044945],"study_design_scores_gemma":[0.00064597017,0.00040457246,0.0006245121,0.00021037142,0.0000430739,0.00030772891,0.0006594452,0.96940666,0.025449188,0.00050376327,0.0013284966,0.0004161888],"about_ca_topic_score_codex":0.000022018556,"about_ca_topic_score_gemma":0.00000937457,"teacher_disagreement_score":0.1115659,"about_ca_system_score_codex":0.00026967126,"about_ca_system_score_gemma":0.00010109275,"threshold_uncertainty_score":0.99993604},"labels":[],"label_agreement":null},{"id":"W4289792473","doi":"10.1109/jstsp.2022.3196562","title":"L-Mix: A Latent-Level Instance Mixup Regularization for Robust Self-Supervised Speaker Representation Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature learning; Embedding; Speech recognition; Artificial intelligence; Regularization (linguistics); Speaker recognition; Pattern recognition (psychology); Supervised learning; Semi-supervised learning; Machine learning; Artificial neural network","score_opus":0.05517068553173211,"score_gpt":0.2693890251686757,"score_spread":0.2142183396369436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289792473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0767728,0.00017750806,0.9215386,0.0007976821,0.00026705273,0.00018444555,0.0000015294942,0.000059055983,0.00020135398],"genre_scores_gemma":[0.6282171,0.000028015851,0.3707991,0.00023396262,0.00029561593,0.000020761992,0.0000067363344,0.000020129291,0.00037857553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819773,0.0002321828,0.0005846494,0.00023683978,0.0005167488,0.0002318392],"domain_scores_gemma":[0.9984915,0.00012818795,0.0005292705,0.00009277465,0.0006989633,0.000059256385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007147576,0.00012021826,0.00022560601,0.00036073974,0.00034875833,0.00019108126,0.00041870636,0.000057228364,0.000028427972],"category_scores_gemma":[0.00016209949,0.00012488989,0.00007442977,0.0012971341,0.000014785725,0.00079699693,0.000043766307,0.00046315655,8.216085e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002647232,0.0005601365,0.005761649,0.00018463482,0.0000919447,0.00013747258,0.0064768284,0.071969084,0.036803737,0.0006096379,0.0004747392,0.8766654],"study_design_scores_gemma":[0.0017204056,0.00030094903,0.0025035504,0.00015460278,0.000031466534,0.00028469384,0.00036321647,0.97224873,0.017248543,0.003904882,0.0009616309,0.0002773414],"about_ca_topic_score_codex":0.00000259287,"about_ca_topic_score_gemma":0.000004023483,"teacher_disagreement_score":0.90027964,"about_ca_system_score_codex":0.00021347663,"about_ca_system_score_gemma":0.00033015103,"threshold_uncertainty_score":0.50928617},"labels":[],"label_agreement":null},{"id":"W4295308567","doi":"10.1109/jstsp.2022.3206084","title":"Self-Supervised Language Learning From Raw Audio: Lessons From the Zero Resource Speech Challenge","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Connaught Fund; Agence Nationale de la Recherche; École des Hautes Etudes en Sciences Sociales","keywords":"Computer science; Speech recognition; Zero (linguistics); Resource (disambiguation); Artificial intelligence; Natural language processing; Linguistics","score_opus":0.030685832581785723,"score_gpt":0.26361765352844096,"score_spread":0.23293182094665524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295308567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84626025,0.006356912,0.12812366,0.015657224,0.0006755281,0.00024734027,0.00000862108,0.00022509288,0.0024453504],"genre_scores_gemma":[0.97235453,0.000070814465,0.026133783,0.0006581428,0.000619373,0.000006101849,0.0000046010105,0.00002075876,0.00013191921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973969,0.00064402417,0.0005677892,0.00029200656,0.0007913809,0.0003078919],"domain_scores_gemma":[0.99838036,0.0006184356,0.00049328525,0.00019985458,0.00021643157,0.00009165846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008284162,0.0001757933,0.00031987252,0.00018187323,0.00051218626,0.00026846258,0.0012364691,0.00008140576,0.00018151826],"category_scores_gemma":[0.00015579576,0.00014305455,0.00010548127,0.0008692889,0.000030760184,0.00039778583,0.00015687398,0.0014696383,0.0000052851924],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008554436,0.0003628676,0.0011211763,0.000023073484,0.00012335746,0.00071212836,0.022819107,0.001725334,0.018358044,0.000081580816,0.0009342124,0.9536536],"study_design_scores_gemma":[0.008175573,0.0014338228,0.011292362,0.0013295076,0.00034275796,0.001107535,0.01959295,0.7535428,0.12479451,0.022125551,0.05401676,0.0022458928],"about_ca_topic_score_codex":0.00007655907,"about_ca_topic_score_gemma":0.000036731348,"teacher_disagreement_score":0.9514077,"about_ca_system_score_codex":0.00017788153,"about_ca_system_score_gemma":0.00034554416,"threshold_uncertainty_score":0.6384929},"labels":[],"label_agreement":null},{"id":"W4312602762","doi":"10.1109/jstsp.2022.3223498","title":"Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Info-communications Media Development Authority; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Computer science; Upload; Computer network; Wireless; Feature (linguistics); Training (meteorology); Server; Local area network; Latency (audio); Distributed computing; Operating system; Telecommunications","score_opus":0.03354321227861804,"score_gpt":0.2523201774147574,"score_spread":0.21877696513613934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312602762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18677396,0.0003132815,0.81094134,0.0016804503,0.00007063949,0.00009718051,6.127305e-7,0.00010145832,0.000021071142],"genre_scores_gemma":[0.872255,0.00003873981,0.12758565,0.000053038246,0.00003486168,0.0000053662334,0.0000017794816,0.000014470219,0.000011060945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979966,0.00033734602,0.0005003547,0.00029102206,0.00051481265,0.00035983708],"domain_scores_gemma":[0.9986499,0.00014005118,0.00052740244,0.0002977617,0.00031535552,0.0000695269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071019575,0.00016554949,0.0003010937,0.00033616376,0.00040256564,0.0002719206,0.0030356871,0.00005742175,0.000008664909],"category_scores_gemma":[0.0007597139,0.00015087417,0.000020466588,0.0013101251,0.00009079058,0.00082223426,0.0015949704,0.0010593217,1.2280238e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071537535,0.000374709,0.0049355347,0.00016312886,0.00012147717,0.001646207,0.0043011373,0.110626996,0.016972685,0.00014567148,0.0023161042,0.857681],"study_design_scores_gemma":[0.0013461394,0.0006784748,0.00029858743,0.00019340318,0.00001550604,0.0012548172,0.00043940384,0.97656965,0.010705287,0.008111354,0.00014463336,0.00024273816],"about_ca_topic_score_codex":0.0000059604354,"about_ca_topic_score_gemma":0.0000021062845,"teacher_disagreement_score":0.86594266,"about_ca_system_score_codex":0.00019497063,"about_ca_system_score_gemma":0.0006378917,"threshold_uncertainty_score":0.615247},"labels":[],"label_agreement":null},{"id":"W4312985618","doi":"10.1109/jstsp.2022.3224591","title":"Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Guangdong Provincial Pearl River Talents Program; National Research Foundation of Korea; National Natural Science Foundation of China; Ministry of Science and ICT, South Korea; Singapore University of Technology and Design; Ministry of Education - Singapore","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; The Internet; Computer network; Resource allocation; Edge computing; Internet privacy; Artificial intelligence; World Wide Web","score_opus":0.03912346999838315,"score_gpt":0.2894215025369622,"score_spread":0.2502980325385791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312985618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29796258,0.000851877,0.6925735,0.007825368,0.00028565733,0.00026780117,0.0000012092131,0.00014789005,0.00008411395],"genre_scores_gemma":[0.9512237,0.000024755893,0.04837203,0.00012124913,0.00013006553,0.000028065024,0.0000061672404,0.000023341978,0.00007064716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720365,0.00034018038,0.0010145468,0.00040471708,0.00056302676,0.00047389444],"domain_scores_gemma":[0.99767,0.00033250492,0.0007832529,0.0007256276,0.0004279602,0.000060662183],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0020237232,0.0001990752,0.0003529728,0.0008172937,0.0002606028,0.00033099417,0.010900648,0.000114988405,0.000013700044],"category_scores_gemma":[0.007431374,0.00021088446,0.000059761616,0.0019986306,0.000042955584,0.00094584475,0.008936832,0.0016955608,5.7415303e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009166554,0.0014151586,0.019969204,0.0008330081,0.00015693526,0.00049870566,0.0114243645,0.12545477,0.074717686,0.001230033,0.05272102,0.7106625],"study_design_scores_gemma":[0.000687074,0.00044678382,0.0005147482,0.00034393545,0.0000069632797,0.00012519884,0.00042902192,0.94659495,0.018433286,0.02677748,0.0053902175,0.0002503497],"about_ca_topic_score_codex":0.000027462616,"about_ca_topic_score_gemma":0.000017992677,"teacher_disagreement_score":0.82114017,"about_ca_system_score_codex":0.0006761563,"about_ca_system_score_gemma":0.0004984258,"threshold_uncertainty_score":0.9990787},"labels":[],"label_agreement":null},{"id":"W4313887286","doi":"10.1109/jstsp.2023.3235302","title":"EyeDrive: A Deep Learning Model for Continuous Driver Authentication","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Biometrics; Authentication (law); Modality (human–computer interaction); Context (archaeology); Artificial intelligence; Deep learning; Frame (networking); Frame rate; Focus (optics); Identification (biology); Computer vision; Machine learning; Computer security; Computer network","score_opus":0.022988178606121786,"score_gpt":0.28708106719560605,"score_spread":0.26409288858948426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313887286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26143253,0.00008531163,0.7377396,0.00047028894,0.0000823093,0.000058754158,1.7314291e-7,0.00010389399,0.000027109614],"genre_scores_gemma":[0.9578164,0.000015100987,0.041787114,0.000030203237,0.00011240864,0.0000055658666,7.2376e-7,0.000010052046,0.00022240807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888515,0.000044311604,0.0003970676,0.00017970649,0.00021494424,0.00027880364],"domain_scores_gemma":[0.998793,0.00010163043,0.00036549132,0.00007926034,0.00061857054,0.000042066735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046214688,0.00010251793,0.00021643989,0.00041872964,0.00013595802,0.00010323106,0.0004704176,0.000102648904,9.2969736e-7],"category_scores_gemma":[0.00015612609,0.00009571164,0.00005269024,0.0009942305,0.000041724124,0.00035482907,0.00003192774,0.00043651697,0.0000024010815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005196206,0.00017509027,0.00795433,0.00013940655,0.000049472557,0.000080110876,0.0061171725,0.090838924,0.092381,0.0033547627,0.00022964622,0.79862815],"study_design_scores_gemma":[0.0004721142,0.00013002352,0.0035519404,0.00011880936,0.000011850604,0.0000347527,0.000059074562,0.97792524,0.004509939,0.012966309,0.00011020151,0.00010976471],"about_ca_topic_score_codex":8.075404e-7,"about_ca_topic_score_gemma":0.000002614952,"teacher_disagreement_score":0.8870863,"about_ca_system_score_codex":0.00006930454,"about_ca_system_score_gemma":0.00016321546,"threshold_uncertainty_score":0.39030075},"labels":[],"label_agreement":null},{"id":"W4322730980","doi":"10.1109/jstsp.2023.3250956","title":"Attentive Deep Image Quality Assessment for Omnidirectional Stitching","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Image stitching; Computer vision; Artificial intelligence; Omnidirectional antenna; Computer science; Image quality; Quality (philosophy); Quality assessment; Feature extraction; Image (mathematics); Evaluation methods; Telecommunications; Engineering","score_opus":0.059698756393452075,"score_gpt":0.38901574315102666,"score_spread":0.3293169867575746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322730980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07801876,0.00010200549,0.91946614,0.0014800346,0.00046004966,0.0001469372,0.0000018554613,0.00006554108,0.00025866373],"genre_scores_gemma":[0.8249426,0.00002042117,0.17399423,0.00021057694,0.00067389157,0.000013908804,0.0000032266835,0.000014333803,0.00012682319],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975886,0.00024017644,0.00087080104,0.00026751563,0.000649205,0.00038371992],"domain_scores_gemma":[0.9976666,0.00038827516,0.00060600915,0.00012557923,0.0011282265,0.00008533553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022311616,0.00015968313,0.000341838,0.0004000015,0.00024247258,0.00037139276,0.0005503704,0.00007834629,0.0000066164653],"category_scores_gemma":[0.00015668609,0.00014911233,0.00012064445,0.0012874372,0.000037573835,0.0014181038,0.00006469961,0.0005031308,0.0000025900856],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019552639,0.0009734903,0.008219977,0.0008169784,0.0002605446,0.00032359353,0.008238495,0.0076214094,0.20400302,0.007708089,0.0020730458,0.75956583],"study_design_scores_gemma":[0.005397868,0.0010770792,0.13841428,0.00117985,0.00009283384,0.00026222525,0.0018644712,0.7086147,0.062048275,0.07736428,0.0023821306,0.0013020055],"about_ca_topic_score_codex":0.000013022699,"about_ca_topic_score_gemma":0.000016061498,"teacher_disagreement_score":0.7582638,"about_ca_system_score_codex":0.00027444746,"about_ca_system_score_gemma":0.000738964,"threshold_uncertainty_score":0.60806245},"labels":[],"label_agreement":null},{"id":"W4376851419","doi":"10.1109/jstsp.2023.3276595","title":"User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Carleton University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Cache; Scalability; Scheduling (production processes); Computer network; Virtual reality; Mobile edge computing; Frame (networking); Enhanced Data Rates for GSM Evolution; Server; Real-time computing; Multimedia; Artificial intelligence; Operating system","score_opus":0.024619112589854667,"score_gpt":0.28670996603880816,"score_spread":0.2620908534489535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376851419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53131616,0.00013624987,0.46799067,0.00021062586,0.00020839929,0.000073720694,0.000001614501,0.00004758585,0.000014973939],"genre_scores_gemma":[0.9980211,0.000018951625,0.0014201087,0.000078957055,0.0003678102,0.0000023804237,0.0000021567632,0.000011541218,0.00007695678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986364,0.00008654754,0.00049830956,0.00022221802,0.00025957983,0.00029690462],"domain_scores_gemma":[0.9988646,0.0002462214,0.00030907683,0.00009558118,0.00040634337,0.00007817244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092383986,0.00013073452,0.00026541084,0.00029199722,0.00023033896,0.0002552223,0.00039351924,0.00008167163,4.3396957e-7],"category_scores_gemma":[0.00007036047,0.00012273341,0.000059188984,0.000714893,0.000031234737,0.0005547333,0.0000778734,0.00044844317,4.406291e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012872288,0.00018324188,0.016250942,0.00039881913,0.00008027082,0.0002106726,0.004776146,0.094282605,0.015419184,0.0014600665,0.00078990444,0.8660194],"study_design_scores_gemma":[0.00060126407,0.00023226738,0.0029039981,0.00032624547,0.000011593888,0.00011135029,0.0002512065,0.9937822,0.0005262753,0.0010053429,0.00008406447,0.00016417558],"about_ca_topic_score_codex":0.000024167863,"about_ca_topic_score_gemma":0.000027410028,"teacher_disagreement_score":0.8994996,"about_ca_system_score_codex":0.0001278036,"about_ca_system_score_gemma":0.00021505397,"threshold_uncertainty_score":0.50049233},"labels":[],"label_agreement":null},{"id":"W4389880061","doi":"10.1109/jstsp.2023.3343626","title":"Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multicast; Computer science; Computer network; Resource management (computing); Video streaming; Xcast; Source-specific multicast; Multimedia","score_opus":0.03233416119960012,"score_gpt":0.31155516580734205,"score_spread":0.2792210046077419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389880061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051998816,0.00010913955,0.9461114,0.00083194894,0.00015562063,0.00024925484,0.0000026425603,0.000081551036,0.0004596445],"genre_scores_gemma":[0.95840377,0.000008420093,0.040705614,0.00021804425,0.00032860166,0.000010058055,0.000004313695,0.000018972356,0.00030222305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978694,0.00007176336,0.00073069765,0.00028161943,0.00058618427,0.00046038418],"domain_scores_gemma":[0.99881905,0.0002532676,0.00026380128,0.00016833829,0.00039099652,0.0001045674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075321615,0.00017449349,0.0002803344,0.00061096484,0.00015011022,0.00059312757,0.00068768347,0.00007028672,0.000002327309],"category_scores_gemma":[0.00008078549,0.0001632725,0.00009768527,0.0017947246,0.000028306773,0.0010417054,0.000078749785,0.00031444264,0.0000024582446],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010059075,0.00040348654,0.0029570195,0.00050062366,0.000072267394,0.0005348544,0.0008818306,0.011612264,0.002227533,0.00033419213,0.00152947,0.9788459],"study_design_scores_gemma":[0.0037175317,0.0006828276,0.008896081,0.0015826059,0.0000852943,0.000106164975,0.0010158076,0.94254345,0.019645385,0.0017462842,0.019199036,0.00077955343],"about_ca_topic_score_codex":0.0000012249152,"about_ca_topic_score_gemma":0.0000017291787,"teacher_disagreement_score":0.9780663,"about_ca_system_score_codex":0.00017998763,"about_ca_system_score_gemma":0.00023491314,"threshold_uncertainty_score":0.66580594},"labels":[],"label_agreement":null},{"id":"W4394744448","doi":"10.1109/jstsp.2024.3386054","title":"Modeling and Analysis of Near-Field ISAC","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Computer science; Field (mathematics); Mathematics","score_opus":0.018347365285071453,"score_gpt":0.2544811946475776,"score_spread":0.23613382936250618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394744448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9171303,0.0066416613,0.0758941,0.000038774786,0.00008038986,0.00002851982,5.0170996e-7,0.000019488782,0.00016624494],"genre_scores_gemma":[0.99790597,0.00011295208,0.0018806929,0.000010241175,0.000073062365,4.665043e-7,3.6713772e-7,0.000008473371,0.000007794939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991089,0.000020647034,0.0004437212,0.00008264412,0.0002092339,0.00013483751],"domain_scores_gemma":[0.9996458,0.000051235696,0.00004160059,0.00004389322,0.0001777111,0.000039707353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029515053,0.000083288476,0.00025624462,0.00035086757,0.000023215038,0.000063422005,0.000079132325,0.00005945824,0.000015332891],"category_scores_gemma":[0.000031937812,0.00007813123,0.000052673975,0.0011302251,0.000014707975,0.00015751163,0.0000049345135,0.00034345008,1.0605066e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039305527,0.000052753054,0.0039437,0.00076994364,0.00057360943,0.000031575335,0.0022830626,0.6470366,0.14034836,0.000021687823,0.00003336519,0.20486604],"study_design_scores_gemma":[0.000116416304,0.000113842856,0.00082061696,0.00032631436,0.000265723,0.000013047967,0.000025280888,0.98520267,0.012614503,0.00040639547,0.000022165787,0.00007305036],"about_ca_topic_score_codex":0.000010920659,"about_ca_topic_score_gemma":0.000035926758,"teacher_disagreement_score":0.33816606,"about_ca_system_score_codex":0.000048216294,"about_ca_system_score_gemma":0.00010632772,"threshold_uncertainty_score":0.3186099},"labels":[],"label_agreement":null},{"id":"W4395096392","doi":"10.1109/jstsp.2024.3369289","title":"Guest Editorial Signal Processing for Digital Twin in 6G Multi-Tier Computing Systems","year":2024,"lang":"en","type":"editorial","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Signal processing; Digital signal processing; Computer hardware","score_opus":0.016418478126031,"score_gpt":0.26782955621772625,"score_spread":0.2514110780916953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395096392","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012769566,0.003258945,0.043628823,0.000019744211,0.950195,0.0005726381,0.00015408901,0.00021211346,0.00068165845],"genre_scores_gemma":[0.14226925,0.000028955355,0.0007645099,0.0000035933056,0.8564917,0.00002396029,0.000080465055,0.00019787296,0.00013972481],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944962,0.00005487986,0.0026759433,0.00042473042,0.0015092475,0.00083895214],"domain_scores_gemma":[0.99670166,0.0005607113,0.00072409376,0.0001194793,0.0017090088,0.00018504848],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.000970602,0.00071096397,0.0011699833,0.0011809659,0.00011128467,0.0020861793,0.00068644225,0.0017428148,0.0000026630441],"category_scores_gemma":[0.00027866574,0.0007221561,0.00020494829,0.0013420929,0.00008573079,0.0021537303,0.00003494747,0.004613539,0.000005024303],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020322244,0.00024367042,0.00008812719,0.012170289,0.00017929838,0.00017337556,0.0021014586,0.1580557,0.00046023473,0.0000033062397,0.79226613,0.034055218],"study_design_scores_gemma":[0.004161563,0.00038641252,0.0000075922544,0.025807561,0.00017682723,0.00012355004,0.0007055495,0.26317173,0.00080993585,0.0002159796,0.70280147,0.0016318208],"about_ca_topic_score_codex":0.0000066329353,"about_ca_topic_score_gemma":0.000008100002,"teacher_disagreement_score":0.1409923,"about_ca_system_score_codex":0.0012521709,"about_ca_system_score_gemma":0.0016528802,"threshold_uncertainty_score":0.99955314},"labels":[],"label_agreement":null},{"id":"W4399800998","doi":"10.1109/jstsp.2024.3416681","title":"Near-Field Multiuser Communications Based on Sparse Arrays","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Multiuser detection; Field (mathematics); Algorithm; Telecommunications; Code division multiple access; Mathematics","score_opus":0.02436027726013315,"score_gpt":0.26690685450626606,"score_spread":0.2425465772461329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399800998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056138866,0.0049717696,0.93526965,0.0014350764,0.000332589,0.00009029455,0.0000034312882,0.00019304485,0.0015652444],"genre_scores_gemma":[0.99203366,0.0001102477,0.007388464,0.00014724843,0.00024034303,0.000001758514,0.0000013574152,0.000023651508,0.000053259628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990997,0.000042128806,0.00039415693,0.00008662619,0.00020573704,0.0001716795],"domain_scores_gemma":[0.9994329,0.00015724287,0.000059225928,0.00013410968,0.00015788448,0.000058635316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019038764,0.00012121803,0.00020476812,0.00029074593,0.00007221105,0.0001664546,0.00027108405,0.000087538676,0.000041058993],"category_scores_gemma":[0.000037213977,0.00010625378,0.00007761451,0.00084009534,0.00003184306,0.00018791785,0.0000066216753,0.0007284216,0.000008484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014011019,0.00046919062,0.0046111443,0.0010144918,0.00035032385,0.0005865608,0.0028982204,0.4751366,0.24732774,0.00017666708,0.0039815283,0.2633074],"study_design_scores_gemma":[0.00018634788,0.00006772044,0.00012737008,0.0006818017,0.000044270193,0.000017135417,0.00004443732,0.99346066,0.0035166682,0.0002024562,0.001532418,0.000118722746],"about_ca_topic_score_codex":0.0000033369533,"about_ca_topic_score_gemma":0.000008167069,"teacher_disagreement_score":0.9358948,"about_ca_system_score_codex":0.000086379776,"about_ca_system_score_gemma":0.00014360907,"threshold_uncertainty_score":0.4332903},"labels":[],"label_agreement":null},{"id":"W4400276083","doi":"10.1109/jstsp.2024.3422823","title":"Incongruity-Aware Cross-Modal Attention for Audio-Visual Fusion in Dimensional Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Audio visual; Modal; Speech recognition; Emotion recognition; Artificial intelligence; Sensor fusion; Visual attention; Fusion; Pattern recognition (psychology); Computer vision; Perception; Psychology; Multimedia","score_opus":0.024534494778918708,"score_gpt":0.31605500027419214,"score_spread":0.2915205054952734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400276083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6428968,0.0005445956,0.35548255,0.00033874664,0.0005547003,0.00011251748,0.0000013786637,0.000041702697,0.000027065245],"genre_scores_gemma":[0.97969437,0.000019226238,0.019332102,0.00007829921,0.0007838634,0.0000061867395,0.0000075882167,0.000016430738,0.000061936014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800646,0.00008507819,0.0007857334,0.00033262253,0.00044694022,0.00034318314],"domain_scores_gemma":[0.99860775,0.00012917069,0.00032295563,0.000061853105,0.0008008755,0.00007736152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009879761,0.00017568462,0.00026458703,0.0006904928,0.00016362556,0.00062395865,0.0002892954,0.00016681783,0.0000081310445],"category_scores_gemma":[0.00010392162,0.00016292742,0.000091784306,0.0013236669,0.00003927131,0.002230147,0.000041380863,0.00059162994,0.0000029316525],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013785978,0.00022223417,0.0070084217,0.0005476532,0.000017687025,0.00016517889,0.0005845269,0.0017226038,0.11977373,0.000023261722,0.00006742514,0.8697294],"study_design_scores_gemma":[0.0035994998,0.001116824,0.043663885,0.008371307,0.00004955551,0.00096377067,0.00012715555,0.6366845,0.2628404,0.04149735,0.0002507585,0.00083502266],"about_ca_topic_score_codex":0.0000072326056,"about_ca_topic_score_gemma":0.000022664804,"teacher_disagreement_score":0.8688944,"about_ca_system_score_codex":0.00029830233,"about_ca_system_score_gemma":0.0006601297,"threshold_uncertainty_score":0.6643987},"labels":[],"label_agreement":null},{"id":"W4402041053","doi":"10.1109/jstsp.2024.3451290","title":"Fractional Chirp Rate Based CSS Division Multiple Access Over LEO Satellite Internet-of-Things","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Satellite Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Chirp; Satellite; Communications satellite; Telecommunications; Computer network; Remote sensing; Geology; Optics; Physics; Astronomy","score_opus":0.03133523149259236,"score_gpt":0.29744615418047093,"score_spread":0.26611092268787856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402041053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6596957,0.020873575,0.3161554,0.00014169174,0.00143857,0.00019691793,0.0000025767786,0.00017360688,0.0013219607],"genre_scores_gemma":[0.9967998,0.0003022268,0.0024376651,0.000042925727,0.00030956647,0.0000036187378,0.0000054000448,0.0000439955,0.000054782085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980102,0.0001247455,0.0010833129,0.00015429837,0.00041167726,0.00021572315],"domain_scores_gemma":[0.9985839,0.0004672522,0.00031539003,0.00014318207,0.00041702655,0.00007325737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007742729,0.00019093894,0.00036603303,0.00054268504,0.000040705243,0.00028424995,0.0005036403,0.00014876689,0.000060887523],"category_scores_gemma":[0.00011138033,0.00017582631,0.000101526406,0.0009412041,0.000037394988,0.0012948498,0.000031553867,0.00087914476,0.0000034879927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022468783,0.00019483353,0.03497915,0.0029635623,0.00025176583,0.0001226575,0.004059268,0.07236976,0.20874998,0.000062539024,0.00027685493,0.67574495],"study_design_scores_gemma":[0.00071910786,0.00006352973,0.022581957,0.0029019162,0.000027886983,0.00004752573,0.00003970276,0.86531657,0.09900936,0.00041886902,0.008590679,0.00028288356],"about_ca_topic_score_codex":0.00002073571,"about_ca_topic_score_gemma":0.000006680548,"teacher_disagreement_score":0.7929468,"about_ca_system_score_codex":0.00018401102,"about_ca_system_score_gemma":0.00021442995,"threshold_uncertainty_score":0.7169989},"labels":[],"label_agreement":null},{"id":"W4402810893","doi":"10.1109/jstsp.2024.3468037","title":"Learning-Based Intermittent CSI Estimation With Adaptive Intervals in Integrated Sensing and Communication Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Estimation; Artificial intelligence; Engineering","score_opus":0.016640641991052734,"score_gpt":0.2559240260667358,"score_spread":0.2392833840756831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402810893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17293744,0.0017841569,0.8247387,0.00023603629,0.00014476333,0.00007655407,3.6447378e-7,0.00005092044,0.00003108246],"genre_scores_gemma":[0.9461342,0.0000370261,0.053713948,0.0000265971,0.00006031765,0.0000011356683,0.0000025204129,0.000010859553,0.00001338579],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985431,0.00029202134,0.0005525225,0.00019192795,0.00024973555,0.00017071616],"domain_scores_gemma":[0.9989283,0.000245302,0.00027551787,0.000104961306,0.0003985363,0.000047388974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007217645,0.00013465266,0.00023905323,0.00047000166,0.00007490001,0.00055622275,0.0002666295,0.000083802515,9.115578e-7],"category_scores_gemma":[0.00006109563,0.00010518367,0.000021016918,0.0010296904,0.00005248656,0.0007193921,0.0000323726,0.0009497334,4.355851e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010129884,0.000056617362,0.0009827587,0.00016484782,0.000021498696,0.00022455204,0.0032226911,0.4264335,0.00095837784,0.00015388326,0.000055488676,0.56762445],"study_design_scores_gemma":[0.00032517602,0.0002692183,0.000629272,0.005893788,0.000009920169,0.00027124945,0.00026789887,0.9912185,0.0006466722,0.0002549799,0.00009516023,0.00011817147],"about_ca_topic_score_codex":0.00005231543,"about_ca_topic_score_gemma":0.000038560047,"teacher_disagreement_score":0.77319676,"about_ca_system_score_codex":0.00015131562,"about_ca_system_score_gemma":0.00023849787,"threshold_uncertainty_score":0.5363669},"labels":[],"label_agreement":null},{"id":"W4403095623","doi":"10.1109/jstsp.2024.3474254","title":"Integrated Sensing and Communications for End-to-End Predictive Beamforming Design in Vehicle-to-Infrastructure Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Alliance de recherche numérique du Canada; Deutsche Forschungsgemeinschaft","keywords":"Beamforming; End-to-end principle; Computer science; Computer network; Telecommunications","score_opus":0.023488742406878205,"score_gpt":0.2625484489879265,"score_spread":0.2390597065810483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403095623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12074344,0.0015757674,0.8770625,0.0001457702,0.00015187736,0.00023787879,0.0000030048905,0.000046673595,0.000033110886],"genre_scores_gemma":[0.8807887,0.00009868852,0.11880677,0.0000664831,0.00019510252,0.0000053109898,0.0000028720067,0.000030385892,0.0000056734884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988663,0.000058284364,0.00054982473,0.00013816616,0.00013865434,0.0002487246],"domain_scores_gemma":[0.9992643,0.00020455231,0.00006463722,0.00007971267,0.0002867449,0.00010007089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051728595,0.00015438949,0.00023936492,0.0005538853,0.000081644015,0.00015640043,0.00015473537,0.00011307231,0.0000028468717],"category_scores_gemma":[0.00007643817,0.000146076,0.000029749655,0.00087673543,0.000023285293,0.00027706864,0.000022534789,0.0007102756,2.0592445e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044013737,0.0000067794895,0.000047082034,0.00009654932,0.000024295727,0.000008340637,0.00219441,0.6684802,0.060521364,0.000003684712,0.000045703822,0.26852757],"study_design_scores_gemma":[0.00025287303,0.00010577398,0.00009394011,0.0012814904,0.000024792987,0.00005827616,0.00023131177,0.9811876,0.015819244,0.00060790306,0.00019435583,0.00014248116],"about_ca_topic_score_codex":0.000005638614,"about_ca_topic_score_gemma":0.000029735189,"teacher_disagreement_score":0.7600453,"about_ca_system_score_codex":0.00023438835,"about_ca_system_score_gemma":0.00015272327,"threshold_uncertainty_score":0.5956806},"labels":[],"label_agreement":null},{"id":"W4405180667","doi":"10.1109/jstsp.2024.3511403","title":"Signal Processing and Learning for Next Generation Multiple Access in 6G","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Signal processing; Artificial intelligence; SIGNAL (programming language); Telecommunications; Radar","score_opus":0.03740119847410139,"score_gpt":0.2886143616873036,"score_spread":0.2512131632132022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405180667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24335416,0.006723747,0.7494052,0.000041184147,0.0001735778,0.00018734188,6.03971e-7,0.00006974001,0.000044448007],"genre_scores_gemma":[0.9843421,0.00012033343,0.014590932,0.000010940892,0.00083562575,0.000015243781,0.0000041643452,0.00004897791,0.00003168809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986963,0.00005027762,0.00065663207,0.00017722789,0.00016469175,0.00025483896],"domain_scores_gemma":[0.999366,0.00011800627,0.00014951073,0.000030894833,0.0002833425,0.000052244763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004268233,0.00016929567,0.0002673948,0.00048955035,0.00009581493,0.00045928557,0.0001197605,0.00012971647,0.0000037087675],"category_scores_gemma":[0.000080492995,0.00016947147,0.000029798372,0.0007803995,0.000020332644,0.0018844142,0.000010950857,0.0006487981,2.2897207e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021126598,0.00001342131,0.0011768499,0.0008121981,0.000009751937,0.000019477957,0.0010362577,0.71760374,0.09558547,0.000003398439,0.000020205647,0.18369807],"study_design_scores_gemma":[0.0005270657,0.00006440886,0.00017302317,0.0012592239,0.000015863068,0.00007359719,0.0001281571,0.98281515,0.014366339,0.00017989276,0.00021909489,0.0001781976],"about_ca_topic_score_codex":0.0000040784757,"about_ca_topic_score_gemma":0.000037335514,"teacher_disagreement_score":0.74098796,"about_ca_system_score_codex":0.00022164502,"about_ca_system_score_gemma":0.00016814051,"threshold_uncertainty_score":0.6910846},"labels":[],"label_agreement":null},{"id":"W4406138065","doi":"10.1109/jstsp.2024.3522437","title":"Editorial Introduction to the Special Issue on Learning-Based Signal Processing for Integrated Sensing and Communications","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Signal processing; Telecommunications; Radar","score_opus":0.016978317916384756,"score_gpt":0.2869723455077209,"score_spread":0.2699940275913361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406138065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002900767,0.00045641413,0.9633589,0.027174452,0.0056947568,0.00029408737,0.0000012247858,0.00006433477,0.000055082983],"genre_scores_gemma":[0.695744,0.000024709043,0.02929137,0.00028911102,0.27452448,0.000011432191,0.0000036183274,0.000022237475,0.00008904932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987537,0.000107712694,0.00041575145,0.00023440128,0.00028355315,0.00020483651],"domain_scores_gemma":[0.9987115,0.00025987544,0.00018717106,0.00013886622,0.000637784,0.00006475522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006142623,0.00013250904,0.00017090372,0.00022177058,0.00042192644,0.00070186553,0.00047053205,0.000077330005,0.000002864074],"category_scores_gemma":[0.00007070822,0.000096744996,0.000040240175,0.0012710014,0.000057247045,0.0003553023,0.000037523638,0.0008512339,0.0000013593851],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006590764,0.000051233885,0.00000842066,0.000055672175,0.000009912256,0.000003055258,0.0008560876,0.031745862,0.0046285028,0.00020355477,0.037489273,0.92488253],"study_design_scores_gemma":[0.0001977637,0.0002549746,0.000015035007,0.0003470833,0.000015943027,0.000030214309,0.00005644435,0.6378989,0.0024508627,0.000944471,0.3576767,0.000111557965],"about_ca_topic_score_codex":0.0000043487444,"about_ca_topic_score_gemma":0.000019049607,"teacher_disagreement_score":0.9340675,"about_ca_system_score_codex":0.000104413106,"about_ca_system_score_gemma":0.00038938207,"threshold_uncertainty_score":0.67681056},"labels":[],"label_agreement":null},{"id":"W4410491803","doi":"10.1109/jstsp.2025.3558652","title":"R-Bench: Are Your Large Multimodal Model Robust to Real-World Corruptions?","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.04899085643834207,"score_gpt":0.33231877452336195,"score_spread":0.2833279180850199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410491803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013550958,0.00009868684,0.9823399,0.00240062,0.0001858427,0.00015001357,0.000005630989,0.00010790758,0.0011603937],"genre_scores_gemma":[0.73895365,0.000035517078,0.25950754,0.00057727157,0.00013004632,0.000009292622,0.0000017811789,0.000009664502,0.000775239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981051,0.00007962409,0.00076391164,0.00030022205,0.00039456968,0.00035656808],"domain_scores_gemma":[0.9979955,0.000050218958,0.00050603144,0.00028230742,0.0010633493,0.00010261022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005683632,0.00017299535,0.00031726324,0.0009648846,0.00016852713,0.0002572036,0.001098236,0.00010421182,0.000005084862],"category_scores_gemma":[0.00012575275,0.00016812743,0.000053030508,0.0025353676,0.000027195214,0.0009928339,0.00010713084,0.0006132442,0.000002408148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034470105,0.0025795707,0.0585973,0.00073860283,0.00015105419,0.00029918773,0.0054852855,0.33831105,0.14555712,0.04725592,0.042406306,0.3582739],"study_design_scores_gemma":[0.0006391781,0.000073466595,0.012906177,0.00074408064,0.00001511214,0.000023677529,0.00006930951,0.9606959,0.015553318,0.007826787,0.001173612,0.00027937855],"about_ca_topic_score_codex":0.000017144703,"about_ca_topic_score_gemma":0.00009468114,"teacher_disagreement_score":0.7254027,"about_ca_system_score_codex":0.00033433634,"about_ca_system_score_gemma":0.0006902778,"threshold_uncertainty_score":0.68560374},"labels":[],"label_agreement":null},{"id":"W4411446549","doi":"10.1109/jstsp.2025.3581484","title":"Split Fine-Tuning for Large Language Models in Wireless Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Wireless; Wireless network; Computer network; Artificial intelligence; Telecommunications","score_opus":0.019486117815965456,"score_gpt":0.2893391874151269,"score_spread":0.26985306959916144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411446549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0387044,0.0012066404,0.95890355,0.00035711878,0.00020178875,0.00014425286,4.2942725e-7,0.00003409843,0.00044774878],"genre_scores_gemma":[0.9699144,0.000021863374,0.02961478,0.00013902734,0.0001982302,0.00000955155,5.223163e-7,0.0000080872,0.000093541224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862945,0.00007569124,0.00063844206,0.00017882638,0.0001618594,0.00031572665],"domain_scores_gemma":[0.99916327,0.000107493695,0.00029712947,0.00011440784,0.00027968653,0.000038023485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008607164,0.00012536475,0.00031988468,0.00041738985,0.000073657386,0.00017481606,0.0005614632,0.00012061364,8.9420047e-7],"category_scores_gemma":[0.00002134237,0.00011376218,0.000055522152,0.00097462896,0.000008855071,0.0006463826,0.00004878081,0.0004579139,5.1655295e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013207532,0.00053465733,0.008027904,0.0008269878,0.00007991696,0.00025421183,0.006395532,0.020795912,0.010877036,0.046563424,0.0018239508,0.9036884],"study_design_scores_gemma":[0.00075600937,0.0000738272,0.00030238018,0.0011337076,0.0000055767505,0.000027441623,0.00008561565,0.9820598,0.0036053066,0.011571763,0.00024094683,0.00013762759],"about_ca_topic_score_codex":0.00001812058,"about_ca_topic_score_gemma":0.00007663414,"teacher_disagreement_score":0.9612639,"about_ca_system_score_codex":0.00012109525,"about_ca_system_score_gemma":0.00024960132,"threshold_uncertainty_score":0.4639087},"labels":[],"label_agreement":null},{"id":"W4416286402","doi":"10.1109/jstsp.2025.3633550","title":"MobiLLM: Enabling On-Device Fine-Tuning of Billion-Sized LLMs via Server-Assisted Side-Tuning","year":2025,"lang":"","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Mobile device; Backpropagation; Mobile computing; Quantization (signal processing); Mobile telephony; Computation; Server; Scheme (mathematics); Mobile processor","score_opus":0.03175548512140474,"score_gpt":0.30207165474353476,"score_spread":0.27031616962213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416286402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21818979,0.004670132,0.77356905,0.0017497934,0.0007158859,0.000512613,0.0000019183465,0.00007199771,0.0005188416],"genre_scores_gemma":[0.95621437,0.00033937793,0.042083196,0.00041856038,0.000639461,0.000014801296,0.0000020072464,0.00005023267,0.00023801615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931973,0.00053567416,0.0032904842,0.0008507216,0.0011607998,0.00096498645],"domain_scores_gemma":[0.9912887,0.0014374927,0.0033236705,0.0006109086,0.0030810796,0.0002581379],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001497824,0.0006724199,0.0013890201,0.0017129439,0.00063342036,0.0003774682,0.0020627445,0.0004062456,0.000017516657],"category_scores_gemma":[0.000501221,0.00064093317,0.00029648928,0.010030935,0.00023400896,0.0016111743,0.00027075264,0.0024821598,0.0000026544058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033614755,0.0007127707,0.0010486307,0.00075222645,0.00017418624,0.00014324793,0.0015005564,0.16724767,0.26583812,0.00065333646,0.00008327803,0.56150985],"study_design_scores_gemma":[0.0043525565,0.0010582322,0.0028529314,0.014634525,0.00028473794,0.00032896825,0.00032538958,0.7597213,0.20087624,0.013551831,0.0008686389,0.0011446457],"about_ca_topic_score_codex":0.00002549355,"about_ca_topic_score_gemma":0.000044556986,"teacher_disagreement_score":0.73802453,"about_ca_system_score_codex":0.00057571445,"about_ca_system_score_gemma":0.0020772659,"threshold_uncertainty_score":0.99981916},"labels":[],"label_agreement":null}]}