{"meta":{"query_hash":"f7243cc23602","filters":{"venue":"Machine Learning and Knowledge Extraction"},"cohort_total":51,"direct_labels_cover":0,"predictions_cover":51,"exported":51,"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/f7243cc23602","api":"https://metacan.xera.ac/api/v1/cohort?venue=Machine+Learning+and+Knowledge+Extraction"},"results":[{"id":"W2739573821","doi":"10.3390/make1010002","title":"Learning to Teach Reinforcement Learning Agents","year":2017,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Reinforcement Learning in Robotics","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":"University of Alberta","funders":"Washington State University; U.S. Department of Agriculture; National Aeronautics and Space Administration; National Science Foundation","keywords":"Reinforcement learning; Advice (programming); Heuristics; Statistic; Action (physics); Variance (accounting); Quality (philosophy); Discounting","score_opus":0.02352967150582671,"score_gpt":0.323069848083718,"score_spread":0.29954017657789134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739573821","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":[0.03505318,0.00023764993,0.8910411,0.0010241542,0.0012575646,0.00036896786,1.4197289e-7,0.0008464083,0.07017086],"genre_scores_gemma":[0.91461504,0.00015906061,0.0041728416,0.0000586977,0.00026724476,0.000025750309,0.000015023882,0.000043109365,0.08064327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735963,0.00035411556,0.000456608,0.00075952185,0.00043709046,0.0006330081],"domain_scores_gemma":[0.9981717,0.00019131438,0.00054444995,0.00061358797,0.00016230605,0.000316649],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014046244,0.00037357668,0.00033641563,0.00032448347,0.0036350524,0.0013996185,0.0008401965,0.00016105433,0.00009134802],"category_scores_gemma":[0.0017603529,0.0003808316,0.00010504374,0.00019598128,0.00006832341,0.0010743252,0.000966385,0.0018394416,0.0006477701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003218047,0.000047223773,0.057554558,0.00006122237,0.00005602143,0.000020331541,0.0042685713,0.75943893,0.001677009,0.0011707431,0.00042618517,0.17524704],"study_design_scores_gemma":[0.0004936064,0.0005111747,0.015120032,0.000085862506,0.000017736664,0.000030829764,0.00016109046,0.6942566,0.00023626171,0.000019954561,0.2887004,0.00036643143],"about_ca_topic_score_codex":0.000171842,"about_ca_topic_score_gemma":0.000018173652,"teacher_disagreement_score":0.88686824,"about_ca_system_score_codex":0.00014907225,"about_ca_system_score_gemma":0.00005948714,"threshold_uncertainty_score":0.99986434},"labels":[],"label_agreement":null},{"id":"W2901533770","doi":"10.3390/make1020036","title":"Real-Time Vehicle Make and Model Recognition System","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Vehicle License Plate Recognition","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 Regina","funders":"","keywords":"Computer science; Support vector machine; License; Artificial intelligence; Set (abstract data type); Feature (linguistics); Random forest; Class (philosophy); Identification (biology); Component (thermodynamics); Feature extraction; Machine learning; Real-time computing; Pattern recognition (psychology); Computer vision","score_opus":0.008471277298213386,"score_gpt":0.2261265976645268,"score_spread":0.2176553203663134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901533770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95970464,0.00035793683,0.00067575433,0.000017995864,0.00018977208,0.00016201322,0.000007816261,0.00080240215,0.038081672],"genre_scores_gemma":[0.9965917,0.0005150766,0.00089749147,0.0000021966687,0.00009992576,0.000012459842,0.0000755577,0.00005048315,0.0017551363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992572,0.00006936744,0.00017998334,0.0002283225,0.00007802426,0.00018710028],"domain_scores_gemma":[0.99966764,0.00008931569,0.000046342408,0.00007304057,0.000045848567,0.000077834666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024778245,0.00016223307,0.00017724263,0.00012538841,0.00011271982,0.00006140541,0.000019677675,0.0001384613,0.000043364955],"category_scores_gemma":[0.000015350539,0.00017521378,0.000028078983,0.00010055851,0.000012670045,0.00020671,0.000018403287,0.0003698938,0.0007052386],"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":[0.00009407001,0.000060716495,0.0047814194,0.0013048758,0.00008203631,0.000009982384,0.0010862813,0.015463527,0.4486725,0.000079416066,0.00014300471,0.52822214],"study_design_scores_gemma":[0.00047609315,0.000051027808,0.0016778749,0.0001405005,0.000034052027,0.000115432966,0.00012375292,0.9944003,0.0018698353,0.00004814156,0.00084748084,0.00021550298],"about_ca_topic_score_codex":0.000034509794,"about_ca_topic_score_gemma":0.000015336162,"teacher_disagreement_score":0.9789368,"about_ca_system_score_codex":0.000070396745,"about_ca_system_score_gemma":0.000008888271,"threshold_uncertainty_score":0.90646505},"labels":[],"label_agreement":null},{"id":"W2907210592","doi":"10.3390/make1010018","title":"Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Ontario Power Generation; Ontario Tech University","funders":"","keywords":"Autoregressive integrated moving average; Interface (matter); Computer science; Situation awareness; Time series; Process (computing); Sequence (biology); Autoregressive model; Human–machine interface; Human–machine system; Data mining; Operator (biology); Human error; State (computer science); Series (stratigraphy); Artificial intelligence; Machine learning; Engineering; Econometrics; Algorithm; Reliability engineering; Mathematics","score_opus":0.07389168470611468,"score_gpt":0.4390646777614761,"score_spread":0.36517299305536144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907210592","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8319785,0.0013765455,0.081762195,0.0001049398,0.0022099037,0.0010072893,0.00006408758,0.0002738729,0.08122264],"genre_scores_gemma":[0.98331916,0.000025423104,0.00015098762,0.000009368591,0.00009332525,0.00008676326,0.00013594552,0.00002858958,0.016150454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819416,0.0005392339,0.00046122374,0.0003658942,0.00026365306,0.00017584256],"domain_scores_gemma":[0.99868876,0.00019133631,0.00034909998,0.00017254286,0.0005433278,0.00005495187],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021446482,0.00016735856,0.00022049608,0.0002274531,0.0002023077,0.00003477028,0.00006683415,0.00011303393,0.0018613896],"category_scores_gemma":[0.00011589632,0.000165994,0.000079357895,0.000118451644,0.000036293848,0.0003512647,0.000022668863,0.00040660088,0.00015666943],"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":[0.0011681695,0.0012229076,0.021097414,0.0004414532,0.0006038878,0.0000011464506,0.02827792,0.08616661,0.09602397,0.014681371,0.0013414873,0.74897367],"study_design_scores_gemma":[0.0023118677,0.00050074555,0.009899241,0.000078145094,0.00013200224,0.000032892258,0.00062088406,0.965008,0.0007827891,0.0025491307,0.017894296,0.00019000837],"about_ca_topic_score_codex":0.0002492798,"about_ca_topic_score_gemma":0.00013715467,"teacher_disagreement_score":0.8788414,"about_ca_system_score_codex":0.00013636162,"about_ca_system_score_gemma":0.00004341264,"threshold_uncertainty_score":0.99905103},"labels":[],"label_agreement":null},{"id":"W2954062317","doi":"10.3390/make1030045","title":"Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université TÉLUQ; Institut National de la Recherche Scientifique","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Statistic; Kinematics; Curse of dimensionality; Artificial intelligence; Pattern recognition (psychology); Computer science; Classifier (UML); Sample size determination; Mathematics; Sample (material); Statistics; Machine learning; Data mining","score_opus":0.009507961157756373,"score_gpt":0.2808299173983609,"score_spread":0.2713219562406045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954062317","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9511335,0.0045446074,0.039642945,0.0007107962,0.00018055413,0.0013051187,0.000011549406,0.000117024414,0.0023539176],"genre_scores_gemma":[0.9945642,0.00012577466,0.00032001614,0.000071837916,0.00008994728,0.00010186628,0.00015370683,0.000025568379,0.004547099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913603,0.00009585963,0.00019865179,0.0002903107,0.00012443429,0.00015468469],"domain_scores_gemma":[0.99942714,0.00019344118,0.0000957148,0.00012766692,0.00005534106,0.000100722296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659058,0.00014510563,0.00018851158,0.00007811138,0.00019384964,0.00005958604,0.000023660594,0.00006923955,0.000079781734],"category_scores_gemma":[0.000056652156,0.00011288996,0.00002622902,0.00011066738,0.000022322864,0.00007964997,0.00001844629,0.0002512596,0.00023356943],"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":[0.00005699425,0.000059687933,0.011923527,0.00009720687,0.00001181998,0.0000014241817,0.00055424223,0.0000116609,0.119097516,0.000073772295,0.000053963446,0.8680582],"study_design_scores_gemma":[0.0277456,0.03377461,0.15588139,0.0033262845,0.0017507786,0.001990975,0.007899551,0.4099405,0.028572071,0.0016032277,0.32483703,0.0026779764],"about_ca_topic_score_codex":0.000047208618,"about_ca_topic_score_gemma":0.000052143132,"teacher_disagreement_score":0.8653802,"about_ca_system_score_codex":0.00003142085,"about_ca_system_score_gemma":0.0000134568445,"threshold_uncertainty_score":0.46035188},"labels":[],"label_agreement":null},{"id":"W2977512614","doi":"10.3390/make1040059","title":"Towards Image Classification with Machine Learning Methodologies for Smartphones","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Transfer of learning; Deep learning; Inductive transfer; Android (operating system); Butterfly; Robot learning; Mobile robot","score_opus":0.040413905756543966,"score_gpt":0.36179164142149467,"score_spread":0.3213777356649507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977512614","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":[0.008105407,0.0017257996,0.9843132,0.00038520276,0.00020592628,0.000342416,0.000001685253,0.0007895216,0.004130838],"genre_scores_gemma":[0.57093674,0.0006090948,0.42186335,0.000025511254,0.00010357896,0.00005948188,0.000037646823,0.00003437297,0.006330266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985668,0.0002884226,0.00022219279,0.0005227147,0.00013508095,0.00026475385],"domain_scores_gemma":[0.99877375,0.0005322808,0.00021872087,0.00021610374,0.00019694277,0.000062226885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095532695,0.0002207164,0.00026459072,0.00017964157,0.00033572628,0.00019405088,0.00020369742,0.000100471574,0.00001857696],"category_scores_gemma":[0.00050099177,0.00017409326,0.000063938955,0.00031351025,0.00005093764,0.00091306673,0.00010889606,0.0005723083,0.000034548135],"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":[0.00017653088,0.00005966006,0.010179262,0.00014011298,0.000028219156,0.0000025235,0.0006294574,0.000072148534,0.060773417,0.0030432139,0.00006068105,0.9248348],"study_design_scores_gemma":[0.0017721698,0.0025452871,0.022554062,0.0001550888,0.000060528506,0.00015936601,0.00043172427,0.554139,0.08193883,0.00487351,0.33046496,0.0009054511],"about_ca_topic_score_codex":0.00004895266,"about_ca_topic_score_gemma":0.000015934156,"teacher_disagreement_score":0.92392933,"about_ca_system_score_codex":0.000049386246,"about_ca_system_score_gemma":0.000043104956,"threshold_uncertainty_score":0.70993173},"labels":[],"label_agreement":null},{"id":"W2986573554","doi":"10.3390/make1040061","title":"Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Threat Reduction Agency; Nvidia","keywords":"Thresholding; Pattern recognition (psychology); Linear discriminant analysis; Binary number; Artificial intelligence; Partial least squares regression; Sample (material); Artificial neural network; Computer science; Noise (video); Relevance (law); Mathematics; Machine learning; Chemistry; Chromatography","score_opus":0.024540741268471326,"score_gpt":0.30879774378637936,"score_spread":0.28425700251790803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986573554","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6594006,0.0005220437,0.3390952,0.000017893472,0.000061156774,0.00016541974,0.0000021840897,0.00056470104,0.00017083947],"genre_scores_gemma":[0.9102049,0.00007892443,0.08901754,0.0000016561658,0.00003329314,0.000031397587,0.00006686636,0.000029936074,0.0005355125],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993285,0.000014305192,0.00014729996,0.0002598632,0.000058002628,0.0001920469],"domain_scores_gemma":[0.999683,0.00007193336,0.000059533853,0.00010811828,0.000043171705,0.00003419254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056955774,0.00015885061,0.00019554587,0.00018341526,0.0001136613,0.00003912733,0.00005054756,0.00011228624,0.000014980468],"category_scores_gemma":[0.000024481245,0.00014453754,0.000042263215,0.00029523685,0.00002574035,0.00016565541,0.0000121529165,0.0003637316,0.000020533536],"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":[0.000040168055,0.00007500991,0.027168456,0.00008579176,0.00013336304,4.4611096e-7,0.00009289954,0.038463984,0.9189029,0.000055881785,0.000004115631,0.0149770295],"study_design_scores_gemma":[0.0008719006,0.000081968465,0.008621525,0.000023367693,0.00012581315,0.000003515931,0.00010708646,0.92273045,0.066623755,0.0000053006984,0.000629215,0.0001760924],"about_ca_topic_score_codex":0.0000060214265,"about_ca_topic_score_gemma":0.000019507092,"teacher_disagreement_score":0.8842665,"about_ca_system_score_codex":0.00010070148,"about_ca_system_score_gemma":0.0000020045416,"threshold_uncertainty_score":0.589407},"labels":[],"label_agreement":null},{"id":"W3005415419","doi":"10.3390/make2010003","title":"Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"University of Regina","keywords":"Random forest; Remote sensing; Pixel; Convolutional neural network; Canopy; Computer science; Lidar; Environmental science; Satellite imagery; Geography; Artificial intelligence","score_opus":0.01660234135065939,"score_gpt":0.2684972853422704,"score_spread":0.251894943991611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005415419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8418395,0.000813457,0.14634109,0.0013494323,0.00024100306,0.00019713827,0.0000033726378,0.00023574359,0.00897927],"genre_scores_gemma":[0.9967031,0.000030524632,0.0020859623,0.000063342595,0.00020331895,0.0000015834789,0.00007911879,0.00001525938,0.00081779686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914235,0.0001215847,0.00015881438,0.0002879488,0.000116231284,0.00017305171],"domain_scores_gemma":[0.9996453,0.000059744398,0.00009540598,0.00006496223,0.000011782615,0.00012283743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015276794,0.00012063975,0.00010290584,0.000024065514,0.00062442326,0.00003779227,0.000039262406,0.00007905781,0.00023225015],"category_scores_gemma":[0.000058215046,0.00011769004,0.00003443918,0.00019089175,0.00007599176,0.00014274266,0.000072502095,0.00030626217,0.00012555631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010363775,0.000052969386,0.072191305,0.000015520547,0.000013277871,0.0000030129852,0.00082606747,0.8333486,0.01986967,0.00006629891,0.0014401828,0.07206946],"study_design_scores_gemma":[0.00021453146,0.00004072704,0.019203346,0.000006313044,0.000020178704,0.000050187216,0.000020636531,0.9469087,0.000106574684,0.00001819949,0.03328717,0.0001234463],"about_ca_topic_score_codex":0.00036956518,"about_ca_topic_score_gemma":0.00014469915,"teacher_disagreement_score":0.1548636,"about_ca_system_score_codex":0.00016997455,"about_ca_system_score_gemma":0.000007196901,"threshold_uncertainty_score":0.48026222},"labels":[],"label_agreement":null},{"id":"W3046519084","doi":"10.3390/make2030011","title":"Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Social media; Immigration; Computer science; Word (group theory); Character (mathematics); Task (project management); Voice activity detection; Recall; Precision and recall; Internet privacy; Artificial intelligence; World Wide Web; Speech processing; Political science; Psychology; Linguistics; Law; Cognitive psychology; Engineering","score_opus":0.02308940963608441,"score_gpt":0.2834093222664004,"score_spread":0.260319912630316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046519084","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8718384,0.00022925215,0.12431106,0.00054079166,0.0010585256,0.00016882361,3.667873e-7,0.00062689965,0.0012259085],"genre_scores_gemma":[0.9973692,0.00012046414,0.0012915421,0.000044394874,0.0005693316,0.000019274747,0.0000036684546,0.00002011784,0.0005619884],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987456,0.00016077864,0.00020183293,0.00047813726,0.00019173181,0.00022187918],"domain_scores_gemma":[0.9994827,0.00005671251,0.00011469427,0.00013875673,0.00006672653,0.00014039381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023878703,0.00019037367,0.00014876435,0.0001451971,0.00044260526,0.0002466922,0.00012276892,0.00012107714,0.000013109939],"category_scores_gemma":[0.00007567192,0.00018913516,0.00006109479,0.00037218086,0.000016057,0.0004963148,0.000054836015,0.0006703404,0.00022940747],"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":[0.000031270418,0.00007874458,0.02114125,0.0000213111,0.000011572283,0.000018819788,0.001421634,0.00011422642,0.13336933,0.000030796517,0.000034041925,0.843727],"study_design_scores_gemma":[0.0012263325,0.0017650092,0.119256265,0.00011687561,0.00007134646,0.00023866647,0.0004384567,0.26723206,0.54652923,0.000064683205,0.062204674,0.00085640117],"about_ca_topic_score_codex":0.000093142364,"about_ca_topic_score_gemma":0.00002050642,"teacher_disagreement_score":0.8428706,"about_ca_system_score_codex":0.000055625827,"about_ca_system_score_gemma":0.000015804791,"threshold_uncertainty_score":0.7712708},"labels":[],"label_agreement":null},{"id":"W3176714582","doi":"10.3390/make3030027","title":"Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":345,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Interpretability; Artificial intelligence; Computer science; Machine learning; Cluster analysis; Classifier (UML); Stability (learning theory); Random forest; Popularity; Pattern recognition (psychology)","score_opus":0.02470034312009067,"score_gpt":0.3228493839746241,"score_spread":0.2981490408545334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176714582","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":[0.010277559,0.00085634843,0.9842946,0.00039596888,0.0003959655,0.00019201144,0.0000052686373,0.00021673267,0.0033655812],"genre_scores_gemma":[0.97500426,0.00006858991,0.019571176,0.00003964221,0.00006834042,0.00010558692,0.000027186317,0.000018240393,0.005097012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841213,0.00019130361,0.00032079214,0.0005779847,0.00013055411,0.00036721793],"domain_scores_gemma":[0.9982202,0.0008977762,0.000101262805,0.00029493214,0.00035669125,0.00012914336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005984294,0.00018223107,0.00021350467,0.00012044952,0.0006560164,0.00026447073,0.00020338371,0.00009439039,0.000037475074],"category_scores_gemma":[0.0012919524,0.00019408882,0.00007390993,0.0003184442,0.0000625087,0.0007288034,0.00016250063,0.00034568243,0.000041806383],"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":[0.00012130531,0.0010224695,0.0025753288,0.00050613924,0.000073524345,0.00006964559,0.009029557,0.22742808,0.010324885,0.07777836,0.0009866345,0.67008406],"study_design_scores_gemma":[0.00015392939,0.00011899626,0.00009733292,0.000038208556,0.000019611414,0.000054534783,0.00039851942,0.96410286,0.006159951,0.007254877,0.021390202,0.0002109849],"about_ca_topic_score_codex":0.000080500526,"about_ca_topic_score_gemma":0.00047324336,"teacher_disagreement_score":0.9647267,"about_ca_system_score_codex":0.00013610153,"about_ca_system_score_gemma":0.00020974311,"threshold_uncertainty_score":0.7914712},"labels":[],"label_agreement":null},{"id":"W3187934274","doi":"10.3390/make3030030","title":"Proposing an Ontology Model for Planning Photovoltaic Systems","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Photovoltaic System Optimization Techniques","field":"Energy","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":"Concordia University; Université du Québec en Outaouais","funders":"","keywords":"Photovoltaic system; Computer science; Maximum power point tracking; Ontology; Reuse; Controller (irrigation); Systems engineering; Power (physics); Engineering; Electrical engineering","score_opus":0.03439009288818463,"score_gpt":0.33945899330691176,"score_spread":0.30506890041872714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187934274","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":[0.08898508,0.0073713576,0.8825345,0.000048255482,0.0008597599,0.0006494565,0.0000099163935,0.0014132953,0.018128365],"genre_scores_gemma":[0.97822064,0.00007006376,0.011204153,0.00002376498,0.00021378549,0.00018061795,0.0001824627,0.000064126776,0.009840404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851173,0.00031004232,0.00035044897,0.00045067695,0.00010380226,0.00027329903],"domain_scores_gemma":[0.9990971,0.00015854259,0.00018847764,0.0001712338,0.00028384713,0.000100762096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048690234,0.00020396036,0.00030466734,0.00015445244,0.00044050705,0.00013880907,0.00006513907,0.00020830483,0.000018973495],"category_scores_gemma":[0.00026247973,0.00021038261,0.00005728921,0.00016005692,0.000029084398,0.0002897723,0.000033779466,0.00031950857,0.00000656676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002415701,0.00039436505,0.010518358,0.0006284527,0.000120853445,0.000029932171,0.005254994,0.49062708,0.4529071,0.005940559,0.00055744074,0.032779288],"study_design_scores_gemma":[0.00045975376,0.000109192864,0.00007543476,0.00011291358,0.00003704433,0.00014679127,0.00043403398,0.96018744,0.012919169,0.00034906247,0.024936354,0.00023280951],"about_ca_topic_score_codex":0.00070530205,"about_ca_topic_score_gemma":0.00047524125,"teacher_disagreement_score":0.88923556,"about_ca_system_score_codex":0.000106001906,"about_ca_system_score_gemma":0.000098111304,"threshold_uncertainty_score":0.8579154},"labels":[],"label_agreement":null},{"id":"W3189505573","doi":"10.3390/make4020017","title":"Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Missing data; Computer science; Estimation; Artificial neural network; Temporal database; Position (finance); Graph; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Theoretical computer science; Engineering; Business","score_opus":0.015742407613713535,"score_gpt":0.2819110839778797,"score_spread":0.26616867636416613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189505573","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":[0.21042544,0.008266969,0.750872,0.0008474081,0.0035902003,0.0010249196,0.00006252891,0.014063273,0.010847235],"genre_scores_gemma":[0.997349,0.00007978618,0.0016469533,0.000020249625,0.000096145915,0.000033194126,0.0005701316,0.000029120096,0.00017541104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991254,0.00013539617,0.00021236089,0.00023997581,0.000106928266,0.00017993338],"domain_scores_gemma":[0.999697,0.00004738319,0.000049097518,0.00015151125,0.000011239796,0.000043740485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042453353,0.00013456342,0.00011967816,0.00020891814,0.00036372396,0.00006049492,0.000107277134,0.00004437007,0.00005063019],"category_scores_gemma":[0.000018101573,0.00015652897,0.000023745177,0.00028732314,0.0000138812375,0.00034599262,0.00012442243,0.00062447693,0.000004820115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003151017,0.00008056868,0.015533241,0.00007302965,0.000022556571,0.0000139796875,0.00035239413,0.6402645,0.000363979,0.00006350875,0.009391114,0.33380958],"study_design_scores_gemma":[0.00028405283,0.000040889583,0.009972835,0.000023742547,0.000015755628,0.000029637338,0.000096443924,0.95438284,0.000010564785,0.00003595584,0.03496096,0.00014630538],"about_ca_topic_score_codex":0.00008603817,"about_ca_topic_score_gemma":0.0001241853,"teacher_disagreement_score":0.7869236,"about_ca_system_score_codex":0.000074476615,"about_ca_system_score_gemma":0.0000074725804,"threshold_uncertainty_score":0.6383066},"labels":[],"label_agreement":null},{"id":"W3195446130","doi":"10.3390/make3030034","title":"A Survey of Machine Learning-Based Solutions for Phishing Website Detection","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phishing; Computer science; The Internet; Computer security; Internet security; World Wide Web; Information security; Security service","score_opus":0.03124810316152414,"score_gpt":0.2902130198347076,"score_spread":0.25896491667318344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195446130","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":[0.046746023,0.004101609,0.9469688,0.00022457448,0.00097383757,0.00015454346,0.0000104863675,0.00033882319,0.00048129895],"genre_scores_gemma":[0.99641144,0.000092772876,0.0020940658,0.000015838119,0.00012523556,0.00002414897,0.00009645087,0.000024182715,0.0011158518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980321,0.00069072004,0.00032150332,0.00048286197,0.00016478801,0.00030801725],"domain_scores_gemma":[0.9981569,0.0008478569,0.0002612423,0.00020094708,0.00044380204,0.00008923014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001815693,0.0001876149,0.00024982696,0.00023410576,0.00087771425,0.00019318184,0.00013743917,0.00014749385,0.00001975535],"category_scores_gemma":[0.0015849242,0.00020220417,0.0001109947,0.00070942024,0.00003603886,0.00044273605,0.00009616778,0.00065369625,0.000011556147],"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":[0.00031424547,0.0006769979,0.07859819,0.0004352889,0.00013925506,0.000009640134,0.0016965165,0.026598407,0.107320935,0.00061961205,0.00014836944,0.78344256],"study_design_scores_gemma":[0.0007619748,0.00027851446,0.03993197,0.000049726932,0.00003068625,0.00003782632,0.000021534172,0.929416,0.009667629,0.000116433235,0.019466367,0.00022132618],"about_ca_topic_score_codex":0.0011105611,"about_ca_topic_score_gemma":0.004669997,"teacher_disagreement_score":0.9496654,"about_ca_system_score_codex":0.00007132317,"about_ca_system_score_gemma":0.000112937545,"threshold_uncertainty_score":0.8245647},"labels":[],"label_agreement":null},{"id":"W3212509273","doi":"10.3390/make3040045","title":"A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":54,"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); University of Alberta","funders":"","keywords":"Transparency (behavior); Computer science; Component (thermodynamics); Artificial intelligence; Knowledge management; Management science; Engineering","score_opus":0.06261654919714393,"score_gpt":0.3471028790874849,"score_spread":0.28448632989034095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212509273","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":[0.004657322,0.0038488074,0.99046147,0.0005065777,0.00021964351,0.00020563508,0.0000011261037,0.000061616105,0.00003781497],"genre_scores_gemma":[0.7927445,0.00043381026,0.20635302,0.00001551894,0.00006353656,0.000041948057,0.0000032729868,0.00000938037,0.00033501623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858195,0.00029137998,0.000333386,0.00042729508,0.00012441116,0.00024158877],"domain_scores_gemma":[0.99707305,0.0021774112,0.00016895999,0.00026548,0.0002479466,0.00006716324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010929536,0.00014642398,0.00023814497,0.00018231294,0.0005637202,0.00020327812,0.00019896353,0.000088537556,0.000020391508],"category_scores_gemma":[0.0009810526,0.000120342585,0.00009268065,0.0009545727,0.000083151994,0.0002578353,0.00013992665,0.00033810822,0.0000075976413],"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":[0.000105162646,0.00049727265,0.0021289908,0.00012986576,0.0003750182,0.00001499209,0.00821155,0.04707439,0.026767721,0.09364557,0.000012482247,0.821037],"study_design_scores_gemma":[0.000036225007,0.00008717148,0.00083081186,0.00003081785,0.00011912773,0.000014337331,0.0011730965,0.9417075,0.04413404,0.010649058,0.0010822845,0.0001355358],"about_ca_topic_score_codex":0.0001653431,"about_ca_topic_score_gemma":0.00016079182,"teacher_disagreement_score":0.8946331,"about_ca_system_score_codex":0.000027889226,"about_ca_system_score_gemma":0.00005563951,"threshold_uncertainty_score":0.49074283},"labels":[],"label_agreement":null},{"id":"W4225492836","doi":"10.3390/make4020015","title":"An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Western University","funders":"Government of Canada","keywords":"Autoencoder; Computer science; Artificial intelligence; Thresholding; Anomaly detection; Pattern recognition (psychology); Feature (linguistics); Multivariate statistics; Feature learning; Machine learning; Data mining; Deep learning; Image (mathematics)","score_opus":0.006973311791659286,"score_gpt":0.2750364673034,"score_spread":0.2680631555117407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225492836","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":[0.16561924,0.00008175683,0.83301216,0.00022639926,0.00007362884,0.0003581297,0.000004776115,0.00045100198,0.00017289413],"genre_scores_gemma":[0.97641796,0.0000048136244,0.022133395,0.000020619485,0.000023250275,0.00047236917,0.000037257083,0.00002125781,0.0008691062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890405,0.00017441234,0.0001980563,0.00043463075,0.000103411425,0.00018541871],"domain_scores_gemma":[0.99949104,0.00008806995,0.0001251464,0.00017801698,0.00006644244,0.0000512882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046101952,0.0001442165,0.00013905304,0.00026122193,0.00092716486,0.00011687608,0.00015154202,0.000049300485,0.00003179244],"category_scores_gemma":[0.000014712991,0.00014457645,0.00004177069,0.0004459933,0.000027590133,0.0004720145,0.00004712415,0.00035947145,0.0000041417497],"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":[0.0008152677,0.0014047825,0.030345371,0.00016098822,0.0000634724,0.000012477432,0.0020617188,0.08397781,0.40628025,0.0033899157,0.0000261228,0.4714618],"study_design_scores_gemma":[0.0006616719,0.0007025768,0.0143048875,0.000010963143,0.000010166057,0.00003330937,0.00009872211,0.9776884,0.0014347415,0.00021340365,0.004649152,0.00019200884],"about_ca_topic_score_codex":0.0001742533,"about_ca_topic_score_gemma":0.0002373125,"teacher_disagreement_score":0.8937106,"about_ca_system_score_codex":0.00012531248,"about_ca_system_score_gemma":0.000047996073,"threshold_uncertainty_score":0.7131096},"labels":[],"label_agreement":null},{"id":"W4283076847","doi":"10.3390/make4020026","title":"Fairness and Explanation in AI-Informed Decision Making","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":148,"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":"Austrian Science Fund","keywords":"Transparency (behavior); Perspective (graphical); Perception; Fairness measure; Affect (linguistics); Computer science; Reciprocal; Psychology; Social psychology; Artificial intelligence; Computer security","score_opus":0.024374707503598263,"score_gpt":0.4105602472728633,"score_spread":0.386185539769265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283076847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93526995,0.0014222742,0.0008991361,0.003100706,0.00062770525,0.00018543664,0.0000022481279,0.000096608725,0.058395907],"genre_scores_gemma":[0.99808335,0.0004505565,0.00010397304,0.000077715405,0.00011151091,0.000011689995,0.0000068762183,0.000007706401,0.0011465906],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990036,0.0003264333,0.000142097,0.00014915253,0.00021103997,0.000167675],"domain_scores_gemma":[0.9992235,0.00055780716,0.000076248114,0.000032090924,0.0000576088,0.000052782045],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001728631,0.00006818767,0.0000973221,0.00017118333,0.001655781,0.00015254339,0.00004869825,0.00007425028,0.00011670358],"category_scores_gemma":[0.0011059681,0.00007446762,0.000018459888,0.00024304604,0.00005940048,0.00041504917,0.000062656174,0.00068619155,0.0000034515508],"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":[0.00011715626,0.0001011036,0.10600939,0.00003023067,0.000008424931,0.000008557929,0.09877102,0.00031474262,0.00013830754,0.020467794,0.00030397953,0.7737293],"study_design_scores_gemma":[0.0015615387,0.00032727356,0.15987645,0.00015923135,0.000025906626,0.00002223766,0.06927201,0.029253155,0.000015350939,0.03430675,0.70460695,0.00057312567],"about_ca_topic_score_codex":0.0017136546,"about_ca_topic_score_gemma":0.011749078,"teacher_disagreement_score":0.77315617,"about_ca_system_score_codex":0.00016068303,"about_ca_system_score_gemma":0.000110564644,"threshold_uncertainty_score":0.9996439},"labels":[],"label_agreement":null},{"id":"W4285595287","doi":"10.3390/make4030032","title":"Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Dalhousie University","funders":"","keywords":"Data envelopment analysis; RDM; Entropy (arrow of time); Computer science; Econometrics; Information Criteria; Data mining; Mathematical optimization; Statistics; Mathematics; Model selection; Artificial intelligence","score_opus":0.067863478137799,"score_gpt":0.3723473594554385,"score_spread":0.3044838813176395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285595287","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5591637,0.005926698,0.4123866,0.0010029214,0.0008585663,0.00059096597,0.000048787242,0.00038005054,0.019641707],"genre_scores_gemma":[0.98805183,0.00005749339,0.0027187602,0.000024735218,0.00009283536,0.000032220687,0.00025659744,0.000015253452,0.008750254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99486446,0.0018693836,0.0007563337,0.0011742351,0.0010084655,0.00032710316],"domain_scores_gemma":[0.99824834,0.0006954085,0.00041473447,0.00043346582,0.0001223389,0.000085701344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010071074,0.00020977207,0.00044796983,0.0021054135,0.0010317548,0.0003278639,0.0006380025,0.00007464693,0.0004873395],"category_scores_gemma":[0.0014949131,0.00018880656,0.00010056724,0.006232249,0.00004648086,0.00042716454,0.0006693709,0.0009868835,0.000046463967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019741544,0.0015127892,0.39484078,0.000018989702,0.00042933054,0.000012379739,0.0052095973,0.40617275,0.0027384737,0.000557915,0.0019823613,0.1863272],"study_design_scores_gemma":[0.00029907518,0.000057580466,0.025727473,0.0000036849488,0.00018869714,0.000031030777,0.0011120937,0.8535165,0.000018175873,0.0002348042,0.11861963,0.00019122155],"about_ca_topic_score_codex":0.0005685605,"about_ca_topic_score_gemma":0.0006106868,"teacher_disagreement_score":0.44734377,"about_ca_system_score_codex":0.0002211872,"about_ca_system_score_gemma":0.00012757872,"threshold_uncertainty_score":0.79355276},"labels":[],"label_agreement":null},{"id":"W4297236464","doi":"10.3390/make4040041","title":"Comparison of Imputation Methods for Missing Rate of Perceived Exertion Data in Rugby","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Sports Performance and Training","field":"Medicine","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":"Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"Imputation (statistics); Missing data; Statistics; Random forest; Perceived exertion; Mean squared error; Elastic net regularization; Computer science; Regression; Mathematics; Medicine; Artificial intelligence","score_opus":0.08415591073145363,"score_gpt":0.48443963850910543,"score_spread":0.4002837277776518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297236464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.979668,0.0016762719,0.017695943,0.00010203903,0.0001608618,0.00021556161,0.000004072354,0.000024119503,0.00045308613],"genre_scores_gemma":[0.98613125,0.00004059357,0.013122493,0.000006095666,0.000043586264,0.000013397226,0.00041958166,0.000012789472,0.00021018385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921924,0.00010845843,0.0003357517,0.00018172515,0.00005976357,0.00009508428],"domain_scores_gemma":[0.99942654,0.00016047095,0.0002289954,0.00010598617,0.000052879306,0.000025150313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015671451,0.00007314439,0.00026532108,0.00020466787,0.00014139968,0.000005113107,0.000037394042,0.00003401015,0.00004149103],"category_scores_gemma":[0.00016335747,0.00007330207,0.0000296879,0.00017341838,0.000022096176,0.00012146667,0.000042601245,0.0003075208,2.9167848e-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":[0.00054722663,0.00023741073,0.35921985,0.0002802398,0.000019780286,6.294286e-7,0.004812267,0.00087949773,0.07006171,0.00001776721,0.000014629585,0.563909],"study_design_scores_gemma":[0.0012729189,0.00039863403,0.3600529,0.00008141844,0.00007668911,0.000016799146,0.002324532,0.62781674,0.0017593135,0.000045692672,0.006089815,0.00006453421],"about_ca_topic_score_codex":0.00010740249,"about_ca_topic_score_gemma":0.000015980051,"teacher_disagreement_score":0.62693727,"about_ca_system_score_codex":0.00003459535,"about_ca_system_score_gemma":0.0000485377,"threshold_uncertainty_score":0.29891717},"labels":[],"label_agreement":null},{"id":"W4307765015","doi":"10.3390/make4040048","title":"Lottery Ticket Structured Node Pruning for Tabular Datasets","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Data Stream Mining 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":"University of Windsor","funders":"","keywords":"Pruning; Computer science; Inference; Reduction (mathematics); Range (aeronautics); Ticket; Artificial neural network; Node (physics); Iterative method; Machine learning; Artificial intelligence; Data mining; Algorithm; Mathematics","score_opus":0.014513956100050959,"score_gpt":0.30008244950784685,"score_spread":0.2855684934077959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307765015","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":[0.03304979,0.0014066462,0.9606777,0.00069979276,0.0009778029,0.00045844345,0.00031308364,0.0012994092,0.001117375],"genre_scores_gemma":[0.8980947,0.000023597026,0.09956363,0.00009264467,0.00014273672,0.00012370951,0.0012303375,0.000031097006,0.000697517],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987752,0.00020301472,0.00018525662,0.00045443745,0.0001477849,0.0002343457],"domain_scores_gemma":[0.99924505,0.00019185657,0.00014042832,0.00033015627,0.000031273114,0.00006124004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067308766,0.00015230032,0.00015171731,0.00017527859,0.0007896076,0.00016530706,0.00040408433,0.00004135143,0.000040334588],"category_scores_gemma":[0.0001605328,0.00016099925,0.000036592875,0.00021707335,0.000023897668,0.00041587395,0.0005707931,0.00050249376,0.0000059562294],"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":[0.00012076242,0.0003000084,0.01250382,0.00017098915,0.000082339306,0.000039356102,0.0027591104,0.00086142623,0.019116579,0.0072004884,0.04102267,0.91582245],"study_design_scores_gemma":[0.00042776833,0.00026850894,0.0013013888,0.0000173006,0.00001987975,0.000120586556,0.00008703191,0.3185893,0.0013331006,0.0008402005,0.67672765,0.0002672813],"about_ca_topic_score_codex":0.000051691295,"about_ca_topic_score_gemma":0.000019439683,"teacher_disagreement_score":0.9155552,"about_ca_system_score_codex":0.00006281018,"about_ca_system_score_gemma":0.000038212438,"threshold_uncertainty_score":0.65653586},"labels":[],"label_agreement":null},{"id":"W4308718476","doi":"10.3390/make4040047","title":"Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR; Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky; Austrian Science Fund","keywords":"Class (philosophy); Range (aeronautics); Domain (mathematical analysis); Function (biology); Space (punctuation); Artificial intelligence; Computer science; Binary classification; Mathematics; Machine learning; Support vector machine","score_opus":0.04673742173121412,"score_gpt":0.3243509899479155,"score_spread":0.27761356821670136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308718476","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":[0.0058338735,0.0005618431,0.98873305,0.0018454636,0.00029341373,0.0009317512,0.000025436004,0.0003211472,0.0014540432],"genre_scores_gemma":[0.96114355,0.00006879192,0.025670161,0.00006222005,0.0001484655,0.0016887212,0.00029582833,0.000044517397,0.01087776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781597,0.00037384877,0.00036075676,0.00077159255,0.00024957315,0.00042824165],"domain_scores_gemma":[0.9975794,0.0013595031,0.0003284611,0.00020919867,0.00039029616,0.00013312418],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0012385171,0.0002576838,0.00024773125,0.00038640312,0.0034878384,0.00036753717,0.00015590132,0.000089017216,0.00006464191],"category_scores_gemma":[0.0005858237,0.00026228093,0.00006495818,0.0005743881,0.00007342084,0.0016938178,0.0001587838,0.00084066694,0.000010986057],"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":[0.0035172137,0.0020790368,0.010551556,0.0004017959,0.0004261554,0.000017299164,0.018817566,0.07173761,0.0140133165,0.35105246,0.005576878,0.5218091],"study_design_scores_gemma":[0.00051645696,0.0012910174,0.0002232889,0.000020384685,0.000049177244,0.0001377542,0.005202887,0.73173654,0.00045942352,0.0010857832,0.2590193,0.00025799207],"about_ca_topic_score_codex":0.0007387496,"about_ca_topic_score_gemma":0.0006828542,"teacher_disagreement_score":0.9630629,"about_ca_system_score_codex":0.00028386177,"about_ca_system_score_gemma":0.00019811779,"threshold_uncertainty_score":0.99998295},"labels":[],"label_agreement":null},{"id":"W4313575157","doi":"10.3390/make5010004","title":"IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","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":"Royal Bank of Canada","funders":"","keywords":"Embedding; Computer science; Benchmarking; Representation (politics); Artificial intelligence; Graph; Machine learning; Feature learning; Knowledge representation and reasoning; Iterative refinement; Task (project management); Transfer of learning; Theoretical computer science","score_opus":0.026325188613280908,"score_gpt":0.34010690381531417,"score_spread":0.31378171520203324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313575157","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":[0.07738749,0.0023461406,0.91433764,0.0007721506,0.0007759383,0.00041155436,0.0000024932108,0.0015282976,0.002438272],"genre_scores_gemma":[0.98816895,0.00022077783,0.003446305,0.000029366412,0.00031048123,0.000083716826,0.000090573216,0.000042325122,0.007607504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819666,0.00029064334,0.00029327854,0.0006566909,0.0001300529,0.00043268985],"domain_scores_gemma":[0.99865466,0.0007224363,0.000097529184,0.0001428999,0.00026781656,0.00011468396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054659636,0.00023980899,0.0002327622,0.00035531557,0.0009746287,0.00026840784,0.00017919704,0.000119808225,0.000007120046],"category_scores_gemma":[0.0003498064,0.00023394306,0.00010461931,0.0010819947,0.000047979924,0.0010553135,0.00009027306,0.0007063609,0.000058581132],"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":[0.00009179237,0.00007878058,0.0032842562,0.0001617267,0.000026892272,0.000008834158,0.010599561,0.008242996,0.014262342,0.0021023029,0.00024124717,0.9608993],"study_design_scores_gemma":[0.0007291352,0.0003115342,0.004170518,0.00009506521,0.000019810273,0.000037153965,0.00045128004,0.95266277,0.0011954476,0.0009180579,0.039083607,0.00032565335],"about_ca_topic_score_codex":0.000011847728,"about_ca_topic_score_gemma":0.000047044203,"teacher_disagreement_score":0.9605736,"about_ca_system_score_codex":0.000043688186,"about_ca_system_score_gemma":0.000042529366,"threshold_uncertainty_score":0.9539921},"labels":[],"label_agreement":null},{"id":"W4321231457","doi":"10.3390/make5010015","title":"Can Principal Component Analysis Be Used to Explore the Relationship of Rowing Kinematics and Force Production in Elite Rowers during a Step Test? A Pilot Study","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Sports Performance and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; Simon Fraser University; University of Victoria","funders":"","keywords":"Rowing; Kinematics; Physical medicine and rehabilitation; Elbow; Stroke (engine); Physical therapy; Mathematics; Simulation; Medicine; Engineering; Physics; Surgery; Mechanical engineering","score_opus":0.08999170683810115,"score_gpt":0.35666462255622855,"score_spread":0.2666729157181274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321231457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984966,0.00011322794,0.00003250043,0.00062266644,0.00006843372,0.0004889896,6.1345537e-7,0.00007719973,0.00009980399],"genre_scores_gemma":[0.99894065,0.00003525704,0.000084112864,0.0000055500905,0.000077548226,0.00004941692,0.000029070172,0.000017041371,0.0007613473],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990109,0.00005715334,0.00033275352,0.00025767565,0.00017883227,0.00016264794],"domain_scores_gemma":[0.9994345,0.00018814806,0.00012078046,0.00014299515,0.000052540305,0.00006108899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000973208,0.0001220072,0.00026383405,0.00061760045,0.0002484254,0.00002136674,0.000026525891,0.000027421034,0.0000043526443],"category_scores_gemma":[0.0005073651,0.00009580458,0.000033517153,0.001232701,0.000025351961,0.00009719619,0.000037722904,0.0003938462,0.0000016894867],"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":[0.00014929728,0.00022172308,0.96170896,0.00014098623,0.00008772239,0.000007821086,0.028110716,0.004043736,0.0036170427,0.0000037979257,0.0000015048244,0.0019067129],"study_design_scores_gemma":[0.00085125066,0.00049327617,0.9508435,0.0001859361,0.00031059782,0.000016488742,0.013177406,0.033883333,0.000104343686,0.0000036691295,0.000043831777,0.00008633981],"about_ca_topic_score_codex":0.00023260571,"about_ca_topic_score_gemma":0.0013183497,"teacher_disagreement_score":0.029839596,"about_ca_system_score_codex":0.000048349597,"about_ca_system_score_gemma":0.000021529977,"threshold_uncertainty_score":0.39067975},"labels":[],"label_agreement":null},{"id":"W4321371507","doi":"10.3390/make5010016","title":"A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Corrosion; Pipeline transport; Artificial neural network; Downtime; Reliability (semiconductor); Cathodic protection; Submarine pipeline; Environmental science; Fossil fuel; Pipeline (software); Petroleum engineering; Engineering; Reliability engineering; Computer science; Materials science; Metallurgy; Geotechnical engineering; Environmental engineering; Waste management; Artificial intelligence; Mechanical engineering","score_opus":0.05312106828459529,"score_gpt":0.32163520580308463,"score_spread":0.26851413751848935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321371507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9519636,0.0037670543,0.033588827,0.0010677131,0.0021155216,0.00061601406,0.00006925833,0.0017791112,0.0050329203],"genre_scores_gemma":[0.9984108,0.00011200079,0.00022349555,0.000025976344,0.00032137457,0.000010351106,0.00063204765,0.000022102742,0.00024184995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989368,0.0001400022,0.00021792218,0.00031539815,0.00019878111,0.00019114454],"domain_scores_gemma":[0.99929017,0.00032713547,0.000048514572,0.00019457632,0.00007022788,0.000069367394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094461424,0.00017196662,0.00019910779,0.00015426756,0.0002702034,0.0000972135,0.00006380681,0.00012825309,0.00008851329],"category_scores_gemma":[0.0004229216,0.0001601629,0.0000369859,0.00029759057,0.000048114318,0.00019441929,0.000044754517,0.00065314776,0.000012995654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004804721,0.000020080191,0.0038225704,0.00010418929,0.000048207745,0.0000053709296,0.00015632594,0.94380724,0.003132445,0.000024019142,0.000973411,0.04785807],"study_design_scores_gemma":[0.0006572514,0.00003270837,0.024215735,0.000065324595,0.00009027291,0.000017936354,0.00004826074,0.9693072,0.000043400185,0.00016560058,0.005186036,0.00017026119],"about_ca_topic_score_codex":0.00011814969,"about_ca_topic_score_gemma":0.0004208655,"teacher_disagreement_score":0.04768781,"about_ca_system_score_codex":0.000048801332,"about_ca_system_score_gemma":0.000015463931,"threshold_uncertainty_score":0.65312535},"labels":[],"label_agreement":null},{"id":"W4323565362","doi":"10.3390/make5010017","title":"Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministero della Salute; Università degli Studi di Siena","keywords":"Usability; Computer science; Perception; Relevance (law); Transparency (behavior); Reading (process); Data science; Human–computer interaction; Psychology; Computer security","score_opus":0.023069008272530413,"score_gpt":0.32517115363827465,"score_spread":0.30210214536574426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323565362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8414201,0.00024273315,0.15261945,0.0010956544,0.0006379332,0.00032793096,0.0000013067931,0.0009125536,0.0027423247],"genre_scores_gemma":[0.9918453,0.0000128030815,0.005797698,0.000100195575,0.00034697913,0.00005577051,0.00001788057,0.000033369302,0.001789974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979512,0.00035795328,0.0003205463,0.00065153796,0.00022729061,0.0004914391],"domain_scores_gemma":[0.99881554,0.00054321834,0.000119196775,0.00030306494,0.00006746842,0.00015148974],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0017673606,0.00022109268,0.00017506337,0.00023921837,0.0013659495,0.0007025484,0.00041422126,0.00008645751,0.000040171242],"category_scores_gemma":[0.00044078505,0.00019541716,0.000056048542,0.0008565997,0.00004689288,0.0008588991,0.00037574908,0.00061644707,0.00054219365],"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":[0.000041921998,0.00015441995,0.057346515,0.000041198786,0.000041650652,0.000046259313,0.1071003,0.010911113,0.26986742,0.0025085963,0.0011387381,0.5508019],"study_design_scores_gemma":[0.00010058859,0.00012976192,0.003970169,0.00008815331,0.000008493915,0.000013667372,0.013430889,0.93419623,0.03401347,0.00037397209,0.0133593045,0.00031527012],"about_ca_topic_score_codex":0.0004581802,"about_ca_topic_score_gemma":0.00011313529,"teacher_disagreement_score":0.9232851,"about_ca_system_score_codex":0.0000983256,"about_ca_system_score_gemma":0.000027616252,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W4366390417","doi":"10.3390/make5020024","title":"Lottery Ticket Search on Untrained Models with Applied Lottery Sample Selection","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lottery; Computer science; Ticket; Fraction (chemistry); Machine learning; Sample (material); Set (abstract data type); Selection (genetic algorithm); Artificial intelligence; Artificial neural network; Process (computing); Mathematics; Statistics; Computer security","score_opus":0.08505546188253313,"score_gpt":0.3895661560022754,"score_spread":0.3045106941197423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366390417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9756993,0.00007341336,0.0050097248,0.00023677651,0.00036394893,0.00027288622,0.000010813907,0.0006803617,0.017652756],"genre_scores_gemma":[0.9899744,0.000024531451,0.00018640643,0.000046575507,0.00013914601,0.00008016349,0.00021275235,0.00006005702,0.009275984],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99863553,0.00017243989,0.00016787823,0.00048531353,0.0001675723,0.00037127896],"domain_scores_gemma":[0.9993732,0.00028246248,0.00006238456,0.00013822355,0.000048325277,0.0000954214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035675557,0.00021940346,0.00019026651,0.00036376118,0.00038019655,0.000067706496,0.000049702183,0.000118453434,0.0002378273],"category_scores_gemma":[0.000016824746,0.00018683696,0.00003957905,0.00047458446,0.000042213425,0.00008991748,0.000021226404,0.0006017582,0.00070921023],"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":[0.0035995455,0.0024439683,0.4280541,0.0001353392,0.0005678866,0.00013594629,0.013558262,0.012100693,0.009092808,0.0049539204,0.0048234113,0.5205341],"study_design_scores_gemma":[0.011938394,0.004540152,0.8297279,0.00021612705,0.0004963187,0.00041562517,0.002633471,0.10054942,0.0019716802,0.0012826803,0.04454787,0.0016803509],"about_ca_topic_score_codex":0.00039265,"about_ca_topic_score_gemma":0.000094513925,"teacher_disagreement_score":0.5188538,"about_ca_system_score_codex":0.000064670436,"about_ca_system_score_gemma":0.000022768001,"threshold_uncertainty_score":0.91156995},"labels":[],"label_agreement":null},{"id":"W4385424060","doi":"10.3390/make5030045","title":"Efficient Latent Space Compression for Lightning-Fast Fine-Tuning and Inference of Transformer-Based Models","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; Inference; Embedding; Encoder; Transformer; Fine-tuning; Artificial intelligence; Pattern recognition (psychology); Voltage","score_opus":0.02609713780614908,"score_gpt":0.2920949468936843,"score_spread":0.26599780908753523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385424060","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":[0.06897446,0.0005835943,0.92923516,0.00034671804,0.00018295957,0.0001540424,0.0000026755283,0.00011042945,0.0004099832],"genre_scores_gemma":[0.98962146,0.00006687779,0.009845049,0.0000046202413,0.000051235947,0.00001502597,0.000010400646,0.000010488581,0.00037484753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912536,0.000108233406,0.00018410086,0.00029262522,0.00010009777,0.0001895647],"domain_scores_gemma":[0.99923354,0.0004056544,0.00010666942,0.000094283714,0.00009457757,0.00006524475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041765315,0.00013499969,0.00018600016,0.00015670632,0.00031867652,0.000069311296,0.00008625551,0.000058154554,0.0000031425077],"category_scores_gemma":[0.00007427021,0.000114776565,0.000048305832,0.00026420478,0.000037032492,0.00013953997,0.000042890624,0.00017593472,0.0000024461672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004806717,0.00007096844,0.0010174504,0.00011181719,0.000015322026,0.0000011099478,0.0016101856,0.7771892,0.044601366,0.0010634778,0.000052827014,0.17421824],"study_design_scores_gemma":[0.00048457697,0.00016223897,0.00119429,0.0001234343,0.000013780556,0.0000017431679,0.000039716626,0.98960954,0.0067134984,0.00016883334,0.0013618505,0.00012651733],"about_ca_topic_score_codex":0.000040499886,"about_ca_topic_score_gemma":0.000022706608,"teacher_disagreement_score":0.920647,"about_ca_system_score_codex":0.00001219749,"about_ca_system_score_gemma":0.000028821034,"threshold_uncertainty_score":0.46804526},"labels":[],"label_agreement":null},{"id":"W4386135062","doi":"10.3390/make5030057","title":"Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"Eli Lilly Canada; Fonds de Recherche du Québec - Santé; Otsuka Canada Pharmaceutical; Eli Lilly and Company","keywords":"Computer science; Machine learning; Artificial intelligence; Support vector machine; Perceptron; Naive Bayes classifier; Classifier (UML); Decision tree; Artificial neural network","score_opus":0.08972079733234087,"score_gpt":0.42494684368794694,"score_spread":0.33522604635560604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386135062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99147975,0.0013648941,0.000100845355,0.00057447667,0.00047945097,0.00031232164,0.000002725398,0.000065481516,0.0056200465],"genre_scores_gemma":[0.9980767,0.00021824347,0.0000278413,0.000013312207,0.00003590575,0.00010099113,0.00003869415,0.000019130199,0.0014692191],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982601,0.0006702807,0.0005501478,0.00019876225,0.00012436758,0.00019633897],"domain_scores_gemma":[0.99889207,0.00058083254,0.00030442773,0.00016697065,0.000034968132,0.000020736843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013712224,0.00012575343,0.00019359365,0.00035947585,0.0001715561,0.0000244501,0.0001511188,0.00004508753,0.0000873151],"category_scores_gemma":[0.000048363032,0.00008837388,0.000059189137,0.0007531179,0.00008088407,0.00015990771,0.000020634416,0.0007973647,0.00006772419],"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":[0.00015155449,0.00079611776,0.50807923,0.00016601851,0.00004194842,0.0000012893612,0.011953892,0.0005092198,0.0013406985,0.00066785264,0.00004084719,0.47625133],"study_design_scores_gemma":[0.0005978626,0.00027317746,0.657883,0.00018819513,0.0000076581355,0.000020116817,0.0015529534,0.33574277,0.00008874739,0.000081605525,0.0034968753,0.00006706114],"about_ca_topic_score_codex":0.00064778735,"about_ca_topic_score_gemma":0.00092249434,"teacher_disagreement_score":0.47618428,"about_ca_system_score_codex":0.000045905555,"about_ca_system_score_gemma":0.000013352876,"threshold_uncertainty_score":0.36037824},"labels":[],"label_agreement":null},{"id":"W4386776962","doi":"10.3390/make5030060","title":"Gradient-Based Neural Architecture Search: A Comprehensive Evaluation","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"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":"Rashtriya Uchchatar Shiksha Abhiyan; Canadian Institute for Advanced Research","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Artificial neural network; Architecture; Gradient descent; Machine learning; Resource (disambiguation); Deep learning","score_opus":0.04902472630016129,"score_gpt":0.3505566359418409,"score_spread":0.3015319096416796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386776962","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":[0.3499535,0.0017203944,0.63923347,0.004125346,0.0006640415,0.0007599881,0.000003360662,0.0018253678,0.0017145098],"genre_scores_gemma":[0.9952875,0.00004353515,0.003810665,0.000052929805,0.00013072659,0.00007008076,0.00006437975,0.000016365717,0.00052385166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856776,0.00035738936,0.00015221238,0.0004241031,0.00023379787,0.0002647628],"domain_scores_gemma":[0.99906874,0.00040532704,0.00007395857,0.00021322489,0.00014236169,0.00009640972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035435476,0.00014405733,0.00011821138,0.0002495313,0.00048289695,0.000090972106,0.0001719984,0.000051638875,0.000010986738],"category_scores_gemma":[0.00007946011,0.00013644605,0.000046585676,0.0009567296,0.000036692567,0.00019504478,0.000106000734,0.0005259921,0.00012339492],"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":[0.00001732028,0.000048706508,0.001431703,0.00002686234,0.000008989014,0.000005012821,0.0006860619,0.18137652,0.0065550054,0.001365393,0.00022853848,0.8082499],"study_design_scores_gemma":[0.00037501001,0.00008071681,0.011006381,0.000013074615,0.000009185888,0.000024229137,0.00003267356,0.9623845,0.00029728576,0.00079630496,0.024845112,0.0001355047],"about_ca_topic_score_codex":0.000017622531,"about_ca_topic_score_gemma":0.000029055085,"teacher_disagreement_score":0.8081144,"about_ca_system_score_codex":0.000045625166,"about_ca_system_score_gemma":0.000036799367,"threshold_uncertainty_score":0.55641085},"labels":[],"label_agreement":null},{"id":"W4387331439","doi":"10.3390/make5040069","title":"Entropy-Aware Time-Varying Graph Neural Networks with Generalized Temporal Hawkes Process: Dynamic Link Prediction in the Presence of Node Addition and Deletion","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"University of Toronto","keywords":"Computer science; Graph; Entropy (arrow of time); Point process; Theoretical computer science; Representation (politics); Node (physics); Artificial intelligence; Dynamic network analysis; Enhanced Data Rates for GSM Evolution; Mathematics","score_opus":0.014607877925904228,"score_gpt":0.273304304026124,"score_spread":0.25869642610021976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387331439","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941715,0.00041062466,0.0028605242,0.0015981297,0.00019191469,0.00035540145,0.00001634876,0.00020990225,0.0001856375],"genre_scores_gemma":[0.99912703,0.00022418048,0.00003657887,0.00004852212,0.00011566092,0.00007510479,0.00008705946,0.000018045945,0.0002678449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985196,0.00047620488,0.00020426331,0.00039902923,0.00020715786,0.00019374581],"domain_scores_gemma":[0.99808615,0.0015715103,0.00016111784,0.0000846692,0.000063147345,0.00003341088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004816636,0.00015825969,0.000165107,0.00022483384,0.00047935438,0.000056768964,0.00006103062,0.000070378715,0.000014098355],"category_scores_gemma":[0.0011792147,0.00011818614,0.00002881069,0.00067785534,0.00012875718,0.00034486956,0.000035388704,0.00046732643,0.0000039502606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002172972,0.00047319973,0.3201012,0.0007104959,0.00007345124,0.000056499743,0.008425212,0.46857342,0.1049804,0.00021451271,0.0011817862,0.09303686],"study_design_scores_gemma":[0.00061390206,0.00025598324,0.03956948,0.00009095699,0.000021855494,0.000069434594,0.00025160026,0.958155,0.00033828928,0.00015397137,0.00036333123,0.00011619516],"about_ca_topic_score_codex":0.00006862224,"about_ca_topic_score_gemma":0.00013870085,"teacher_disagreement_score":0.48958158,"about_ca_system_score_codex":0.000026605456,"about_ca_system_score_gemma":0.000018816228,"threshold_uncertainty_score":0.48194906},"labels":[],"label_agreement":null},{"id":"W4388945936","doi":"10.3390/make5040085","title":"FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Segmentation; Class (philosophy); Function (biology); Computer science; Terrain; Artificial intelligence; Machine learning; Geography; Cartography","score_opus":0.01779713589971847,"score_gpt":0.29313076773951846,"score_spread":0.2753336318398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388945936","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":[0.063859016,0.00046273446,0.93399256,0.000063976666,0.00031817274,0.0005602666,0.0000012531113,0.000477904,0.0002641376],"genre_scores_gemma":[0.989948,0.00003345309,0.008625791,0.000004291548,0.0001496655,0.00033984822,0.000052458836,0.000013768122,0.0008327277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887794,0.0001406367,0.00022812336,0.00042518612,0.000091587775,0.00023654893],"domain_scores_gemma":[0.99941355,0.00020910491,0.00014051246,0.00013172683,0.000056334367,0.00004879639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000483916,0.0001282132,0.00013576142,0.00025145817,0.00034624184,0.00010749234,0.0001044018,0.00007890058,4.2363064e-7],"category_scores_gemma":[0.000086066924,0.00013321298,0.00003280842,0.00080649764,0.000015107736,0.000399728,0.000060119644,0.00030688618,0.000012639446],"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":[0.00005018872,0.00016623923,0.007831006,0.00056344044,0.00001661373,0.0000026932419,0.0012108369,0.088569865,0.1808825,0.0015027836,0.00006896033,0.71913487],"study_design_scores_gemma":[0.00038348942,0.00006724244,0.0051319757,0.000009443464,0.000007967667,0.000016229333,0.00012989018,0.991069,0.0012388246,0.00014961646,0.0016587225,0.00013757069],"about_ca_topic_score_codex":0.000115130366,"about_ca_topic_score_gemma":0.00009048518,"teacher_disagreement_score":0.926089,"about_ca_system_score_codex":0.00009285375,"about_ca_system_score_gemma":0.000015640888,"threshold_uncertainty_score":0.5432267},"labels":[],"label_agreement":null},{"id":"W4389203808","doi":"10.3390/make5040089","title":"Analysing Semi-Supervised ConvNet Model Performance with Computation Processes","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","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":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Machine learning; Computation; Classifier (UML); Artificial neural network; Supervised learning; Training set; Data pre-processing; Algorithm","score_opus":0.02850362207233759,"score_gpt":0.3039833642872269,"score_spread":0.2754797422148893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389203808","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":[0.45012382,0.00023174185,0.5465813,0.00019644314,0.00007457156,0.00008887908,4.0956337e-7,0.0005258148,0.0021770257],"genre_scores_gemma":[0.99325955,0.00019664009,0.0046275374,0.00001708147,0.00005492564,0.0000150550895,0.000023628802,0.00001626446,0.0017893037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988962,0.00008675885,0.00019509689,0.00038539077,0.00017505084,0.00026153304],"domain_scores_gemma":[0.99931335,0.00016639984,0.000103724466,0.00011993039,0.00022418583,0.00007239341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044809165,0.00015258334,0.00015128806,0.0002672771,0.0005414367,0.00023545888,0.00014600539,0.000054353,0.00000498799],"category_scores_gemma":[0.000096071315,0.00013460037,0.000021087215,0.0013032602,0.00004094403,0.0009181714,0.00007326652,0.0003012508,0.000117008596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043871096,0.00008054573,0.019402152,0.00027394498,0.000028841676,0.000011349629,0.005944073,0.7300737,0.0038375687,0.00051143725,0.00010599685,0.23968652],"study_design_scores_gemma":[0.00012761039,0.00012974131,0.0012941038,0.0000637211,0.000012997111,0.000022219838,0.00025397236,0.9940101,0.0028321291,0.00023855493,0.0008350673,0.00017979491],"about_ca_topic_score_codex":0.00005040513,"about_ca_topic_score_gemma":0.000096029005,"teacher_disagreement_score":0.54313576,"about_ca_system_score_codex":0.000038380673,"about_ca_system_score_gemma":0.00009862882,"threshold_uncertainty_score":0.5488844},"labels":[],"label_agreement":null},{"id":"W4392957858","doi":"10.3390/make6010032","title":"Analyzing the Impact of Oncological Data at Different Time Points and Tumor Biomarkers on Artificial Intelligence Predictions for Five-Year Survival in Esophageal Cancer","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","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":"Institute of Cancer Research","funders":"","keywords":"Esophageal cancer; Cancer; Medicine; Oncology; Artificial intelligence; Internal medicine; Computer science","score_opus":0.05788579022705994,"score_gpt":0.4176247959946425,"score_spread":0.35973900576758255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392957858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9849213,0.012482953,0.0005633377,0.00069301174,0.00015062637,0.0005298105,0.00020847646,0.000047641697,0.00040284818],"genre_scores_gemma":[0.99834275,0.0006416082,0.000043324017,0.0000022482718,0.00017635155,0.000044818098,0.0001421768,0.000013683911,0.00059306517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990279,0.00015377918,0.00019859502,0.000327212,0.00010876951,0.00018374725],"domain_scores_gemma":[0.999046,0.0006535045,0.000045883542,0.0001245167,0.000034816792,0.000095262185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725279,0.00013116586,0.00021872664,0.00015514647,0.0001595232,0.000035614416,0.000051681636,0.000047359914,0.00019041362],"category_scores_gemma":[0.00024314871,0.00007553972,0.00006469616,0.0001817183,0.000088524765,0.000061531304,0.00010084269,0.0003623921,0.000011045779],"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":[0.007984485,0.0013324141,0.46034384,0.0005885579,0.0010563032,0.0001460505,0.0010985277,0.0006523393,0.003141789,0.00032636512,0.0008154638,0.52251387],"study_design_scores_gemma":[0.00073305896,0.0027217395,0.42721856,0.00035387446,0.00019236401,0.000047905363,0.0001471332,0.5672565,0.00062996615,0.00031724133,0.00026655392,0.00011508354],"about_ca_topic_score_codex":0.00055583613,"about_ca_topic_score_gemma":0.00045277,"teacher_disagreement_score":0.5666042,"about_ca_system_score_codex":0.00028370897,"about_ca_system_score_gemma":0.00007778106,"threshold_uncertainty_score":0.30804205},"labels":[],"label_agreement":null},{"id":"W4394956176","doi":"10.3390/make6020041","title":"Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Term (time); Convolutional neural network; Computer science; Long short term memory; Sentiment analysis; Natural language processing; Artificial intelligence; Information retrieval; Artificial neural network; Recurrent neural network","score_opus":0.03887216470533473,"score_gpt":0.37624500442286146,"score_spread":0.33737283971752674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394956176","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7327635,0.012031353,0.20648192,0.0011693572,0.0032793614,0.00047701845,0.00001021437,0.0009031026,0.04288414],"genre_scores_gemma":[0.9763156,0.00014610954,0.00039761869,0.000018027695,0.0011223449,0.000022514634,0.000031885465,0.000020157071,0.021925716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980448,0.0003713612,0.00035943324,0.00045262623,0.00033446675,0.0004373398],"domain_scores_gemma":[0.9993278,0.00026356545,0.00006668372,0.000092176,0.00009759413,0.0001521936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014612701,0.00018472834,0.000219543,0.00021490142,0.0012366947,0.00047042346,0.000104243925,0.0001300321,0.00045358873],"category_scores_gemma":[0.000093634706,0.00018686935,0.00017533218,0.0006255624,0.00018852988,0.00065463857,0.000064944616,0.00063519785,0.000090126334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014885988,0.0002836828,0.06158057,0.00017216,0.0014387033,0.00011678692,0.032489154,0.51173234,0.008498983,0.05869933,0.00079195114,0.3240475],"study_design_scores_gemma":[0.000045317815,0.000033124357,0.0018891819,0.00006719485,0.00040046885,0.000010148102,0.001114827,0.9850373,0.00041670442,0.00070684624,0.010008977,0.00026986512],"about_ca_topic_score_codex":0.0018288124,"about_ca_topic_score_gemma":0.01878396,"teacher_disagreement_score":0.47330502,"about_ca_system_score_codex":0.00022802733,"about_ca_system_score_gemma":0.00016012124,"threshold_uncertainty_score":0.99912065},"labels":[],"label_agreement":null},{"id":"W4396952634","doi":"10.3390/make6020050","title":"Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Software Engineering Research","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":"Concordia University","funders":"","keywords":"Vulnerability (computing); Computer science; Vulnerability assessment; Psychology; Computer security","score_opus":0.013586231344363547,"score_gpt":0.335502881454395,"score_spread":0.32191665011003145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396952634","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":[0.1813346,0.0026485184,0.81479806,0.000092244874,0.0003013389,0.00010538324,0.0000068552317,0.0005481326,0.00016486007],"genre_scores_gemma":[0.99387044,0.000047849313,0.005004273,0.0000032740907,0.000037936137,0.000017140394,0.000023749577,0.000018947741,0.000976372],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859834,0.00016377114,0.00023840192,0.00043438526,0.00023714053,0.0003279848],"domain_scores_gemma":[0.9971418,0.0023869066,0.000047904196,0.00019156773,0.00013273816,0.00009909859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011394464,0.00016414063,0.00020262065,0.0001697146,0.00024792965,0.00020354311,0.00017988544,0.000088741624,0.000017871858],"category_scores_gemma":[0.002016442,0.00014940022,0.0000574016,0.00036575267,0.000033859047,0.0004758506,0.00017213065,0.00087913655,0.000005967883],"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":[0.000009488128,0.0003121984,0.7428253,0.0012063329,0.00010048988,0.000019566234,0.0019546102,0.10273584,0.010341799,0.0029234197,0.001091596,0.1364793],"study_design_scores_gemma":[0.00013819244,0.00009411106,0.02339295,0.0000953259,0.000009826301,0.0000093167955,0.000017859673,0.9712119,0.0012083389,0.00025963108,0.0034105252,0.00015202357],"about_ca_topic_score_codex":0.00011086,"about_ca_topic_score_gemma":0.0000057881057,"teacher_disagreement_score":0.86847603,"about_ca_system_score_codex":0.0001550457,"about_ca_system_score_gemma":0.00012790054,"threshold_uncertainty_score":0.6092364},"labels":[],"label_agreement":null},{"id":"W4398349452","doi":"10.3390/make6020052","title":"Locally-Scaled Kernels and Confidence Voting","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Mitacs","keywords":"Voting; Computer science; Political science; Law; Politics","score_opus":0.011671512419377554,"score_gpt":0.29089840322773763,"score_spread":0.2792268908083601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398349452","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":[0.11613072,0.016131926,0.8552191,0.0010840115,0.0010597616,0.0001400702,0.0000011097088,0.0008759852,0.009357293],"genre_scores_gemma":[0.9941857,0.0003889411,0.0017197669,0.000026739957,0.00011996304,0.00000736041,0.0000040216732,0.000009342105,0.0035381608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916464,0.0001034925,0.0001397909,0.00035287015,0.000089596055,0.00014959388],"domain_scores_gemma":[0.9995538,0.00022706887,0.000035480763,0.0000666698,0.000039411752,0.00007759099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037822532,0.00011188998,0.00010118372,0.00011190153,0.00023606556,0.00038706273,0.00007109347,0.000069447044,0.000031970158],"category_scores_gemma":[0.00008146771,0.000096928736,0.000026121608,0.00016728541,0.00003145559,0.000522707,0.00009129085,0.00039973966,0.00012643285],"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":[0.000009686056,0.00003061144,0.002201382,0.00016047635,0.000014946225,0.000025854353,0.0014151869,0.00004940524,0.015595566,0.0066420175,0.00044233783,0.9734125],"study_design_scores_gemma":[0.00018954584,0.00008191305,0.0020834869,0.00035099388,0.0000118254475,0.0001773907,0.000079655714,0.93703586,0.0017048941,0.00092550484,0.057175383,0.00018353929],"about_ca_topic_score_codex":0.000045705914,"about_ca_topic_score_gemma":0.000018884499,"teacher_disagreement_score":0.973229,"about_ca_system_score_codex":0.000013769461,"about_ca_system_score_gemma":0.000022753442,"threshold_uncertainty_score":0.3952639},"labels":[],"label_agreement":null},{"id":"W4404638618","doi":"10.3390/make6040130","title":"Node-Centric Pruning: A Novel Graph Reduction Approach","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","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":"University of New Brunswick","funders":"","keywords":"Computer science; Scalability; Pruning; Graph; Theoretical computer science; Node (physics); Distributed computing; Artificial intelligence; Machine learning; Engineering","score_opus":0.015621257278360324,"score_gpt":0.2815210943851965,"score_spread":0.2658998371068362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404638618","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":[0.0071061025,0.010082453,0.97333544,0.00028696653,0.0014929471,0.00016265736,6.082605e-7,0.0010492094,0.0064835832],"genre_scores_gemma":[0.968661,0.00040513952,0.027134776,0.000011163771,0.00037426545,0.000024936286,0.000014518067,0.000027790176,0.0033464178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865955,0.000104067476,0.00019921445,0.0006109445,0.00014669578,0.0002795146],"domain_scores_gemma":[0.9994996,0.00010699203,0.000070452355,0.00017499753,0.000050080354,0.000097861564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003335608,0.00019447932,0.00014563084,0.000378885,0.00035131953,0.00030934153,0.00016439948,0.000102789345,0.0000054792354],"category_scores_gemma":[0.00004403713,0.00017841771,0.0000761431,0.0012432317,0.000043227577,0.00077081274,0.00010431087,0.0008776614,0.00003403084],"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":[0.0000486589,0.0005218824,0.0013372708,0.0004459948,0.00010182582,0.000029879971,0.0033082007,0.017819623,0.028416604,0.046939325,0.0013104054,0.8997203],"study_design_scores_gemma":[0.0002291039,0.00007747332,0.0006946245,0.00007069139,0.000018690453,0.0005067724,0.00004510113,0.94103163,0.00018703207,0.0008923373,0.056009095,0.00023741781],"about_ca_topic_score_codex":0.000016272801,"about_ca_topic_score_gemma":0.0000022181503,"teacher_disagreement_score":0.9615549,"about_ca_system_score_codex":0.000049267754,"about_ca_system_score_gemma":0.000029412684,"threshold_uncertainty_score":0.7275663},"labels":[],"label_agreement":null},{"id":"W4407192178","doi":"10.3390/make7010012","title":"Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Interpretability; Artificial intelligence; Pixel; Computer science; Medicine; Machine learning","score_opus":0.0202131925955474,"score_gpt":0.36563903158435757,"score_spread":0.3454258389888102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407192178","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":[0.3046402,0.0015010663,0.69052494,0.0007190302,0.00015081032,0.00012587463,0.0000017048056,0.000065512,0.0022708443],"genre_scores_gemma":[0.9979629,0.00007457437,0.0017563015,0.000052710824,0.0000105374465,0.000015799886,0.0000045731776,0.0000042489637,0.00011836671],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977432,0.0005973587,0.00058517954,0.00061886566,0.0001958791,0.0002595417],"domain_scores_gemma":[0.998403,0.00086187915,0.00014854589,0.0002828472,0.00019569784,0.00010802123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019226046,0.0001906993,0.0005150593,0.0006898745,0.00015229578,0.00009338184,0.00026915479,0.00008734594,0.00002343398],"category_scores_gemma":[0.0007759744,0.00018302597,0.000095821124,0.0014485648,0.00019797632,0.0008186737,0.00034032282,0.0007230746,0.0000020347459],"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":[0.0001581862,0.0006663212,0.5246098,0.00019571064,0.00024829793,0.000005814775,0.022936242,0.009740095,0.0022998182,0.01778678,0.000024689562,0.42132825],"study_design_scores_gemma":[0.00013976118,0.00004183576,0.03180299,0.00011197788,0.00006775525,0.00000486463,0.00077353383,0.96039563,0.0007410088,0.0053899866,0.00039332357,0.00013731538],"about_ca_topic_score_codex":0.0010229155,"about_ca_topic_score_gemma":0.0045425477,"teacher_disagreement_score":0.9506556,"about_ca_system_score_codex":0.00012700443,"about_ca_system_score_gemma":0.000118837575,"threshold_uncertainty_score":0.7463583},"labels":[],"label_agreement":null},{"id":"W4409634738","doi":"10.3390/make7020038","title":"Knowledge Graphs and Their Reciprocal Relationship with Large Language Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","field":"Computer Science","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":"Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reciprocal; Computer science; Natural language processing; Linguistics; Philosophy","score_opus":0.012439613366926514,"score_gpt":0.2888355388840247,"score_spread":0.2763959255170982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409634738","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":[0.11622123,0.023313884,0.844137,0.00033052138,0.00028373176,0.00021019184,0.0000017796132,0.00055186584,0.014949796],"genre_scores_gemma":[0.99178684,0.0002441003,0.0035160964,0.000031640884,0.0000344485,0.000019203086,0.0000088818015,0.00001387908,0.0043449025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886894,0.00020263735,0.00016626964,0.00045040573,0.000059385817,0.00025237104],"domain_scores_gemma":[0.99913865,0.0004429547,0.00007788842,0.00019623211,0.00006555308,0.00007873522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003450153,0.00019791709,0.00017704516,0.0002546153,0.00053538487,0.00011489369,0.00013488262,0.000102832724,0.000002301112],"category_scores_gemma":[0.000065475884,0.0001515911,0.000035313296,0.0006851629,0.000058006393,0.0005965575,0.00014054203,0.0007242022,0.0000052639734],"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":[0.0001391334,0.00031546282,0.06143343,0.00020029787,0.00007429061,0.000014921632,0.010508563,0.0020073294,0.00085412466,0.4176661,0.00034791345,0.50643843],"study_design_scores_gemma":[0.0008534071,0.00015531729,0.013087115,0.00016485057,0.00001830051,0.00006330516,0.00034881048,0.9484797,0.0001678373,0.027034663,0.009338779,0.00028793036],"about_ca_topic_score_codex":0.000013059521,"about_ca_topic_score_gemma":0.00021520728,"teacher_disagreement_score":0.94647235,"about_ca_system_score_codex":0.000023543575,"about_ca_system_score_gemma":0.00003067374,"threshold_uncertainty_score":0.61817056},"labels":[],"label_agreement":null},{"id":"W4410240055","doi":"10.3390/make7020042","title":"Leveraging Failure Modes and Effect Analysis for Technical Language Processing","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","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":"Hydro-Québec; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Computer science","score_opus":0.008437007984600979,"score_gpt":0.30890788592712254,"score_spread":0.3004708779425216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410240055","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":[0.10991552,0.0033663826,0.8846742,0.00030872974,0.00003378825,0.00010381045,2.2180102e-7,0.00019311761,0.0014042648],"genre_scores_gemma":[0.979076,0.000013214355,0.019728078,0.000013626741,0.00004391602,0.000021000671,0.0000040740492,0.000005221885,0.0010948135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924594,0.00008588852,0.00013120782,0.00034322144,0.00005501314,0.00013869994],"domain_scores_gemma":[0.99955165,0.00022101188,0.00005093686,0.000107841784,0.000035196237,0.000033372915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005150762,0.00010612022,0.00017290693,0.00026531928,0.00031216748,0.00019512286,0.000090197675,0.00006574894,0.0000012780089],"category_scores_gemma":[0.00013741817,0.00009400565,0.00004962176,0.0003843916,0.000016275208,0.0002122312,0.00008804683,0.00025913175,6.3198837e-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":[0.000017889708,0.000027657157,0.04463736,0.00024580056,0.000071419614,0.0000018765895,0.0010841265,0.0028045797,0.00830956,0.001333295,0.000020417438,0.941446],"study_design_scores_gemma":[0.00032216168,0.00003408606,0.0048195026,0.0000481986,0.000097258504,0.000008062779,0.0000938914,0.9921177,0.0004674852,0.00013908157,0.0017558534,0.00009669198],"about_ca_topic_score_codex":0.000037989455,"about_ca_topic_score_gemma":0.000063912834,"teacher_disagreement_score":0.9893131,"about_ca_system_score_codex":0.000026351017,"about_ca_system_score_gemma":0.000020266702,"threshold_uncertainty_score":0.3833439},"labels":[],"label_agreement":null},{"id":"W4413160539","doi":"10.3390/make7030082","title":"Multilayer Perceptron Mapping of Subjective Time Duration onto Mental Imagery Vividness and Underlying Brain Dynamics: A Neural Cognitive Modeling Approach","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"Children's Hospital of Eastern Ontario; Carleton University","funders":"","keywords":"Cognition; Duration (music); Dynamics (music); Mental image; Psychology; Multilayer perceptron; Artificial intelligence; Neural correlates of consciousness; Cognitive psychology; Artificial neural network; Neuroimaging; Computer science; Neuroscience; Art","score_opus":0.028333824630308542,"score_gpt":0.3081942580353396,"score_spread":0.2798604334050311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413160539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9313526,0.00018649602,0.064093575,0.00020261647,0.00023774971,0.0003280555,0.000010848468,0.000091480826,0.003496558],"genre_scores_gemma":[0.9972997,0.00005063327,0.0001635883,0.000050999995,0.000036813766,0.000018587105,0.00005743551,0.0000205649,0.00230173],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850804,0.00035684416,0.0002932966,0.00051717786,0.000121700024,0.00020294341],"domain_scores_gemma":[0.999215,0.00044332872,0.00015412447,0.00006309294,0.00007587947,0.000048590846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036636728,0.0002047003,0.00022948913,0.0002865811,0.0005291347,0.00010502903,0.000045416004,0.000094571034,0.000009576899],"category_scores_gemma":[0.0004919691,0.00020059098,0.00005731009,0.00027531042,0.00008568237,0.00044468077,0.00007763746,0.00047111037,0.000003165936],"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":[0.00033023884,0.00021268683,0.004163548,0.00020295956,0.00002671874,0.0000024168708,0.003142415,0.0020445397,0.83790386,0.00021010728,0.000007512412,0.151753],"study_design_scores_gemma":[0.00069822924,0.00009777374,0.005279567,0.000111363064,0.000030092522,0.00004149553,0.0018251956,0.9895328,0.0020669175,0.00011281008,0.000027978176,0.00017574186],"about_ca_topic_score_codex":0.00016655869,"about_ca_topic_score_gemma":0.00006935237,"teacher_disagreement_score":0.98748827,"about_ca_system_score_codex":0.00011281192,"about_ca_system_score_gemma":0.000027004518,"threshold_uncertainty_score":0.81798625},"labels":[],"label_agreement":null},{"id":"W4413794792","doi":"10.3390/make7030089","title":"AlzheimerRAG: Multimodal Retrieval-Augmented Generation for Clinical Use Cases","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generative grammar; Search engine indexing; Artificial intelligence; Information retrieval; Machine learning; Natural language processing; Data science","score_opus":0.10801962975602644,"score_gpt":0.4138889138627881,"score_spread":0.3058692841067616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413794792","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":[0.19481112,0.0018308797,0.80073524,0.0004135992,0.0014536087,0.00022010329,0.0000013640542,0.00020285773,0.000331214],"genre_scores_gemma":[0.9722927,0.0002045755,0.022871474,0.00006201213,0.00033175037,0.0000131435,0.00002281854,0.000008853011,0.0041926685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987769,0.00021814306,0.0003242418,0.00044437713,0.00007522097,0.00016112298],"domain_scores_gemma":[0.998742,0.00078652723,0.000101171754,0.0001898284,0.00012566189,0.00005485306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074794237,0.00012099459,0.00015771044,0.00012758435,0.00034803545,0.00019338116,0.000109508364,0.000112299,0.0000050004505],"category_scores_gemma":[0.0012120045,0.00011694882,0.00007006459,0.0001700661,0.00002142311,0.0004075048,0.000083361956,0.00032238555,0.000007184413],"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":[0.00016470769,0.0003173353,0.051007997,0.0000620313,0.00012688055,0.000008202534,0.00046041326,0.0013240343,0.0070860675,0.006259376,0.0018138756,0.93136907],"study_design_scores_gemma":[0.0007864939,0.00010347264,0.004399339,0.000023575314,0.00003114754,0.000013224736,0.000011626668,0.94211453,0.00064212846,0.00007066025,0.05169103,0.00011276014],"about_ca_topic_score_codex":0.000078640536,"about_ca_topic_score_gemma":0.000062060295,"teacher_disagreement_score":0.94079053,"about_ca_system_score_codex":0.00003231649,"about_ca_system_score_gemma":0.000057602356,"threshold_uncertainty_score":0.47690344},"labels":[],"label_agreement":null},{"id":"W4414093251","doi":"10.3390/make7030097","title":"A Review of Large Language Models for Automated Test Case Generation","year":2025,"lang":"en","type":"review","venue":"Machine Learning and Knowledge Extraction","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Test (biology); Natural language generation; Natural language; Focus (optics); Software; Test case","score_opus":0.04220589820370538,"score_gpt":0.4041485293505684,"score_spread":0.361942631146863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414093251","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.7880694e-7,0.75710577,0.237969,0.000013787943,0.00015064454,0.00047256539,0.000025029598,0.0039760205,0.00028702634],"genre_scores_gemma":[0.00004325447,0.9666857,0.03197354,0.000028286828,0.00011042475,0.00018433217,0.00019686883,0.000023613251,0.00075395993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846214,0.00029830527,0.0005207765,0.00045031245,0.00007900445,0.00018944494],"domain_scores_gemma":[0.9975311,0.0014671674,0.0004618606,0.00031390073,0.00017825788,0.000047703732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001137592,0.0002832668,0.0008516959,0.00028920075,0.00022845968,0.00006707022,0.00019094208,0.00019358043,0.0000026336622],"category_scores_gemma":[0.0020782643,0.00024234867,0.00021192015,0.00046801398,0.000013884738,0.00017270927,0.00013282479,0.0004010916,0.0000028777888],"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":[3.7574353e-7,0.000065759916,0.0000013307516,0.090849794,0.0000171212,0.00001672944,0.00009144425,0.000002840266,9.0935856e-7,0.00015286492,0.0064731734,0.90232766],"study_design_scores_gemma":[0.00009291365,0.000080414706,8.198179e-8,0.06901033,0.00021609582,0.00071201316,0.0000012030206,0.49852836,0.0000018522705,0.00007543867,0.4310791,0.00020220512],"about_ca_topic_score_codex":0.00005743802,"about_ca_topic_score_gemma":0.00001622399,"teacher_disagreement_score":0.9021255,"about_ca_system_score_codex":0.000052885218,"about_ca_system_score_gemma":0.00018749185,"threshold_uncertainty_score":0.9882692},"labels":[],"label_agreement":null},{"id":"W4414240954","doi":"10.3390/make7030101","title":"CRISP-NET: Integration of the CRISP-DM Model with Network Analysis","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Dalhousie University","keywords":"Identification (biology); Process (computing); Field (mathematics); Adaptation (eye); Personalization; Data integration; Software; Software development; Software development process","score_opus":0.007368779355266472,"score_gpt":0.2965423148062663,"score_spread":0.28917353545099983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414240954","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":[0.09080105,0.00057012576,0.87721664,0.00016099033,0.000058856313,0.0001350387,0.000002597409,0.00007139526,0.030983334],"genre_scores_gemma":[0.9917836,0.000015483522,0.001672631,0.00000922196,0.0001082672,0.000017376233,0.000034647986,0.00000958309,0.0063491473],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915874,0.00016006379,0.00023127563,0.00022346074,0.00008756414,0.00013891823],"domain_scores_gemma":[0.9993851,0.000091852016,0.00018967611,0.00019339893,0.00011593462,0.000024039156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000280732,0.00014319656,0.0002555193,0.0001634883,0.0003044304,0.000045507608,0.00009255335,0.000039328945,0.00006644147],"category_scores_gemma":[0.0000103359625,0.000097341865,0.0001562363,0.0011299577,0.000046898684,0.000080000886,0.000058419264,0.0003990926,0.0000012450901],"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":[0.00007358552,0.00017349207,0.5774954,0.000021956028,0.0010070748,1.2368184e-7,0.0004718251,0.2773994,0.0010484714,0.013925363,0.0016106268,0.1267727],"study_design_scores_gemma":[0.00015155193,0.000027152993,0.020342674,0.00005629368,0.0009198374,2.436979e-7,0.00011647436,0.9706109,0.00051031006,0.0029940528,0.004162122,0.00010835285],"about_ca_topic_score_codex":0.0004968927,"about_ca_topic_score_gemma":0.00061337923,"teacher_disagreement_score":0.90098256,"about_ca_system_score_codex":0.000022263997,"about_ca_system_score_gemma":0.000035591453,"threshold_uncertainty_score":0.3969486},"labels":[],"label_agreement":null},{"id":"W4415299179","doi":"10.3390/make7040121","title":"Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","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":"Provincial Health Services Authority; University of British Columbia","funders":"","keywords":"Task (project management); Language model; Selection (genetic algorithm); Exploit; Health care; Language understanding; Task analysis","score_opus":0.07084545954893032,"score_gpt":0.3752476515817091,"score_spread":0.30440219203277874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415299179","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":[0.014893502,0.0017074454,0.9798022,0.0010821231,0.00021499033,0.00045602518,0.000004434678,0.00019392153,0.0016453763],"genre_scores_gemma":[0.9104095,0.00015383905,0.05885646,0.00016877709,0.00023672612,0.00025615026,0.000039815404,0.000021234739,0.029857513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988254,0.00010079587,0.00029462008,0.0004403066,0.000067339664,0.00027154555],"domain_scores_gemma":[0.99913,0.0004206529,0.000093311544,0.00022478655,0.00007306241,0.000058148868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005115966,0.00014712705,0.00020470146,0.00023164097,0.0003428802,0.000106188105,0.00021571729,0.00010122652,0.000008383967],"category_scores_gemma":[0.00034333213,0.00012328994,0.00004276591,0.00040242504,0.000009426263,0.00016964352,0.00011610431,0.0002778913,0.0000037251648],"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":[0.00039922702,0.00060355884,0.02430178,0.0012135672,0.000055705845,0.000010146724,0.008722996,0.017126637,0.004822893,0.09361891,0.0008033315,0.84832126],"study_design_scores_gemma":[0.0010387411,0.000039485592,0.00058902573,0.00007373002,0.000009244661,0.000003905101,0.000111604306,0.9364247,0.00015467803,0.00088330416,0.06053272,0.00013890072],"about_ca_topic_score_codex":0.00038664797,"about_ca_topic_score_gemma":0.0050138803,"teacher_disagreement_score":0.9209457,"about_ca_system_score_codex":0.00008925523,"about_ca_system_score_gemma":0.00014793903,"threshold_uncertainty_score":0.5027618},"labels":[],"label_agreement":null},{"id":"W4415357126","doi":"10.3390/make7040124","title":"SemiSeg-CAW: Semi-Supervised Segmentation of Ultrasound Images by Leveraging Class-Level Information and an Adaptive Multi-Loss Function","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Medical Image Segmentation Techniques","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":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Weighting; Scale-space segmentation; Feature (linguistics); Pattern recognition (psychology); Image segmentation; Segmentation-based object categorization; Dependency (UML)","score_opus":0.017657013095751087,"score_gpt":0.31087791044407603,"score_spread":0.29322089734832496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415357126","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":[0.02794263,0.0006636325,0.9700331,0.0001368131,0.00018404699,0.00022592142,0.000010447542,0.00023401118,0.0005693604],"genre_scores_gemma":[0.93614805,0.00042143546,0.06216871,0.000117455726,0.000033365362,0.000035264322,0.00019718982,0.000009898691,0.00086862635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872494,0.00027057712,0.00037498775,0.00029400297,0.00018091075,0.00015459541],"domain_scores_gemma":[0.9991354,0.00023264895,0.00022520113,0.00013449148,0.00018842025,0.00008385019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006168592,0.00017088464,0.00017319049,0.00026705442,0.00027928702,0.00019017536,0.000115374714,0.00010184222,0.000019540217],"category_scores_gemma":[0.00018673103,0.00017179405,0.00002890309,0.00028664948,0.000078671954,0.0026921702,0.00007177938,0.00035246226,0.0000043206105],"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":[0.00007629956,0.00017554426,0.011486905,0.00016648872,0.000044832002,6.6511114e-7,0.002368879,0.00006893636,0.2784661,0.00025990652,0.00098861,0.70589685],"study_design_scores_gemma":[0.0037078192,0.00066763273,0.040415704,0.0003039682,0.00011315634,0.00004384186,0.002948726,0.6423062,0.30492178,0.00062935334,0.0033220588,0.00061975437],"about_ca_topic_score_codex":0.00020513502,"about_ca_topic_score_gemma":0.000016819622,"teacher_disagreement_score":0.90820545,"about_ca_system_score_codex":0.0000715786,"about_ca_system_score_gemma":0.00005025491,"threshold_uncertainty_score":0.7005558},"labels":[],"label_agreement":null},{"id":"W4416295981","doi":"10.3390/make7040147","title":"Adaptive Multi-View Hypergraph Learning for Cross-Condition Bearing Fault Diagnosis","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Hypergraph; Feature (linguistics); Fault (geology); Feature learning; Fusion; Similarity (geometry)","score_opus":0.014321793020525355,"score_gpt":0.3441445669387899,"score_spread":0.3298227739182646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416295981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5083405,0.030927613,0.43112677,0.00021646864,0.0018049544,0.0018791549,0.00003396542,0.006093211,0.019577378],"genre_scores_gemma":[0.99046326,0.0024138584,0.0038357354,0.000018887928,0.00014359987,0.000781256,0.00010428353,0.00007248788,0.002166658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998627,0.00012399,0.00037487992,0.00042454145,0.00009619068,0.00035339687],"domain_scores_gemma":[0.9989782,0.0005848236,0.000090502486,0.0001311133,0.00013533435,0.00008001983],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005010332,0.00033378316,0.00034394037,0.00041790507,0.00057083566,0.00016852576,0.00010680074,0.00022766546,0.00004238649],"category_scores_gemma":[0.0005033666,0.0003592837,0.00014421812,0.00035372013,0.000057234065,0.00034673826,0.000055883404,0.00096727547,0.000020859125],"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":[0.0000754982,0.0003004424,0.31107834,0.00089981715,0.00024275784,0.000004569172,0.000680382,0.044138283,0.0057537216,0.00089925376,0.00155396,0.63437295],"study_design_scores_gemma":[0.0011227294,0.00018898335,0.043602385,0.00046364163,0.00011328283,0.000010083994,0.00010117522,0.7373182,0.008829225,0.0002921328,0.20745277,0.00050538493],"about_ca_topic_score_codex":0.00012410425,"about_ca_topic_score_gemma":0.0001595011,"teacher_disagreement_score":0.6931799,"about_ca_system_score_codex":0.00012739302,"about_ca_system_score_gemma":0.000017388384,"threshold_uncertainty_score":0.9998859},"labels":[],"label_agreement":null},{"id":"W4416322339","doi":"10.3390/make7040148","title":"Model-Aware Automatic Benchmark Generation with Self-Error Instructions for Data-Driven Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Machine Learning and Data Classification","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":"Artificial Intelligence in Medicine (Canada)","funders":"Ministero dello Sviluppo Economico","keywords":"Benchmark (surveying); Data point; Generative grammar; Regression; Data-driven; Data modeling; Experimental data","score_opus":0.05107415529911185,"score_gpt":0.3329024960587661,"score_spread":0.28182834075965424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416322339","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":[0.0075857257,0.00033594255,0.986616,0.0008695757,0.00032762863,0.0003201941,0.000016608818,0.0006453958,0.0032829097],"genre_scores_gemma":[0.8378218,0.00006873941,0.15892842,0.000032661435,0.00010842153,0.000086836924,0.00081756985,0.000016956423,0.0021186175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984859,0.00017154631,0.00028415243,0.0006965277,0.00014397987,0.0002178789],"domain_scores_gemma":[0.9988374,0.00014933156,0.00017984098,0.00059744285,0.00016399183,0.00007198029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005090389,0.00020603862,0.00019774286,0.00027749606,0.0008348008,0.00030119752,0.00039804837,0.000108540036,0.0000047131093],"category_scores_gemma":[0.00009843458,0.00018217505,0.00003304933,0.0004043256,0.00002953461,0.001333411,0.00018859266,0.00042199437,0.0000061903615],"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":[0.00004205169,0.00043904298,0.0031533497,0.00040127404,0.00016739605,0.0000017615487,0.0017081387,0.25335228,0.0014284778,0.05965323,0.003024453,0.67662853],"study_design_scores_gemma":[0.00055738393,0.000089231755,0.00050447613,0.000047504167,0.00006453277,0.000026848957,0.000038525992,0.98346555,0.000025462436,0.0007219054,0.0142612485,0.00019735716],"about_ca_topic_score_codex":0.00005477871,"about_ca_topic_score_gemma":0.00020920302,"teacher_disagreement_score":0.830236,"about_ca_system_score_codex":0.00007296129,"about_ca_system_score_gemma":0.00018229923,"threshold_uncertainty_score":0.7428883},"labels":[],"label_agreement":null},{"id":"W4416363878","doi":"10.3390/make7040149","title":"Explainable Recommendation of Software Vulnerability Repair Based on Metadata Retrieval and Multifaceted LLMs","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metadata; Context (archaeology); Code (set theory); Vulnerability (computing); Knowledge base; Transparency (behavior); Artifact (error); Robustness (evolution)","score_opus":0.016644298966465746,"score_gpt":0.31910019328848227,"score_spread":0.3024558943220165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416363878","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":[0.17558147,0.0006108921,0.82144266,0.0004625027,0.00041778423,0.00026393292,0.0000056016142,0.00072546845,0.0004896672],"genre_scores_gemma":[0.9787631,0.000031435273,0.019632785,0.000013308763,0.000021106174,0.000010319187,0.000036054073,0.000009909239,0.0014820044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986985,0.00033169193,0.00022412534,0.0004453561,0.00012189194,0.00017846558],"domain_scores_gemma":[0.99742997,0.0020013743,0.00007535169,0.00029329842,0.00013968039,0.000060350365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015129062,0.0001355991,0.00018611264,0.00031076348,0.00024016513,0.00009882012,0.00013274794,0.00008771748,0.000016815111],"category_scores_gemma":[0.005140638,0.00013136202,0.000041350533,0.0005372491,0.000039172308,0.0004345802,0.00014006444,0.0005296865,0.000003103345],"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":[0.0004695453,0.00054081396,0.22728775,0.0008888187,0.00008579753,0.0000045545976,0.00069159776,0.0046634423,0.003912797,0.00068690785,0.0005612897,0.7602067],"study_design_scores_gemma":[0.00068025256,0.00021541794,0.0751988,0.00009028196,0.00001364829,0.0000036140086,0.000031410775,0.90216094,0.002821557,0.000074176976,0.01856752,0.00014240739],"about_ca_topic_score_codex":0.000097199794,"about_ca_topic_score_gemma":0.000010990178,"teacher_disagreement_score":0.8974975,"about_ca_system_score_codex":0.00007751811,"about_ca_system_score_gemma":0.00006553197,"threshold_uncertainty_score":0.6154195},"labels":[],"label_agreement":null},{"id":"W4416723482","doi":"10.3390/make7040154","title":"Low-SNR Northern Right Whale Upcall Detection and Classification Using Passive Acoustic Monitoring to Reduce Adverse Human–Whale Interactions","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Marine animal studies overview","field":"Environmental 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":"Dalhousie University","funders":"","keywords":"Support vector machine; Feature extraction; Classifier (UML); Pattern recognition (psychology); Right whale; Underwater; Whale","score_opus":0.0195422294831908,"score_gpt":0.31584453593367134,"score_spread":0.29630230645048056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416723482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98306555,0.0002640344,0.004757964,0.00010343258,0.00043576342,0.00023708922,0.0000010410857,0.00008950457,0.0110456105],"genre_scores_gemma":[0.9950174,0.00013548025,0.00025004262,0.000012208136,0.00014469538,0.00003503339,0.0000037097445,0.000016925213,0.0043844962],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989015,0.00012261332,0.0002355664,0.0004282864,0.00010929078,0.00020277445],"domain_scores_gemma":[0.99953866,0.00009661935,0.00012602663,0.00011401242,0.0000377228,0.00008694698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018828393,0.00018041616,0.0001612758,0.00010645394,0.0009371955,0.000060205577,0.00006069585,0.00006771581,0.00015904331],"category_scores_gemma":[0.00014207284,0.00018236521,0.000039559003,0.0003206863,0.00005917878,0.00031729776,0.00020275867,0.00041829445,0.000098655764],"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":[0.00006954314,0.00017980048,0.19461633,0.00010256289,0.000042642692,0.000004294123,0.000835504,0.0023167676,0.46083766,0.000012461629,0.000060135368,0.3409223],"study_design_scores_gemma":[0.0005153475,0.00016451009,0.87789387,0.00027419897,0.0001570069,0.00003706103,0.00110663,0.065390974,0.009004991,0.00004474395,0.045019083,0.000391569],"about_ca_topic_score_codex":0.0018735678,"about_ca_topic_score_gemma":0.011674831,"teacher_disagreement_score":0.68327755,"about_ca_system_score_codex":0.0004183767,"about_ca_system_score_gemma":0.000009299108,"threshold_uncertainty_score":0.7436637},"labels":[],"label_agreement":null},{"id":"W4416904921","doi":"10.3390/make7040157","title":"SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Digital Imaging for Blood Diseases","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 Calgary","funders":"","keywords":"Deep learning; Lyme disease; Preprocessor; Generalizability theory; Borrelia burgdorferi; Disease; Robustness (evolution)","score_opus":0.019082248411816355,"score_gpt":0.29651888788893216,"score_spread":0.27743663947711583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416904921","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":[0.03607897,0.011415683,0.9417024,0.00014526659,0.00039047503,0.00032727345,0.000009390969,0.00066896516,0.009261574],"genre_scores_gemma":[0.97601897,0.00011448525,0.014962172,0.000052472562,0.0001451484,0.00012949087,0.00015807708,0.000042417214,0.00837678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977907,0.00022182356,0.00034616844,0.0009337893,0.00021330232,0.00049421174],"domain_scores_gemma":[0.99872017,0.00043439577,0.00018189882,0.00030078218,0.00014037672,0.00022236306],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00044869623,0.00033910928,0.0003144921,0.0003950303,0.0009168137,0.0013466165,0.00038866795,0.000050888393,0.0000063010575],"category_scores_gemma":[0.00038457994,0.00036552182,0.00017341842,0.00037187652,0.00006321147,0.002335946,0.00038188006,0.0004617323,0.000015887561],"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":[0.00020765726,0.001320498,0.025907217,0.0010031818,0.00031065932,0.000056232595,0.0029461826,0.082665876,0.0014411866,0.009491558,0.001138191,0.87351155],"study_design_scores_gemma":[0.0009528561,0.000031434145,0.004382727,0.0001665985,0.000098789504,0.000008832854,0.00022104106,0.9722605,0.00076626276,0.013045601,0.007673228,0.0003921828],"about_ca_topic_score_codex":0.00022990246,"about_ca_topic_score_gemma":0.000007336536,"teacher_disagreement_score":0.93994,"about_ca_system_score_codex":0.00009846116,"about_ca_system_score_gemma":0.00009439348,"threshold_uncertainty_score":0.99987966},"labels":[],"label_agreement":null},{"id":"W7116670301","doi":"10.3390/make8010002","title":"Enhancing GNN Explanations for Malware Detection with Dual Subgraph Matching","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Malware Detection 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":"University of New Brunswick","funders":"","keywords":"Malware; Discriminative model; Benchmark (surveying); Generalization; Matching (statistics); Dual (grammatical number); Subgraph isomorphism problem; Control flow graph","score_opus":0.006989772379721288,"score_gpt":0.2894999140025139,"score_spread":0.2825101416227926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116670301","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":[0.014233662,0.00040363046,0.982513,0.00017435825,0.0003458519,0.0002649041,0.0000014477337,0.00097326876,0.0010898904],"genre_scores_gemma":[0.933998,0.000050388386,0.06361418,0.00002495091,0.00007181841,0.00015350628,0.000008436138,0.000015980746,0.0020627182],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900866,0.000087991946,0.00020332505,0.00040613336,0.00009100399,0.00020291316],"domain_scores_gemma":[0.9992278,0.00028865557,0.00012400007,0.00015461113,0.00015894,0.00004598157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034847448,0.00016377144,0.00014732554,0.00043166374,0.0007546212,0.00015089626,0.0000991426,0.00008675961,0.0000035430387],"category_scores_gemma":[0.000120508106,0.00015579782,0.000046012843,0.00051580387,0.000024504287,0.0006100711,0.00006027786,0.00038962803,0.0000035513692],"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":[0.00013268249,0.000116650226,0.0012678662,0.00023289358,0.00006274009,0.0000053275494,0.0013586448,0.0008379216,0.08506326,0.009532444,0.00006538407,0.9013242],"study_design_scores_gemma":[0.0027126693,0.0015585803,0.008535332,0.0006755438,0.00013846613,0.00044535246,0.0013566294,0.21364193,0.52662915,0.021081015,0.22184265,0.0013827063],"about_ca_topic_score_codex":0.00006660483,"about_ca_topic_score_gemma":0.0008073417,"teacher_disagreement_score":0.91976434,"about_ca_system_score_codex":0.00007903276,"about_ca_system_score_gemma":0.00004008071,"threshold_uncertainty_score":0.6353251},"labels":[],"label_agreement":null},{"id":"W7117764744","doi":"10.3390/make8010006","title":"Research Frontiers in Machine Learning &amp; Knowledge Extraction","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Eusko Jaurlaritza; Austrian Science Fund","keywords":"Transparency (behavior); Underpinning; Software deployment; Cornerstone; Applications of artificial intelligence; Embedding; Domain (mathematical analysis); Knowledge integration","score_opus":0.04255175253055986,"score_gpt":0.3903736406979469,"score_spread":0.347821888167387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117764744","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":[0.08758308,0.0252614,0.7846895,0.0017649217,0.003668907,0.0006568379,0.000001470339,0.0008897218,0.09548416],"genre_scores_gemma":[0.9440549,0.0012692849,0.0068110772,0.00002045032,0.00020035943,0.00006657924,0.000022637152,0.000034129745,0.047520578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956366,0.0016956226,0.0005840836,0.0009748755,0.0003247091,0.00078409794],"domain_scores_gemma":[0.99796206,0.00093946775,0.00016164318,0.0004051254,0.00036939868,0.00016230118],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0043021655,0.0003236172,0.00038179875,0.0018211477,0.0012460453,0.00047706792,0.0005140417,0.00027688695,0.000056946225],"category_scores_gemma":[0.001302569,0.00034170615,0.000094818985,0.0026899201,0.0001462243,0.0011137272,0.00039593433,0.0031404207,0.00038148812],"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":[0.0001724195,0.00074392295,0.09421573,0.00023055107,0.000055293196,0.000034227658,0.0060227104,0.0035230923,0.00988131,0.013370038,0.0028378074,0.8689129],"study_design_scores_gemma":[0.00039487233,0.00015426864,0.0061809826,0.00019786617,0.0000128267775,0.000030913194,0.00095370575,0.5254339,0.0023949211,0.0035468969,0.46034122,0.00035764044],"about_ca_topic_score_codex":0.0013454913,"about_ca_topic_score_gemma":0.003050554,"teacher_disagreement_score":0.86855525,"about_ca_system_score_codex":0.00044234254,"about_ca_system_score_gemma":0.0002125682,"threshold_uncertainty_score":0.9999035},"labels":[],"label_agreement":null}]}